Lung Cancer

Overview

Literature Analysis

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Tag cloud generated 08 August, 2015 using data from PubMed, MeSH and CancerIndex

Mutated Genes and Abnormal Protein Expression (726)

How to use this data tableClicking on the Gene or Topic will take you to a separate more detailed page. Sort this list by clicking on a column heading e.g. 'Gene' or 'Topic'.

GeneLocationAliasesNotesTopicPapers
SCLC1 3p23-p21 SCCL, SCLC -SCLC1 and Lung Cancer
3000
EGFR 7p12 ERBB, HER1, mENA, ERBB1, PIG61, NISBD2 -EGFR and Lung Cancer
2685
TP53 17p13.1 P53, BCC7, LFS1, TRP53 -TP53 mutations in Lung Cancer
1213
KRAS 12p12.1 NS, NS3, CFC2, KRAS1, KRAS2, RASK2, KI-RAS, C-K-RAS, K-RAS2A, K-RAS2B, K-RAS4A, K-RAS4B -KRAS and Lung Cancer
899
ALK 2p23 CD246, NBLST3 -ALK and Lung Cancer
555
MKI67 10q26.2 KIA, MIB-, MIB-1, PPP1R105 -MKI67 and Lung Cancer
509
CDKN2A 9p21 ARF, MLM, P14, P16, P19, CMM2, INK4, MTS1, TP16, CDK4I, CDKN2, INK4A, MTS-1, P14ARF, P19ARF, P16INK4, P16INK4A, P16-INK4A -CDKN2A and Lung Cancer
408
ERBB2 17q12 NEU, NGL, HER2, TKR1, CD340, HER-2, MLN 19, HER-2/neu -ERBB2 and Lung Cancer
373
GSTM1 1p13.3 MU, H-B, GST1, GTH4, GTM1, MU-1, GSTM1-1, GSTM1a-1a, GSTM1b-1b -GSTM1 and Lung Cancer
-Tabacco smoke, GSTM1 Polymorphisms and Suceptability to Lung Cancer
236
PTEN 10q23.3 BZS, DEC, CWS1, GLM2, MHAM, TEP1, MMAC1, PTEN1, 10q23del -PTEN and Lung Cancer
294
MET 7q31 HGFR, AUTS9, RCCP2, c-Met -C-MET and Lung Cancer
289
EML4 2p21 C2orf2, ELP120, EMAP-4, EMAPL4, ROPP120 -EML4 and Lung Cancer
267
ERCC1 19q13.32 UV20, COFS4, RAD10 -ERCC1 and Lung Cancer
223
CYP1A1 15q24.1 AHH, AHRR, CP11, CYP1, P1-450, P450-C, P450DX -CYP1A1 and Lung Cancer
213
AKT1 14q32.32 AKT, PKB, RAC, CWS6, PRKBA, PKB-ALPHA, RAC-ALPHA -AKT1 and Lung Cancer
204
PROC 2q13-q14 PC, APC, PROC1, THPH3, THPH4 -PROC and Lung Cancer
191
BRAF 7q34 NS7, BRAF1, RAFB1, B-RAF1 -BRAF and Lung Cancer
188
CTNNB1 3p21 CTNNB, MRD19, armadillo -CTNNB1 and Lung Cancer
183
CCND1 11q13 BCL1, PRAD1, U21B31, D11S287E -CCND1 and Lung Cancer
176
CD9 12p13.3 MIC3, MRP-1, BTCC-1, DRAP-27, TSPAN29, TSPAN-29 -CD9 and Lung Cancer
174
CASP3 4q34 CPP32, SCA-1, CPP32B -CASP3 and Lung Cancer
164
KIT 4q12 PBT, SCFR, C-Kit, CD117 -KIT and Lung Cancer
152
TNF 6p21.3 DIF, TNFA, TNFSF2, TNF-alpha -TNF and Lung Cancer
152
CDKN1A 6p21.2 P21, CIP1, SDI1, WAF1, CAP20, CDKN1, MDA-6, p21CIP1 -CDKN1A and Lung Cancer
149
XRCC1 19q13.2 RCC -XRCC1 and Lung Cancer
139
KITLG 12q22 SF, MGF, SCF, FPH2, FPHH, KL-1, Kitl, SHEP7 -KITLG and Lung Cancer
135
BIRC5 17q25 API4, EPR-1 -Survivin Expression in Non Small Lung Cancer
134
NODAL 10q22.1 HTX5 -NODAL and Lung Cancer
133
PIK3CA 3q26.3 MCM, CWS5, MCAP, PI3K, CLOVE, MCMTC, p110-alpha -PIK3CA and Lung Cancer
130
MTOR 1p36.2 FRAP, FRAP1, FRAP2, RAFT1, RAPT1 -MTOR and Lung Cancer
125
PTGS2 1q25.2-q25.3 COX2, COX-2, PHS-2, PGG/HS, PGHS-2, hCox-2, GRIPGHS -PTGS2 (COX2) and Lung Cancer
124
GSTT1 22q11.23 -GSTT1 and Lung Cancer
120
RET 10q11.2 PTC, MTC1, HSCR1, MEN2A, MEN2B, RET51, CDHF12, CDHR16, RET-ELE1 -RET-KIF5B fusion in Adenocarcinoma Lung Cancer
-RET and Lung Cancer
96
RASSF1 3p21.3 123F2, RDA32, NORE2A, RASSF1A, REH3P21 -RASSF1 and Lung Cancer
119
SRC 20q12-q13 ASV, SRC1, c-SRC, p60-Src -SRC and Lung Cancer
118
STAT3 17q21.31 APRF, HIES, ADMIO -STAT3 and Lung Cancer
112
ERCC2 19q13.3 EM9, TTD, XPD, COFS2, TFIIH -ERCC2 and Lung Cancer
110
RB1 13q14.2 RB, pRb, OSRC, pp110, p105-Rb, PPP1R130 -RB1 and Lung Cancer
103
MYC 8q24.21 MRTL, MYCC, c-Myc, bHLHe39 -MYC and Lung Cancer
93
ROS1 6q22 ROS, MCF3, c-ros-1 -ROS1 and Lung Cancer
92
RRM1 11p15.5 R1, RR1, RIR1 -RRM1 and Lung Cancer
92
KIF5B 10p11.22 KNS, KINH, KNS1, UKHC, HEL-S-61 -KIF5B and Lung Cancer
-RET-KIF5B fusion in Adenocarcinoma Lung Cancer
-KIF5B-ALK Rearrangements in Lung Cancer
40
VEGFA 6p12 VPF, VEGF, MVCD1 -VEGFA and Lung Cancer
88
HIF1A 14q23.2 HIF1, MOP1, PASD8, HIF-1A, bHLHe78, HIF-1alpha, HIF1-ALPHA -HIF1A and Lung Cancer
87
PCNA 20pter-p12 ATLD2 -PCNA and Lung Cancer
83
TUBE1 6q21 TUBE, dJ142L7.2 -TUBE1 and Lung Cancer
81
CEACAM5 19q13.1-q13.2 CEA, CD66e -CEACAM5 and Lung Cancer
80
TERT 5p15.33 TP2, TRT, CMM9, EST2, TCS1, hTRT, DKCA2, DKCB4, hEST2, PFBMFT1 -TERT and Lung Cancer
73
STK11 19p13.3 PJS, LKB1, hLKB1 -STK11 and Lung Cancer
72
FTCDNL1 2q33.1 FONG -FONG and Lung Cancer
68
MUC1 1q21 EMA, MCD, PEM, PUM, KL-6, MAM6, MCKD, PEMT, CD227, H23AG, MCKD1, MUC-1, ADMCKD, ADMCKD1, CA 15-3, MUC-1/X, MUC1/ZD, MUC-1/SEC Overexpression
Prognostic
-MUC1 and Lung Cancer
67
CYP2E1 10q26.3 CPE1, CYP2E, P450-J, P450C2E -CYP2E1 and Lung Cancer
64
HGF 7q21.1 SF, HGFB, HPTA, F-TCF, DFNB39 -HGF and Lung Cancer
61
TGFB1 19q13.1 CED, LAP, DPD1, TGFB, TGFbeta -TGFB1 and Lung Cancer
60
BCL2 18q21.3 Bcl-2, PPP1R50 -BCL2 and Lung Cancer
60
IGF1R 15q26.3 IGFR, CD221, IGFIR, JTK13 -IGF1R and Lung Cancer
60
FGFR1 8p11.23-p11.22 CEK, FLG, HH2, OGD, FLT2, KAL2, BFGFR, CD331, FGFBR, FLT-2, HBGFR, N-SAM, FGFR-1, HRTFDS, bFGF-R-1 -FGFR1 and Lung Cancer
59
FOS 14q24.3 p55, AP-1, C-FOS -FOS and Lung Cancer
58
OGG1 3p26.2 HMMH, MUTM, OGH1, HOGG1 -OGG1 and Lung Cancer
56
TGFBR2 3p22 AAT3, FAA3, LDS2, MFS2, RIIC, LDS1B, LDS2B, TAAD2, TGFR-2, TGFbeta-RII -TGFBR2 and Lung Cancer
53
NQO1 16q22.1 DTD, QR1, DHQU, DIA4, NMOR1, NMORI -NQO1 and Lung Cancer
51
MIR21 17q23.1 MIRN21, miR-21, miRNA21, hsa-mir-21 -MicroRNA miR-21 and Lung Cancer
51
ACHE 7q22 YT, ACEE, ARACHE, N-ACHE -ACHE and Lung Cancer
50
CCNB1 5q12 CCNB -CCNB1 and Lung Cancer
50
TTF1 9q34.13 TTF-1, TTF-I -TTF1 and Lung Cancer
50
XRCC3 14q32.3 CMM6 -XRCC3 and Lung Cancer
49
SOX2 3q26.3-q27 ANOP3, MCOPS3 -SOX2 and Lung Cancer
49
CHRNA5 15q24 LNCR2 -CHRNA5 and Lung Cancer
49
BCL2L1 20q11.21 BCLX, BCL2L, BCLXL, BCLXS, Bcl-X, bcl-xL, bcl-xS, PPP1R52, BCL-XL/S -BCL2L1 and Lung Cancer
49
NFE2L2 2q31 NRF2 -NFE2L2 and Lung Cancer
46
ABCG2 4q22 MRX, MXR, ABCP, BCRP, BMDP, MXR1, ABC15, BCRP1, CD338, GOUT1, CDw338, UAQTL1, EST157481 -ABCG2 and Lung Cancer
45
EPHX1 1q42.1 MEH, EPHX, EPOX, HYL1 -EPHX1 and Lung Cancer
43
XPC 3p25.1 XP3, RAD4, XPCC, p125 -XPC and Lung Cancer
43
ABCC1 16p13.1 MRP, ABCC, GS-X, MRP1, ABC29 -ABCC1 (MRP1) and Lung Cancer
42
CALU 7q32.1 -CALU and Lung Cancer
42
HEBP1 12p13.1 HBP, HEBP -HEBP1 and Lung Cancer
42
RHOA 3p21.3 ARHA, ARH12, RHO12, RHOH12 -RHOA and Lung Cancer
41
GAPDH 12p13 G3PD, GAPD, HEL-S-162eP -GAPDH and Lung Cancer
40
CXCR4 2q21 FB22, HM89, LAP3, LCR1, NPYR, WHIM, CD184, LAP-3, LESTR, NPY3R, NPYRL, WHIMS, HSY3RR, NPYY3R, D2S201E -CXCR4 and Lung Cancer
40
NAT2 8p22 AAC2, PNAT, NAT-2 -NAT2 and Lung Cancer
39
TP63 3q28 AIS, KET, LMS, NBP, RHS, p40, p51, p63, EEC3, OFC8, p73H, p73L, SHFM4, TP53L, TP73L, p53CP, TP53CP, B(p51A), B(p51B) -TP63 and Lung Cancer
39
CHRNA3 15q24 LNCR2, PAOD2, NACHRA3 -CHRNA3 and Lung Cancer
38
AKT2 19q13.1-q13.2 PKBB, PRKBB, HIHGHH, PKBBETA, RAC-BETA -AKT2 and Lung Cancer
38
NME1 17q21.3 NB, AWD, NBS, GAAD, NDKA, NM23, NDPKA, NDPK-A, NM23-H1 -NME1 and Lung Cancer
38
PDLIM4 5q31.1 RIL -PDLIM4 and Lung Cancer
36
CD82 11p11.2 R2, 4F9, C33, IA4, ST6, GR15, KAI1, SAR2, TSPAN27 -CD82 and Lung Cancer
36
BAD 11q13.1 BBC2, BCL2L8 -BAD and Lung Cancer
36
MYCN 2p24.3 NMYC, ODED, MODED, N-myc, bHLHe37 -MYCN in Lung Cancer
35
RAC1 7p22 MIG5, Rac-1, TC-25, p21-Rac1 -RAC1 and Lung Cancer
35
ERBB3 12q13 HER3, LCCS2, ErbB-3, c-erbB3, erbB3-S, MDA-BF-1, c-erbB-3, p180-ErbB3, p45-sErbB3, p85-sErbB3 -ERBB3 and Lung Cancer
34
TWIST1 7p21.2 CRS, CSO, SCS, ACS3, CRS1, BPES2, BPES3, TWIST, bHLHa38 -TWIST1 and Lung Cancer
34
TNFRSF10B 8p22-p21 DR5, CD262, KILLER, TRICK2, TRICKB, ZTNFR9, TRAILR2, TRICK2A, TRICK2B, TRAIL-R2, KILLER/DR5 -TNFRSF10B and Lung Cancer
32
CYP1B1 2p22.2 CP1B, GLC3A, CYPIB1, P4501B1 -CYP1B1 and Lung Cancer
32
VEGFC 4q34.3 VRP, Flt4-L, LMPH1D -VEGFC and Lung Cancer
31
DROSHA 5p13.3 RN3, ETOHI2, RNASEN, RANSE3L, RNASE3L, HSA242976 -DROSHA and Lung Cancer
31
ASCL1 12q23.2 ASH1, HASH1, MASH1, bHLHa46 -ASCL1 and Lung Cancer
31
POMC 2p23.3 LPH, MSH, NPP, POC, ACTH, CLIP -POMC and Lung Cancer
30
NKX2-1 14q13 BCH, BHC, NK-2, TEBP, TTF1, NKX2A, T/EBP, TITF1, TTF-1, NKX2.1 -NKX2-1 and Lung Cancer
30
PARP1 1q41-q42 PARP, PPOL, ADPRT, ARTD1, ADPRT1, PARP-1, ADPRT 1, pADPRT-1 -PARP1 and Lung Cancer
30
SERPINB5 18q21.33 PI5, maspin -SERPIN-B5 and Lung Cancer
29
CXCL1 4q21 FSP, GRO1, GROa, MGSA, NAP-3, SCYB1, MGSA-a -CXCL1 and Lung Cancer
29
CYP2A6 19q13.2 CPA6, CYP2A, CYP2A3, P450PB, CYPIIA6, P450C2A -CYP2A6 and Lung Cancer
29
RELA 11q13 p65, NFKB3 -RELA and Lung Cancer
29
MCC 5q21 MCC1 -MCC and Lung Cancer
29
CDH13 16q23.3 CDHH, P105 -CDH13 and Lung Cancer
29
JUN 1p32-p31 AP1, AP-1, c-Jun -c-Jun and Lung Cancer
28
CXCL12 10q11.1 IRH, PBSF, SDF1, TLSF, TPAR1, SCYB12 -CXCL12 and Lung Cancer
28
RECK 9p13.3 ST15 -RECK and Lung Cancer
28
FOXM1 12p13 MPP2, TGT3, HFH11, HNF-3, INS-1, MPP-2, PIG29, FKHL16, FOXM1B, HFH-11, TRIDENT, MPHOSPH2 -FOXM1 and Lung Cancer
28
ZEB1 10p11.2 BZP, TCF8, AREB6, FECD6, NIL2A, PPCD3, ZFHEP, ZFHX1A, DELTAEF1 -ZEB1 and Lung Cancer
28
TSC2 16p13.3 LAM, TSC4, PPP1R160 -TSC2 and Lung Cancer
27
IL6 7p21 HGF, HSF, BSF2, IL-6, IFNB2 -IL6 and Lung Cancer
27
SLC2A1 1p34.2 PED, DYT9, GLUT, DYT17, DYT18, EIG12, GLUT1, HTLVR, GLUT-1, GLUT1DS Prognostic
-GLUT1 Overexpression and Lung Cancer
27
MIR126 9q34.3 MIRN126, mir-126, miRNA126 -MicroRNA mir-126 and Lung Cancer
27
RHOC 1p13.1 H9, ARH9, ARHC, RHOH9 -RHOC and Lung Cancer
26
CLPTM1L 5p15.33 CRR9 -CLPTM1L and Lung Cancer
26
XPA 9q22.3 XP1, XPAC -XPA and Lung Cancer
26
HMGA2 12q15 BABL, LIPO, HMGIC, HMGI-C, STQTL9 -HMGA2 and Lung Cancer
25
FAS 10q24.1 APT1, CD95, FAS1, APO-1, FASTM, ALPS1A, TNFRSF6 -FAS and Lung Cancer
25
RAD51 15q15.1 RECA, BRCC5, MRMV2, HRAD51, RAD51A, HsRad51, HsT16930 -RAD51 and Lung Cancer
25
SMAD4 18q21.1 JIP, DPC4, MADH4, MYHRS -SMAD4 and Lung Cancer
24
TSC1 9q34 LAM, TSC -TSC1 and Lung Cancer
24
CYP1A2 15q24.1 CP12, P3-450, P450(PA) -CYP1A2 and Lung Cancer
24
MCL1 1q21 TM, EAT, MCL1L, MCL1S, Mcl-1, BCL2L3, MCL1-ES, bcl2-L-3, mcl1/EAT -MCL1 and Lung Cancer
24
PPP2CA 5q31.1 RP-C, PP2Ac, PP2CA, PP2Calpha -PPP2CA and Lung Cancer
24
DICER1 14q32.13 DCR1, MNG1, Dicer, HERNA, RMSE2, Dicer1e, K12H4.8-LIKE -DICER1 and Lung Cancer
23
DDR2 1q23.3 TKT, MIG20a, NTRKR3, TYRO10 -DDR2 and Lung Cancer
23
SMAD2 18q21.1 JV18, MADH2, MADR2, JV18-1, hMAD-2, hSMAD2 -SMAD2 and Lung Cancer
23
MMP1 11q22.3 CLG, CLGN -MMP1 and Lung Cancer
23
PPP2CB 8p12 PP2CB, PP2Abeta -PPP2CB and Lung Cancer
23
AXL 19q13.1 ARK, UFO, JTK11, Tyro7 -AXL and Lung Cancer
23
SKP2 5p13 p45, FBL1, FLB1, FBXL1 -SKP2 and Lung Cancer
22
ANXA8 10q11.22 ANX8, CH17-360D5.2 -ANXA8 and Lung Cancer
22
RAP1A 1p13.3 RAP1, C21KG, G-22K, KREV1, KREV-1, SMGP21 -Lung Cancer and RAP1A
22
POU5F1 6p21.31 OCT3, OCT4, OTF3, OTF4, OTF-3, Oct-3, Oct-4 -POU5F1 and Lung Cancer
22
TERC 3q26 TR, hTR, TRC3, DKCA1, PFBMFT2, SCARNA19 -TERC and Lung Cancer
22
CADM1 11q23.2 BL2, ST17, IGSF4, NECL2, RA175, TSLC1, IGSF4A, Necl-2, SYNCAM, sgIGSF, sTSLC-1, synCAM1 -CADM1 and Lung Cancer
22
CD24 6q21 CD24A -CD24 and Lung Cancer
21
TUBB3 16q24.3 CDCBM, FEOM3, TUBB4, CDCBM1, CFEOM3, beta-4, CFEOM3A -TUBB3 and Lung Cancer
21
SLC2A3 12p13.3 GLUT3 -GLUT3 and Lung cancer
21
PTK2 8q24.3 FAK, FADK, FAK1, FRNK, PPP1R71, p125FAK, pp125FAK -PTK2 and Lung Cancer
21
CISH 3p21.3 CIS, G18, SOCS, CIS-1, BACTS2 -CISH and Lung Cancer
21
MMP3 11q22.3 SL-1, STMY, STR1, CHDS6, MMP-3, STMY1 -MMP3 and Lung Cancer
21
CHRNB4 15q24 -CHRNB4 and Lung Cancer
21
DNMT3B 20q11.2 ICF, ICF1, M.HsaIIIB -DNMT3B and Lung Cancer
20
EGR1 5q31.1 TIS8, AT225, G0S30, NGFI-A, ZNF225, KROX-24, ZIF-268 -EGR1 and Lung Cancer
20
MMP7 11q22.2 MMP-7, MPSL1, PUMP-1 -MMP7 and Lung Cancer
20
HNRNPA2B1 7p15 RNPA2, HNRPA2, HNRPB1, SNRPB1, HNRNPA2, HNRNPB1, IBMPFD2, HNRPA2B1 -HNRNPA2B1 and Lung Cancer
20
CAV1 7q31.1 CGL3, PPH3, BSCL3, LCCNS, VIP21, MSTP085 -CAV1 and Lung Cancer
19
CDK1 10q21.1 CDC2, CDC28A, P34CDC2 -CDK1 and Lung Cancer
19
MAGEA3 Xq28 HIP8, HYPD, CT1.3, MAGE3, MAGEA6 -MAGEA3 and Lung Cancer
19
SMARCA4 19p13.2 BRG1, SNF2, SWI2, MRD16, RTPS2, BAF190, SNF2L4, SNF2LB, hSNF2b, BAF190A -SMARCA4 and Lung Cancer
19
LIMK1 7q11.23 LIMK, LIMK-1 -LIMK1 and Lung Cancer
18
SPARC 5q31.3-q32 ON -SPARC and Lung Cancer
18
POLE 12q24.3 FILS, POLE1, CRCS12 -POLE and Lung Cancer
18
GSTM3 1p13.3 GST5, GSTB, GTM3, GSTM3-3 -GSTM3 and Lung Cancer
17
