AI Article Synopsis
- * This research used a network-based approach to analyze genetic connections and identify 315 common differentially expressed genes (DEGs) related to CC from multiple datasets, eventually identifying key hub genes and modules.
- * The study found important links between CC and other tumors, revealing potential therapeutic targets and suggesting that the candidate drugs discovered could be beneficial for treating advanced CC.
Article Abstract
Background: In today's society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death.
Results: This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI's GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules.
Conclusion: Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9892376 | PMC |
| http://dx.doi.org/10.1186/s43141-023-00469-x | DOI Listing |
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