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Int. J. Mol. Sci. 2016, 17(8), 1272; doi:10.3390/ijms17081272

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

1
Institute of Oral Biology, National Yang-Ming University, Taipei 112, Taiwan
2
School of Medicine, National Yang-Ming University, Taipei 112, Taiwan
3
Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei 112, Taiwan
4
Institute of Clinical Medicine, School of Medicine, National Yang−Ming University, Taipei 112, Taiwan
5
Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
6
Department & Institute of Pharmacology, National Yang−Ming University, Taipei 112, Taiwan
7
Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editor: William Chi-shing Cho
Received: 21 June 2016 / Revised: 22 July 2016 / Accepted: 27 July 2016 / Published: 5 August 2016
(This article belongs to the Special Issue Big Data for Oncology)
View Full-Text   |   Download PDF [3176 KB, uploaded 5 August 2016]   |  

Abstract

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. View Full-Text
Keywords: epithelial ovarian cancer; function; integrative analysis; gene expression microarray; gene set; machine learning epithelial ovarian cancer; function; integrative analysis; gene expression microarray; gene set; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Chang, C.-M.; Chuang, C.-M.; Wang, M.-L.; Yang, Y.-P.; Chuang, J.-H.; Yang, M.-J.; Yen, M.-S.; Chiou, S.-H.; Chang, C.-C. Gene Set−Based Integrative Analysis Revealing Two Distinct Functional Regulation Patterns in Four Common Subtypes of Epithelial Ovarian Cancer. Int. J. Mol. Sci. 2016, 17, 1272.

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