TIMP2 17q25 DDC8, CSC-21K -TIMP2 Expression in Lung Cancer
17
NANOG 12p13.31 -NANOG and Lung Cancer
17
ALDH1A1 9q21.13 ALDC, ALDH1, HEL-9, HEL12, PUMB1, ALDH11, RALDH1, ALDH-E1, HEL-S-53e -ALDH1A1 and Lung Cancer
17
THBS1 15q15 TSP, THBS, TSP1, TSP-1, THBS-1 -THBS1 and Lung Cancer
17
CCL2 17q11.2-q12 HC11, MCAF, MCP1, MCP-1, SCYA2, GDCF-2, SMC-CF, HSMCR30 -CCL2 and Lung Cancer
17
ERCC5 13q33 XPG, UVDR, XPGC, COFS3, ERCM2, ERCC5-201 -ERCC5 and Lung Cancer
17
CBL 11q23.3 CBL2, NSLL, C-CBL, RNF55, FRA11B -CBL and Lung Cancer
17
CD74 5q32 II, DHLAG, HLADG, Ia-GAMMA Translocation
-CD74 and Lung Cancer
-CD74-NTRK1 fusion in Lung Cancer
16
CREB1 2q34 CREB -CREB1 and Lung Cancer
16
MALT1 18q21 MLT, MLT1, IMD12 -MALT1 and Lung Cancer
16
TIMP3 22q12.3 SFD, K222, K222TA2, HSMRK222 -TIMP3 and Lung Cancer
16
TYMS 18p11.32 TS, TMS, HST422 -TYMS and Lung Cancer
16
SSTR2 17q24 -SSTR2 and Lung Cancer
16
PECAM1 17q23.3 CD31, PECA1, GPIIA', PECAM-1, endoCAM, CD31/EndoCAM -PECAM1 and Lung Cancer
16
SIRT1 10q21.3 SIR2, hSIR2, SIR2L1 -SIRT1 and Lung Cancer
16
LOX 5q23.2 -LOX and Lung Cancer
16
PLK1 16p12.2 PLK, STPK13 -PLK1 and Lung Cancer
15
EPCAM 2p21 ESA, KSA, M4S1, MK-1, DIAR5, EGP-2, EGP40, KS1/4, MIC18, TROP1, EGP314, HNPCC8, TACSTD1 -EPCAM and Lung Cancer
15
HES1 3q28-q29 HHL, HRY, HES-1, bHLHb39 -HES1 and Lung Cancer
15
PLAUR 19q13 CD87, UPAR, URKR, U-PAR -PLAUR and Lung Cancer
15
CYP3A4 7q21.1 HLP, CP33, CP34, CYP3A, NF-25, CYP3A3, P450C3, CYPIIIA3, CYPIIIA4, P450PCN1 -CYP3A4 and Lung Cancer
15
SPP1 4q22.1 OPN, BNSP, BSPI, ETA-1 -SPP1 and Lung Cancer
15
RAF1 3p25 NS5, CRAF, Raf-1, c-Raf, CMD1NN -RAF1 and Lung Cancer
15
CRK 17p13.3 p38, CRKII -CRK and Lung Cancer
14
CDC42 1p36.1 G25K, CDC42Hs -CDC42 and Lung Cancer
14
S100A4 1q21 42A, 18A2, CAPL, FSP1, MTS1, P9KA, PEL98 -S100A4 and Lung Cancer
14
DKK1 10q11.2 SK, DKK-1 -DKK1 and Lung Cancer
14
S100A2 1q21 CAN19, S100L -S100A2 and Lung Cancer
14
MARCO 2q14.2 SCARA2 -MARCO and Lung Cancer
14
TUSC2 3p21.3 PAP, FUS1, PDAP2, C3orf11 -TUSC2 and Lung Cancer
14
GPC3 Xq26.1 SGB, DGSX, MXR7, SDYS, SGBS, OCI-5, SGBS1, GTR2-2 -GPC3 and Lung Cancer
14
SCGB1A1 11q12.3 UGB, UP1, CC10, CC16, CCSP, UP-1, CCPBP -SCGB1A1 and Lung Cancer
14
TNFSF11 13q14 ODF, OPGL, sOdf, CD254, OPTB2, RANKL, TRANCE, hRANKL2 -TNFSF11 and Lung Cancer
14
EIF4E 4q23 CBP, EIF4F, AUTS19, EIF4E1, EIF4EL1 -EIF4E and Lung Cancer
14
FOSL1 11q13 FRA, FRA1, fra-1 -FOSL1 and Lung Cancer
14
SOD2 6q25.3 IPOB, MNSOD, MVCD6 -SOD2 and Lung Cancer
14
BECN1 17q21 ATG6, VPS30, beclin1 -BECN1 and Lung Cancer
14
FOXO3 6q21 FOXO2, AF6q21, FKHRL1, FOXO3A, FKHRL1P2 -FOXO3 and Lung Cancer
14
CDC25C 5q31 CDC25, PPP1R60 -CDC25C and Lung Cancer
14
MUC5AC 11p15.5 TBM, leB, MUC5 -MUC5AC and Lung Cancer
14
GPX1 3p21.3 GPXD, GSHPX1 -GPX1 and Lung Cancer
14
CCNA2 4q27 CCN1, CCNA -CCNA2 and Lung Cancer
14
TPM3 1q21.2 TM3, TM5, TRK, CFTD, NEM1, TM-5, TM30, CAPM1, TM30nm, TPMsk3, hscp30, HEL-189, HEL-S-82p, OK/SW-cl.5 -TPM3 and Lung Cancer
14
IGF1 12q23.2 IGFI, IGF-I, IGF1A -IGF1 and Lung Cancer
14
WIF1 12q14.3 WIF-1 -WIF1 and Lung Cancer
13
CD68 17p13 GP110, LAMP4, SCARD1 -CD68 and Lung Cancer
13
MMP12 11q22.3 ME, HME, MME, MMP-12 -MMP12 and Lung Cancer
13
KDR 4q11-q12 FLK1, CD309, VEGFR, VEGFR2 -KDR and Lung Cancer
13
MOS 8q11 MSV -MOS and Lung Cancer
13
WNT5A 3p21-p14 hWNT5A -WNT5A and Lung Cancer
13
SEMA3B 3p21.3 SemA, SEMA5, SEMAA, semaV, LUCA-1 -SEMA3B and Lung Cancer
13
RBM5 3p21.3 G15, H37, RMB5, LUCA15 -RBM5 and Lung Cancer
13
IL1B 2q14 IL-1, IL1F2, IL1-BETA -IL1B and Lung Cancer
13
ZEB2 2q22.3 SIP1, SIP-1, ZFHX1B, HSPC082, SMADIP1 -ZEB2 and Lung Cancer
13
ERBB4 2q33.3-q34 HER4, ALS19, p180erbB4 -ERBB4 and Lung Cancer
12
WARS 14q32.31 IFI53, IFP53, GAMMA-2 -WARS and Lung Cancer
12
PTHLH 12p12.1-p11.2 HHM, PLP, BDE2, PTHR, PTHRP -PTHLH and Lung Cancer
12
RHOB 2p24 ARH6, ARHB, RHOH6, MST081, MSTP081 -RHOB and Lung Cancer
12
VIP 6q25 PHM27 -VIP and Lung Cancer
12
MMP14 14q11.2 MMP-14, MMP-X1, MT-MMP, MT1MMP, MTMMP1, WNCHRS, MT1-MMP, MT-MMP 1 -MMP14 and Lung Cancer
12
SLC19A1 21q22.3 CHMD, FOLT, IFC1, REFC, RFC1 -SLC19A1 and Lung Cancer
12
CDH2 18q11.2 CDHN, NCAD, CD325, CDw325 -CDH2 and Lung Cancer
12
WWOX 16q23 FOR, WOX1, EIEE28, FRA16D, SCAR12, HHCMA56, PRO0128, SDR41C1, D16S432E -WWOX and Lung Cancer
12
RRM2 2p25-p24 R2, RR2, RR2M -RRM2 and Lung Cancer
12
COL18A1 21q22.3 KS, KNO, KNO1 -COL18A1 and Lung Cancer
12
E2F3 6p22 E2F-3 -E2F3 and Lung Cancer
11
BIRC7 20q13.3 KIAP, LIVIN, MLIAP, RNF50, ML-IAP -BIRC7 and Lung Cancer
11
PRKDC 8q11 HYRC, p350, DNAPK, DNPK1, HYRC1, IMD26, XRCC7, DNA-PKcs -PRKDC and Lung Cancer
11
BMP2 20p12 BDA2, BMP2A -BMP2 and Lung Cancer
11
JUND 19p13.2 AP-1 -JUND and Lung Cancer
11
MAGEA4 Xq28 CT1.4, MAGE4, MAGE4A, MAGE4B, MAGE-41, MAGE-X2 -MAGEA4 and Lung Cancer
11
PDK1 2q31.1 -PDK1 and Lung Cancer
11
CTAG1B Xq28 CTAG, ESO1, CT6.1, CTAG1, LAGE-2, LAGE2B, NY-ESO-1 -CTAG1B and Lung Cancer
11
EPHA2 1p36 ECK, CTPA, ARCC2, CTPP1, CTRCT6 -EPHA2 and Lung Cancer
11
BAP1 3p21.1 UCHL2, hucep-6, HUCEP-13 -BAP1 and Lung Cancer
11
NOTCH3 19p13.2-p13.1 IMF2, CASIL, CADASIL -NOTCH3 and Lung Cancer
11
FGFR4 5q35.2 TKF, JTK2, CD334 -FGFR4 and Lung Cancer
11
PPP1R13L 19q13.32 RAI, RAI4, IASPP, NKIP1 -PPP1R13L and Lung Cancer
11
TNFRSF1A 12p13.2 FPF, MS5, p55, p60, TBP1, TNF-R, TNFAR, TNFR1, p55-R, CD120a, TNFR55, TNFR60, TNF-R-I, TNF-R55, TNFR1-d2 -TNFRSF1A and Lung Cancer
10
H19 11p15.5 ASM, BWS, WT2, ASM1, PRO2605, D11S813E, LINC00008, NCRNA00008 -H19 and Lung Cancer
10
NAT1 8p22 AAC1, MNAT, NATI, NAT-1 -NAT1 and Lung Cancer
10
MIF 22q11.23 GIF, GLIF, MMIF -MIF and Lung Cancer
10
CASP10 2q33-q34 MCH4, ALPS2, FLICE2 -CASP10 and Lung Cancer
10
MIRLET7C 21q21.1 LET7C, let-7c, MIRNLET7C, hsa-let-7c -MicroRNA let-7c and Lung Cancer
10
CLDN1 3q28-q29 CLD1, SEMP1, ILVASC -CLDN1 and Lung Cancer
10
DKK3 11p15.2 RIG, REIC -DKK3 and Lung Cancer
10
BCAR1 16q23.1 CAS, CAS1, CASS1, CRKAS, P130Cas -BCAR1 and Lung Cancer
10
ATF1 12q13 TREB36, EWS-ATF1, FUS/ATF-1 -ATF1 and Lung Cancer
10
TFE3 Xp11.22 TFEA, RCCP2, RCCX1, bHLHe33 -TFE3 and Lung Cancer
10
RBL2 16q12.2 Rb2, P130 -RBL2 and Lung Cancer
10
ERCC4 16p13.12 XPF, RAD1, FANCQ, ERCC11 -ERCC4 and Lung Cancer
10
CD63 12q12-q13 MLA1, ME491, LAMP-3, OMA81H, TSPAN30 -CD63 and Lung Cancer
10
VEGFB 11q13 VRF, VEGFL -VEGFB and Lung Cancer
10
CHFR 12q24.33 RNF116, RNF196 -CHFR and Lung Cancer
10
S100P 4p16 MIG9 -S100P and Lung Cancer
10
DLC1 8p22 HP, ARHGAP7, STARD12, p122-RhoGAP -DLC1 and Lung Cancer
10
PLA2G4A 1q25 PLA2G4, cPLA2-alpha -PLA2G4A and Lung Cancer
10
MUC4 3q29 ASGP, MUC-4, HSA276359 -MUC4 and Lung Cancer
9
NEDD9 6p24.2 CAS2, CASL, HEF1, CAS-L, CASS2 -NEDD9 and Lung Cancer
9
TNFRSF11A 18q22.1 FEO, OFE, ODFR, OSTS, PDB2, RANK, CD265, OPTB7, TRANCER, LOH18CR1 -TNFRSF11A and Lung Cancer
9
KRT5 12q13.13 K5, CK5, DDD, DDD1, EBS2, KRT5A -KRT5 and Lung Cancer
9
LGALS1 22q13.1 GBP, GAL1 -LGALS1 and Lung Cancer
9
HYAL2 3p21.3 LUCA2 -HYAL2 and Lung Cancer
9
CXCL5 4q13.3 SCYB5, ENA-78 -CXCL5 and Lung Cancer
9
POSTN 13q13.3 PN, OSF2, OSF-2, PDLPOSTN, periostin -POSTN and Lung Cancer
9
AVPR1B 1q32 AVPR3 -AVPR1B and Lung Cancer
9
PTPRD 9p23-p24.3 HPTP, PTPD, HPTPD, HPTPDELTA, RPTPDELTA -PTPRD and Lung Cancer
9
BRMS1 11q13.2 -BRMS1 and Lung Cancer
9
RARB 3p24.2 HAP, RRB2, NR1B2, MCOPS12 -RARB and Lung Cancer
9
SLIT2 4p15.2 SLIL3, Slit-2 -SLIT2 and Lung Cancer
9
GADD45A 1p31.2 DDIT1, GADD45 -GADD45A and Lung Cancer
9
GATA3 10p15 HDR, HDRS -GATA3 and Lung Cancer
9
XRCC5 2q35 KU80, KUB2, Ku86, NFIV, KARP1, KARP-1 -XRCC5 and Lung Cancer
9
TP53BP1 15q15-q21 p202, 53BP1 -TP53BP1 and Lung Cancer
9
ETS2 21q22.2 ETS2IT1 -ETS2 and Lung Cancer
9
IL17A 6p12 IL17, CTLA8, IL-17, IL-17A -IL17A and Lung Cancer
9
TIMP1 Xp11.3-p11.23 EPA, EPO, HCI, CLGI, TIMP Prognostic
-TIMP1 and Non Small Cell Lung Cancer
9
PARK2 6q25.2-q27 PDJ, PRKN, AR-JP, LPRS2 -PARK2 and Lung Cancer
9
ACTB 7p22 BRWS1, PS1TP5BP1 -ACTB and Lung Cancer
9
EP300 22q13.2 p300, KAT3B, RSTS2 -EP300 and Lung Cancer
9
CFTR 7q31.2 CF, MRP7, ABC35, ABCC7, CFTR/MRP, TNR-CFTR, dJ760C5.1 -CFTR and Lung Cancer
9
IL17C 16q24 CX2, IL-17C -IL17C and Lung Cancer
9
DIABLO 12q24.31 SMAC, DFNA64 -DIABLO and Lung Cancer
9
AVPR1A 12q14-q15 V1aR, AVPR1, AVPR V1a -AVPR1A and Lung Cancer
9
SOCS3 17q25.3 CIS3, SSI3, ATOD4, Cish3, SSI-3, SOCS-3 -SOCS3 and Lung Cancer
9
ANXA2 15q22.2 P36, ANX2, LIP2, LPC2, CAL1H, LPC2D, ANX2L4, PAP-IV, HEL-S-270 -ANXA2 and Lung Cancer
9
CD274 9p24 B7-H, B7H1, PDL1, PD-L1, PDCD1L1, PDCD1LG1 -CD274 and Lung Cancer
9
MMP11 22q11.23 ST3, SL-3, STMY3 -MMP11 and Lung Cancer
9
TOP2A 17q21-q22 TOP2, TP2A -TOP2A Expression in Lung Cancer
9
ABCC4 13q32 MRP4, MOATB, MOAT-B -ABCC4 and Lung Cancer
9
CRKL 22q11.21 -CRKL and Lung Cancer
8
CYP2C19 10q24 CPCJ, CYP2C, P450C2C, CYPIIC17, CYPIIC19, P450IIC19 -CYP2C19 and Lung Cancer
8
HOXA1 7p15.3 BSAS, HOX1, HOX1F -HOXA1 and Lung Cancer
8
MAML2 11q21 MAM2, MAM3, MAM-3, MLL-MAML2 -MAML2 and Lung Cancer
8
ARNT 1q21 HIF1B, TANGO, bHLHe2, HIF1BETA, HIF-1beta, HIF1-beta, HIF-1-beta -ARNT and Lung Cancer
8
EREG 4q13.3 ER -EREG and Lung Cancer
8
STAR 8p11.2 STARD1 -STAR and Lung Cancer
8
SLC34A2 4p15.2 NPTIIb, NAPI-3B, NAPI-IIb -SLC34A2 and Lung Cancer
8
DUSP1 5q34 HVH1, MKP1, CL100, MKP-1, PTPN10 -DUSP1 and Lung Cancer
8
MCM2 3q21 BM28, CCNL1, CDCL1, cdc19, D3S3194, MITOTIN -MCM2 and Lung Cancer
8
LPP 3q28 -LPP and Lung Cancer
8
EPB41L3 18p11.32 4.1B, DAL1, DAL-1 -EPB41L3 and Lung Cancer
8
STMN1 1p36.11 Lag, SMN, OP18, PP17, PP19, PR22, LAP18, C1orf215 -STMN1 and Lung Cancer
8
CCNB2 15q22.2 HsT17299 -CCNB2 and Lung Cancer
8
CCDC6 10q21 H4, PTC, TPC, TST1, D10S170 -CCDC6 and Lung Cancer
8
MIRLET7G 3p21.1 LET7G, let-7g, MIRNLET7G, hsa-let-7g -MicroRNA let-7g and Lung Cancer
8
LATS2 13q12.11 KPM -LATS2 and Lung Cancer
8
MTAP 9p21 BDMF, MSAP, DMSFH, LGMBF, DMSMFH, c86fus, HEL-249 -MTAP and Lung Cancer
8
NDRG1 8q24.3 GC4, RTP, DRG1, NDR1, NMSL, TDD5, CAP43, CMT4D, DRG-1, HMSNL, RIT42, TARG1, PROXY1 -NDRG1 and Lung Cancer
8
ING1 13q34 p33, p47, p33ING1, p24ING1c, p33ING1b, p47ING1a -ING1 and Lung Cancer
8
ALDH3A1 17p11.2 ALDH3, ALDHIII -ALDH3A1 and Lung Cancer
8
TPX2 20q11.2 DIL2, p100, DIL-2, HCTP4, FLS353, HCA519, REPP86, C20orf1, C20orf2, GD:C20orf1 -TPX2 and Lung Cancer
8
CRTC1 19p13.11 MECT1, TORC1, TORC-1, WAMTP1 -CRTC1 and Lung Cancer
8
HYAL1 3p21.31 MPS9, NAT6, LUCA1, HYAL-1 -HYAL1 and Lung Cancer
8
NFKB1 4q24 p50, KBF1, p105, EBP-1, NF-kB1, NFKB-p50, NFkappaB, NF-kappaB, NFKB-p105, NF-kappa-B -NFKB1 and Lung Cancer
8
AIDA 1q41 C1orf80 -AIDA and Lung Cancer
8
CXCL10 4q21 C7, IFI10, INP10, IP-10, crg-2, mob-1, SCYB10, gIP-10 -CXCL10 and Lung Cancer
8
CYP24A1 20q13 CP24, HCAI, CYP24, P450-CC24 -CYP24A1 and Lung Cancer
8
CCR7 17q12-q21.2 BLR2, EBI1, CCR-7, CD197, CDw197, CMKBR7, CC-CKR-7 -CCR7 and Lung Cancer
8
CYR61 1p22.3 CCN1, GIG1, IGFBP10 -CYR61 and Lung Cancer
8
MBD2 18q21 DMTase, NY-CO-41 -MBD2 and Lung Cancer
8
SMARCA2 9p22.3 BRM, SNF2, SWI2, hBRM, NCBRS, Sth1p, BAF190, SNF2L2, SNF2LA, hSNF2a -SMARCA2 and Lung Cancer
8
CEACAM1 19q13.2 BGP, BGP1, BGPI -CEACAM1 and Lung Cancer
8
HSPB1 7q11.23 CMT2F, HMN2B, HSP27, HSP28, Hsp25, SRP27, HS.76067, HEL-S-102 -HSPB1 and Lung Cancer
8
ABCC2 10q24 DJS, MRP2, cMRP, ABC30, CMOAT -ABCC2 and Lung Cancer
7
MAPK8 10q11.22 JNK, JNK1, PRKM8, SAPK1, JNK-46, JNK1A2, SAPK1c, JNK21B1/2 -MAPK8 and Lung Cancer
7
EIF3E 8q22-q23 INT6, EIF3S6, EIF3-P48, eIF3-p46 -EIF3E and Lung Cancer
7
RASSF5 1q32.1 RAPL, Maxp1, NORE1, NORE1A, NORE1B, RASSF3 -RASSF5 and Lung Cancer
7
AKR1B10 7q33 HIS, HSI, ARL1, ARL-1, ALDRLn, AKR1B11, AKR1B12 -AKR1B10 and Lung Cancer
7
CTNNA1 5q31.2 CAP102 -CTNNA1 and Lung Cancer
7
ATF3 1q32.3 -ATF3 and Lung Cancer
7
ADAM9 8p11.22 MCMP, MDC9, CORD9, Mltng -ADAM9 and Lung Cancer
7
RARS 5q35.1 HLD9, ArgRS, DALRD1 -RARS and Lung Cancer
7
ATG5 6q21 ASP, APG5, APG5L, hAPG5, APG5-LIKE -ATG5 and Lung Cancer
7
PRB2 12p13.2 Ps, cP7, IB-9, PRPPRB1 -PRB2 and Lung Cancer
7
CHGA 14q32 CGA -CHGA and Lung Cancer
7
CAST 5q15 BS-17, PLACK -CAST and Lung Cancer
7
DMBT1 10q26.13 GP340, muclin -DMBT1 and Lung Cancer
7
BRD4 19p13.1 CAP, MCAP, HUNK1, HUNKI -BRD4 and Lung Cancer
7
LYN 8q13 JTK8, p53Lyn, p56Lyn -LYN and Lung Cancer
7
CRP 1q23.2 PTX1 -CRP and Lung Cancer
7
MBD1 18q21 RFT, PCM1, CXXC3 -MBD1 and Lung Cancer
7
XRCC2 7q36.1 -XRCC2 and Lung Cancer
7
CYP2A13 19q13.2 CPAD, CYP2A, CYPIIA13 -CYP2A13 and Lung Cancer
