Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer

• Main Authors: Chia-Ming Chang, Chi-Mu Chuang, Mong-Lien Wang, Yi-Ping Yang, Jen-Hua Chuang, Ming-Jie Yang, Ming-Shyen Yen, Shih-Hwa Chiou, Cheng-Chang Chang
• Format: Article
• Language: English
• Published: MDPI AG 2016-08-01
• Series: International Journal of Molecular Sciences
• Subjects: epithelial ovarian cancer; function; integrative analysis; gene expression microarray; gene set; machine learning
• Online Access: http://www.mdpi.com/1422-0067/17/8/1272

Clear cell (CCC), endometrioid (EC), mucinous (MC) and high-grade serous carcinoma (SC) are the four most common subtypes of epithelial ovarian carcinoma (EOC). The widely accepted dualistic model of ovarian carcinogenesis divided EOCs into type I and II categories based on the molecular features. However, this hypothesis has not been experimentally demonstrated. We carried out a gene set-based analysis by integrating the microarray gene expression profiles downloaded from the publicly available databases. These quantified biological functions of EOCs were defined by 1454 Gene Ontology (GO) term and 674 Reactome pathway gene sets. The pathogenesis of the four EOC subtypes was investigated by hierarchical clustering and exploratory factor analysis. The patterns of functional regulation among the four subtypes containing 1316 cases could be accurately classified by machine learning. The results revealed that the ERBB and PI3K-related pathways played important roles in the carcinogenesis of CCC, EC and MC; while deregulation of cell cycle was more predominant in SC. The study revealed that two different functional regulation patterns exist among the four EOC subtypes, which were compatible with the type I and II classifications proposed by the dualistic model of ovarian carcinogenesis.