Ann Urol Res | Volume 1, Issue 1 | Research Article | Open Access
Lushun Yuan1#, Xinyue Cao2#, Liang Chen1, Yuan Zhu1,2, Guofeng Qian3 and Yu Xiao1,2,4*
1Department of Urology, Zhongnan Hospital of Wuhan University, China
2Department of Biological Repositories, Zhongnan Hospital of Wuhan University, China
3Department of Endocrinology, The First Affiliated Hospital of Zhejiang University, China
4Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, China
Prostate Cancer (PCa) is one of the leading causes of cancer-related deaths among the men around the world. In this study, we aim to identify candidate biomarkers in PCa using bioinformatics analysis combined with the analysis of the common database of tumors and uncover possible mechanisms. The gene expression profiles of GSE55945 including 13 PCa samples (with Gleason score of 6 or 7) and 8 normal prostate samples were downloaded from GEO database. Firstly, Differentially Expressed Genes (DEGs) were obtained using “limma” R package followed by preprocession of raw expression data. A total of 581 genes, including 204 up-regulated genes and 377 down-regulated genes, were screened out in PCa tissues compared with normal prostate tissues with the cut-off criteria p<0.05 and |log2FC|>1. Secondly, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed using DAVID database. Thirdly, protein-protein interaction (PPI) network of the DEGs was constructed by Cytoscape software. Modules in PPI network were screened using Molecular Complex Detection (MCODE). At last, 7 hub genes, ANXA1, CHRM3, UTS2, PROK1, AGT, CCK and EDN3 were identified from the modules of PPI network, and then validated by Oncomine database and Protein atlas database. In conclusion, our study suggested that the identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of PCa, and might reveal preliminary information with regard to carcinogenesis of prostate cancer.
Prostate cancer; Bioinformatic analysis; microarray; PPI
Yuan L, Cao X, Chen L, Zhu Y, Qian G, Xiao Y. Identification of Potential Biomarkers in Human Prostate Cancer Using Bioinformatics Analysis. Ann Urol Res. 2017; 1(1): 1002. Copyright © 2017 Yu Xiao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.