7
FEN1 11q12 MF1, RAD2, FEN-1 -FEN1 and Lung Cancer
7
SPRR1B 1q21-q22 SPRR1, GADD33, CORNIFIN -SPRR1B and Lung Cancer
7
SOX4 6p22.3 EVI16 -SOX4 and Lung Cancer
7
ID2 2p25 GIG8, ID2A, ID2H, bHLHb26 Prognostic
-ID2 Expression in Lung Cancer
7
XRCC4 5q14.2 -XRCC4 and Lung Cancer
7
BDNF 11p13 ANON2, BULN2 -BDNF and Lung Cancer
7
WNT7A 3p25 -WNT7A and Lung Cancer
7
DNAJB4 1p31.1 DjB4, HLJ1, DNAJW -DNAJB4 and Lung Cancer
7
SPRY2 13q31.1 hSPRY2 -SPRY2 and Lung Cancer
7
DDR1 6p21.3 CAK, DDR, NEP, HGK2, PTK3, RTK6, TRKE, CD167, EDDR1, MCK10, NTRK4, PTK3A -DDR1 and Lung Cancer
7
TBK1 12q14.1 NAK, T2K -TBK1 and Lung Cancer
7
TNFRSF10A 8p21 DR4, APO2, CD261, TRAILR1, TRAILR-1 -TNFRSF10A and Lung Cancer
7
FOLR1 11q13.3-q14.1 FBP, FOLR -FOLR1 and Lung Cancer
7
CALCA 11p15.2 CT, KC, CGRP, CALC1, CGRP1, CGRP-I -CALCA and Lung Cancer
7
ING4 12p13.31 my036, p29ING4 -ING4 and Lung Cancer
7
MSN Xq11.1 HEL70 -MSN and Lung Cancer
7
MVP 16p11.2 LRP, VAULT1 -MVP and Lung Cancer
7
MAP2K4 17p12 JNKK, MEK4, MKK4, SEK1, SKK1, JNKK1, SERK1, MAPKK4, PRKMK4, SAPKK1, SAPKK-1 -MAP2K4 and Lung Cancer
7
SEMA3F 3p21.3 SEMA4, SEMAK, SEMA-IV -SEMA3F and Lung Cancer
7
PPP2R1B 11q23.2 PR65B, PP2A-Abeta -PPP2R1B and Lung Cancer
7
ZMYND10 3p21.3 BLU, FLU, CILD22 -ZMYND10 and Lung Cancer
7
AMFR 16q21 GP78, RNF45 -AMFR and Lung Cancer
7
MTDH 8q22.1 3D3, AEG1, AEG-1, LYRIC, LYRIC/3D3 -MTDH and Lung Cancer
7
BCHE 3q26.1-q26.2 E1, CHE1, CHE2 -BCHE and Lung Cancer
7
NUMB 14q24.3 S171, C14orf41, c14_5527 -NUMB and Lung Cancer
6
SPRR2C 1q21-q22 -SPRR2C and Lung Cancer
6
MAX 14q23 bHLHd4 -MAX and Lung Cancer
6
PTPRF 1p34 LAR, BNAH2 -PTPRF and Lung Cancer
6
ARHGDIB 12p12.3 D4, GDIA2, GDID4, LYGDI, Ly-GDI, RAP1GN1, RhoGDI2 -ARHGDIB and Lung Cancer
6
PDCD1 2q37.3 PD1, PD-1, CD279, SLEB2, hPD-1, hPD-l, hSLE1 -PDCD1 and Lung Cancer
6
UCHL1 4p14 NDGOA, PARK5, PGP95, PGP9.5, Uch-L1, HEL-117, PGP 9.5 -UCHL1 and Lung Cancer
6
GATA5 20q13.33 GATAS, bB379O24.1 -GATA5 and Lung Cancer
6
SPRR2A 1q21-q22 -SPRR2A and Lung Cancer
6
PITX1 5q31.1 BFT, CCF, POTX, PTX1, LBNBG -PITX1 and Lung Cancer
6
FOXA2 20p11 HNF3B, TCF3B -FOXA2 and Lung Cancer
6
DLEC1 3p21.3 F56, DLC1, CFAP81 -DLEC1 and Lung Cancer
6
PTTG1 5q35.1 EAP1, PTTG, HPTTG, TUTR1 -PTTG1 and Lung Cancer
6
PDPN 1p36.21 T1A, GP36, GP40, Gp38, OTS8, T1A2, TI1A, T1A-2, AGGRUS, HT1A-1, PA2.26 -PDPN and Lung Cancer
6
HOXA5 7p15.2 HOX1, HOX1C, HOX1.3 -HOXA5 and Lung Cancer
6
ITGB3 17q21.32 GT, CD61, GP3A, BDPLT2, GPIIIa, BDPLT16 -ITGB3 and Lung Cancer
6
SNAI2 8q11 SLUG, WS2D, SLUGH1, SNAIL2 -SNAI2 and Lung Cancer
6
NEUROD1 2q32 BETA2, BHF-1, MODY6, NEUROD, bHLHa3 -NEUROD1 and Lung Cancer
6
HOTAIR 12q13.13 HOXAS, HOXC-AS4, HOXC11-AS1, NCRNA00072 -HOTAIR and Lung Cancer
6
REST 4q12 XBR, NRSF -REST and Lung Cancer
6
TTL 2q13 -TTL and Lung Cancer
6
RAD52 12p13-p12.2 -RAD52 and Lung Cancer
6
GATA4 8p23.1-p22 TOF, ASD2, VSD1, TACHD -GATA4 and Lung Cancer
6
CHIA 1p13.2 CHIT2, AMCASE, TSA1902 -CHIA and Lung Cancer
6
SHC1 1q21 SHC, SHCA -SHC1 and Lung Cancer
6
TGFBR3 1p33-p32 BGCAN, betaglycan -TGFBR3 and Lung Cancer
6
GAB1 4q31.21 -GAB1 and Lung Cancer
6
ENO1 1p36.2 NNE, PPH, MPB1, ENO1L1 -ENO1 and Lung Cancer
6
EXO1 1q43 HEX1, hExoI -EXO1 and Lung Cancer
6
SPRR1A 1q21-q22 SPRK -SPRR1A and Lung Cancer
6
CLMP 11q24.1 ACAM, ASAM, CSBM, CSBS -CLMP and Lung Cancer
6
KL 13q12 -KL and Lung Cancer
6
CDCP1 3p21.31 CD318, TRASK, SIMA135 -CDCP1 and Lung Cancer
6
UBE2C 20q13.12 UBCH10, dJ447F3.2 -UBE2C and Lung Cancer
6
SPRR2B 1q21-q22 -SPRR2B and Lung Cancer
6
ACTA2 10q23.3 AAT6, ACTSA, MYMY5 -ACTA2 and Lung Cancer
6
AKR1C1 10p15-p14 C9, DD1, DDH, DDH1, H-37, HBAB, MBAB, HAKRC, DD1/DD2, 2-ALPHA-HSD, 20-ALPHA-HSD -AKR1C1 and Lung Cancer
6
MMP10 11q22.3 SL-2, STMY2 -MMP10 and Lung Cancer
6
FEV 2q36 PET-1, HSRNAFEV -FEV and Lung Cancer
6
DDB2 11p12-p11 DDBB, UV-DDB2 -DDB2 and Lung Cancer
6
SNAI1 20q13.2 SNA, SNAH, SNAIL, SLUGH2, SNAIL1, dJ710H13.1 -SNAI1 and Lung Cancer
6
ECT2 3q26.1-q26.2 ARHGEF31 -ECT2 and Lung Cancer
6
ROBO1 3p12 SAX3, DUTT1 -ROBO1 and Lung Cancer
6
PWAR1 15q11.2 PAR1, PAR-1, D15S227E -PAR1 and Lung Cancer
6
MIRLET7B 22q13.31 LET7B, let-7b, MIRNLET7B, hsa-let-7b -MicroRNA let-7b and Lung Cancer
6
HDGF 1q23.1 HMG1L2 -HDGF and Lung Cancer
6
MDC1 6p21.3 NFBD1 -MDC1 and Lung Cancer
6
RAG2 11p13 RAG-2 -RAG2 and Lung Cancer
6
RPA1 17p13.3 HSSB, RF-A, RP-A, REPA1, RPA70, MST075 -RPA1 and Lung Cancer
5
BRAP 12q24 IMP, BRAP2, RNF52 -BRAP and Lung Cancer
5
DUSP6 12q22-q23 HH19, MKP3, PYST1 -DUSP6 and Lung Cancer
5
CYP2C9 10q24 CPC9, CYP2C, CYP2C10, CYPIIC9, P450IIC9 -CYP2C9 and Lung Cancer
5
MINA 3q11.2 ROX, MDIG, NO52, MINA53 -MINA and Lung Cancer
5
ERCC6 10q11.23 CSB, CKN2, COFS, ARMD5, COFS1, RAD26, UVSS1 -ERCC6 and Lung Cancer
5
EPHB6 7q33-q35 HEP -EPHB6 and Lung Cancer
5
CXCR3 Xq13 GPR9, MigR, CD182, CD183, Mig-R, CKR-L2, CMKAR3, IP10-R -CXCR3 and Lung Cancer
5
FPGS 9q34.1 -FPGS and Lung Cancer
5
PTGER2 14q22 EP2 -PTGER2 and Lung Cancer
5
TJP1 15q13 ZO-1 -TJP1 and Lung Cancer
5
MBD4 3q21.3 MED1 -MBD4 and Lung Cancer
5
EFEMP1 2p16 DHRD, DRAD, FBNL, MLVT, MTLV, S1-5, FBLN3, FIBL-3 -EFEMP1 and Lung Cancer
5
LIG4 13q33-q34 LIG4S -LIG4 and Lung Cancer
5
IL1A 2q14 IL1, IL-1A, IL1F1, IL1-ALPHA -IL1A and Lung Cancer
5
CRTC2 1q21.3 TORC2, TORC-2 -CRTC2 and Lung Cancer
5
NOTO 2p13.2 -NOTO and Lung Cancer
5
SSTR1 14q13 SS1R, SS1-R, SRIF-2, SS-1-R -SSTR1 and Lung Cancer
5
SULF2 20q13.12 HSULF-2 -SULF2 and Lung Cancer
5
DLX4 17q21.33 BP1, DLX7, DLX8, DLX9 -DLX4 and Lung Cancer
5
BMP7 20q13 OP-1 -BMP7 and Lung Cancer
5
HSPA1B 6p21.3 HSP70-2, HSP70-1B -HSPA1B and Lung Cancer
5
CDK5 7q36 PSSALRE -CDK5 and Lung Cancer
5
CD46 1q32 MCP, TLX, AHUS2, MIC10, TRA2.10 -CD46 and Lung Cancer
5
ASPSCR1 17q25.3 TUG, ASPL, ASPS, RCC17, UBXD9, UBXN9, ASPCR1 -ASPSCR1 and Lung Cancer
5
CDC25B 20p13 -CDC25B and Lung Cancer
5
TFG 3q12.2 TF6, HMSNP, SPG57, TRKT3 -TFG and Lung Cancer
5
RALBP1 18p11.3 RIP1, RLIP1, RLIP76 -RALBP1 and Lung Cancer
5
TMEFF2 2q32.3 TR, HPP1, TPEF, TR-2, TENB2, CT120.2 -TMEFF2 and Lung Cancer
5
ALCAM 3q13.1 MEMD, CD166 -ALCAM and Lung Cancer
5
CCNA1 13q12.3-q13 CT146 -CCNA1 and Lung Cancer
5
TAGLN 11q23.2 SM22, SMCC, TAGLN1, WS3-10 -TAGLN and Lung Cancer
5
TSG101 11p15 TSG10, VPS23 -TSG101 and Lung Cancer
5
ACTN4 19q13 FSGS, FSGS1, ACTININ-4 -ACTN4 and Lung Cancer
5
SCGB3A1 5q35.3 HIN1, HIN-1, LU105, UGRP2, PnSP-2 -SCGB3A1 and Lung Cancer
5
DRD2 11q23 D2R, D2DR -DRD2 and Lung Cancer
5
PRDX1 1p34.1 PAG, PAGA, PAGB, PRX1, PRXI, MSP23, NKEFA, TDPX2, NKEF-A -PRDX1 and Lung Cancer
5
PDPK1 16p13.3 PDK1, PDPK2, PDPK2P, PRO0461 -PDPK1 and Lung Cancer
5
PPARD 6p21.2 FAAR, NUC1, NUCI, NR1C2, NUCII, PPARB -PPAR delta and Lung Cancer
5
TP73 1p36.3 P73 -TP73 and Lung Cancer
5
LEPR 1p31 OBR, OB-R, CD295, LEP-R, LEPRD -LEPR and Lung Cancer
5
TRAF6 11p12 RNF85, MGC:3310 -TRAF6 and Lung Cancer
5
AQP1 7p14 CO, CHIP28, AQP-CHIP -AQP1 and Lung Cancer
5
S100A9 1q21 MIF, NIF, P14, CAGB, CFAG, CGLB, L1AG, LIAG, MRP14, 60B8AG, MAC387 -S100A9 and Lung Cancer
5
IGFBP5 2q35 IBP5 -IGFBP5 and Lung Cancer
5
PGLS 19p13.2 6PGL -PGLS and Lung Cancer
5
SEMA3A 7p12.1 HH16, SemD, COLL1, SEMA1, SEMAD, SEMAL, coll-1, Hsema-I, SEMAIII, Hsema-III -SEMA3A and Lung Cancer
5
GAS6 13q34 AXSF, AXLLG -GAS6 and Lung Cancer
5
SUV39H1 Xp11.23 MG44, KMT1A, SUV39H, H3-K9-HMTase 1 -SUV39H1 and Lung Cancer
5
HDAC4 2q37.3 HD4, AHO3, BDMR, HDACA, HA6116, HDAC-4, HDAC-A -HDAC4 and Lung Cancer
5
SQSTM1 5q35 p60, p62, A170, OSIL, PDB3, ZIP3, p62B -SQSTM1 and Lung Cancer
5
IL15 4q31 IL-15 -IL15 and Lung Cancer
5
CCL19 9p13 ELC, CKb11, MIP3B, MIP-3b, SCYA19 -CCL19 and Lung Cancer
5
SLC29A1 6p21.1 ENT1 -SLC29A1 and Lung Cancer
5
ADAMTS1 21q21.2 C3-C5, METH1 -ADAMTS1 and Lung Cancer
5
BTG2 1q32 PC3, TIS21 -BTG2 and Lung Cancer
5
CTCFL 20q13.31 CT27, BORIS, CTCF-T, HMGB1L1, dJ579F20.2 -CTCFL and Lung Cancer
5
PYCARD 16p11.2 ASC, TMS, TMS1, CARD5, TMS-1 -PYCARD and Lung Cancer
5
SATB1 3p23 -SATB1 and Lung Cancer
4
LIN28B 6q21 CSDD2 -LIN28B and Lung Cancer
4
FER 5q21 TYK3, PPP1R74, p94-Fer -FER and Lung Cancer
4
INHA 2q35 -INHA and Lung Cancer
4
SKP1 5q31 OCP2, p19A, EMC19, SKP1A, OCP-II, TCEB1L -SKP1 and Lung Cancer
4
ATG7 3p25.3 GSA7, APG7L, APG7-LIKE -ATG7 and Lung Cancer
4
LRP1B 2q21.2 LRPDIT, LRP-DIT -LRP1B and Lung Cancer
4
RALGDS 9q34.3 RGF, RGDS, RalGEF -RALGDS and Lung Cancer
4
AQP3 9p13 GIL, AQP-3 -AQP3 and Lung Cancer
4
ATF2 2q32 HB16, CREB2, TREB7, CREB-2, CRE-BP1 -ATF2 and Lung Cancer
4
SMAD6 15q22.31 AOVD2, MADH6, MADH7, HsT17432 -SMAD6 and Lung Cancer
4
MIRLET7E 19q13.41 LET7E, let-7e, MIRNLET7E, hsa-let-7e -MicroRNA let-7e and Lung Cancer
4
HOXA11 7p15.2 HOX1, HOX1I -HOXA11 and Lung Cancer
4
WNT2 7q31.2 IRP, INT1L1 -WNT2 and Lung Cancer
4
HOXA4 7p15.2 HOX1, HOX1D -HOXA4 and Lung Cancer
4
CDK7 5q12.1 CAK1, HCAK, MO15, STK1, CDKN7, p39MO15 -CDK7 and Lung Cancer
4
PLAU 10q22.2 ATF, QPD, UPA, URK, u-PA, BDPLT5 -PLAU and Lung Cancer
4
FOXC2 16q24.1 LD, MFH1, MFH-1, FKHL14 -FOXC2 and Lung Cancer
4
AGO2 8q24 Q10, EIF2C2 -EIF2C2 and Lung Cancer
4
MAPK14 6p21.3-p21.2 RK, p38, CSBP, EXIP, Mxi2, CSBP1, CSBP2, CSPB1, PRKM14, PRKM15, SAPK2A, p38ALPHA -MAPK14 and Lung Cancer
4
SERPINA1 14q32.1 PI, A1A, AAT, PI1, A1AT, PRO2275, alpha1AT -SERPINA1 and Lung Cancer
4
TACC3 4p16.3 ERIC1, ERIC-1 -TACC3 and Lung Cancer
4
CCL21 9p13 ECL, SLC, CKb9, TCA4, 6Ckine, SCYA21 -CCL21 and Lung Cancer
4
IKBKE 1q32.1 IKKE, IKKI, IKK-E, IKK-i -IKBKE and Lung Cancer
4
CCL20 2q36.3 CKb4, LARC, ST38, MIP3A, Exodus, MIP-3a, SCYA20, MIP-3-alpha -CCL20 and Lung Cancer
4
MALL 2q13 BENE -MALL and Lung Cancer
4
TP53I3 2p23.3 PIG3 -TP53I3 and Lung Cancer
4
SERPINB2 18q21.3 PAI, PAI2, PAI-2, PLANH2, HsT1201 -SERPINB2 and Lung Cancer
4
CXCR1 2q35 C-C, CD128, CD181, CKR-1, IL8R1, IL8RA, CMKAR1, IL8RBA, CDw128a, C-C-CKR-1 -CXCR1 and Lung Cancer
4
WISP1 8q24.22 CCN4, WISP1c, WISP1i, WISP1tc -WISP1 and Lung Cancer
4
FGF9 13q11-q12 GAF, FGF-9, SYNS3, HBFG-9, HBGF-9 -FGF9 and Lung Cancer
4
NEK2 1q32.3 NLK1, RP67, NEK2A, HsPK21, PPP1R111 -NEK2 and Lung Cancer
4
KLK10 19q13 NES1, PRSSL1 -KLK10 and Non-Small Cell Lung Cancer
4
ASH1L 1q22 ASH1, KMT2H, ASH1L1 -ASH1L and Lung Cancer
4
CDH3 16q22.1 CDHP, HJMD, PCAD -CDH3 and Lung Cancer
4
DEC1 9q32 CTS9 -DEC1 and Lung Cancer
4
KIAA1524 3q13.13 p90, CIP2A -KIAA1524 and Lung Cancer
4
CASP6 4q25 MCH2 -CASP6 and Lung Cancer
4
VIPR1 3p22 II, HVR1, RDC1, V1RG, VIPR, VIRG, VAPC1, VPAC1, VPAC1R, VIP-R-1, VPCAP1R, PACAP-R2, PACAP-R-2 -VIPR1 and Lung Cancer
4
JAG2 14q32 HJ2, SER2 -JAG2 and Lung Cancer
4
NFKBIA 14q13 IKBA, MAD-3, NFKBI -NFKBIA and Lung Cancer
4
CD151 11p15.5 GP27, MER2, RAPH, SFA1, PETA-3, TSPAN24 -CD151 and Lung Cancer
4
CCR1 3p21 CKR1, CD191, CKR-1, HM145, CMKBR1, MIP1aR, SCYAR1 -CCR1 and Lung Cancer
4
FOSB 19q13.32 AP-1, G0S3, GOS3, GOSB -FOSB and Lung Cancer
4
BCL2L11 2q13 BAM, BIM, BOD -BCL2L11 and Lung Cancer
4
TANK 2q24.2 ITRAF, TRAF2, I-TRAF -TANK and Lung Cancer
4
LAMC2 1q25-q31 B2T, CSF, EBR2, BM600, EBR2A, LAMB2T, LAMNB2 -LAMC2 and Lung Cancer
4
MAP3K8 10p11.23 COT, EST, ESTF, TPL2, AURA2, MEKK8, Tpl-2, c-COT -MAP3K8 and Lung Cancer
4
MED1 17q12 PBP, CRSP1, RB18A, TRIP2, PPARBP, CRSP200, DRIP205, DRIP230, PPARGBP, TRAP220 -MED1 and Lung Cancer
4
MAGEB2 Xp21.3 DAM6, CT3.2, MAGE-XP-2 -MAGEB2 and Lung Cancer
4
ROCK1 18q11.1 ROCK-I, P160ROCK -ROCK1 and Lung Cancer
4
SPINK1 5q32 TCP, PCTT, PSTI, TATI, Spink3 -SPINK1 and Lung Cancer
4
MIR107 10q23.31 MIRN107, miR-107 -MIRN107 microRNA, human and Lung Cancer
4
ZBTB7A 19p13.3 LRF, FBI1, FBI-1, ZBTB7, ZNF857A, pokemon -ZBTB7A and Lung Cancer
4
CD81 11p15.5 S5.7, CVID6, TAPA1, TSPAN28 -CD81 and Lung Cancer
4
NRP1 10p12 NP1, NRP, BDCA4, CD304, VEGF165R -NRP1 and Lung Cancer
4
PDCD6 5p15.33 ALG2, ALG-2, PEF1B -PDCD6 and Lung Cancer
4
IGFBP4 17q21.2 BP-4, IBP4, IGFBP-4, HT29-IGFBP -IGFBP4 and Lung Cancer
4
MACC1 7p21.1 7A5, SH3BP4L -MACC1 and Lung Cancer
4
DOK2 8p21.3 p56DOK, p56dok-2 -DOK2 and Lung Cancer
4
DLL4 15q14 hdelta2 -DLL4 and Lung Cancer
4
MAD1L1 7p22 MAD1, PIG9, TP53I9, TXBP181 -MAD1L1 and Lung Cancer
4
SOS1 2p21 GF1, HGF, NS4, GGF1, GINGF -SOS1 and Lung Cancer
4
NR0B1 Xp21.3 AHC, AHX, DSS, GTD, HHG, AHCH, DAX1, DAX-1, NROB1, SRXY2 -NR0B1 and Lung Cancer
4
NRP2 2q33.3 NP2, NPN2, PRO2714, VEGF165R2 -NRP2 and Lung Cancer
4
WHSC1L1 8p11.2 NSD3, pp14328 -WHSC1L1 and Lung Cancer
4
TFPI 2q32 EPI, TFI, LACI, TFPI1 -TFPI and Lung Cancer
4
