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

• DOI: 10.21608/eajbsc.2023.317702
• Publication Date: 2023-09-26T19:26:49Z
• Authors: Bushra Mohammed; Sherzad Mustafa; Bayar Zeebaree
• Keywords: Identification

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