Qiong Wu1, Bo Zhang1, Ziheng Wang2, Xinyi Hu2, Yidan Sun3, Ran Xu4, Xinming Chen5, Qiuhong Wang6, Fei Ju6, Shiqi Ren2, Chenlin Zhang7, Fuwei Qi8, Qianqian Ma9, Qun Xue10, You Lang Zhou11. 1. Medical School of Nantong University, Nantong 226001, PR China; The Hand Surgery Research Center, Department of Hand Surgery, Affiliated Hospital of Nantong University, Nantong 226001, PR China. 2. Department of Medicine, Nantong University Xinling College, Nantong, Jiangsu, 226001, PR China. 3. Department of Oncology, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, PR China. 4. Medical School of Nantong University, Nantong 226001, PR China. 5. Department of Thoracic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, PR China. 6. The Hand Surgery Research Center, Department of Hand Surgery, Affiliated Hospital of Nantong University, Nantong 226001, PR China. 7. Department of Spine, Chinese Medicine Hospital,Wuxi Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi 214000, PR China. 8. Department of anesthesiology, The First people's Hospital of Taicang City, Taicang Affiliated Hospital of Soochow University, Suzhou 215400, P.R. China. 9. Emergency Office, Wuxi Center for Disease Control and Prevention, Wuxi, Jiangsu, 214023, PR China. 10. Department of Thoracic Surgery, Affiliated Hospital of Nantong University, Nantong, 226001, PR China. Electronic address: qunyuemi@sina.com. 11. The Hand Surgery Research Center, Department of Hand Surgery, Affiliated Hospital of Nantong University, Nantong 226001, PR China. Electronic address: zhouyoulang@ntu.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC. METHODS: We integrated five microarray datasets from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GC and normal samples were analyzed with limma package. Gene ontology (GO) and KEGG enrichment analysis were performed using DAVID. Then we established the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING). The prognostic analysis of hub genes were performed through Gene Expression Profiling Interactive Analysis (GEPIA). Additionally, we used real-time quantitative PCR to validate the expression of hub genes in 5 pairs of tumor tissues and corresponding adjacent tissues. Finally, the candidate small molecules as potential drugs to treat GC were predicted in CMap database. RESULTS: Through integrating five microarray datasets, a total of 172 overlap DEGs were detected including 79 up-regulated and 93 down-regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in digestion, collagen fibril organization and cell adhesion. Signaling pathway analysis indicated that these DEGs played an vital in ECM-receptor interaction, focal adhesion and metabolism of xenobiotics by cytochrome P450. Protein-protein interaction network among the overlap DEGs was established with 124 nodes and 365 interactions. Three DEGs with high degree of connectivity (NID2, COL4A1 and COL4A2) were selected as hub genes. The GEPIA database confirmed that overexpression levels of hub genes were significantly associated with worse survival of patients. Finally, the 20 most significant small molecules were obtained based on CMap database and spiradoline was the most promising small molecule to reverse the GC gene expression. CONCLUSIONS: Our results indicated that NID2, COL4A1 and COL4A2 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of GC initiation and progression.
BACKGROUND AND OBJECTIVE: The underlying molecular mechanisms of gastric cancer (GC) have yet not been investigated clearly. In this study, we aimed to identify hub genes involved in the pathogenesis and prognosis of GC. METHODS: We integrated five microarray datasets from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between GC and normal samples were analyzed with limma package. Gene ontology (GO) and KEGG enrichment analysis were performed using DAVID. Then we established the protein-protein interaction (PPI) network of DEGs by the Search Tool for the Retrieval of Interacting Genes database (STRING). The prognostic analysis of hub genes were performed through Gene Expression Profiling Interactive Analysis (GEPIA). Additionally, we used real-time quantitative PCR to validate the expression of hub genes in 5 pairs of tumor tissues and corresponding adjacent tissues. Finally, the candidate small molecules as potential drugs to treat GC were predicted in CMap database. RESULTS: Through integrating five microarray datasets, a total of 172 overlap DEGs were detected including 79 up-regulated and 93 down-regulated genes. Biological process analysis of functional enrichment showed these DEGs were mainly enriched in digestion, collagen fibril organization and cell adhesion. Signaling pathway analysis indicated that these DEGs played an vital in ECM-receptor interaction, focal adhesion and metabolism of xenobiotics by cytochrome P450. Protein-protein interaction network among the overlap DEGs was established with 124 nodes and 365 interactions. Three DEGs with high degree of connectivity (NID2, COL4A1 and COL4A2) were selected as hub genes. The GEPIA database confirmed that overexpression levels of hub genes were significantly associated with worse survival of patients. Finally, the 20 most significant small molecules were obtained based on CMap database and spiradoline was the most promising small molecule to reverse the GC gene expression. CONCLUSIONS: Our results indicated that NID2, COL4A1 and COL4A2 could be the potential novel biomarkers for GC diagnosis prognosis and the promising therapeutic targets. The present study may be crucial to understanding the molecular mechanism of GC initiation and progression.