Research Papers:
Identification of cancer prognosis-associated functional modules using differential co-expression networks
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Abstract
Wenshuai Yu1, Shengjie Zhao1,2, Yongcui Wang3, Brian Nlong Zhao4, Weiling Zhao5 and Xiaobo Zhou6,7,8
1Key Laboratory of Embedded System and Service Computing, College of Electronics and Information Engineering, The Ministry of Education, Tongji University, Shanghai, China
2College of Software Engineering, Tongji University, Shanghai, China
3Key Laboratory of Adaptation and Evolution of Plateau Biota, Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining, China
4Shanghai High School International Division, Shanghai, China
5Department of Radiology and Comprehensive Cancer Center, Wake Forest University School of Medicine, Winston Salem, NC, USA
6College of Electronics and Information Engineering, Tongji University, Shanghai, China
7Center for Big Data Sciences and Network Security, Tongji University, Shanghai, China
8Center for Bioinformatics and System Biology, Wake Forest University School of Medicine, Winston Salem, NC, USA
Correspondence to:
Shengjie Zhao, email: [email protected]
Xiaobo Zhou, email: [email protected]
Keywords: co-expression network; prognosis; HO-GSVD; gene module; cancer
Abbreviations: DAVID: the Database for Annotation, Visualization, and Integrated Discovery; TCGA: The Cancer Genome Atlas; KEGG: Kyoto Encyclopedia of Genes and Genomes
Received: July 12, 2017 Accepted: November 15, 2017 Published: December 04, 2017
ABSTRACT
The rapid accumulation of cancer-related data owing to high-throughput technologies has provided unprecedented choices to understand the progression of cancer and discover functional networks in multiple cancers. Establishment of co-expression networks will help us to discover the systemic properties of carcinogenesis features and regulatory mechanisms of multiple cancers. Here, we proposed a computational workflow to identify differentially co-expressed gene modules across 8 cancer types by using combined gene differential expression analysis methods and a higher-order generalized singular value decomposition. Four co-expression modules were identified; and oncogenes and tumor suppressors were significantly enriched in these modules. Functional enrichment analysis demonstrated the significantly enriched pathways in these modules, including ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. The top-ranked miRNAs (mir-199, mir-29, mir-200) and transcription factors (FOXO4, E2A, NFAT, and MAZ) were identified, which play an important role in deregulating cellular energetics; and regulating angiogenesis and cancer immune system. The clinical significance of the co-expressed gene clusters was assessed by evaluating their predictability of cancer patients’ survival. The predictive power of different clusters and subclusters was demonstrated. Our results will be valuable in cancer-related gene function annotation and for the evaluation of cancer patients’ prognosis.
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