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| Bioinformatics analysis of prostate cancer related genes and its function |
| HE Yi-chen 1, SUN Hao 2, JIAO Zhi-min 1, ZHOU Yang 1, WANG Cheng-yue 1, ZHANG Jin-hu 1, CHEN Bing-hai 2 |
| （1. School of Medicine, Jiangsu University, Zhenjiang Jiangsu 212013; 2. Department of Urinary Surgery, the Affiliated Hospital of Jiangsu University, Zhenjiang Jiangsu 212001,China) |
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Abstract Objective: To find the key genes in the occurrence of prostate cancer by using bioinformatics methods and in vitro experiments. Methods: Gene chip data GSE60502, including 48 normal prostate tissue samples and 47 prostate cancer tissue samples, was downloaded from the GEO database.The microarray date were analyzed by the online software Morpheus (https:∥software.broadinstitute.org／morpheus／),which displayed differentially expressed genes between these two kinds of samples.Then,GO enrichment and Pathway analysis of differentially expressed genes were performed by the GCBI, an online analysis tool. Gene co-expression network and gene interaction network were constructed using the common genes included in GO enrichment and Pathway analysis. Finally, cell proliferation was varified by the Cell Counting Kit-8. Results: A total of 6 903 differentially expressed genes were confirmed.The top 15 genes include 9 up-regulated genes and 6 down-regulated genes, and the most differentially expressed gene was CRISP3. The differentially expressed genes were mostly involved in small molecule metabolic process, signal transduction, DNA-dependent transcription and positive regulation of transcription from RNA polymerase Ⅱ promoter. The differentially expressed genes were mostly involved in Metabolic pathways, PI3K-Akt signaling pathway, MAPK signaling pathway and Wnt signaling pathway. CACNA1A was at the centre of the co-expression network, while ADCY5 and PIK3CB were at the core of the gene interaction network. Down-regulation of CACNA1A could significantly inhibit cell proliferation in vitro. Conclusion: CRISP3,CACNA1A, ADCY5 and PIK3CB are probably the critical genes in the occurrence of prostate cancer.
［Key words］bioinformatics analysis; prostate cancer; CACNA1A; CRISP3; ADCY5; PIK3CB
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Received: 12 November 2018
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2019, Vol. 29 