HPSE 4q21.3 HPA, HPA1, HPR1, HSE1, HPSE1 -HPSE and Lung Cancer
4
POLK 5q13 DINP, POLQ, DINB1 -POLK and Lung Cancer
4
VCAN 5q14.3 WGN, ERVR, GHAP, PG-M, WGN1, CSPG2 -VCAN and Lung Cancer
4
MIRLET7D 9q22.32 LET7D, let-7d, MIRNLET7D, hsa-let-7d -None and MicroRNA let-d Cancer
4
RPS6 9p21 S6 -RPS6 and Lung Cancer
4
PRKCDBP 11p15.4 SRBC, HSRBC, CAVIN3, cavin-3 -PRKCDBP and Lung Cancer
3
GPX2 14q24.1 GPRP, GPx-2, GI-GPx, GPRP-2, GPx-GI, GSHPx-2, GSHPX-GI -GPX2 and Lung Cancer
3
CDK9 9q34.1 TAK, C-2k, CTK1, CDC2L4, PITALRE -CDK9 and Lung Cancer
3
LOXL2 8p21.3 LOR2, WS9-14 -LOXL2 and Lung Cancer
3
FGF10 5p13-p12 -FGF10 and Lung Cancer
3
REV1 2q11.1-q11.2 REV1L -REV1 and Lung Cancer
3
LARS 5q32 LRS, LEUS, LFIS, ILFS1, LARS1, LEURS, PIG44, RNTLS, HSPC192, hr025Cl -LARS and Lung Cancer
3
PLA2G2A 1p35 MOM1, PLA2, PLA2B, PLA2L, PLA2S, PLAS1, sPLA2 -PLA2G2A and Lung Cancer
3
GUSB 7q21.11 BG, MPS7 -GUSB and Lung Cancer
3
SNRPE 1q32 SME, Sm-E, B-raf, HYPT11 -SNRPE and Lung Cancer
3
CMBL 5p15.2 JS-1 -CMBL and Lung Cancer
3
AKAP12 6q24-q25 SSeCKS, AKAP250 -AKAP12 and Lung Cancer
3
SMPD1 11p15.4-p15.1 ASM, NPD, ASMASE -SMPD1 and Lung Cancer
3
LZTS1 8p22 F37, FEZ1 -LZTS1 and Lung Cancer
3
KLK14 19q13.3-q13.4 KLK-L6 -KLK14 and Lung Cancer
3
CX3CR1 3p21.3 V28, CCRL1, GPR13, CMKDR1, GPRV28, CMKBRL1 -CX3CR1 and Lung Cancer
3
U2AF1 21q22.3 RN, FP793, U2AF35, U2AFBP, RNU2AF1 -U2AF1 and Lung Cancer
3
RALB 2q14.2 -RALB and Lung Cancer
3
FRS2 12q15 SNT, SNT1, FRS2A, SNT-1, FRS2alpha -FRS2 and Lung Cancer
3
CASP2 7q34-q35 ICH1, NEDD2, CASP-2, NEDD-2, PPP1R57 -CASP2 and Lung Cancer
3
LAPTM4B 8q22.1 LC27, LAPTM4beta -LAPTM4B and Lung Cancer
3
TRA 14q11.2 IMD7, TCRA, TCRD, TRA@, TRAC -TRA and Lung Cancer
3
ROR1 1p31.3 NTRKR1, dJ537F10.1 -ROR1 and Lung Cancer
3
PEA15 1q21.1 PED, MAT1, HMAT1, MAT1H, PEA-15, HUMMAT1H -PEA15 and Lung Cancer
3
TNFRSF1B 1p36.22 p75, TBPII, TNFBR, TNFR2, CD120b, TNFR1B, TNFR80, TNF-R75, p75TNFR, TNF-R-II -TNFRSF1B and Lung Cancer
3
PIAS3 1q21 ZMIZ5 -PIAS3 and Lung Cancer
3
HOXB7 17q21.3 HOX2, HOX2C, HHO.C1, Hox-2.3 -HOXB7 and Lung Cancer
3
SSTR3 22q13.1 SS3R, SS3-R, SS-3-R, SSR-28 -SSTR3 and Lung Cancer
3
ARID2 12q12 p200, BAF200 -ARID2 and Lung Cancer
3
PRDX6 1q25.1 PRX, p29, AOP2, 1-Cys, NSGPx, aiPLA2, HEL-S-128m -PRDX6 and Lung Cancer
3
IRF7 11p15.5 IRF7A, IRF7B, IRF7C, IRF7H, IRF-7H -IRF7 and Lung Cancer
3
CLDN3 7q11.23 RVP1, HRVP1, C7orf1, CPE-R2, CPETR2 -CLDN3 and Lung Cancer
3
DUSP4 8p12-p11 TYP, HVH2, MKP2, MKP-2 -DUSP4 and Lung Cancer
3
HSP90AB1 6p12 HSP84, HSPC2, HSPCB, D6S182, HSP90B -HSP90AB1 and Lung Cancer
3
FGF19 11q13.1 -FGF19 and Lung Cancer
3
MALAT1 11q13.1 HCN, NEAT2, PRO2853, mascRNA, LINC00047, NCRNA00047 -MALAT1 and Lung Cancer
3
CLDN7 17p13.1 CLDN-7, CEPTRL2, CPETRL2, Hs.84359, claudin-1 -CLDN7 and Lung Cancer
3
POLB 8p11.2 -POLB and Lung Cancer
3
OPCML 11q25 OPCM, OBCAM, IGLON1 -OPCML and Lung Cancer
3
CEACAM6 19q13.2 NCA, CEAL, CD66c -CEACAM6 and Lung Cancer
3
TNFRSF10D 8p21 DCR2, CD264, TRUNDD, TRAILR4, TRAIL-R4 -TNFRSF10D and Lung Cancer
3
SOX18 20q13.33 HLTS -SOX18 and Lung Cancer
3
TBX21 17q21.32 TBET, T-PET, T-bet, TBLYM -TBX21 and Lung Cancer
3
CASP5 11q22.2-q22.3 ICH-3, ICEREL-III, ICE(rel)III -CASP5 and Lung Cancer
3
MTSS1 8p22 MIM, MIMA, MIMB -MTSS1 and Lung Cancer
3
CCL22 16q13 MDC, ABCD-1, SCYA22, STCP-1, DC/B-CK, A-152E5.1 -CCL22 and Lung Cancer
3
PTK7 6p21.1-p12.2 CCK4, CCK-4 -PTK7 and Lung Cancer
3
ROCK2 2p24 ROCK-II -ROCK2 and Lung Cancer
3
MAPKAPK2 1q32 MK2, MK-2, MAPKAP-K2 -MAPKAPK2 and Lung Cancer
3
PGK1 Xq13.3 PGKA, MIG10, HEL-S-68p -PGK1 and Lung Cancer
3
ING2 4q35.1 ING1L, p33ING2 -ING2 and Lung Cancer
3
CAV2 7q31.1 CAV -CAV2 and Lung Cancer
3
GREM1 15q13.3 DRM, HMPS, MPSH, PIG2, CRAC1, CRCS4, DAND2, HMPS1, IHG-2, DUP15q, C15DUPq, GREMLIN, CKTSF1B1 -GREM1 and Lung Cancer
3
SPDEF 6p21.3 PDEF, bA375E1.3 -SPDEF and Lung Cancer
3
BMP6 6p24-p23 VGR, VGR1 -BMP6 and Lung Cancer
3
TNKS 8p23.1 TIN1, ARTD5, PARPL, TINF1, TNKS1, pART5, PARP5A, PARP-5a -TNKS and Lung Cancer
3
MTA2 11q12-q13.1 PID, MTA1L1 -MTA2 and Lung Cancer
3
CXCL14 5q31 KEC, KS1, BMAC, BRAK, NJAC, MIP2G, MIP-2g, SCYB14 -CXCL14 and Lung Cancer
3
BAG3 10q25.2-q26.2 BIS, MFM6, BAG-3, CAIR-1 -BAG3 and Lung Cancer
3
PBRM1 3p21 PB1, BAF180 -PBRM1 and Lung Cancer
3
SLC7A5 16q24.3 E16, CD98, LAT1, 4F2LC, MPE16, hLAT1, D16S469E -SLC7A5 and Lung Cancer
3
NNAT 20q11.2-q12 Peg5 -NNAT and Lung Cancer
3
S100A11 1q21 MLN70, S100C, HEL-S-43 -S100A11 and Lung Cancer
3
PRC1 15q26.1 ASE1 -PRC1 and Lung Cancer
3
MIRLET7I 12q14.1 LET7I, MIRNLET7I, hsa-let-7i -MicroRNA let-7i and Lung Cancer
3
MT1G 16q13 MT1, MT1K -MT1G and Lung Cancer
3
RALA 7p15-p13 RAL -RALA and Lung Cancer
3
LGALS4 19q13.2 GAL4, L36LBP -LGALS4 and Lung Cancer
3
PPARGC1A 4p15.1 LEM6, PGC1, PGC1A, PGC-1v, PPARGC1, PGC-1(alpha) -PPARGC1A and Lung Cancer
3
RAD23B 9q31.2 P58, HR23B, HHR23B -RAD23B and Lung Cancer
3
BOLL 2q33 BOULE -BOLL and Lung Cancer
3
MYH9 22q13.1 MHA, FTNS, EPSTS, BDPLT6, DFNA17, NMMHCA, NMHC-II-A, NMMHC-IIA -MYH9 and Lung Cancer
3
MARS 12q13.3 MRS, METRS, MTRNS, SPG70 -MARS and Lung Cancer
3
LINC00632 Xq27.1 -RP1-177G6.2 and Lung Cancer
3
GJA1 6q22.31 HSS, CMDR, CX43, GJAL, ODDD, AVSD3, HLHS1 -GJA1 and Lung Cancer
3
HIP1 7q11.23 HIP-I, ILWEQ -HIP1 and Lung Cancer
3
TLE1 9q21.32 ESG, ESG1, GRG1 -TLE1 and Lung Cancer
3
TFRC 3q29 T9, TR, TFR, p90, CD71, TFR1, TRFR -TFRC and Lung Cancer
3
BAGE 21p11.1 not on ref BAGE1, CT2.1 -BAGE and Lung Cancer
3
HTATIP2 11p15.1 CC3, TIP30, SDR44U1 -HTATIP2 and Lung Cancer
3
MCM4 8q11.2 NKCD, CDC21, CDC54, NKGCD, hCdc21, P1-CDC21 -MCM4 and Lung Cancer
3
DAB2IP 9q33.1-q33.3 AIP1, AIP-1, AF9Q34, DIP1/2 -DAB2IP and Lung Cancer
3
TGFBI 5q31 CSD, CDB1, CDG2, CSD1, CSD2, CSD3, EBMD, LCD1, BIGH3, CDGG1 -TGFBI and Lung Cancer
3
ROR2 9q22 BDB, BDB1, NTRKR2 -ROR2 and Lung Cancer
3
SOX1 13q34 -SOX1 and Lung Cancer
3
MEST 7q32 PEG1 -MEST and Lung Cancer
3
EZR 6q25.3 CVL, CVIL, VIL2, HEL-S-105 -EZR and Lung Cancer
3
HOXD10 2q31.1 HOX4, HOX4D, HOX4E, Hox-4.4 -HOXD10 and Lung Cancer
3
IQGAP1 15q26.1 SAR1, p195, HUMORFA01 -IQGAP1 and Lung Cancer
3
RAP1GDS1 4q23-q25 GDS1, SmgGDS -RAP1GDS1 and Lung Cancer
3
KLK5 19q13.33 SCTE, KLKL2, KLK-L2 -KLK5 and Lung Cancer
3
SKIL 3q26 SNO, SnoA, SnoI, SnoN -SKIL and Lung Cancer
3
LMO4 1p22.3 -LMO4 and Lung Cancer
2
KISS1R 19p13.3 HH8, CPPB1, GPR54, AXOR12, KISS-1R, HOT7T175 -KISS1R and Lung Cancer
2
DACH1 13q22 DACH -DACH1 and Lung Cancer
2
PTGER4 5p13.1 EP4, EP4R -PTGER4 and Lung Cancer
2
GAGE1 Xp11.23 CT4.1, GAGE-1 -GAGE1 and Lung Cancer
2
GOPC 6q21 CAL, FIG, PIST, GOPC1, dJ94G16.2 -GOPC and Lung Cancer
2
NEFL 8p21 NFL, NF-L, NF68, CMT1F, CMT2E, PPP1R110 -NEFL and Lung Cancer
2
CDH17 8q22.1 HPT1, CDH16, HPT-1 -CDH17 and Lung Cancer
2
SLPI 20q12 ALP, MPI, ALK1, BLPI, HUSI, WAP4, WFDC4, HUSI-I -SLPI and Lung Cancer
2
LIMD1 3p21.3 -LIMD1 and Lung Cancer
2
PLAT 8p12 TPA, T-PA -PLAT and Lung Cancer
2
SACS 13q12 SPAX6, ARSACS, DNAJC29, PPP1R138 -SACS and Lung Cancer
2
ATIC 2q35 PURH, AICAR, AICARFT, IMPCHASE, HEL-S-70p -ATIC and Lung Cancer
2
MAFG 17q25.3 hMAF -MAFG and Lung Cancer
2
UGT2B17 4q13 BMND12, UDPGT2B17 -UGT2B17 and Lung Cancer
2
RIN1 11q13.2 -RIN1 and Lung Cancer
2
NOX1 Xq22 MOX1, NOH1, NOH-1, GP91-2 -NOX1 and Lung Cancer
2
POLI 18q21.1 RAD30B, RAD3OB -POLI and Lung Cancer
2
IL24 1q32 C49A, FISP, MDA7, MOB5, ST16, IL10B -IL24 and Lung Cancer
2
RARRES3 11q23 RIG1, TIG3, HRSL4, HRASLS4, PLA1/2-3 -RARRES3 and Lung Cancer
2
FRAT1 10q24.1 -FRAT1 and Lung Cancer
2
ST5 11p15 HTS1, p126, DENND2B -ST5 and Lung Cancer
2
SIRT3 11p15.5 SIR2L3 -SIRT3 and Lung Cancer
2
PPP1R3A 7q31.1 GM, PP1G, PPP1R3 -PPP1R3A and Lung Cancer
2
STK4 20q11.2-q13.2 KRS2, MST1, YSK3, TIIAC -STK4 and Lung Cancer
2
TPM1 15q22.1 CMH3, TMSA, CMD1Y, LVNC9, C15orf13, HTM-alpha -TPM1 and Lung Cancer
2
IL23R 1p31.3 -IL23R and Lung Cancer
2
RCVRN 17p13.1 RCV1 -RCVRN and Lung Cancer
2
MMP8 11q22.3 HNC, CLG1, MMP-8, PMNL-CL -MMP8 and Lung Cancer
2
NEMF 14q22 NY-CO-1, SDCCAG1 -NEMF and Lung Cancer
2
CCL17 16q13 TARC, ABCD-2, SCYA17, A-152E5.3 -CCL17 and Lung Cancer
2
ARL11 13q14.2 ARLTS1 -ARL11 and Lung Cancer
2
AKAP9 7q21-q22 LQT11, PRKA9, AKAP-9, CG-NAP, YOTIAO, AKAP350, AKAP450, PPP1R45, HYPERION, MU-RMS-40.16A -AKAP9 and Lung Cancer
2
MYCL 1p34.2 LMYC, L-Myc, MYCL1, bHLHe38 -MYCL and Lung Cancer
2
TNFSF13 17p13.1 APRIL, CD256, TALL2, ZTNF2, TALL-2, TRDL-1, UNQ383/PRO715 -TNFSF13 and Lung Cancer
2
NR3C2 4q31.1 MR, MCR, MLR, NR3C2VIT -NR3C2 and Lung Cancer
2
CXCL11 4q21.2 IP9, H174, IP-9, b-R1, I-TAC, SCYB11, SCYB9B -CXCL11 and Lung Cancer
2
MUC7 4q13.3 MG2 -MUC7 and Lung Cancer
2
HTRA1 10q26.3 L56, HtrA, ARMD7, ORF480, PRSS11, CARASIL -HTRA1 and Lung Cancer
2
TLR7 Xp22.3 TLR7-like -TLR7 and Lung Cancer
2
BAI1 8q24.3 GDAIF -BAI1 and Lung Cancer
2
S100A10 1q21 42C, P11, p10, GP11, ANX2L, CAL1L, CLP11, Ca[1], ANX2LG -S100A10 and Lung Cancer
2
ALOX5 10q11.2 5-LO, 5LPG, LOG5, 5-LOX -ALOX5 and Lung Cancer
2
HOXB3 17q21.3 HOX2, HOX2G, Hox-2.7 -HOXB3 and Lung Cancer
2
OCLN 5q13.1 BLCPMG, PPP1R115 -OCLN and Lung Cancer
2
CLTC 17q23.1 Hc, CHC, CHC17, CLH-17, CLTCL2 -CLTC and Lung Cancer
2
ASAH1 8p22 AC, PHP, ASAH, PHP32, ACDase, SMAPME -ASAH1 and Lung Cancer
2
KDM5A 12p11 RBP2, RBBP2, RBBP-2 -KDM5A and Lung Cancer
2
PVT1 8q24 LINC00079, NCRNA00079 -PVT1 and Lung Cancer
2
AGTR2 Xq22-q23 AT2, ATGR2, MRX88 -AGTR2 and Lung Cancer
2
DKC1 Xq28 DKC, CBF5, DKCX, NAP57, NOLA4, XAP101 -DKC1 and Lung Cancer
2
MIR1258 2q31.3 MIRN1258, mir-1258, hsa-mir-1258 -MicroRNA miR-1258 and Lung Cancer
2
CD276 15q23-q24 B7H3, B7-H3, B7RP-2, 4Ig-B7-H3 -CD276 and Lung Cancer
2
LAMB3 1q32 AI1A, LAM5, LAMNB1, BM600-125KDA -LAMB3 and Lung Cancer
2
LRP1 12q13.3 APR, LRP, A2MR, CD91, APOER, LRP1A, TGFBR5, IGFBP3R -LRP1 and Lung Cancer
2
HHIP 4q28-q32 HIP -HHIP and Lung Cancer
2
TXNRD1 12q23-q24.1 TR, TR1, TXNR, TRXR1, GRIM-12 -TXNRD1 and Lung Cancer
2
KPNA2 17q24.2 QIP2, RCH1, IPOA1, SRP1alpha -KPNA2 and Lung Cancer
2
EPHB3 3q27.1 ETK2, HEK2, TYRO6 -EPHB3 and Lung Cancer
2
RAD17 5q13 CCYC, R24L, RAD24, HRAD17, RAD17SP -RAD17 and Lung Cancer
2
GFRA1 10q26.11 GDNFR, RET1L, RETL1, TRNR1, GDNFRA, GFR-ALPHA-1 -GFRA1 and Lung Cancer
2
SRPX Xp21.1 DRS, ETX1, SRPX1, HEL-S-83p -SRPX and Lung Cancer
2
XRCC6 22q13.2 ML8, KU70, TLAA, CTC75, CTCBF, G22P1 -XRCC6 and Lung Cancer
2
C2orf44 2p23.3 WDCP, PP384 -C2orf44 and Lung Cancer
1
CEACAM3 19q13.2 CEA, CGM1, W264, W282, CD66D -CEACAM3 and Lung Cancer
1
MIR1297 13 MIRN1297, hsa-mir-1297 -MicroRNA miR-1297 and Lung Cancer
1
NTRK1 1q21-q22 MTC, TRK, TRK1, TRKA, Trk-A, p140-TrkA Translocation
-CD74-NTRK1 fusion in Lung Cancer
1
KMT2A 11q23 HRX, MLL, MLL1, TRX1, ALL-1, CXXC7, HTRX1, MLL1A, WDSTS, MLL/GAS7, TET1-MLL -KMT2A and Lung Cancer
1
MIR10B 2q31.1 MIRN10B, mir-10b, miRNA10B, hsa-mir-10b -MIR10B and Lung Cancer
1
HERPUD1 16q13 SUP, HERP, Mif1 -HERPUD1 and Lung Cancer
1
GPHN 14q23.3 GPH, GEPH, HKPX1, GPHRYN, MOCODC -GPHN and Lung Cancer
1
ZNRF3 22q12.1 RNF203, BK747E2.3 -ZNRF3 and Lung Cancer
1
TNFRSF6B 20q13.3 M68, TR6, DCR3, M68E, DJ583P15.1.1 -TNFRSF6B Amplification in Lung Cancer
1
CTNND1 11q11 CAS, p120, CTNND, P120CAS, P120CTN, p120(CAS), p120(CTN) -CTNND1 and Lung Cancer
1
MIR1301 2 MIRN1301, mir-1301, hsa-mir-1301 -MicroRNA miR-1301 and Lung Cancer
1
CBLC 19q13.2 CBL-3, RNF57, CBL-SL -CBLC and Lung Cancer
1
CHCHD7 8q12.1 COX23 -CHCHD7 and Lung Cancer
1
TRD 14q11.2 TCRD, TRD@, TCRDV1 -TRD and Lung Cancer
1
MIR1271 5q35 MIRN1271, hsa-mir-1271 -MIRN1271 microRNA, human and Lung Cancer
1
MIR125A 19q13.41 MIRN125A, miRNA125A -MIR125A and Lung Cancer
1
RXRB 6p21.3 NR2B2, DAUDI6, RCoR-1, H-2RIIBP -RXRB and Lung Cancer
1
PRIM1 12q13 p49 -PRIM1 and Lung Cancer
1
PATZ1 22q12.2 ZSG, MAZR, PATZ, RIAZ, ZBTB19, ZNF278, dJ400N23 -PATZ1 and Lung Cancer
1
ARHGAP26 5q31 GRAF, GRAF1, OPHN1L, OPHN1L1 -ARHGAP26 and Lung Cancer
1
BANP 16q24 BEND1, SMAR1, SMARBP1 -BANP and Lung Cancer
1
RXRG 1q22-q23 RXRC, NR2B3 -RXRG and Lung Cancer
1
PCSK7 11q23-q24 LPC, PC7, PC8, SPC7 -PCSK7 and Lung Cancer
1
IDO1 8p12-p11 IDO, INDO, IDO-1 -IDO1 and Lung Cancer
1
PLCD1 3p22-p21.3 NDNC3, PLC-III -PLCD1 and Lung Cancer
1
ENDOU 12q13.1 P11, PP11, PRSS26 -ENDOU and Lung Cancer
1
VIPR2 7q36.3 VPAC2, VPAC2R, VIP-R-2, VPCAP2R, PACAP-R3, DUP7q36.3, PACAP-R-3, C16DUPq36.3 -VIPR2 and Lung Cancer
1
GJB2 13q11-q12 HID, KID, PPK, CX26, DFNA3, DFNB1, NSRD1, DFNA3A, DFNB1A -GJB2 and Lung Cancer
1

Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).

Latest Publications

Kobyakov DS, Avdalyan AM, Klimachev VV, et al.
[Non-small cell lung cancer: HER2 oncogene status].
Arkh Patol. 2015 Mar-Apr; 77(2):3-9 [PubMed] Related Publications
OBJECTIVE: to study HER2 protein and HER2 gene, their heterogeneity in non-small cell lung cancer.
MATERIAL AND METHODS: 218 intraoperative non-small cell lung samples were examined using tissue matrix methods. HER2 protein was determined by immunohistochemistry (clone 4B5, and HER2 gene and CEP1 7 were evaluated by in situ hybridization (SISH, ).
RESULTS: Positive and indefinite statuses were found in 59 (27%) and 47 (22%) cases, respectively; intratumor heterogeneity was detected in 32 (30%) cases. Amplification of the HER-2 gene was found in 12 (6%) cases; that of the HER2 gene along with an increase in CEPI 7 was observed in 7 (3%) cases; elevated CEP1 7 levels were seen in 19 (9%) cases. Intratumor heterogeneity of HER2 gene amplification was not found; however, one case of adenocarcinoma showed high-level HER2 gene amplification in the gland-like areas and low-level HER2 gene amplification in the solid areas. HER2-positive status and amplification were more common in adenocarcinoma than in squamous cell carcinoma (p<0.001). There was a moderate correlation between HER2 immunohistochemical status and amplification (r=0.38; p<0.001).
CONCLUSION: Thus, in non-small cell lung cancer, there is an elevated HER2 protein level and, well less frequently, altered activity in the HER2 gene (amplification) as a cause of enhanced protein synthesis.

Jin G, Zhu M, Yin R, et al.
Low-frequency coding variants at 6p21.33 and 20q11.21 are associated with lung cancer risk in Chinese populations.
Am J Hum Genet. 2015; 96(5):832-40 [PubMed] Related Publications
Genome-wide association studies have successfully identified a subset of common variants associated with lung cancer risk. However, these variants explain only a fraction of lung cancer heritability. It has been proposed that low-frequency or rare variants might have strong effects and contribute to the missing heritability. To assess the role of low-frequency or rare variants in lung cancer development, we analyzed exome chips representing 1,348 lung cancer subjects and 1,998 control subjects during the discovery stage and subsequently evaluated promising associations in an additional 4,699 affected subjects and 4,915 control subjects during the replication stages. Single-variant and gene-based analyses were carried out for coding variants with a minor allele frequency less than 0.05. We identified three low-frequency missense variants in BAT2 (rs9469031, c.1544C>T [p.Pro515Leu]; odds ratio [OR] = 0.55, p = 1.28 × 10(-10)), FKBPL (rs200847762, c.410C>T [p.Pro137Leu]; OR = 0.25, p = 9.79 × 10(-12)), and BPIFB1 (rs6141383, c.850G>A [p.Val284Met]; OR = 1.72, p = 1.79 × 10(-7)); these variants were associated with lung cancer risk. rs9469031 in BAT2 and rs6141383 in BPIFB1 were also associated with the age of onset of lung cancer (p = 0.001 and 0.006, respectively). BAT2 and FKBPL at 6p21.33 and BPIFB1 at 20q11.21 were differentially expressed in lung tumors and paired normal tissues. Gene-based analysis revealed that FKBPL, in which two independent variants were identified, might account for the association with lung cancer risk at 6p21.33. Our results highlight the important role low-frequency variants play in lung cancer susceptibility and indicate that candidate genes at 6p21.33 and 20q11.21 are potentially biologically relevant to lung carcinogenesis.

Ren Y, Yin Z, Li K, et al.
TGFβ-1 and TGFBR2 polymorphisms, cooking oil fume exposure and risk of lung adenocarcinoma in Chinese nonsmoking females: a case control study.
BMC Med Genet. 2015; 16:22 [PubMed] Free Access to Full Article Related Publications
BACKGROUND: Transforming growth factor-β (TGF-β) plays an important role in regulating cellular functions, and many studies have demonstrated important roles for TGF-β in various cancers. Single nucleotide polymorphisms (SNPs) of TGF-β may influence lung carcinogenesis. The aim of this study was to test whether TGF-β1 C509T and TGF-β receptor II (TGFBR2) G-875A polymorphisms were associated with lung adenocarcinoma in nonsmoking females.
METHODS: A hospital-based case-control study was performed in Chinese nonsmoking females. Genotyping was performed using TaqMan SNP genotyping assay, and demographic data and environmental exposure were collected by trained interviewers after informed consents were obtained.
RESULTS: A total of 272 (95.4%) cases and 313 (99.4%) controls were successfully genotyped, and the results showed that the polymorphic allele frequencies of C509T and G875A were similar among lung adenocarcinoma patients and controls (P=0.589 and 0.643, respectively). However, when the data were stratified for cooking oil fume exposure, the TT genotype of the TGFB1 C509T polymorphism showed a significantly decreased risk for lung adenocarcinoma compared with the CC genotype (adjusted OR=0.362, 95% CI=0.149-0.878, P=0.025).
CONCLUSIONS: TGF-β1 gene C509T polymorphism might be associated with decreased risk of lung adenocarcinoma in Chinese females exposed to cooking oil fumes, but no association was observed TGFBR2 gene G875A polymorphism.

Lan YT, Jen-Kou L, Lin CH, et al.
Mutations in the RAS and PI3K pathways are associated with metastatic location in colorectal cancers.
J Surg Oncol. 2015; 111(7):905-10 [PubMed] Related Publications
BACKGROUND AND OBJECTIVES: Identification of mutations in the downstream epidermal growth factor receptor (EGFR) signaling pathway could provide important insights of EGFR-targeted therapies in colorectal cancers. We analyzed the mutation spectra of the PI3K/PTEN/AKT and RAS/RAF/MAPK pathways in colorectal cancers and the associations of these mutations with sites of metastases or recurrence.
METHODS: The study population comprised 1,492 retrospectively collected stages I-IV colorectal cancer specimens. Tissue was obtained between 2000 and 2010 at a single hospital. We analyzed 61 hot spots using MALDI-TOF mass spectrometry for nucleic acid analysis.
RESULTS: Mutations were found in the RAS pathway in 47.3% of patients and in the PI3K pathway in 14.3% of patients, with 9.2% of patients carrying mutations in both pathways. Both the RAS and PI3K pathway mutations were significantly associated with proximal tumors, mucinous tumors, and microsatellite instability. Tumors carrying a RAS pathway mutation exhibited a higher frequency of lung and peritoneal metastasis than did tumors with a wild-type gene (P = 0.025 and 0.009, respectively). NRAS gene mutation was significantly associated with lung metastasis (P = 0.001).
CONCLUSIONS: Somatic mutations in the RAS pathway of the primary tumor in colorectal cancer can influence patterns of metastasis and recurrence.

Vlasov VV, Rykova EIu, Ponomareva AA, et al.
[Circulating microRNAs in lung cancer: prospects for diagnostics, prognosis and prediction of antitumor treatment efficiency].
Mol Biol (Mosk). 2015 Jan-Feb; 49(1):55-66 [PubMed] Related Publications
The major methods of microRNA extraction from different biological fluids (particularly, serum and plasma), approaches to the analysis of microRNA concentration and composition, normalization methods used in data analysis are outlined in the review. The advantages and disadvantages of the described methodological approaches are being highlighted. Special attention is given to microRNAs, circulating in blood, which could be used as the markers for minimally invasive lung cancer diagnostics, prediction of antitumor treatment efficiency and disease prognosis. Prospects and limitations arising from the evaluation of clinical significance of microRNAs as the potential tumor markers, and emerging as roles of various microRNAs in the pathogenesis of lung cancer become known, are discussed.

Svaton M, Pesek M, Chudacek Z, Vosmiková H
Current two EGFR mutations in lung adenocarcinoma -  case report.
Klin Onkol. 2015; 28(2):134-7 [PubMed] Related Publications
Nowadays, EGFR TKIs (epidermal growth factor receptor-tyrosine kinase inhibitors) targeted therapy is well established treatment for patients with the so-called EGFR common mutations with advanced or metastatic nonsmall cell lung cancer. The efficacy for the so-called rare and especially for the very rare complex EGFR mutations is not clear. We describe a case of a 63- year-old female with metastatic nonsmall cell lung cancer with complex EGFR mutation (G719X + S768I) who had been treated by gefitinib. She achieved progression free survival within eight months. Then, we discuss our case with other literature case reports. Together, it seems that described complex EGFR mutation has a relatively good sensitivity for EGFR TKIs treatment.Key words: nonsmall cell lung cancer -  EGFR gene -  EGFR protein -  complex mutations -  rare EGFR mutations -  EGFR TKIs.

Ma L, Chen Y, Yang C, et al.
[Association of UGT1A1 (*28, *60 and * 93) polymorphism with the adverse reactions of irinotecan chemotherapy in extensive stage small cell lung cancer].
Zhonghua Zhong Liu Za Zhi. 2015; 37(1):29-32 [PubMed] Related Publications
OBJECTIVE: To explore the correlation between UGT1A1 (*28, *60 and * 93) polymorphism and the adverse reactions in small cell lung cancer patients after irinotecan chemotherapy.
METHODS: Clinical data of 58 small cell lung cancer patients in extensive stage treated in our hospital were retrospectively analyzed. Polymerase chain reaction was used to amplify the UTG, and direct sequencing was performed to determine the UGT polymorphism. The adverse reactions ≥ grade 3 after irinotecan chemotherapy in patients with different UGT genotype were analyzed.
RESULTS: Amongthe 58 patients with extensive stage small cell lung cancer, there were 45 (77.6%) cases of wild type UGT1A1*28, 40 (69.0%) cases of wild type UGT1A1* 93, 38 (65.5%) cases of wild type UGT1A1*60, 18 cases of mutation in UGT1A1* 93 and 20 cases of mutation in UGT1A1*60. In UGT1A1 promoter position 28, there were 8 (13.8%) cases of TA5 mutation and 5 (8.6%) cases of TA7 mutation. Among the patients with TA5 mutation, 5 cases had ≥ grade 3 diarrhea, 3 cases had ≥ grade 3 leucopenia and 3 cases had ≥ grade 3 neutropenia, while among the patients with UGT1A1 * 93 mutation, 7 cases had ≥ grade 3 diarrhea, 6 cases had ≥ grade 3 leucopenia and 4 cases had ≥ grade 3 neutropenia.
CONCLUSIONS: TA5 and UGT1A1* 93 mutation increase the risk of diarrhea and ≥ grade 3 leukopenia and neutropenia, however, wild type UGT1A1 (*28, * 93, *60) and mutant UGT1A1*60 do not increase those risks. Further prospective study in a larger number of patients is needed to clarify the association between UGT1A1*28, UGT1A1* 93 and UGT1A1*60 polymorphism and adverse reactions of irinotecan, and to help clinicians in choosing a better therapeutic modality for personalized chemotherapy to improve curative effect and reduce adverse reactions.

Li Q, Jiang M, Zhao S, et al.
[Interaction between smoking and nicotine acetylcholine receptor subunits alpha 5 gene rs17486278 polymorphisms on lung cancer].
Zhonghua Liu Xing Bing Xue Za Zhi. 2015; 36(1):67-70 [PubMed] Related Publications
OBJECTIVE: To investigate the association and interaction between smoking and the nicotine acetylcholine receptor subunits alpha 5(CHRNA5) gene polymorphisms on lung cancer in Chinese men.
METHODS: A case-control study was employed with a total of 204 male lung cancer patients and 821 healthy control subjects enrolled in the study. All the subjects were interviewed under a structured questionnaire with the contents on socio-demographic status and smoking behavior. Venous blood samples were collected to measure single nucleotide polymorphism of rs17486278 in CHRNA5. A series of multivariate logistic regression models were performed to assess the association and interaction between smoking and the CHRNA5 gene polymorphisms on lung cancer.
RESULTS: After controlling for potential confounding factors, data from the multivariate logistic regression analysis showed that individuals with smoking >15 cigarettes per day would significantly increase the risk of lung cancer when compared to the non-smokers (OR = 3.49, 95%CI:2.29-5.32). However, no associations between CHRNA5 rs17486278 polymorphisms and lung cancer were found. Furthermore, those who smoked 1-15 cigarettes per day had a positive interactive effect between rs17486278 CC genotype and lung cancer (OR = 16.13, 95% CI:1.27-205.33). Results from further stratified analysis on smoking behaviors and rs17486278 genotypes indicated that when compared with non-smokers on rs17486278 AA genotype, those individuals who smoked 1-15 cigarettes per day with rs17486278 CC genotype, individuals smoking >15 cigarettes per day with AA genotype and individuals smoking >15 cigarettes per day with AC genotype, all had a higher risk of developing lung cancer, with their OR value as 8.14(95% CI:1.17-56.56), 3.84 (95% CI:1.30-11.40) and 5.32 (95% CI:1.78-15.93), respectively.
CONCLUSION: There was an interaction between smoking and CHRNA5 gene polymorphism on lung cancer.

Mavridis K, Gueugnon F, Petit-Courty A, et al.
The oncomiR miR-197 is a novel prognostic indicator for non-small cell lung cancer patients.
Br J Cancer. 2015; 112(9):1527-35 [PubMed] Article available free on PMC after 28/04/2016 Related Publications
BACKGROUND: MicroRNA expression signatures can promote personalised care for non-small cell lung cancer (NSCLC) patients. Our aim was to evaluate the previously unexplored prognostic potential of miR-197, a key oncogenic molecule for NSCLC.
METHODS: Total RNA isolation (n=124 NSCLC and n=21 tumour-adjacent normal tissues), was performed using the QIAsymphony SP workstation. The quantity and quality of RNA were assessed by spectrophotometric analysis and an Agilent 2100 bioanalyzer. Polyadenylation and reverse transcription were subsequently carried out. MiR-197 expression levels were measured by qPCR, after quality control (inter-assay CV=7.8%). Internal validation procedures were followed by assigning training and test sets and robust biostatistical analyses were performed, including bootstrap resampling.
RESULTS: MiR-197 is associated with larger tumours (P=0.042) and the squamous cell carcinoma histotype (P=0.032). Interestingly, after adjusting for important prognostic indicators, miR-197 expression was identified as a novel independent predictor of unfavourable prognosis for NSCLC patients (HR=1.97, 95% CI=1.10-3.38, P=0.013). We also demonstrate that miR-197 retains its prognostic performance in both early-stage I (P=0.045) and more advanced-stage individuals (P=0.036).
CONCLUSIONS: The cost-effective expression analysis of miR-197 could constitute a novel molecular tool for NSCLC management.

Li J, Ching T, Huang S, Garmire LX
Using epigenomics data to predict gene expression in lung cancer.
BMC Bioinformatics. 2015; 16 Suppl 5:S10 [PubMed] Article available free on PMC after 28/04/2016 Related Publications
BACKGROUND: Epigenetic alterations are known to correlate with changes in gene expression among various diseases including cancers. However, quantitative models that accurately predict the up or down regulation of gene expression are currently lacking.
METHODS: A new machine learning-based method of gene expression prediction is developed in the context of lung cancer. This method uses the Illumina Infinium HumanMethylation450K Beadchip CpG methylation array data from paired lung cancer and adjacent normal tissues in The Cancer Genome Atlas (TCGA) and histone modification marker CHIP-Seq data from the ENCODE project, to predict the differential expression of RNA-Seq data in TCGA lung cancers. It considers a comprehensive list of 1424 features spanning the four categories of CpG methylation, histone H3 methylation modification, nucleotide composition, and conservation. Various feature selection and classification methods are compared to select the best model over 10-fold cross-validation in the training data set.
RESULTS: A best model comprising 67 features is chosen by ReliefF based feature selection and random forest classification method, with AUC = 0.864 from the 10-fold cross-validation of the training set and AUC = 0.836 from the testing set. The selected features cover all four data types, with histone H3 methylation modification (32 features) and CpG methylation (15 features) being most abundant. Among the dropping-off tests of individual data-type based features, removal of CpG methylation feature leads to the most reduction in model performance. In the best model, 19 selected features are from the promoter regions (TSS200 and TSS1500), highest among all locations relative to transcripts. Sequential dropping-off of CpG methylation features relative to different regions on the protein coding transcripts shows that promoter regions contribute most significantly to the accurate prediction of gene expression.
CONCLUSIONS: By considering a comprehensive list of epigenomic and genomic features, we have constructed an accurate model to predict transcriptomic differential expression, exemplified in lung cancer.

Luo H, Sun Y, Wei G, et al.
Functional Characterization of Long Noncoding RNA Lnc_bc060912 in Human Lung Carcinoma Cells.
Biochemistry. 2015; 54(18):2895-902 [PubMed] Related Publications
Long noncoding RNAs (lncRNAs) are pervasively transcribed in the human genome. Recent studies suggest that the involvement of lncRNAs in human diseases could be far more prevalent than previously appreciated. Here we have identified a lncRNA termed Lnc_bc060912 whose expression is increased in human lung and other tumors. Lnc_bc060912 is 1.2 kb in length and is composed of two exons. The expression of Lnc_bc060912 was repressed by p53. Lnc_bc060912 suppressed cell apoptosis. Using a recently developed method for RNA-pulldown with formaldehyde cross-linking, we found that Lnc_bc060912 interacted with the two DNA damage repair proteins PARP1 and NPM1. Together, these results suggest that Lnc_bc060912, via PARP1 and NPM1, affects cell apoptosis and may play important roles in tumorigenesis and cancer progression.

Sheng H, Ying L, Zheng L, et al.
Down Expression of FBP1 Is a Negative Prognostic Factor for Non-Small-Cell Lung Cancer.
Cancer Invest. 2015; 33(5):197-204 [PubMed] Related Publications
Downregulation of fructose-1,6-bisphosphatse-1 (FBP1) was observed in several cancers but its role in the lung cancer still remains unknown. We examined the cancer tissues from 140 patients with nonsmall cell lung cancer patients and found that the relative gene expression of FBP1 was significantly lower in lung cancer tissues as compared to incisal marginal tissues and normal tissues. The patients with higher level of FBP1 RNA expression have significantly longer disease free survival and overall survival as compared to the lower expression groups. There was a negative correlation with the level of FBP1 and recurrence of the lung cancer.

Yin LG, Zou ZQ, Zhao HY, et al.
[Analysis of tissue-specific differentially methylated genes with differential gene expression in non-small cell lung cancer].
Mol Biol (Mosk). 2014 Sep-Oct; 48(5):797-804 [PubMed] Related Publications
Adenocarcinoma (ADC) and squamous cell carcinomas (SCC) are two subtypes of non-small cell lung carcinomas which are regarded as the leading cause of cancer-related malignancy worldwide. The aim of this study is to detect the differentially methylated loci (DMLs) and differentially methylated genes (DMGs) of these two tumor sets, and then to illustrate the different expression level of specific methylated genes. Using TCGA database and Illumina HumanMethylation 27 arrays, we first screened the DMGs and DMLs in tumor samples. Then, we explored the BiologicalProcess terms of hypermethylated and hypomethylated genes using Functional Gene Ontology (GO) catalogues. Hypermethylation intensively occurred in CpG-island, whereas hypomethylation was located in non-CpG-island. Most SCC and ADC hypermethylated genes involved GO function of DNA dependenit regulation of transcription, and hypomethylated genes mainly 'enriched in the term of immune responses. Additionally, the expression level of specific differentially methylated genesis distinctbetween ADC and SCC. It is concluded that ADC and SCC have different methylated status that might play an important role in carcinogenesis.

Califano R, Abidin A, Tariq NU, et al.
Beyond EGFR and ALK inhibition: unravelling and exploiting novel genetic alterations in advanced non small-cell lung cancer.
Cancer Treat Rev. 2015; 41(5):401-11 [PubMed] Related Publications
During the last decade, thoracic oncology has witnessed an unprecedented outburst of knowledge regarding molecular biology of non small-cell lung cancer (NSCLC). The implementation of high-throughput sequencing analysis and genomic technologies has led to the identification of novel molecular events that characterize NSCLC transformation and may represent critical oncogenic drivers amenable to targeted therapy. Among these, the presence of activating mutations of the epidermal growth factor receptor (EGFR) gene and of chromosomic rearrangements in the anaplastic-lymphoma kinase (ALK) proto-oncogene, have been the first well characterized genetic alterations with corresponding targeted agents to enter the clinical arena. Nevertheless, in the recent years a number of other oncogenic drivers beyond EGFR and ALK inhibition have emerged as novel molecular targets with potential therapeutic implications, including mutations in the genes KRAS, BRAF, HER2, PI3KCA and DDR2, as well as ROS1 and RET rearrangements and MET, HER2 and FGFR1 gene amplifications. The aim of this review is to provide comprehensive information on the novel therapeutic targets identified by recent preclinical evidence and to discuss developments in molecular treatments targeting these oncogenic drivers or actionable mutations beyond EGFR and ALK in advanced NSCLC.

Zhai W, Feng R, Wang H, Wang Y
Note of clarification of data in the paper titled X-ray repair cross-complementing group 1 codon 399 polymorphism and lung cancer risk: an updated meta-analysis.
Tumour Biol. 2015; 36(5):3179-89 [PubMed] Related Publications
We read with great interest the paper titled "X-ray repair cross-complementing group 1 codon 399 polymorphism and lung cancer risk: an updated meta-analysis" published by Wang et al in Tumor Biology, 2014, 35:411-418. Their results suggest that codon 399 polymorphism of XRCC1 gene might contribute to individual's susceptibility to lung cancer in Asian population and especially in nonsmoking Chinese women. The result is encouraging. Nevertheless, several key issues are worth noticing.

