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Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.

ABSTRACT: High-throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivity-coverage iteration strategy and outputting those with significant coverage and exclusivity for both cancers. The associations of the cancer pairs are further evaluated by Fisher's exact test. Being applied to 11 TCGA cancer datasets, ComCovEx identifies 13 significantly associated cancer pairs with plenty of biologically significant common gene modules. The novel results of cancer relationship and common gene modules reveal the relevant pathological basis of different cancer types and provide new clues to diagnosis and drug treatment in associated cancers.

SUBMITTER: Gao B

PROVIDER: S-EPMC8414517 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.

Gao Bo B Zhao Yue Y Gao Yonghang Y Li Guojun G Wu Ling-Yun LY

Global challenges (Hoboken, NJ) 20210619 9

High-throughput biological data has created an unprecedented opportunity for illuminating the mechanisms of tumor emergence and evolution. An important and challenging problem in deciphering cancers is to investigate the commonalities of driver genes and pathways and the associations between cancers. Aiming at this problem, a tool ComCovEx is developed to identify common cancer driver gene modules between two cancers by searching for the candidates in local signaling networks using an exclusivit ...[more]

PMID: 34504716

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Project description:BackgroundApproximately 61 million Americans have a disability. Little research has explored whether disability is associated with subsequent diagnosis of cancer, the second-leading cause of death in the United States. The objective of this study was to explore associations between cancer and disability, focusing on 4 cancers that may present with nonspecific symptoms that could be conflated with aspects of disability, thus delaying cancer diagnoses. An analysis of a nationally representative survey using sampling weights to produce national estimates was performed.MethodsCivilian, noninstitutionalized US residents responding to the 2010-2017 National Health Interview Surveys totaling 259,392 Sample Adult Core survey respondents were included. We used self-reported functional status limitations to identify persons with movement difficulties (MD), complex activity limitations (CAL), and no disability. Multivariable regressions predicting cancer diagnosis included sociodemographic characteristics, tobacco use, body mass index, access to care indicators, and disability status.ResultsPersons with preexisting disability had significantly higher rates of cancer (ranging from 0.40 [SE, 0.05] for ovarian to 3.38 [0.14] for prostate) than did those without disability (0.20 [0.02] and 1.26 [0.04] for the same cancers; all P<.0001). Multivariable analyses found strong associations of preexisting MD and CAL with colorectal cancer, with adjusted odds ratios (aORs) of 1.5 (95% CI, 1.2-1.9) and 1.9 (1.5-2.4), respectively. For non-Hodgkin's lymphoma, the aOR for CAL was 1.5 (1.1-2.1). For prostate cancer, aORs for MD were 1.2 (1.0-1.3) and 1.1 (1.0-1.3) for CAL. Using cross-sectional survey data, we could only identify statistical associations, not causality.ConclusionsOur population-based analyses suggest that persons with disability may constitute a high-risk population, with higher cancer incidence. Optimizing appropriate screening and fully investigating new signs and symptoms are therefore critical for patients with disability.

Dataset Information

Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.

Publications

Identification of Common Driver Gene Modules and Associations between Cancers through Integrated Network Analysis.

Similar Datasets

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