Research IndicatorsGraph generated 30 August 2019 using data from PubMed using criteria.
Mouse over the terms for more detail; many indicate links which you can click for dedicated pages about the topic. Tag cloud generated 30 August, 2019 using data from PubMed, MeSH and CancerIndex
Specific Cancers (5)
Data table showing topics related to specific cancers and associated disorders. Scope includes mutations and abnormal protein expression.
Note: list is not exhaustive. Number of papers are based on searches of PubMed (click on topic title for arbitrary criteria used).
OMIM, Johns Hopkin University
Referenced article focusing on the relationship between phenotype and genotype.
International Cancer Genome Consortium.
Summary of gene and mutations by cancer type from ICGC
Cancer Genome Anatomy Project, NCI
COSMIC, Sanger Institute
Somatic mutation information and related details
GEO Profiles, NCBI
Search the gene expression profiles from curated DataSets in the Gene Expression Omnibus (GEO) repository.
Latest Publications: CBLC (cancer-related)
Metastatic melanoma is an aggressive skin cancer and is one of the global malignancies with high mortality and morbidity. It is essential to identify and verify diagnostic biomarkers of early metastatic melanoma. Previous studies have systematically assessed protein biomarkers and mRNA-based expression characteristics. However, molecular markers for the early diagnosis of metastatic melanoma have not been identified. To explore potential regulatory targets, we have analyzed the gene microarray expression profiles of malignant melanoma samples by co-expression analysis based on the network approach. The differentially expressed genes (DEGs) were screened by the EdgeR package of R software. A weighted gene co-expression network analysis (WGCNA) was used for the identification of DEGs in the special gene modules and hub genes. Subsequently, a protein-protein interaction network was constructed to extract hub genes associated with gene modules. Finally, twenty-four important hub genes (RASGRP2, IKZF1, CXCR5, LTB, BLK, LINGO3, CCR6, P2RY10, RHOH, JUP, KRT14, PLA2G3, SPRR1A, KRT78, SFN, CLDN4, IL1RN, PKP3, CBLC, KRT16, TMEM79, KLK8, LYPD3 and LYPD5) were treated as valuable factors involved in the immune response and tumor cell development in tumorigenesis. In addition, a transcriptional regulatory network was constructed for these specific modules or hub genes, and a few core transcriptional regulators were found to be mostly associated with our hub genes, including GATA1, STAT1, SP1, and PSG1. In summary, our findings enhance our understanding of the biological process of malignant melanoma metastasis, enabling us to identify specific genes to use for diagnostic and prognostic markers and possibly for targeted therapy.
Based on a series of basic, preclinical and clinical studies, the Poly (ADP-ribose) Polymerase 1 (PARP1) inhibitor, olaparib, has recently been approved for use in ovarian cancer patients with BRCA1 or BRCA2 mutations. By identifying novel predictive biomarkers of tumour cell sensitivity to olaparib, it is possible that the utility of PARP inhibitors could be extended beyond this patient subgroup. Many of the known genetic determinants of PARP inhibitor response have key roles in DNA damage response (DDR) pathways. Although protein ubiquitylation is known to play an important role in regulating the DDR, the exact mechanisms by which this occurs are not fully understood. Using two parallel RNA interference-based screening approaches, we identified the E3 ubiquitin ligase, CBLC, as a candidate biomarker of response to olaparib. We validated this observation by demonstrating that silencing of CBLC causes increased sensitivity to olaparib in breast cancer cell line models and that defective homologous recombination (HR) DNA repair is the likely cause. This data provides an example of how defects in the ubiquitin machinery have the potential to influence the response of tumour cells to PARP inhibitors.
