Network-based prioritization of cancer genes by integrative ranks from multi-omics data

• Published in: Computers in biology and medicine Vol. 119; p. 103692
• Main Authors: Shang, Haixia, Liu, Zhi-Ping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-04-2020; Elsevier Limited
• Subjects: Adenocarcinoma; Algorithms; Cancer; Cancer gene prioritization; Computer applications; Constrained PageRank; Deoxyribonucleic acid; DNA; DNA methylation; Genes; Genomes; Hepatocellular carcinoma; Identification methods; Methods; Multi-omics data integration; Multiplex networks; Mutation; Pathogenesis; Prostate cancer; Proteins; Random walk; Search engines; Multiplex networks; Constrained PageRank; Multi-omics data integration; Cancer gene prioritization
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.103692

Finding disease genes related to cancer is of great importance for diagnosis and treatment. With the development of high-throughput technologies, more and more multiple-level omics data have become available. Thus, it is urgent to develop computational methods to identify cancer genes by integrating these data. We propose an integrative rank-based method called iRank to prioritize cancer genes by integrating multi-omics data in a unified network-based framework. The method was used to identify the disease genes of hepatocellular carcinoma (HCC) in humans using the multi-omics data for HCC from TCGA after building up integrated networks in the corresponding molecular levels. The kernel of iRank is based on an improved PageRank algorithm with constraints. To demonstrate the validity and the effectiveness of the method, we performed experiments for comparison between single-level omics data and multiple omics data as well as with other algorithms: random walk (RW), random walk with restart on heterogeneous network (RWH), PRINCE and PhenoRank. We also performed a case study on another cancer, prostate adenocarcinoma (PRAD). The results indicate the effectiveness and efficiency of iRank which demonstrates the significance of integrating multi-omics data and multiplex networks in cancer gene prioritization.
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•A method named iRank has been proposed to identify cancer genes in multiplex networks by integrating multi-omics data.•iRank is based on the PageRank algorithm with constraints for prioritizing disease genes in multi-layer networks.•iRank prioritizes cancer genes efficiently by integrating the multi-omics data of TCGA.•The comparison studies demonstrate the rationality and advantage of iRank.