A comparative mRNA- and miRNA transcriptomics reveals novel molecular signatures associated with metastatic prostate cancers - Shinawi, Thoraia; Nasser, Khalidah Khalid; Moradi, Fatima Amanullah; Mujalli, Abdulrahman; Albaqami, Walaa F.; Almukadi, Haifa S.; Elango, Ramu; Shaik, Noor Ahmad; Banaganapalli, Babajan (Frontiers in Genetics, 2022)
Background: Prostate cancer (PC) is a fatally aggressive urogenital cancer killing millions of men, globally. Thus, this study aims to identify key miRNAs, target genes, and drug targets associated with prostate cancer metastasis. Methods: The miRNA and mRNA expression datasets of 148 prostate tissue biopsies (39 tumours and 109 normal tissues), were analysed by differential gene expression analysis, protein interactome mapping, biological pathway analysis, miRNA-mRNA networking, drug target analysis, and survival curve analysis. Results: The Results show the dysregulated expression of 53 miRNAs and their 250 target genes involved in Hedgehog, ErbB, and cAMP signalling pathways connected to cell growth, migration, and proliferation of prostate cancer cells. The subsequent miRNA-mRNA network and expression status analysis have helped us in narrowing down their number to 3 hub miRNAs (hsa-miR-455-3p, hsa-miR-548c-3p and hsa-miR-582-5p) and 9 hub genes (NFIB, DICER1, GSK3B, DCAF7, FGFR1OP, ABHD2, NACC2, NR3C1 and FGF2). Further investigations with different systems biology methods have prioritized NR3C1, ABHD2 and GSK3B as potential genes involved in prostate cancer metastasis owing to their high mutation load and expression status. Interestingly, down regulation of NR3C1 seems to improve the prostate cancer patient survival rate beyond 150 months. The NR3C1, ABHD2, and GSK3B genes are predicted to be targeted by hsa-miR-582-5p, besides some antibodies, PROTACs and inhibitory molecules. Conclusion: This study identified key miRNAs (miR-548c-3p and miR-582-5p) and target genes (NR3C1, ABHD2, and GSK3B) as potential biomarkers for metastatic prostate cancers from large-scale gene expression data using systems biology approaches.