A novel Aurora-A-mediated phosphorylation of p53 inhibits its interaction with MDM2

Hsueh, Kai Wei; Fu, Shu Ling; Chang, Yu-Ling; Chao, Lin

doi:10.1016/j.bbapap.2012.11.005

Cited by 34 publications

(33 citation statements)

References 41 publications

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“…Network analysis of the gene set made up of TP53 and these eight kinase-encoding genes using IPA (Ingenuity Pathway Analysis) software shows that TP53 directly interacts with all of these genes, confirming the relevance of these identified genes to p53 (Figure 4). In fact, previous studies have revealed that all the eight kinase-encoding genes interact with TP53 [19–25]. …”

Section: Resultsmentioning

confidence: 99%

TP53 mutations, expression and interaction networks in human cancers

2016

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Although the associations of p53 dysfunction, p53 interaction networks and oncogenesis have been widely explored, a systematic analysis of TP53 mutations and its related interaction networks in various types of human cancers is lacking. Our study explored the associations of TP53 mutations, gene expression, clinical outcomes, and TP53 interaction networks across 33 cancer types using data from The Cancer Genome Atlas (TCGA). We show that TP53 is the most frequently mutated gene in a number of cancers, and its mutations appear to be early events in cancer initiation. We identified genes potentially repressed by p53, and genes whose expression correlates significantly with TP53 expression. These gene products may be especially important nodes in p53 interaction networks in human cancers. This study shows that while TP53-truncating mutations often result in decreased TP53 expression, other non-truncating TP53 mutations result in increased TP53 expression in some cancers. Survival analyses in a number of cancers show that patients with TP53 mutations are more likely to have worse prognoses than TP53-wildtype patients, and that elevated TP53 expression often leads to poor clinical outcomes. We identified a set of candidate synthetic lethal (SL) genes for TP53, and validated some of these SL interactions using data from the Cancer Cell Line Project. These predicted SL genes are promising candidates for experimental validation and the development of personalized therapeutics for patients with TP53-mutated cancers.

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Section: Resultsmentioning

confidence: 99%

TP53 mutations, expression and interaction networks in human cancers

2016

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“…AURKA encodes a kinase that regulates cell cycle by involved in microtubule formation and/or stabilization at the spindle pole during chromosome segregation. The interaction between p53 and AURKA has been investigated [33,34]. BUB1 encodes a kinase involved in mitotic spindle checkpoint function.…”

Section: Resultsmentioning

confidence: 99%

Identification of potential synthetic lethal genes to p53 using a computational biology approach

Wang

Simon

2013

BMC Med Genomics

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BackgroundIdentification of genes that are synthetic lethal to p53 is an important strategy for anticancer therapy as p53 mutations have been reported to occur in more than half of all human cancer cases. Although genome-wide RNAi screening is an effective approach to finding synthetic lethal genes, it is costly and labor-intensive.MethodsTo illustrate this approach, we identified potentially druggable genes synthetically lethal for p53 using three microarray datasets for gene expression profiles of the NCI-60 cancer cell lines, one next-generation sequencing (RNA-Seq) dataset from the Cancer Genome Atlas (TCGA) project, and one gene expression data from the Cancer Cell Line Encyclopedia (CCLE) project. We selected the genes which encoded kinases and had significantly higher expression in the tumors with functional p53 mutations (somatic mutations) than in the tumors without functional p53 mutations as the candidates of druggable synthetic lethal genes for p53. We identified important regulatory networks and functional categories pertinent to these genes, and performed an extensive survey of literature to find experimental evidence that support the synthetic lethality relationships between the genes identified and p53. We also examined the drug sensitivity difference between NCI-60 cell lines with functional p53 mutations and NCI-60 cell lines without functional p53 mutations for the compounds that target the kinases encoded by the genes identified.ResultsOur results indicated that some of the candidate genes we identified had been experimentally verified to be synthetic lethal for p53 and promising targets for anticancer therapy while some other genes were putative targets for development of cancer therapeutic agents.ConclusionsOur study indicated that pre-screening of potential synthetic lethal genes using gene expression profiles is a promising approach for improving the efficiency of synthetic lethal RNAi screening.

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“…A number of Aurora A-overexpressing phenotypes, including supernumerary centrosomes, are associated with multipolar spindles, DNA copy number, aneuploidy, increasing resistance to apoptosis and deficient cell cycle checkpoint functions. Previous work demonstrated that Aurora-A and p53 are involved in a negative feedback loop in carcinoma [15,16]. Elevated Aurora-A in tumors targets the inactivation of the p53-related protein p73, contributing to the loss of mitotic checkpoint and promoting apoptosis resistance [17].…”

Section: Discussionmentioning

confidence: 99%

Role of Aurora-A in Ovarian Cancer: A Meta-Analysis

Wu²,

Qian

et al. 2015

Oncol Res Treat

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Background: Recently, several studies have examined associations between Aurora-A expression and clinical outcome in patients with ovarian cancer, but yielded conflicting results with respect to survival. Therefore, the aim of this study was to evaluate the prognostic significance of Aurora-A in ovarian cancer by performing a meta-analysis. Methods: PubMed, Cochrane library, Web of Science, Embase, Medline and Chinese BioMed Database (CBM) databases were searched systematically and only articles in which Aurora-A expression was detected by immunohistochemical staining were included. Hazard ratios (HRs) with 95% confidence intervals (CIs) were extracted and pooled for overall survival (OS) and disease-free survival (DFS). Results: Our results show that the pooled HR for OS was 1.40 (95% CI 0.82-1.98, p < 0.01) by univariate analysis in 7 articles (1,028) and 0.32 (95% CI 0.04-0.615, p = 0.23) by multivariate analysis in 3 studies (155). The association between Aurora-A expression and DFS was also statistically significant in 5 studies (HR = 1.14, 95% CI 0.50-1.78, p < 0.01). Conclusion: This present meta-analysis suggests that the Aurora-A expression may be associated with poor prognosis in patients with ovarian cancer. Furthermore, studies of larger scale and well-matched regimes are warranted to confirm the findings in the future.

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A novel Aurora-A-mediated phosphorylation of p53 inhibits its interaction with MDM2

Cited by 34 publications

References 41 publications

TP53 mutations, expression and interaction networks in human cancers

TP53 mutations, expression and interaction networks in human cancers

Identification of potential synthetic lethal genes to p53 using a computational biology approach

Role of Aurora-A in Ovarian Cancer: A Meta-Analysis

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