Name
Abstract | ||
|---|---|---|
The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omits 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 nonvariant 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1142/S0219720019500380 | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY |
Keywords | DocType | Volume |
Multi-omits data,gene regulation,network model,clustering,feature selection | Journal | 17 |
Issue | ISSN | Citations |
SP6 | 0219-7200 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |