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In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then genome-wide regulatory networks are constructed. Next, by comparing the regulatory networks of key candidate genes in variant samples and non-variant samples, a differential expression regulatory network is generated. The differential network contains a collection of abnormal regulatory genes of key candidate genes. Then, by introducing the functional similarity as a distance metric for gene sets, a density-based clustering method is used to mine gene modules related to cancer. We applied this method to LUSC (lung squamous cell carcinoma) and mined cancer-related gene modules composed of 20 genes. GO function and KEGG pathway analyses indicated that the modules were closely related to cancer. A survival analysis was used to verify that the excavated gene modules can effectively distinguish between high- and low-risk groups. Overall, these results suggest that the proposed method can be used to identify cancer-related gene modules, providing a basis for the development of biomarkers for diagnosis and treatment.<\/jats:p>","DOI":"10.1142\/s0219720019500380","type":"journal-article","created":{"date-parts":[[2019,11,25]],"date-time":"2019-11-25T09:37:48Z","timestamp":1574674668000},"page":"1950038","source":"Crossref","is-referenced-by-count":4,"title":["Integration of multi-omics data to mine cancer-related gene modules"],"prefix":"10.1142","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7054-2564","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing 100875, P. R. China"},{"name":"School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, P. R. 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