Oncotarget

Research Papers:

Identification of core genes and outcome in gastric cancer using bioinformatics analysis

Chenhua Sun, Qi Yuan, Dongdong Wu, Xiaohu Meng and Baolin Wang _

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Oncotarget. 2017; 8:70271-70280. https://doi.org/10.18632/oncotarget.20082

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Abstract

Chenhua Sun1,*, Qi Yuan2,*, Dongdong Wu1, Xiaohu Meng1 and Baolin Wang1,3

1The Second Clinical Medical College of Nanjing Medical University, Nanjing, Jiangsu, China

2Department of Endocrinology, Huai’an First People’s Hospital, Nanjing Medical University, Huai’an, Jiangsu, China

3Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China

*These authors contributed equally to this work and co-first authors

Correspondence to:

Baolin Wang, email: [email protected]

Keywords: gastric cancer, bioinformatics analysis, protein-protein interaction, diagnosis, combination medicine

Received: June 14, 2017     Accepted: July 25, 2017     Published: August 09, 2017

ABSTRACT

Gastric cancer (GC) is a common malignant neoplasm of gastrointestinal tract. We chose gene expression profile of GSE54129 from GEO database aiming to find key genes during the occurrence and development of GC. 132 samples, including 111 cancer and 21 normal gastric mucosa epitheliums, were included in this analysis. Differentially expressed genes (DEGs) between GC patients and health people were picked out using GEO2R tool, then we performed gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using The Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) and Molecular Complex Detection (MCODE) plug-in was utilized to visualize protein-protein interaction (PPI) of these DEGs. There were 971 DEGs, including 468 up-regulated genes enriched in focal adhesion, ECM-receptor interaction and PI3K-Akt signaling pathway, while 503 down-regulated genes enriched in metabolism of xenbiotics and drug by cytochrome P450, chemical carcinogenesis, retinol metabolism and gastric acid secretion. Three important modules were detected from PPI network using MCODE software. Besides, Fifteen hub genes with high degree of connectivity were selected, including BGN, MMP2, COL1A1, and FN1. Moreover, the Kaplan–Meier analysis for overall survival and correlation analysis were applied among those genes. In conclusion, this bioinformatics analysis demonstrated that DEGs and hub genes, such as BGN, might promote the development of gastric cancer, especially in tumor metastasis. In addition, it could be used as a new biomarker for diagnosis and to guide the combination medicine of gastric cancer.


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