Identification of Potential miRNAs Biomarkers for High-Grade Prostate Cancer by Integrated Bioinformatics Analysis

Abstract

The increasing number of datasets available in the GEO database offers a new approach to identify new miRNAs related to PCa. The aim of our study was to suggest a miRNA signature for the detection of high-grade PCa (Gleason score ≥ 7) using bioinformatics tools. Three mRNA datasets (GSE26022, GSE30521, GSE46602) were selected to identify the differentially expressed genes (DEGs) in high-grade PCa. Furthermore, two miRNA datasets (GSE45604, GSE46738) were analyzed to select the differentially expressed miRNAs (DEMs). Functional and pathway enrichment analysis was performed using DAVID and a protein-protein interaction network (PPI) was constructed through STRING. Besides, miRNAs which regulate hub genes were predicted using microRNA.org. A total of 973 DEGs were identified after the analyses of the mRNA datasets, enriched in key mechanisms underlying PCa development. Furthermore, we identified 10 hub genes (EGFR, VEGFA, IGF1, PIK3R1, CD44, ITGB4, ANXA1, BCL2, LPAR3, LPAR1). The most significant KEGG Pathway was PI3K-Akt signaling pathway, involved in cell proliferation and survival. Moreover, we identified 30 common miRNAs between significant DEMs and the predicted hub gene regulators. Twelve of these miRNAs (miR-1, −365, −132, −195, −133a, −133b, −200c, −339, −222, −21, −221, −708) regulate two or more hub genes identified in our study. We suggested a signature including these 12 miRNAs for high-grade PCa detection. These miRNAs have been associated with aggressive PCa, poor survival and resistance to treatment in the last years.

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