Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38927277", "kind": "science-record", "revision": 2, "content_hash": "sha256:5c824c5524c6be930f6ae97768210f98b732728431fc2e9f2eee17cbcc0a27dd", "created_at": "2026-11-01T20:29:00Z", "updated_at": "2026-11-01T21:33:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases.", "doi": "10.3390/biology13060397", "pmid": "38927277", "authors": ["Elaine C Pasamba", "Marco A Orda", "Brian Harvey Avanceña Villanueva", "Po-Wei Tsai", "Lemmuel L Tayo"], "journal": "Biology", "year": 2024, "abstract": "Gynecological diseases are triggered by aberrant molecular pathways that alter gene expression, hormonal balance, and cellular signaling pathways, which may lead to long-term physiological consequences. This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database. Five highly preserved modules were observed across the EM (GSE51981), OC (GSE63885), CC (GSE63514), and EC (GSE17025) datasets. The functional annotation and pathway enrichment analysis revealed that the highly preserved modules were heavily involved in several inflammatory pathways that are associated with transcription dysregulation, such as NF-kB signaling, JAK-STAT signaling, MAPK-ERK signaling, and mTOR signaling pathways. Furthermore, the results also include pathways that are relevant in gynecological disease prognosis through viral infections. Mutations in the ESR1 gene that encodes for ERα, which were shown to also affect signaling pathways involved in inflammation, further indicate its importance in gynecological disease prognosis. Potential drugs were screened through the Drug Repurposing Encyclopedia (DRE) based on the up-and downregulated hub genes, wherein a bacterial ribosomal subunit inhibitor and a benzodiazepine receptor agonist were the top candidates. Other drug candidates include a dihydrofolate reductase inhibitor, glucocorticoid receptor agonists, cholinergic receptor agonists, selective serotonin reuptake inhibitors, sterol demethylase inhibitors, a bacterial antifolate, and serotonin receptor antagonist drugs which have known anti-inflammatory effects, demonstrating that the gene network highlights specific inflammatory pathways as a therapeutic avenue in designing drug candidates for gynecological diseases.", "claims": [{"id": "claim-method", "statement": "This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38927277", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene microarray datasets used for the study are openly available in the NCBI Gene Expression Omnibus (GEO) database under the accession IDs GSE51981, GSE63885, GSE63514, and GSE17025 datasets at https://www.ncbi.nlm.nih.gov/geo/ accessed on 30 October 2023.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38927277", "locator": "sections[33] \"Data Availability Statement\" paragraph 2", "quote": "The gene microarray datasets used for the study are openly available in the NCBI Gene Expression Omnibus (GEO) database under the accession IDs GSE51981, GSE63885, GSE63514, and GSE17025 datasets at https://www.ncbi.nlm.nih.gov/geo/ accessed on 30 October 2023."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Weighted gene co-expression network analysis of GEO microarray datasets identified five highly preserved modules across endometriosis, ovarian cancer, cervical cancer, and endometrial cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38927277", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Five highly preserved modules were observed across the EM (GSE51981), OC (GSE63885), CC (GSE63514), and EC (GSE17025) datasets."}, {"asset": "source:pmid:38927277", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Weighted gene co-expression network analysis of GEO microarray datasets identified five highly preserved modules across endometriosis, ovarian cancer, cervical cancer, and endometrial cancer.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38927277"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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