ICan: an integrated co-alteration network to identify ovarian cancer-related genes

• Published in: PloS one 2015-03, Vol.10 (3), p.e0116095
• Main Authors: Zhou, Yuanshuai, Liu, Yongjing, Li, Kening, Zhang, Rui, Qiu, Fujun, Zhao, Ning, Xu, Yan
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Angiogenesis; Apoptosis; Bioinformatics; BRCA1 protein; Breast cancer; Cancer; Cancer genetics; Carcinogenesis; Carcinogens; Computational Biology - methods; Copy number; Data analysis; Data processing; Databases, Genetic; DNA Copy Number Variations; DNA Methylation; EphA2 protein; Female; Fibroblasts; Gene expression; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genes; Genetic aspects; Genomes; Gynecology; Humans; Kaplan-Meier Estimate; Medical prognosis; Medical research; Metastasis; Methods; Methylation; Mutation; Oncogenes; Ovarian cancer; Ovarian carcinoma; Ovarian Neoplasms - genetics; Ovarian Neoplasms - mortality; p53 Protein; Pathology; Prognosis; Proteins; PTEN protein; Science; Survival analysis; Tumor proteins; Tumor suppressor genes; Tumorigenesis; Workflow
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0116095

Over the last decade, an increasing number of integrative studies on cancer-related genes have been published. Integrative analyses aim to overcome the limitation of a single data type, and provide a more complete view of carcinogenesis. The vast majority of these studies used sample-matched data of gene expression and copy number to investigate the impact of copy number alteration on gene expression, and to predict and prioritize candidate oncogenes and tumor suppressor genes. However, correlations between genes were neglected in these studies. Our work aimed to evaluate the co-alteration of copy number, methylation and expression, allowing us to identify cancer-related genes and essential functional modules in cancer. We built the Integrated Co-alteration network (ICan) based on multi-omics data, and analyzed the network to uncover cancer-related genes. After comparison with random networks, we identified 155 ovarian cancer-related genes, including well-known (TP53, BRCA1, RB1 and PTEN) and also novel cancer-related genes, such as PDPN and EphA2. We compared the results with a conventional method: CNAmet, and obtained a significantly better area under the curve value (ICan: 0.8179, CNAmet: 0.5183). In this paper, we describe a framework to find cancer-related genes based on an Integrated Co-alteration network. Our results proved that ICan could precisely identify candidate cancer genes and provide increased mechanistic understanding of carcinogenesis. This work suggested a new research direction for biological network analyses involving multi-omics data.