Identification of novel microRNAs as potential biomarkers for the early diagnosis of ovarian cancer using an in-silico approach
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Ovarian cancer (OC) is the most fatal gynaecologic malignancy that is generally diagnosed in the advanced stages, resulting in a low survival rate of about 40%. This emphasizes the need to identify a biomarker that can allow for accurate diagnosis at stage I. MicroRNAs (miRNAs) are appealing as biomarkers due to their stability, non-invasiveness, and differential expression in tumour tissue compared to healthy tissue. Since they are non-coding, their biological functions can be uncovered by examining their target genes and thus identifying their regulatory pathways and processes. This study aimed to identify miRNAs and genes as candidate biomarkers for early stage OC diagnosis, through two distinct in silico approaches. The first pipeline was based on sequence similarity between miRNAs with a proven mechanism in OC and miRNAs with no known role. This resulted in 9 candidate miRNAs, that have not been previously implicated in OC, that showed 90-99% similarity to a miRNA involved in OC. Following a series of in silico experimentations, it was uncovered that these miRNAs share 12 gene targets that are expressed in the ovary and also have proven implications in the disease. Since the miRNAs target genes contribute to OC onset and progression, it strengthens the notion that the miRNAs may be dysregulated as well. Using TCGA, the second pipeline involved analysing patient clinical data along with implementing statistical measures to isolate miRNAs and genes with high expression in OC. This resulted in 26 miRNAs and 25 genes being shortlisted as the potential candidates for OC management. It was also noted that targeting interactions occur between 15 miRNAs and 16 genes identified through this pipeline. In total, 35 miRNAs and 37 genes were identified from both pipelines.