To investigate the effect of valproate treatment on the K562 cell line, a model for chronic myelogenous leukaemia, the growth and survival of the K562 cell line were investigated using the Annexin-V/PI dual staining method, and global profiles of gene expression and alternative splicing in K562 cells were assessed using exon microarrays. A significant increase in cell apoptosis was observed in valproate-exposed K562 cells using flow cytometry. A total of 628 transcripts were identified as being significantly differentially expressed. The number of genes demonstrating increased expression levels was greater than the number of genes demonstrating decreased expression levels (445 genes vs. 183 genes, respectively). The significant enrichment analysis of GO terms for the differentially expressed genes revealed that these genes are involved in many important biological processes such as apoptosis. Six of the genes observed to be differentially expressed that might be involved in apoptosis were selected to undergo qRT-PCR validation. In total, 198 candidates of alternative splicing variants were identified. Among them, three alternative splicing events were selected for validation, and CBLC and TBX1 were confirmed to be alternatively spliced by semi-nested PCR. In conclusion, valproate exposure facilitated cell apoptosis, altered mRNA expression and alternative splicing events in the K562 cell line.
Loewy AD, Niles KM, Anastasio N, et al.Epigenetic modification of the gene for the vitamin B(12) chaperone MMACHC can result in increased tumorigenicity and methionine dependence.
Mol Genet Metab. 2009; 96(4):261-7 [PubMed
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Methionine dependence, the inability of cells to grow when the amino acid methionine is replaced in culture medium by its metabolic precursor homocysteine, is characteristic of many cancer cell lines and some tumors in situ. Most cell lines proliferate normally under these conditions. The methionine dependent tumorigenic human melanoma cell line MeWo-LC1 was derived from the methionine independent non-tumorigenic line, MeWo. MeWo-LC1 has a cellular phenotype identical to that of cells from patients with the cblC inborn error of cobalamin metabolism, with decreased synthesis of cobalamin coenzymes and decreased activity of the cobalamin-dependent enzymes methionine synthase and methylmalonylCoA mutase. Inability of cblC cells to complement the defect in MeWo-LC1 suggested that it was caused by decreased activity of the MMACHC gene. However, no potentially disease causing mutations were detected in the coding sequence of MMACHC in MeWo-LC1. No MMACHC expression was detected in MeWo-LC1 by quantitative or non-quantitative PCR. There was virtually complete methylation of a CpG island at the 5'-end of the MMACHC gene in MeWo-LC1, consistent with inactivation of the gene by methylation. The CpG island was partially methylated (30-45%) in MeWo and only lightly methylated (2-11%) in control fibroblasts. Infection of MeWo-LC1 with wild type MMACHC resulted in correction of the defect in cobalamin metabolism and restoration of the ability of cells to grow in medium containing homocysteine. We conclude that epigenetic inactivation of the MMACHC gene is responsible for methionine dependence in MeWo-LC1.
Kim B, Lee HJ, Choi HY, et al.Clinical validity of the lung cancer biomarkers identified by bioinformatics analysis of public expression data.
Cancer Res. 2007; 67(15):7431-8 [PubMed
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Identification of molecular markers often leads to important clinical applications such as early diagnosis, prognosis, and drug targeting. Lung cancer, the leading cause of cancer-related deaths, still lacks reliable molecular markers. We have combined the bioinformatics analysis of the public gene expression data and clinical validation to identify biomarker genes for non-small-cell lung cancer. The serial analysis of gene expression and the expressed sequence tag data were meta-analyzed to produce a list of the differentially expressed genes in lung cancer. Through careful inspection of the predicted genes, we selected 20 genes for experimental validation using semiquantitative reverse transcriptase-PCR. The microdissected clinical specimens used in the study consisted of three groups: lung tissues from benign diseases and the paired (cancer and pathologic normal) tissues from non-small-cell lung cancer patients. After extensive statistical analyses, seven genes (CBLC, CYP24A1, ALDH3A1, AKR1B10, S100P, PLUNC, and LOC147166) were identified as potential diagnostic markers. Quantitative real-time PCR was carried out to additionally assess the value of the seven identified genes leading to the confirmation of at least two genes (CBLC and CYP24A1) as highly probable novel biomarkers. The gene properties of the identified markers, especially their relationship to lung cancer and cell signaling pathway regulation, further suggest their potential value as drug targets as well.