Identification of Ovarian Cancer Using in Silico-Based Analysis of the Downregulated Expressed miRNAs

Abstract

Ovarian cancer (OC) is one of the top global reasons of death among women with high prevalence. Ovarian cancer can be categorized into epithelial, non-epithelial, and metastatic types. Animal models such as mice are intensively utilized to investigate the molecular mechanism controlling cancer development in the human beings. Recently, several approaches have been extremely studied to control ovarian cancer at the transcriptional or posttranscriptional levels using small RNAs molecules including microRNAs. These molecules have played a key role in the growth of malignant tumour of ovary including cellular proliferation and metastasis. We carried out a metaanalysis of previously published miRNA expression datasets (two human datasets GSE83693 and GSE119055) and one mouse GSE98391 to identify the downregulated miRNA and its target genes with biological processes and pathways. Meta-analysis of miRNA datasets showed that miR-378a-3p, miR-378a -5p and miR-378c are commonly downregulated miRNAs among the three databases in cancerous samples in comparison to normal samples. A total of 405 common gene targets for miR-378a-3p, -5p and miR-378c were identified using miRWALK. Enrichment analysis revealed that miRNAs target genes were predominantly linked to protein binding as well as in Ras signalling pathways. In addition, multiple hub miRNA target genes in the PPI network provided poor prognosis for the patients with OC including FLT1, its level was closely relevant to ovarian cancer. Overall, these investigations exhibited that the defined miRNAs and their target genes could be exploited as biomarkers to identify ovarian malignancies and achieve an early effective therapy.