| dc.description.abstract | Prostate Cancer is the leading cause of cancer-related death in males in the Western world. It is a common biological disease originating from the reproductive system of the male namely, the prostate gland, usually in older patients (over the age of 50) and with a family history of this disease. The disease shows clinical aggressiveness due to genetic alterations of gene expression in prostate epithelial cells. Prostate cancer is
currently diagnosed by biopsy and prostate cancer screening via the Prostate-Specific Antigen (PSA) blood test. Early detection is critical and although PSA was discovered to aid in the diagnoses of this cancer at its early stages, it has a disadvantage due to its low specificity thus causing unnecessary biopsies of healthy individuals and overtreatment of patients. Although various studies and efforts have been made to identify the ideal biomarker for prostate cancer and many even being applied to clinical use, it is still challenging and has not replaced the best-known biomarker PSA. PSA test has minimal invasive characteristics, at relatively low cost together with high sensitivity but low specificity. Biomarker discovery is a challenging process and a good biomarker has to be sensitive, specific and its test highly standardized and reproducible as well as identify risk for or diagnose a disease, assess disease severity or progression, predict prognosis or guide treatment. Computational biology plays a significant role in the discovery of new biomarkers, the analyses of disease states and the validation of potential biomarkers. Bioinformatic approaches are effective for the detection of potential micro ribonucleic acid (miRNA) in cancer. Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. Small non-protein coding RNA, miRNA are small regulatory RNA molecules that modulate the expression of their target genes. miRNAs influence numerous cancer-relevant processes such as proliferation, cell cycle control, apoptosis, differentiation, migration and metabolism. Discovery and existence of extracellular miRNAs that circulate in the blood of cancer patients has raised the possibility that miRNAs may serve as novel diagnostic markers. Since a single miRNA is said to be able to target several mRNAs, aberrant miRNA expression is capable of disrupting the expression of several mRNAs and proteins. Biomarker discovery for prostate cancer of mRNA and miRNA expression are strongly needed to enable more accurate detection of prostate cancer, improve prediction of tumour aggressiveness and facilitate diagnosis. The aim of this project was to focus on functional analyses of genes and their protein products regulated by previously identified miRNA in prostate cancer using bioinformatics as a tool. Most proteins function in collaboration with other proteins and therefore this study further aims to identify these protein-protein interactions and the biological relevance of these interactions as it relates to Prostate cancer. Various computational databases were used such as STRING, DAVID and GeneHub-GEPIS for functional analyses of these miRNA regulated genes. The main focus was on the 21 genes regulated by several miRNAs identified in a previous study. Results from this study identified six genes; ERP44, GP1BA, IFNG, SEPT2, TNFRSF13C and TNFSF4, as possible diagnostic biomarkers for prostate cancer. These results are promising, since the targeted biomarkers would be easily detectable in bodily fluids with the Gene Ontology (GO) analysis of these gene products showing enrichment for cell surface expression. The six genes identified in silico were associated to transcription factors (TFs) to confirm regulatory control of these TFs in cancer promoting processes and more specifically prostate cancer. The CREB, E2F, Nkx3-1 and p53 TFs were discovered to be linked to the genes IFNG, GP1BA, SEPT2 and TNFRSF13C respectively. The expression of these TFs show strong association with cancer and cancer related pathways specifically prostate cancer and thus demonstrates that these genes can be assessed as possible biomarkers for prostate cancer. The prognostic and predictive values of the candidate genes were evaluated to assess their relationship to prognosis of this disease by means of several in silico prognostic databases. The results revealed expression differences for the majority of the candidate genes were not significantly sufficient to be distinguished as strong prognostic biomarkers in several prostate cancer populations. Although one marker, GP1BA was supported as having prognostic value for prostate cancer based on it's statistical pvalue in one of the prostate cancer patient datasets used. Another candidate gene SEPT2 showed promise as it has some prognostic value in the early stages of the disease. Although the results yielded, based on the in silico analysis, were not the discovery of an ideal diagnostic marker based on the set criteria in this study, further analysis using a molecular approach qRT-PCR can be considered for a detailed followup study on selected candidate genes to evaluate their roles in disease initiation and progression of prostate cancer using cell lines as well as patient samples. | en_US |
