ArticleBiology2023
Identification of Driver Genes and miRNAs in Ovarian Cancer through an Integrated In-Silico Approach.
Article in Biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Decoding Tumor-Immune Interactions in Hepatocellular Carcinoma Through Network-Centered Identification of CXCR2.International journal of molecular sciences · 2026Article
- Article
- Review
- Emerging biologic and clinical implications of miR-182-5p in gynecologic cancers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Unveiling Novel miRNA-mRNA Interactions and Their Prognostic Roles in Triple-Negative Breast Cancer: Insights into miR-210, miR-183, miR-21, and miR-181b.International journal of molecular sciences · 2025Article
- Exosomal insights into ovarian cancer stem cells: revealing the molecular hubs.Journal of ovarian research · 2025Article
- Multi-Omics Analysis Revealed That TAOK1 Can Be Used as a Prognostic Marker and Target in a Variety of Tumors, Especially in Cervical Cancer.OncoTargets and therapy · 2025Article
- Investigating mechanistic insights of curcumin in blocking the Interleukin-8 signaling pathway associated with Breast Cancer: An in-silico approach.Saudi journal of biological sciences · 2024Article
- Unravelling driver genes as potential therapeutic targets in ovarian cancer via integrated bioinformatics approach.Journal of ovarian research · 2024Article
- Current strategies for early epithelial ovarian cancer detection using miRNA as a potential tool.Frontiers in molecular biosciences · 2024Review
- Non-coding RNA's prevalence as biomarkers for prognostic, diagnostic, and clinical utility in breast cancer.Functional & integrative genomics · 2023Review
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5 authors.
Funding
Abstract
Ovarian cancer is the eighth-most common cancer in women and has the highest rate of death among all gynecological malignancies in the Western world. Increasing evidence shows that miRNAs are connected to the progression of ovarian cancer. In the current study, we focus on the identification of miRNA and its associated genes that are responsible for the early prognosis of patients with ovarian cancer. The microarray dataset GSE119055 used in this study was retrieved via the publicly available GEO database by NCBI for the analysis of DEGs. The miRNA GSE119055 dataset includes six ovarian carcinoma samples along with three healthy/primary samples. In our study, DEM analysis of ovarian carcinoma and healthy subjects was performed using R Software to transform and normalize all transcriptomic data along with packages from Bioconductor. Results: We identified miRNA and its associated hub genes from the samples of ovarian cancer. We discovered the top five upregulated miRNAs (hsa-miR-130b-3p, hsa-miR-18a-5p, hsa-miR-182-5p, hsa-miR-187-3p, and hsa-miR-378a-3p) and the top five downregulated miRNAs (hsa-miR-501-3p, hsa-miR-4324, hsa-miR-500a-3p, hsa-miR-1271-5p, and hsa-miR-660-5p) from the network and their associated genes, which include seven common genes (SCN2A, BCL2, MAF, ZNF532, CADM1, ELAVL2, and ESRRG) that were considered hub genes for the downregulated network. Similarly, for upregulated miRNAs we found two hub genes (PRKACB and TAOK1).
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