Liu Q, Ghosh P, Magpayo N, et al.
Lung cancer cell line screen links fanconi anemia/BRCA pathway defects to increased relative biological effectiveness of proton radiation.
Int J Radiat Oncol Biol Phys. 2015; 91(5):1081-9 [PubMed] Related Publications
PURPOSE: Growing knowledge of genomic heterogeneity in cancer, especially when it results in altered DNA damage responses, requires re-examination of the generic relative biological effectiveness (RBE) of 1.1 of protons.
METHODS AND MATERIALS: For determination of cellular radiosensitivity, we irradiated 17 lung cancer cell lines at the mid-spread-out Bragg peak of a clinical proton beam (linear energy transfer, 2.5 keV/μm). For comparison, 250-kVp X rays and (137)Cs γ-rays were used. To estimate the RBE of protons relative to (60)Co (Co60eq), we assigned an RBE(Co60Eq) of 1.1 to X rays to correct the physical dose measured. Standard DNA repair foci assays were used to monitor damage responses. FANCD2 was depleted using RNA interference.
RESULTS: Five lung cancer cell lines (29.4%) exhibited reduced clonogenic survival after proton irradiation compared with X-irradiation with the same physical doses. This was confirmed in a 3-dimensional sphere assay. Corresponding proton RBE(Co60Eq) estimates were statistically significantly different from 1.1 (P≤.05): 1.31 to 1.77 (for a survival fraction of 0.5). In 3 of these lines, increased RBE was correlated with alterations in the Fanconi anemia (FA)/BRCA pathway of DNA repair. In Calu-6 cells, the data pointed toward an FA pathway defect, leading to a previously unreported persistence of proton-induced RAD51 foci. The FA/BRCA-defective cells displayed a 25% increase in the size of subnuclear 53BP1 foci 18 hours after proton irradiation.
CONCLUSIONS: Our cell line screen has revealed variations in proton RBE that are partly due to FA/BRCA pathway defects, suggesting that the use of a generic RBE for cancers should be revisited. We propose that functional biomarkers, such as size of residual 53BP1 foci, may be used to identify cancers with increased sensitivity to proton radiation.

Zhang J, Zhang C, Hu L, et al.
Abnormal Expression of miR-21 and miR-95 in Cancer Stem-Like Cells is Associated with Radioresistance of Lung Cancer.
Cancer Invest. 2015; 33(5):165-71 [PubMed] Related Publications
This study demonstrated that miR-21 and miR-95 expression were significantly higher in the ALDH1(+)CD133(+)subpopulation than in the ALDH1(-)CD133(-) subpopulation of lung cancer cells. Combined delivery of anti-miR-21 and anti-miR-95 by calcium phosphate nanoparticles significantly inhibited tumor growth in a xenograft tumor model and sensitized radiotherapy. The anti-miRNAs significantly reduced miR-21 and miR-95 levels, increased PTEN, SNX1, and SGPP1 protein expression, but reduced Akt Ser(473) and Thr(308) phosphorylation. ALDH1(+)CD133(+) subpopulation of NSCLC tumor cells confers radioresistance due to high expression of miR-21 and miR-95. Targeting inhibition of miR-21 and miR-95 can inhibit tumor growth through elevating PTEN, SNX1, and SGPP1 expression and inhibiting Akt phosphorylation.

Ikeda K, Shiraishi K, Koga T, et al.
Prognostic Significance of Aberrant Methylation of Solute Carrier Gene Family 5A8 in Lung Adenocarcinoma.
Ann Thorac Surg. 2015; 99(5):1755-9 [PubMed] Related Publications
BACKGROUND: Solute carrier family 5 member A8 (SLC5A8) is a sodium-coupled transporter for several chemicals. The SLC5A8 gene has been reported to function as a tumor suppressor gene that contributes to carcinogenesis and tumor progression. The expression of SLC5A8 is silenced in colon neoplasia by hypermethylation of CpG-rich islands located in exon 1. In this study, we assessed the significance of aberrant methylation of the SLC5A8 gene as a prognostic factor for lung adenocarcinoma (AD).
METHODS: We analyzed the methylation levels of a consecutive series of 143 node-negative stage I and II lung AD samples using pyrosequencing.
RESULTS: The methylation level of exon 1 in the SLC5A8 gene was significantly associated with poor prognosis in cases of node-negative stage I and II lung AD.
CONCLUSIONS: Gene silencing of SLC5A8 by hypermethylation was associated with poor prognosis in cases of node-negative stage I and II lung AD.

Wood SL, Pernemalm M, Crosbie PA, Whetton AD
Molecular histology of lung cancer: from targets to treatments.
Cancer Treat Rev. 2015; 41(4):361-75 [PubMed] Related Publications
Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. In addition, molecular profiling studies identify gene products and their mutations which predict tumour responses to targeted therapies such as protein tyrosine kinase inhibitors and also can offer explanation for drug resistance mechanisms. The profiling of circulating micro-RNAs has also provided an ability to discriminate patients in terms of prognosis/diagnosis and high-throughput DNA sequencing strategies are beginning to elucidate cell signalling pathway mutations associated with oncogenesis, including potential stem cell associated pathways, offering the promise that future therapies may target this sub-population, preventing disease relapse post treatment and improving patient survival. This review provides an assessment of molecular profiling within lung cancer concerning molecular mechanisms, treatment options and disease-progression. Current areas of development within lung cancer profiling are discussed (i.e. profiling of circulating tumour cells) and future challenges for lung cancer treatment addressed such as detection of micro-metastases and cancer stem cells.

Harada H, Miyamoto K, Yamashita Y, et al.
Methylated DLX4 Predicts Response to Pathologic Stage I Non-Small Cell Lung Cancer Resection.
Ann Thorac Surg. 2015; 99(5):1746-54 [PubMed] Related Publications
BACKGROUND: Surgery with curative intent is the standard treatment for patients with stage I non-small cell lung cancer (NSCLC). Even after curative resection, however, many patients have recurrent disease. Thus, there is a need to identify molecular biomarkers for the biological characteristics and prognosis of tumors.
METHODS: Methylation-specific polymerase chain reaction analysis was performed for the distal-less homeobox 4 (DLX4) gene in cancer tissues from 109 patients who underwent curative resection for pathologic stage I NSCLC from June 2005 to November 2011. We investigated possible correlations between DLX4 methylation status and disease outcome.
RESULTS: Methylated DLX4 was detected in 54 of 109 patients (49.5%). No significant relationship between DLX4 methylation status and clinicopathologic features was found. Multivariate logistic regression analysis revealed that DLX4 methylation was an independent risk factor for recurrence (p < 0.0001). Patients with DLX4 methylation showed significantly poorer recurrence-free, cancer-specific, and overall survival than patients without DLX4 methylation (p < 0.0001, p = 0.0001, p = 0.0004, respectively). Cox's proportional hazard regression analysis revealed that DLX4 methylation was an independent risk factor for poor prognosis regarding recurrence-free, cancer-specific, and overall survival (p < 0.0001, p = 0.0005, p = 0.0018, respectively).
CONCLUSIONS: Methylated DLX4 is a potential biomarker that predicts poor prognosis after curative resection of pathologic stage I NSCLC. Identification of patients with methylated DLX4 may assist stratification for appropriate adjuvant treatment strategies.

Iyevleva AG, Raskin GA, Tiurin VI, et al.
Novel ALK fusion partners in lung cancer.
Cancer Lett. 2015; 362(1):116-21 [PubMed] Related Publications
Detection of ALK rearrangements in patients with non-small cell lung cancer (NSCLC) presents a significant technical challenge due to the existence of multiple translocation partners and break-points. To improve the performance of PCR-based tests, we utilized the combination of 2 assays, i.e. the variant-specific PCR for the 5 most common ALK rearrangements and the test for unbalanced 5'/3'-end ALK expression. Overall, convincing evidence for the presence of ALK translocation was obtained for 34/400 (8.5%) cases, including 14 EML4ex13/ALKex20, 12 EML4ex6/ALKex20, 3 EML4ex18/ALKex20, 2 EML4ex20/ALKex20 variants and 3 tumors with novel translocation partners. 386 (96.5%) out of 400 EGFR mutation-negative NSCLCs were concordant for both tests, being either positive (n = 26) or negative (n = 360) for ALK translocation; 49 of these samples (6 ALK+, 43 ALK-) were further evaluated by FISH, and there were no instances of disagreement. Among the 14 (3.5%) "discordant" tumors, 5 demonstrated ALK translocation by the first but not by the second PCR assay, and 9 had unbalanced ALK expression in the absence of known ALK fusion variants. 5 samples from the latter group were subjected to FISH, and the presence of translocation was confirmed in 2 cases. Next generation sequencing analysis of these 2 samples identified novel translocation partners, DCTN1 and SQSTM1; furthermore, the DCTN1/ALK fusion was also found in another NSCLC sample with unbalanced 5'/3'-end ALK expression, indicating a recurrent nature of this translocation. We conclude that the combination of 2 different PCR tests is a viable approach for the diagnostics of ALK rearrangements. Systematic typing of ALK fusions is likely to reveal new NSCLC-specific ALK partners.

Flacco A, Ludovini V, Bianconi F, et al.
MYC and human telomerase gene (TERC) copy number gain in early-stage non-small cell lung cancer.
Am J Clin Oncol. 2015; 38(2):152-8 [PubMed] Related Publications
OBJECTIVES: We investigated the frequency of MYC and TERC increased gene copy number (GCN) in early-stage non-small cell lung cancer (NSCLC) and evaluated the correlation of these genomic imbalances with clinicopathologic parameters and outcome.
MATERIALS AND METHODS: Tumor tissues were obtained from 113 resected NSCLCs. MYC and TERC GCNs were tested by fluorescence in situ hybridization (FISH) according to the University of Colorado Cancer Center (UCCC) criteria and based on the receiver operating characteristic (ROC) classification.
RESULTS: When UCCC criteria were applied, 41 (36%) cases for MYC and 41 (36%) cases for TERC were considered FISH-positive. MYC and TERC concurrent FISH-positive was observed in 12 cases (11%): 2 (17%) cases with gene amplification and 10 (83%) with high polysomy. By using the ROC analysis, high MYC (mean ≥ 2.83 copies/cell) and TERC (mean ≥ 2.65 copies/cell) GCNs were observed in 60 (53.1%) cases and 58 (51.3%) cases, respectively. High TERC GCN was associated with squamous cell carcinoma (SCC) histology (P=0.001). In univariate analysis, increased MYC GCN was associated with shorter overall survival (P=0.032 [UCCC criteria] or P=0.02 [ROC classification]), whereas high TERC GCN showed no association. In multivariate analysis including stage and age, high MYC GCN remained significantly associated with worse overall survival using both the UCCC criteria (P=0.02) and the ROC classification (P=0.008).
CONCLUSIONS: Our results confirm MYC as frequently amplified in early-stage NSCLC and increased MYC GCN as a strong predictor of worse survival. Increased TERC GCN does not have prognostic impact but has strong association with squamous histology.

Sneddon S, Leon JS, Dick IM, et al.
Absence of germline mutations in BAP1 in sporadic cases of malignant mesothelioma.
Gene. 2015; 563(1):103-5 [PubMed] Related Publications
Malignant mesothelioma (MM) is a uniformly fatal tumour caused predominantly by exposure to asbestos. It is not known why some exposed individuals get mesothelioma and others do not. There is some epidemiological evidence of host susceptibility. BAP1 gene somatic mutations and allelic loss are common in mesothelioma and recently a BAP1 cancer syndrome was described in which affected individuals and families had an increased risk of cancer of multiple types, including MM. To determine if BAP1 mutations could underlie any of the sporadic mesothelioma cases in our cohort of patients, we performed targeted deep sequencing of the BAP1 exome on the IonTorrent Proton sequencer in 115 unrelated MM cases. No exonic germline BAP1 mutations of known functional significance were observed, further supporting the notion that sporadic germline BAP1 mutations are not relevant to the genetic susceptibility of MM.

Bei L, Xiao-Dong T, Yu-Fang G, et al.
DNA repair gene XRCC3 Thr241Met polymorphisms and lung cancer risk: a meta-analysis.
Bull Cancer. 2015; 102(4):332-9 [PubMed] Related Publications
BACKGROUND: The X-ray repair cross-complementing group 3 (XRCC3) is a highly suspected candidate gene for cancer susceptibility, and a large amount studies have examined the association of the rs861539 in XRCC3 (Thr241Met) with lung cancer risk in various populations. However, the results remain inconclusive.
METHODS: The electronic database of PubMed, Medline, Embase and CNKI (China National Knowledge Infrastructure) were searched for case-control studies published up to December 05, 2013. A systematic review and meta-analysis was performed to evaluate the relationship between XRCC3 Thr241Met polymorphism and lung cancer risk. Data were extracted and pooled odds ratio (OR) with its 95% confidence intervals (CI) were calculated.
RESULTS: Total 21 studies, including 6880 lung cancer cases and 8329 controls, were available for meta-analysis. Overall, our results showed that the XRCC3 Thr241Met polymorphism was not associated with risk of lung cancer in all genetic contrast models (P>0.05). Stratified analyses by ethnicity (Asians, Caucasians and mixed population) showed similar results. Additionally, no evidence of publication bias was observed by using the funnel plot.
CONCLUSIONS: There is no clear evidence showing a significant correlation between XRCC3 Thr241Met polymorphism and lung cancer risk in total population and stratified analysis by ethnicity. However, studies assessing the gene-gene interactions should be considered to further estimate this gene variant in lung cancer risk.

Sabapathy DG, Guillerman RP, Orth RC, et al.
Radiographic screening of infants and young children with genetic predisposition for rare malignancies: DICER1 mutations and pleuropulmonary blastoma.
AJR Am J Roentgenol. 2015; 204(4):W475-82 [PubMed] Related Publications
OBJECTIVE: The purpose of this study was to compare the risks of radiation in screening strategies using chest radiographs and CT to detect a rare cancer in a genetically predisposed population against the risks of undetected disease.
MATERIALS AND METHODS: A decision analytic model of diagnostic imaging screening strategies was built to predict outcomes and cumulative radiation doses for children with DICER1 mutations screened for pleuropulmonary blastoma. Screening strategies compared were chest radiographs followed by chest CT for a positive radiographic result and CT alone. Screening frequencies ranged from once in 3 years to once every 3 months. BEIR VII (model VII proposed by the Committee on the Biological Effects of Ionizing Radiation) risk tables were used to predict excess cancer mortality for each strategy, and the corresponding loss of life expectancy was calculated using Surveillance Epidemiologic and End Results (SEER) statistics. Loss of life expectancy owing to undetected progressive pleuropulmonary blastoma was estimated on the basis of data from the International Pleuropulmonary Blastoma Registry. Sensitivity analysis was performed for all model parameters.
RESULTS: Loss of life expectancy owing to undetected disease in an unscreened population exceeded that owing to radiation-induced cancer for all screening scenarios investigated. Increases in imaging frequency decreased loss of life expectancy for the combined (chest radiographs and CT) screening strategy but increased that for the CT-only strategy. This was because loss of life expectancy for combined screening is dominated by undetected disease, whereas loss of life expectancy for CT screening is dominated by radiation-induced cancers.
CONCLUSION: Even for a rare disease such as pleuropulmonary blastoma, radiographic screening of infants and young children with cancer-predisposing mutations may result in improved life expectancy compared with the unscreened population. The benefit of screening will be greater for diseases with a higher screening yield.

Durieux E, Descotes F, Nguyen AM, et al.
Somatic DICER1 gene mutation in sporadic intraocular medulloepithelioma without pleuropulmonary blastoma syndrome.
Hum Pathol. 2015; 46(5):783-7 [PubMed] Related Publications
Germline DICER1 gene mutation has been described in ocular medulloepithelioma associated with pleuropulmonary blastoma family tumor and dysplasia syndrome. We present a case of sporadic ocular medulloepithelioma in an 18-year-old woman with D1709N somatic mutation in DICER1 gene, which has not been previously described. This case highlights the potential use of DICER1 gene sequencing to resolve the diagnostic challenge in recurrent and metastatic malignant medulloepithelioma, when morphology and immunohistochemistry are inconclusive. Further studies in larger series of this type of tumor are needed to confirm the relevance of this molecular abnormality in the tumorigenesis of this embryonic-type ocular tumor.

Hinrichs JW, van Blokland WT, Moons MJ, et al.
Comparison of next-generation sequencing and mutation-specific platforms in clinical practice.
Am J Clin Pathol. 2015; 143(4):573-8 [PubMed] Related Publications
OBJECTIVES: To compare next-generation sequencing (NGS) platforms with mutation-specific analysis platforms in a clinical setting, in terms of sensitivity, mutation specificity, costs, capacity, and ease of use.
METHODS: We analyzed 25 formalin-fixed, paraffin-embedded lung cancer samples of different size and tumor percentage for known KRAS and EGFR hotspot mutations with two dedicated genotyping platforms (cobas [Roche Diagnostics, Almere, The Netherlands] and Rotor-Gene [QIAGEN, Venlo, The Netherlands]) and two NGS platforms (454 Genome Sequencer [GS] junior [Roche Diagnostics] and Ion Torrent Personal Genome Machine [Life Technologies, Bleiswijk, The Netherlands]).
RESULTS: All platforms, except the 454 GS junior, detected the mutations originally detected by Sanger sequencing and high-resolution melting prescreening and detected an additional KRAS mutation. The dedicated genotyping platforms outperformed the NGS platforms in speed and ease of use. The large sequencing capacity of the NGS platforms enabled them to deliver all mutation information for all samples at once.
CONCLUSIONS: Sensitivity for detecting mutations was highly comparable among all platforms. The choice for either a dedicated genotyping platform or an NGS platform is basically a trade-off between speed and genetic information.

Xu H, Sun W, Zhang G, Cheng Y
Detection of epidermal growth factor receptor mutation in non-small-cell lung carcinoma using cytological and histological specimens.
J BUON. 2015 Jan-Feb; 20(1):142-5 [PubMed] Related Publications
PURPOSE: Epidermal growth factor receptor (EGFR) mutations are prerequisites for the targeted therapy with anti-EGFR tyrosine kinase inhibitors (TKIs) in non-small-cell lung carcinomas (NSCLCs). In patients with advanced-stage NSCLC, sometimes cytological specimens, including those from fine-needle aspiration cytology (FNAC) and pleural effusion, are the only materials for mutation analysis. The purpose of this study was to compare the results of EGFR mutation detection from cytological specimens and histological samples and to evaluate the difference between them, therefore to assess if cell block is a valid source for detection of EGFR mutation.
METHODS: Forty-seven samples from advanced-stage NSCLCs were obtained with individually matched cell blocks (CBs) from FNAC (29 cases) or pleural fluid (18 cases), and formalin-fixed paraffin-embedded (FFPE) blocks from biopsy (34 cases) or surgical excision (13 cases). CBs and FFPE blocks were simultaneously tested for EGFR hot mutations in exons 18, 19, 20 and 21 by polymerase chain reaction (PCR)-direct sequencing and amplification refractory mutation system (ARMS)-PCR.
RESULTS: EGFR mutations were identified in 18/47 (38.3%) or 21/47 (44.7%) cases using CBs and 16/47 (34.0%) or 19/47 (40.4%) using FPPE blocks by PCR-direct sequencing or ARMS-PCR, respectively. The incidence of EGFR mutation was not statistically significant between CBs and FFPE blocks using PCR-direct sequencing or ARMS-PCR (p=0.668 or p=0.677, respectively).
CONCLUSION: Our study suggests that cytological specimens are optimal for advanced NSCLC. The successful use of these non-invasive specimens in molecular pathology is beneficial for patients requiring targeted therapy.

