Gene co-expression network reveals shared modules predictive of stage and grade in serous ovarian cancers
Metrics: PDF 2403 views | HTML 3675 views | ?
Qian Sun1, Haiyue Zhao1, Cong Zhang1, Ting Hu1, Jianli Wu1, Xingguang Lin1, Danfeng Luo1, Changyu Wang1, Li Meng1, Ling Xi1, Kezhen Li1, Junbo Hu1, Ding Ma1 and Tao Zhu1
1Cancer Biology Research Center, Key Laboratory of the Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
Tao Zhu, email: email@example.com
Keywords: ovarian cancer, WGCNA, gene co-expression network, grade, stage
Received: October 13, 2016 Accepted: April 15, 2017 Published: May 11, 2017
Serous ovarian cancer (SOC) is the most lethal gynecological cancer. Clinical studies have revealed an association between tumor stage and grade and clinical prognosis. Identification of meaningful clusters of co-expressed genes or representative biomarkers related to stage or grade may help to reveal mechanisms of tumorigenesis and cancer development, and aid in predicting SOC patient prognosis. We therefore performed a weighted gene co-expression network analysis (WGCNA) and calculated module-trait correlations based on three public microarray datasets (GSE26193, GSE9891, and TCGA), which included 788 samples and 10402 genes. We detected four modules related to one or more clinical features significantly shared across all modeling datasets, and identified one stage-associated module and one grade-associated module. Our analysis showed that MMP2, COL3A1, COL1A2, FBN1, COL5A1, COL5A2, and AEBP1 are top hub genes related to stage, while CDK1, BUB1, BUB1B, BIRC5, AURKB, CENPA, and CDC20 are top hub genes related to grade. Gene and pathway enrichment analyses of the regulatory networks involving hub genes suggest that extracellular matrix interactions and mitotic signaling pathways are crucial determinants of tumor stage and grade. The relationships between gene expression modules and tumor stage or grade were validated in five independent datasets. These results could potentially be developed into a more objective scoring system to improve prediction of SOC outcomes.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 3.0 License.