Gene co-expression network reveals shared modules predictive of stage and grade in serous ovarian cancers

作者：Qian Sun, Haiyue Zhao, Cong Zhang, Ting Hu, Jianli Wu, Xingguang Lin, Danfeng Luo, Changyu Wang, Meng Li, Ling Xi, Kezhen Li, Junbo Hu, Ding Ma, Tao Zhu · 发表于：Oncotarget · 年份：2017 · DOI：10.18632/oncotarget.17785 · 被引用次数：63 · 研究领域：Bioinformatics and Genomic Networks、Ovarian cancer diagnosis and treatment、Ferroptosis and cancer prognosis

// Qian Sun 1 , Haiyue Zhao 1 , Cong Zhang 1 , Ting Hu 1 , Jianli Wu 1 , Xingguang Lin 1 , Danfeng Luo 1 , Changyu Wang 1 , Li Meng 1 , Ling Xi 1 , Kezhen Li 1 , Junbo Hu 1 , Ding Ma 1 and Tao Zhu 1 1 Cancer 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 Correspondence to: Tao Zhu, email: zhutao@tjh.tjmu.edu.cn Keywords: ovarian cancer, WGCNA, gene co-expression network, grade, stage Received: October 13, 2016      Accepted: April 15, 2017      Published: May 11, 2017 ABSTRACT 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-associa...