Damyanov D, Koynov K, Naseva E, Bichev S
EGFR mutations in patients with non small-cell lung cancer in Bulgaria and treatment with gefitinib.
J BUON. 2015 Jan-Feb; 20(1):136-41 [PubMed] Related Publications
PURPOSE: To evaluate the EGFR mutations in non small cell lung cancer (NSCLC) patients in Bulgaria, as well as to summarize the outcomes of patients with EGFR mutations, treated with gefitinib as first- or subsequent-line therapy.
METHODS: From January 2010 to March 2012 tumor samples from773 NSCLC patients were evaluated for EGFR mutations.
RESULTS: Seventy-one mutations were found and 34 patients were treated with gefitinib. Complete remission (CR) was achieved in 2 patients (6.9%), partial remission (PR) in 11 (37.9%), stable disease (SD) in 13 (44.8%), and disease progression (PD) in 3 (10.3%). Higher objective response rate was seen in women and in never-smokers.The mean progression-free survival (PFS) was 11.1 months (95% CI 9.1-13.1), registered in 29 patients (median PFS 10 months ; 95% CI 8.9-11.1).Tolerability to gefitinib was acceptable, with prevalence of skin toxicity, and it did not lead to any significant decline of the patients' quality of life.
CONCLUSION: This is the first study in Bulgaria to evaluate EGFR mutations in NSCLC patients,which were encountered in 9.4% of the studied population. The present study confirms the benefits of first- and subsequent-lines of gefitinib for the treatment of this patient group. Our data give grounds for the conclusion that gefitinib is an effective and well-tolerated therapeutic option for patients with locally advanced and metastatic NSCLC harboring EGFR mutations.

Siyar Ekinci A, Demirci U, Cakmak Oksuzoglu B, et al.
KRAS discordance between primary and metastatic tumor in patients with metastatic colorectal carcinoma.
J BUON. 2015 Jan-Feb; 20(1):128-35 [PubMed] Related Publications
PURPOSE: Adding targeted therapies to chemotherapy in metastatic colorectal cancer (CRC) improves response rates and survival. KRAS is a predictive indicator for anti-epidermal growth factor receptor (EGFR) treatments. The most important reasons for KRAS discordance are intratumoral heterogeneity and incorrect mutation analysis. Evaluating the status of KRAS in primary and metastatic lesions becomes even more crucial to ensure efficient usage of anti-EGFR treatments.
METHODS: Patients with metastatic CRC, whose primary disease and liver and/or lung metastases were operated, were retrospectively evaluated, and KRAS assessment was performed on 31 patients who were suitable for DNA analysis. Pyrosequencing with polymerase chain reaction (PCR) was used for KRAS analysis.
RESULTS: The median age of 31 patients diagnosed with rectal cancer (N=13) and colon cancer (N=18) was 63 years (range 33-73). Metastasectomy locations included the liver (N=27), lung (N=3), and both lung and liver (N=1). KRAS discordance was detected in 22% (7/31) of the patients. While 3 patients with detected discordance had mutated KRAS in the primary material, wild type KRAS was detected in their liver or lung lesions. On the other hand, while 4 patients had wild type KRAS in the primary material, mutated KRAS was determined in their liver or lung lesions. The McNemar test revealed no significant discordance between primary and metastatic disease (p=1.00). No progression free survival (PFS) difference was detected between patients with determined discordance and patients with undetermined discordance (10.6 vs 14.7 months, p=0.719).
CONCLUSION: This is the first study to evaluate KRAS discordance between primary and metastasis in CRC patients, who underwent metastasectomy, together with survival data. In the literature and recent studies with large patient numbers in which modern KRAS tests were used, the KRAS discordance rate varies between 3-12%. In our study, a higher KRAS discordance (22%) was detected, and no survival difference was determined between patients with or without discordance. In recent years, the rising interest in borderline resectable disease may bring forward discussions related to which material the KRAS status should be analyzed.

Recurrent Structural Abnormalities

Selected list of common recurrent structural abnormalities

This is a highly selective list aiming to capture structural abnormalies which are frequesnt and/or significant in relation to diagnosis, prognosis, and/or characterising specific cancers. For a much more extensive list see the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer.

del(3p) in Lung Cancer

Hung J, Kishimoto Y, Sugio K, et al.
Allele-specific chromosome 3p deletions occur at an early stage in the pathogenesis of lung carcinoma.
JAMA. 1995; 273(7):558-63 [PubMed] Related Publications
BACKGROUND: Deletions in the short arm of chromosome 3 (3p) are present in most lung carcinomas.
OBJECTIVE: To investigate the role of these chromosome 3p deletions in the pathogenesis of non-small cell lung carcinomas.
DESIGN: Seven archival, paraffin-embedded, surgically resected lung cancer specimens were studied. Fifty precisely identified malignant and preneoplastic lesions present in bronchi, bronchioles, and alveoli were microdissected from stained slides and analyzed for allele loss using polymerase chain reaction-based assays for dinucleotide repeat polymorphisms at three chromosome 3p loci (3p14, 3p21.3, and 3p25).
SETTING: University-based medical center and affiliated hospitals.
SUBJECTS: Samples were analyzed from seven patients who underwent surgical resection with curative intent for non-small cell lung cancer and whose specimens included extensive multifocal areas of preneoplastic lesions (hyperplasia, metaplasia, dysplasia, or noninvasive cancer).
RESULTS: Lymphocytes from all seven cases were heterozygous (ie, informative) for all three microsatellites analyzed. Six (86%) of seven invasive cancers had loss of heterozygosity at one or more chromosome 3p sites. In the accompanying preneoplastic lesions, loss of heterozygosity was detected in none of two normal bronchioles, 13 (76%) of 17 hyperplasias, six (86%) of seven dysplasias, and four (100%) of four noninvasive cancers. Loss of heterozygosity was detected throughout the respiratory tract, in bronchi, bronchioles, and alveoli. In 18 (78%) of 23 preneoplastic lesions, the specific alleles lost were identical to those lost in the corresponding carcinomas. The probability of this happening by chance is 5.3 x 10(-3).
CONCLUSIONS: Deletions in the short arm of chromosome 3 occur at the earliest stage (hyperplasia) in the pathogenesis of lung cancer and involve all regions of the respiratory tract. Allele loss is highly specific, but its mechanism remains unknown. Our findings may be of considerable biologic, prognostic, and clinical significance.

Hosoe S, Shigedo Y, Ueno K, et al.
Detailed deletion mapping of the short arm of chromosome 3 in small cell and non-small cell carcinoma of the lung.
Lung Cancer. 1994; 10(5-6):297-305 [PubMed] Related Publications
We constructed a detailed deletion map of the short arm of chromosome 3 (3p) for 55 lung cancer cases by using 17 restriction fragment length polymorphism (RFLP) probes. Initially, we examined 40 small cell lung cancer (SCLC) cases and found three regions of deletion at 3p25-26, 3p21.3 and 3p14-cen, suggesting the possibility of at least three different tumor-suppressor genes on 3p. In order to obtain more detailed deletion area, and to compare the pattern of 3p deletion, we also examined 15 non-small cell lung cancer (NSCLC) cases. Compared to NSCLC cases, most of SCLC cases have widespread deletion on 3p, suggesting multiple tumor-suppressor genes on 3p may be inactivated in this type of cancer. In 3p21.3 area, minimum overlapping area of deletion lays between two probes which are close to each other. These data will be useful to isolate the putative tumor-suppressor genes located on the chromosome 3p.

Kohno H, Hiroshima K, Toyozaki T, et al.
p53 mutation and allelic loss of chromosome 3p, 9p of preneoplastic lesions in patients with nonsmall cell lung carcinoma.
Cancer. 1999; 85(2):341-7 [PubMed] Related Publications
BACKGROUND: An accumulation of mutations can result in carcinogenesis. Comparing genetic alterations in preneoplastic lesions with those seen in cancer in the same patient may be helpful in the early diagnosis of lung carcinoma or preneoplastic lesions.
METHODS: To identify genetic alterations that may play a role in the development of nonsmall cell lung carcinoma (NSCLC), the authors examined the p53 gene and microsatellite markers on chromosome 3p (D3S643, D3S1317), 9p (D9S171, IFNA) in 35 bronchial metaplastic lesions and 28 alveolar hyperplastic lesions from 61 patients.
RESULTS: A total of 8 metaplastic lesions (1 squamous metaplasia and 7 dysplasias) and 3 alveolar hyperplastic lesions (with atypia) showed genetic alterations, including loss of heterozygosity (LOH) of 3p, 9p and mutations of the p53 gene. In an analysis of microsatellite markers, 5 of 35 cases of squamous cell carcinoma (SCC) and 3 of 26 cases of adenocarcinoma (Ad) showed LOH in both preneoplastic lesions and synchronous cancers. Nine patients (25.7%) with SCC and 6 patients (23.1%) with Ad were shown to have mutations of the p53 gene by single-strand conformation polymorphism. In 2 of these 9 patients with SCC, the same mutation was observed in both dysplasia and SCC.
CONCLUSIONS: These findings suggest that several genetic alterations may occur in preneoplastic lesions or the early stage of SCC of the lung, whereas the genetic alterations examined appeared to occur relatively late in the pathogenesis of pulmonary adenocarcinoma.

del(9p) in Lung Cancer

Kishimoto Y, Sugio K, Hung JY, et al.
Allele-specific loss in chromosome 9p loci in preneoplastic lesions accompanying non-small-cell lung cancers.
J Natl Cancer Inst. 1995; 87(16):1224-9 [PubMed] Related Publications
BACKGROUND: Carcinogenesis is a multistep process, which may begin as a consequence of chromosomal changes. Deletions in the short arm of chromosome 9 (9p) have been observed in lung carcinomas. In addition, morphologically recognizable preneoplastic lesions, frequently multiple in number, precede onset of invasive carcinomas.
PURPOSE: We tested for deletions and loss of heterozygosity (LOH) at 9p loci in preneoplastic and neoplastic foci in lungs of patients with non-small-cell lung carcinomas (NSCLCs).
METHODS: Seven archival, paraffin-embedded, surgically resected NSCLC specimens were selected. They were predominantly from patients with adenocarcinomas and contained multiple preneoplastic lesions, including hyperplasia, metaplasia, dysplasia, and carcinoma in situ (CIS). Fifty-three histologically identified preneoplastic and malignant lesions present in bronchi, bronchioles, and alveoli were precisely microdissected from stained tissue sections with a micromanipulator. Stromal lymphocytes were used to determine constitutional heterozygosity. The specimens were analyzed for LOH using polymerase chain reaction-based assays for polymorphism in dinucleotide repeats (microsatellite markers) in interferon alfa (IFNA) and D9S171 loci on 9p.
RESULTS: All seven cases were constitutionally heterozygous for one or both microsatellite markers. Five of seven cases had LOH at one or both 9p loci in the invasive primary cancers (doubly informative cases). Four of these five cases also revealed LOH in preneoplastic foci. In the doubly informative cases, LOH was detected in five (38%) of 13 foci of hyperplasia, four (80%) of five foci of dysplasia, and three (100%) of three CIS lesions. LOH was detected in preneoplastic lesions from all regions of the respiratory tract, including bronchi, bronchioles, and alveoli, and involved five different cell types. The identical allele was lost from both the preneoplastic lesions and the corresponding tumors (12 of 12 lesions, 17 of 17 comparisons), a phenomenon we have referred to as "allele-specific mutation." Statistical analyses employing a cumulative binomial test demonstrated that the probabilities of such findings occurring by chance are 2.4 x 10(-4) and 7.6 x 10(-6), respectively. From comparisons with the previously published data on other chromosomal abnormalities in the same tissue specimens, it appears that LOH at 3p and 9p loci occurred early in the hyperplasia stage, but the ras gene point mutations were relatively late, at the CIS stage.
CONCLUSIONS: LOH at 9p loci occurs at the earliest stage in the pathogenesis of lung cancer and involves all regions of the respiratory tract. LOH in NSCLC is not random but targets a specific allele in individuals. Studying preneoplastic lesions may help identify intermediate markers for risk assessment and chemoprevention.

Kohno H, Hiroshima K, Toyozaki T, et al.
p53 mutation and allelic loss of chromosome 3p, 9p of preneoplastic lesions in patients with nonsmall cell lung carcinoma.
Cancer. 1999; 85(2):341-7 [PubMed] Related Publications
BACKGROUND: An accumulation of mutations can result in carcinogenesis. Comparing genetic alterations in preneoplastic lesions with those seen in cancer in the same patient may be helpful in the early diagnosis of lung carcinoma or preneoplastic lesions.
METHODS: To identify genetic alterations that may play a role in the development of nonsmall cell lung carcinoma (NSCLC), the authors examined the p53 gene and microsatellite markers on chromosome 3p (D3S643, D3S1317), 9p (D9S171, IFNA) in 35 bronchial metaplastic lesions and 28 alveolar hyperplastic lesions from 61 patients.
RESULTS: A total of 8 metaplastic lesions (1 squamous metaplasia and 7 dysplasias) and 3 alveolar hyperplastic lesions (with atypia) showed genetic alterations, including loss of heterozygosity (LOH) of 3p, 9p and mutations of the p53 gene. In an analysis of microsatellite markers, 5 of 35 cases of squamous cell carcinoma (SCC) and 3 of 26 cases of adenocarcinoma (Ad) showed LOH in both preneoplastic lesions and synchronous cancers. Nine patients (25.7%) with SCC and 6 patients (23.1%) with Ad were shown to have mutations of the p53 gene by single-strand conformation polymorphism. In 2 of these 9 patients with SCC, the same mutation was observed in both dysplasia and SCC.
CONCLUSIONS: These findings suggest that several genetic alterations may occur in preneoplastic lesions or the early stage of SCC of the lung, whereas the genetic alterations examined appeared to occur relatively late in the pathogenesis of pulmonary adenocarcinoma.

del(1p36) in Lung Cancer

Nomoto S, Haruki N, Tatematsu Y, et al.
Frequent allelic imbalance suggests involvement of a tumor suppressor gene at 1p36 in the pathogenesis of human lung cancers.
Genes Chromosomes Cancer. 2000; 28(3):342-6 [PubMed] Related Publications
The short arm of chromosome 1 is among the most frequently affected regions in various types of common adult cancers as well as in neuroblastoma. In a previous study of ours, frequent allelic imbalance at the TP73 locus at 1p36 was noted in lung cancer despite the absence of TP73 mutations. This suggested the possible existence of an as yet unidentified tumor suppressor gene on 1p. Our initial attempt using the candidate gene approach did not yield any somatic mutations in the 14-3-3sigma gene (official gene symbol, SFN), a mediator of G2 arrest by TP53. Detailed deletion mapping of the telomeric region of 1p was thus carried out as an initial step toward positional cloning. We used seven polymorphic markers in addition to TP73 to examine 61 primary lung cancers. Allelic imbalance at one or more loci of 1p36 was observed in 30 of the 61 cases, whereas D1S508 at 1p36.2 exhibited the highest frequency (45%) of allelic imbalance among the 1p36 markers examined. In contrast, two proximal markers at 1p32-34 showed significantly less frequent (11-14%) allelic imbalance. Consequently, the present study identified the shortest region of overlap between D1S507 and TP73, which included the most frequently affected marker, D1S508. In addition, several cases exhibited allelic imbalance confined to a subtelomeric region distal to D1S2845 at 1p36.3. The present findings warrant future studies to identify the putative tumor suppressor gene(s) at 1p36 to gain a better understanding of the molecular pathogenesis of lung cancer. Genes Chromosomes Cancer 28:342-346, 2000.

Yanada M, Yaoi T, Shimada J, et al.
Frequent hemizygous deletion at 1p36 and hypermethylation downregulate RUNX3 expression in human lung cancer cell lines.
Oncol Rep. 2005; 14(4):817-22 [PubMed] Related Publications
Runt-related transcription factor 3 (RUNX3) has been recognized as a tumor suppressor gene in gastric cancer because its expression level was reduced or disappeared due to epigenetic changes. To evaluate the usefulness of the RUNX3 gene as a biomarker of lung cancer, we have analyzed the expression of the RUNX3 gene in 15 lung cancer cell lines by real-time reverse transcription-polymerase chain reaction (RT-PCR), and demonstrated that RUNX3 gene expression was reduced or disappeared in all cell lines examined (100%). In addition, we have attempted to classify all the cell lines into three groups according to the expression level; less than 10% (group I), 10-30% (group II) and approximately 50% (group III). We further investigated methylation status of the CpG sites in the exon 1 region of RUNX3 by methylation specific PCR (MSP), and studied the correlation between the expression level and hemizygous deletion as revealed by bicolor fluorescence in situ hybridization (FISH). The CpG sites were hypermethylated in 8 cell lines (53%) and the RUNX3 loci were hemizygously deleted in another 8 cell lines (53%). Furthermore group I, II, and III corresponded well to methylation-positive cell lines, cell lines showing hemizygous deletion, and the rest of cell lines without methylation or hemizygous deletion, respectively. These results suggest that a comprehensive study on RUNX3 using real-time RT-PCR, MSP, and FISH could be beneficial in understanding the pathogenetic mechanisms of human lung cancer at the molecular level.

Shibukawa K, Miyokawa N, Tokusashi Y, et al.
High incidence of chromosomal abnormalities at 1p36 and 9p21 in early-stage central type squamous cell carcinoma and squamous dysplasia of bronchus detected by autofluorescence bronchoscopy.
Oncol Rep. 2009; 22(1):81-7 [PubMed] Related Publications
Heavy smokers with central type squamous cell carcinoma (SCC) frequently have multiple cancerous lesions in the bronchus. Autofluorescence bronchoscopy (AFB) is useful in the detection of early bronchogenic cancer and dysplastic lesions. We investigated the loss of heterozygosity (LOH) and microsatellite instability (MSI) and expression of four proteins in 13 early stage SCC (early SCC) and 9 squamous dysplasia detected by AFB and 19 cases of surgically resected invasive SCC (invasive SCC). In early SCC and squamous dysplasia, LOH/MSI of chromosome 1p36 was found in 62 and 33%, respectively, and of 9p21 in 54 and 63%, respectively. TAp73 expression of early SCC and squamous dysplasia was lower than that of normal bronchial epithelium, and p16 expression was not detectable in these lesions. These results suggested that the genetic abnormalities had already developed in the early stage of carcinogenesis of SCC, including squamous dysplasia. The AFB system was able to reveal abnormal autofluorescence in these precancerous lesions, including squamous dysplasia.

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