Mina Jahandideh - Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran
Ebrahim Barzegari - Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran

     MicroRNAs are interesting as cancer diagnostic and prognostic biomarkers because of their unique tissue expression profiles, higher stability in the blood in comparison to mRNAs, and the possibility for reliable quantification. In the case of prostate cancer (PCa), it is currently emphasized to explore new biomarkers, particularly from microRNAs which are freely available in the bloodstream. In this study, the gene expression omnibus database (GEO), a repository of microarray data for PCa circulating extracellular vesicle-free microRNAs profiling, was analyzed for differentially expressed miRNAs (DE-miRs). Top ۲۰ most differentially expressed miRs with significant (adjusted p-value < ۰.۰۱) high expression (fold change) levels were extracted by the simultaneous application of different filtering criteria. Then, microRNA-gene networks were constructed for the two sets of positively (n=۲۰) or negatively (n=۲۰) regulated miRNAs. Gene ontology annotations of the target gene sets were also extracted and analyzed. Results indicated that human miR-۱۵۸۷, miR-۲۲۳-۳p, miR-۳۱۲۵, and miR-۶۴۲b-۳p are highly significant DE-miRs in PCa. In addition, human miR-۴۴۵۹, miR-۱۲۷۳g, miR ۶۴۲a-۳p, and miR-۶۴۲b-۳p were identified as top-ranked hubs in the relevant miRNA-gene networks. FOXK۱, PML, CD۲۴, ATN۱, BAZ۲A, CDKN۱A, NUFIP۲, and HARNPU were identified as microRNA target genes with significant dysregulation. miR-۴۴۵۹, miR-۱۲۷۳g-۳p, miR-۳۱۳۵b, miR-۵۰۰۱-۵p, and miR-۱۵۸۷ were proposed as novel microRNAs with the potential to be utilized as diagnostic biomarkers of prostate cancer among circulating vesicle-free miRNAs.

Bioinformatics, Prostate Cancer, English, Gene expression, diagnosis, Data analysis, Biomarker, Systems biology, Vesicle-free microRNA, Cancer biomarkers, Gene Ontology