Analysis, expression profiling and characterization of hsa-miR-5698 target genes as putative dynamic network biomarkers for prostate cancer: a combined in silico and molecular approach

• Main Author: Lombe, Chipampe Patricia
• Other Authors: Pretorius, Ashley
• Language: en
• Published: University of the Western Cape 2019
• Subjects: MicroRNA; Triplex; DNA sequence motif; Prostate cancer; Transcription factor
• Online Access: http://hdl.handle.net/11394/7026

Philosophiae Doctor - PhD === 2018, the International Agency for Research on Cancer (IARC) estimated that prostate cancer (PCa) was the second leading cause of death in males worldwide. The number of deaths are expected to raise by 50 % in the next decade. This rise is attributed to the shortcomings of the current diagnostic, prognostic, and therapeutic biomarkers used in the management of the disease. Therefore, research into more sensitive, specific and effective biomarkers is a requirement. The use of biomarkers in PCa diagnosis and management takes advantage of the genetic alterations and abnormalities that characterise the disease. In this regard, a microRNA, hsa-miR-5698 was identified in a previous study as a differentiating biomarker between prostate adenocarcinoma and bone metastasis. Six putative translational targets (CDKN1A, CTNND1, FOXC1, LRP8, ELK1 and BIRC2) of this microRNA were discovered using in silico approaches. The aim of this study was to analyse via expression profiling and characterization, the target genes of hsa-miR-5698 in order to determine their ability to act as putative dynamic network biomarkers for PCa. The study was conducted using a combined in silico and molecular approach. The in silico part of the study investigated the putative transcriptional effects of hsa-miR-5698 on the promotors of its translational targets, the correlation between hsa-miR-5698 and mRNA expression profiles as well as the co-expression analysis, pathway analysis and prognostic ability of the target genes. A number of computational software were employed for these purposes, including, R Studio, Trident algorithm, STRING, KEGG, MEME Suite, SurvExpress and ProGgene. The molecular part of the study involved expression profiling of the genes in two PCa cell line LNCaP and PC3 via qPCR.