Identification of genes associated with distinguished Gleason patterns of prostate cancer by analyzing TCGA database

doi:10.16150/j.1671-2870.2018.06.013

Abstract

Abstract: Objective: To investigate the differentially expressed genes involved in distinguished Gleason patterns of prostate cancer (PC) and to reveal the potential molecular mechanisms by applying integrated bioinformatics. Methods: The expression profiles and information for Gleason score were downloaded from the cancer genome atlas (TCGA) database. The differentially expressed genes (DEGs) were obtained and were further analyzed for their interaction and biological processes involved by bioinformatics methods. The gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were used for processing by DAVID online analyses. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. Results: A total of 312 DEGs were identified in the TCGA datasets, of which 157 genes were upregulated and 155 genes were downregulated, in the Gleason pattern 4 PC. The 22 most closely related genes among DEGs were identified from the PPI network and were focused primarily on cell cycle and mitotic division. Conclusions: This study indicated that cancer cells in Gleason pattern 4 PC are more dynamic in the process of mitotic cell cycle. In clinical practice, detection of protein associated with cell cycle may allow the more rational prediction for risk and prognosis of PC. Meanwhile, for patients with Gleason score ≥7 PC, genes associated with cell cycle may provide the effective targets for treatment of prostate cancer.

Key words: Prostate cancer, Gleason score, The cancer genome atlas, Bioinformatics, Cell cycle

CLC Number:

R737.25

WANG Tao, DENG Yu, ZHAO Ping, YÜ Baohua, WANG Xiang, WANG Chaofu. Identification of genes associated with distinguished Gleason patterns of prostate cancer by analyzing TCGA database[J]. Journal of Diagnostics Concepts & Practice, 2018, 17(06): 694-700.

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