ArticleInternational journal of reproductive biomedicine2023
Identification of druggable hub genes and key pathways associated with cervical cancer by protein-protein interaction analysis: An in silico study.
Article in International journal of reproductive biomedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Identification and validation of stemness-associated hub genes in cervical cancer: a bioinformatics and experimental study.World journal of surgical oncology · 2026Article
- Article
- Synergistic mechanism of olaparib and cisplatin on breast cancer elucidated by network pharmacology.Scientific reports · 2025Article
- Identification of diagnostic microRNAs and their target genes in multiple sclerosis based on interactome networks using an in silico approach.Current journal of neurology · 2025Article
- Predicting interactome networks of up/down regulated proteins and drug-gene interaction analysis associated with peri-implantitis.Dental research journal · 2025Article
- Mechanism of Action of Isoflavone Derived from Soy-Based Tempeh as an Antioxidant and Breast Cancer Inhibitor via Potential Upregulation of miR-7-5p: A Multimodal Analysis Integrating Pharmacoinformatics and Cellular Studies.Antioxidants (Basel, Switzerland) · 2024Article
- Novel Functional Food Properties of Forest Onion (Nutrients · 2024Article
- Revealing Novel Source of Breast Cancer Inhibitors from SeagrassMolecules (Basel, Switzerland) · 2024Article
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Authors and funding
3 authors.
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Abstract
Background: The uncontrolled growth of abnormal cells in the cervix leads to cervical cancer (CC), the fourth most common gynecologic cancer. So far, many studies have been conducted on CC; however, it is still necessary to discover the hub gene, key pathways, and the exact underlying mechanisms involved in developing this disease. Objective: This study aims to use gene expression patterns and protein-protein interaction (PPI) network analysis to identify key pathways and druggable hub genes in CC. Materials and Methods: In this in silico analysis, 2 microarray gene expression datasets; GSE63514 (104 cancer and 24 normal samples), and GSE9750 (42 cancer and 24 normal samples) were extracted from gene expression omnibus to identify common differentially expressed genes between them. Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis were performed via the Enrichr database. STRING 12.0 database and CytoHubba plugin in Cytoscape 3.9.1 software were implemented to create and analyze the PPI network. Finally, druggable hub genes were screened. Results: Based on the degree method, 10 key genes were known as the hub genes after the screening of PPI networks by the CytoHubba plugin. Conclusion: This research provides a novel vision for designing therapeutic targets in patients with CC. However, these findings should be verified through additional experiments.
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