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Identification of hub genes and outcome in colon cancer based on bioinformatics analysis

Authors Yang W, Ma J, Zhou W, Li Z, Zhou X, Cao B, Zhang Y, Liu J, Yang Z, Zhang H, Zhao Q, Hong L, Fan D

Received 6 May 2018

Accepted for publication 29 October 2018

Published 27 December 2018 Volume 2019:11 Pages 323—338


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 4

Editor who approved publication: Dr Beicheng Sun

Wanli Yang,1,* Jiaojiao Ma,1,* Wei Zhou,1,* Zichao Li,2 Xin Zhou,2 Bo Cao,2 Yujie Zhang,1 Jinqiang Liu,1 Zhiping Yang,1 Hongwei Zhang,3 Qingchuan Zhao,3 Liu Hong,1 Daiming Fan1

1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, Xijing Hospital of Digestive Diseases, Air Force Military Medical University, Xi’an, China; 2The First Brigade of Student, Air Force Military Medical University, Xi’an, China; 3Department of Digestive Surgery, Xijing Hospital, Air Force Military Medical University, Xi’an, China

*These authors contributed equally to this work

Background: Colon cancer is one of the leading malignant neoplasms worldwide. Until now, the concrete mechanisms of colonic cancerogenesis are largely unknown; identification of driven genes and pathways is, therefore, of great importance for monitoring and conquering this disease. This study aims to explore the potential biomarkers and therapeutic targets for colon cancer treatment.
Methods: The gene expression profile of GSE44076 from Gene Expression Omnibus database, including 98 primary colon cancers and 98 normal distant colon mucosa, was deeply analyzed. GEO2R tool was used to screen the differentially expressed genes (DEGs) between colon cancer tissues and normal samples. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed for screening DEGs using Database for Annotation, Visualization and Integrated Discovery database and Panther database. Moreover, Search Tool for the Retrieval of Interacting Genes, Cytoscape software, and Molecular Complex Detection plug-in were used to visualize the protein–protein interaction of these DEGs.
Results: A total of 497 DEGs were obtained, including 129 upregulated genes mainly enriched in Hippo signaling pathway, Wnt signaling pathway, and cytokine–cytokine receptor interaction and 368 downregulated genes enriched in retinol metabolism, steroid hormone biosynthesis, drug metabolism, and chemical carcinogenesis. Using Molecular Complex Detection software, three important modules were selected from the protein–protein interaction network. Moreover, 20 hub genes with high degree of connectivity were selected, including COL1A1, CXCL5, GNG4, TIMP1, and so on. The Kaplan–Meier analysis for overall survival and correlation analysis were applied among the hub genes.
Conclusion: Taken together, DEGs, especially the hub genes such as COL1A1, might be the driven genes in colon cancer progression. More importantly, they might be the novel biomarkers for diagnosis and guiding therapeutic strategies of colon cancer.

colon cancer, protein–protein interaction, bioinformatics analysis, diagnosis, prognosis

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