Identification of potential molecular mechanisms and therapeutic targets for recurrent pelvic organ prolapse.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37681155", "kind": "science-record", "revision": 5, "content_hash": "sha256:45b600238643cbc9f0b68a0c8e094e3a94ba6f5a1030475a000731fbf0b1c2bd", "created_at": "2026-11-01T19:02:28Z", "updated_at": "2026-11-01T22:52:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential molecular mechanisms and therapeutic targets for recurrent pelvic organ prolapse.", "doi": "10.1016/j.heliyon.2023.e19440", "pmid": "37681155", "authors": ["Quan Zhou", "Man Lu", "Guo-Sheng Li", "Gan-Lu Peng", "Yan-Feng Song"], "journal": "Heliyon", "year": 2023, "abstract": "Background: The pathogenesis of recurrent pelvic organ prolapse (POP) is currently unclear. Therefore, developing targeted preventive measures is difficult. This study identified potential key pathways, crucial genes, comorbidities, and therapeutic targets associated with the occurrence and development of recurrent POP.\nMethods: The original microarray data GSE28660, GSE53868, and GSE12852 were downloaded from the GEO database. Identification and validation of differentially expressed genes (DEGs) and hub genes associated with recurrent POP were performed using R software and cytoHubba of Cytoscape. Protein-protein interaction (PPI) networks were constructed using the STRING tool and visualized using Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enrichment analyses were effectively performed using DAVID platforms. In addition, the NetworkAnalyst platform was used to explore and visualize the miRNA-hub gene network, TF-hub gene network, hub gene-disease network, and hub gene-drug/chemical network.\nResults: A total of 110 DEGs and 6 hub genes (ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE) were identified in this study. These genes were primarily enriched in the PPAR, AMPK, and adipocytokine, non-alcoholic fatty liver disease, and signaling pathways related to glycerol metabolism. Moreover, 96 miRNAs and 97 TFs were identified to as being associated with recurrent POP. These genes were closely linked to adipocyte metabolism and distribution, energy metabolism, and the longevity regulatory pathway. In addition, 192 diseases or chronic complications were potentially related to the recurrence of POP, including diabetes, hypertension, obesity, inflammatory diseases, and chronic obstructive pulmonary disease. Furthermore, 954 drugs or compounds were shown to have therapeutic potential for recurrent POP, and the most critical target drugs were dexamethasone, bisphenol A, efavirenz, 1-methyl-3-isobutylxanthine, and estradiol.\nConclusions: The results of this study revealed that ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE as potential hub genes associated with recurrent POP, and these hub genes may aid in the understanding of the mechanism underlying POP recurrence and the development of potential molecular drugs.", "claims": [{"id": "claim-method", "statement": "The raw data files were downloaded using the R software (version 3.5.2) with the GEO query packages, and the mRNA expression data were analyzed to identify differential expression genes (DEGs) by using the R package limma function [24]. Based on the results of paired Student's t-test, hierarchical clustering analysis categorized the samples into two groups with expression patterns in POP and normal controls. Moreover, multiple tests and P values of results were corrected according to Benjamin and Hochberg methods so as to control the error detection rate at <5%.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37681155", "locator": "sections[4] \"DEG identification\" paragraph 2", "quote": "The raw data files were downloaded using the R software (version 3.5.2) with the GEO query packages, and the mRNA expression data were analyzed to identify differential expression genes (DEGs) by using the R package limma function [24]. Based on the results of paired Student's t-test, hierarchical clustering analysis categorized the samples into two groups with expression patterns in POP and normal controls. Moreover, multiple tests and P values of results were corrected according to Benjamin and Hochberg methods so as to control the error detection rate at <5%."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data associated with this study has been deposited at The data of this study are derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37681155", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "Data associated with this study has been deposited at The data of this study are derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results of this study revealed that ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE as potential hub genes associated with recurrent POP, and these hub genes may aid in the understanding of the mechanism underlying POP recurrence and the development of potential molecular drugs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37681155", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The results of this study revealed that ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE as potential hub genes associated with recurrent POP, and these hub genes may aid in the understanding of the mechanism underlying POP recurrence and the development of potential molecular drugs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The results of this study revealed that ADIPOQ, IL6, PPARG, CEBPA, LPL, and LIPE as potential hub genes associated with recurrent POP, and these hub genes may aid in the understanding of the mechanism underlying POP recurrence and the development of potential molecular drugs.", "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:37681155"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE12852"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:student_t", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"p_value": "th multiple testing and P-value correction according to"}, "inputs": ["data:geo:GSE12852"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:student_t"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37681155", "role": "manuscript", "format": "xml", 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Based on the results of paired Student's t-test, hierarchical clustering analysis categorized the samples into two groups with expression patterns in POP and normal controls. 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