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

• Published in: International journal of molecular sciences Vol. 17; no. 8; p. 1272
• Main Authors: Chang, Chia-Ming, Chuang, Chi-Mu, Wang, Mong-Lien, Yang, Yi-Ping, Chuang, Jen-Hua, Yang, Ming-Jie, Yen, Ming-Shyen, Chiou, Shih-Hwa, Chang, Cheng-Chang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05.08.2016; MDPI
• Subjects: Analysis; Artificial intelligence; Carcinoma, Ovarian Epithelial; Cellular biology; Databases, Genetic; Down-Regulation - genetics; Factor Analysis, Statistical; Female; Gene expression; Gene Expression Regulation, Neoplastic; Gene Ontology; Gene Regulatory Networks; Genes, Neoplasm; Humans; Machine Learning; Neoplasms, Glandular and Epithelial - classification; Neoplasms, Glandular and Epithelial - genetics; Oligonucleotide Array Sequence Analysis; Ovarian cancer; Ovarian Neoplasms - classification; Ovarian Neoplasms - genetics; Transcriptome; Up-Regulation - genetics; gene expression microarray; integrative analysis; epithelial ovarian cancer; gene set; machine learning; function
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms17081272

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.