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)

The p53 regulatory network responds to cellular stresses by initiating processes such as cell cycle arrest and apoptosis. These responses inhibit cellular transformation and mediate the response to many forms of cancer therapies. Functional variants in the genes comprising this network could help identify individuals at greater risk for cancer and patients with poorer responses to therapies, but few such variants have been identified as yet. We use the NCI60 human tumor cell line anticancer drug screen in a scan of single nucleotide polymorphisms (SNP) in 142 p53 stress response genes and identify 7 SNPs that exhibit allelic differences in cellular responses to a large panel of cytotoxic chemotherapeutic agents. The greatest differences are observed for SNPs in 14-3-3tau (YWHAQ; rs6734469, P=5.6x10(-47)) and CD44 (rs187115, P=8.1x10(-24)). In soft-tissue sarcoma patients, we find that the alleles of these SNPs that associate with weaker growth responses to chemotherapeutics associate with poorer overall survival (up to 2.89 relative risk, P=0.011) and an earlier age of diagnosis (up to 10.7 years earlier, P=0.002). Our findings define genetic markers in 14-3-3tau and CD44 that might improve the treatment and prognosis of soft-tissue sarcomas.
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PMID:Chemosensitivity profiles identify polymorphisms in the p53 network genes 14-3-3tau and CD44 that affect sarcoma incidence and survival. 1999 85

Germline variation in the TP53 network genes PRKAG2, PPP2R2B, CCNG1, PIAS1 and YWHAQ was previously suggested to have an impact on drug response in vitro. Here, we investigated the effect on breast cancer survival of germline variation in these genes in 925 Finnish breast cancer patients and further analyzed five single nucleotide polymorphisms (SNPs) in PRKAG2 (rs1029946, rs4726050, rs6464153, rs7789699) and PPP2R2B (rs10477313) for 10-year survival in breast cancer patients, interaction with TP53 R72P and MDM2-SNP309, outcome after specific adjuvant therapy and correlation to tumor characteristics in 4,701 invasive cases from four data sets. We found evidence for carriers of PRKAG2-rs1029946 and PRKAG2-rs4726050 having improved survival in the pooled data (HR 0.53, 95% CI 0.3-0.9; p = 0.023 for homozygous carriers of the rare G-allele and HR 0.85, 95% CI 0.7-0.9; p = 0.049 for carriers of the rare G allele, respectively). PRKAG2-rs4726050 showed a significant interaction with MDM2-SNP309, with PRKAG2-rs4726050 rare G-allele having a dose-dependent effect for better breast cancer survival confined only to MDM2 SNP309 rare G-allele carriers (HR 0.45, 95% CI 0.2-0.7; p = 0.001). This interaction also emerged as an independent predictor of better survival (p = 0.047). PPP2R2B-rs10477313 rare A-allele was found to predict better survival (HR 0.82, 95% CI 0.6-0.9; p = 0.018), especially after hormonal therapy (HR 0.66, 95% CI 0.5-0.9; p = 0.048). These findings warrant further studies and suggest that genetic markers in TP53 network genes such as PRKAG2 and PPP2R2B might affect prognosis and treatment outcome in breast cancer patients.
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PMID:Germline variation in TP53 regulatory network genes associates with breast cancer survival and treatment outcome. 2303 90

Factors that contribute to the onset of atherosclerosis may be elucidated by bioinformatic techniques applied to multiple sources of genomic and proteomic data. The results of genome wide association studies, such as the CardioGramPlusC4D study, expression data, such as that available from expression quantitative trait loci (eQTL) databases, along with protein interaction and pathway data available in Ingenuity Pathway Analysis (IPA), constitute a substantial set of data amenable to bioinformatics analysis. This study used bioinformatic analyses of recent genome wide association data to identify a seed set of genes likely associated with atherosclerosis. The set was expanded to include protein interaction candidates to create a network of proteins possibly influencing the onset and progression of atherosclerosis. Local average connectivity (LAC), eigenvector centrality, and betweenness metrics were calculated for the interaction network to identify top gene and protein candidates for a better understanding of the atherosclerotic disease process. The top ranking genes included some known to be involved with cardiovascular disease (APOA1, APOA5, APOB, APOC1, APOC2, APOE, CDKN1A, CXCL12, SCARB1, SMARCA4 and TERT), and others that are less obvious and require further investigation (TP53, MYC, PPARG, YWHAQ, RB1, AR, ESR1, EGFR, UBC and YWHAZ). Collectively these data help define a more focused set of genes that likely play a pivotal role in the pathogenesis of atherosclerosis and are therefore natural targets for novel therapeutic interventions.
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PMID:Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of Atherosclerosis. 2936 37