Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UNIPROT:P04637 (p53)
 77,613 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Formation and progression of complex diseases are generally the joint effect of genetic and epigenetic disorders, thus an integrative analysis of epigenetic and genetic data is essential for understanding mechanism of the diseases. In this study, we integrate Illuminate 450k DNA methylation and gene expression data to calculate the weights of gene network using Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA). The approach considers all methylation values of CpG sites in a gene, rather than averaging them which was used in other studies ignoring the variability of the methylation sites. Through comparing topological features of control network with those of case network, including global and local features, candidate disease-associated genes and gene modules are identified. We apply the approach to real data, breast invasive carcinoma (BRCA). It successfully identifies susceptibility breast cancer-related genes, such as TP53, BRCA1, EP300, CDK2, MCM7 and so forth, within which most are previously known to breast cancer. Also, GO and pathway enrichment analysis indicate that these genes enrich in cell apoptosis and regulation of cell death which are cancer-related biological processes. Importantly, through analyzing the functions and comparing expression and methylation values of these genes between cases and controls, we find some genes, such as VASN, SNRPD3, and gene modules, targeted by POLR2C, CHMP1B and TAF9, which might be novel breast cancer-related biomarkers.
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PMID:A network-based approach to identify disease-associated gene modules through integrating DNA methylation and gene expression. 2628 1

Reinstating wild-type tumor suppressor p53 activity could be a valuable option for the treatment of cancer. To contribute to development of new treatment options for non-small cell lung cancer (NSCLC), we performed genome-wide siRNA screens for determinants of p53 activity in NSCLC cells. We identified many genes not previously known to be involved in regulating p53 activity. Silencing p53 pathway inhibitor genes was associated with loss of cell viability. The largest functional gene cluster influencing p53 activity was mRNA splicing. Prominent p53 activation was observed upon silencing of specific spliceosome components, rather than by general inhibition of the spliceosome. Ten genes were validated as inhibitors of p53 activity in multiple NSCLC cell lines: genes encoding the Ras pathway activator SOS1, the zinc finger protein TSHZ3, the mitochondrial membrane protein COX16, and the spliceosome components SNRPD3, SF3A3, SF3B1, SF3B6, XAB2, CWC22, and HNRNPL. Silencing these genes generally increased p53 levels, with distinct effects on CDKN1A expression, induction of cell cycle arrest and cell death. Silencing spliceosome components was associated with alternative splicing of MDM4 mRNA, which could contribute to activation of p53. In addition, silencing splice factors was particularly effective in killing NSCLC cells, albeit in a p53-independent manner. Interestingly, silencing SNRPD3 and SF3A3 exerted much stronger cytotoxicity to NSCLC cells than to lung fibroblasts, suggesting that these genes could represent useful therapeutic targets.
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PMID:A genome-wide siRNA screen for regulators of tumor suppressor p53 activity in human non-small cell lung cancer cells identifies components of the RNA splicing machinery as targets for anticancer treatment. 2829 43