Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38267482", "kind": "science-record", "revision": 4, "content_hash": "sha256:33647835b36bb02060e28640d7e57d49792d3167ffc365f7be745cc713f0b656", "created_at": "2026-11-01T19:20:11Z", "updated_at": "2026-11-01T22:53:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of shared pathogenetic mechanisms between COVID-19 and IC through bioinformatics and system biology.", "doi": "10.1038/s41598-024-52625-z", "pmid": "38267482", "authors": ["Zhenpeng Sun", "Li Zhang", "Ruihong Wang", "Zheng Wang", "Xin Liang", "Jiangang Gao"], "journal": "Scientific reports", "year": 2024, "abstract": "COVID-19 increased global mortality in 2019. Cystitis became a contributing factor in SARS-CoV-2 and COVID-19 complications. The complex molecular links between cystitis and COVID-19 are unclear. This study investigates COVID-19-associated cystitis (CAC) molecular mechanisms and drug candidates using bioinformatics and systems biology. Obtain the gene expression profiles of IC (GSE11783) and COVID-19 (GSE147507) from the Gene Expression Omnibus (GEO) database. Identified the common differentially expressed genes (DEGs) in both IC and COVID-19, and extracted a number of key genes from this group. Subsequently, conduct Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the DEGs. Additionally, design a protein-protein interaction (PPI) network, a transcription factor gene regulatory network, a TF miRNA regulatory network, and a gene disease association network using the DEGs. Identify and extract hub genes from the PPI network. Then construct Nomogram diagnostic prediction models based on the hub genes. The DSigDB database was used to forecast many potential molecular medicines that are associated with common DEGs. Assess the precision of hub genes and Nomogram models in diagnosing IC and COVID-19 by employing Receiver Operating Characteristic (ROC) curves. The IC dataset (GSE57560) and the COVID-19 dataset (GSE171110) were selected to validate the models' diagnostic accuracy. A grand total of 198 DEGs that overlapped were found and chosen for further research. FCER1G, ITGAM, LCP2, LILRB2, MNDA, SPI1, and TYROBP were screened as the hub genes. The Nomogram model, built using the seven hub genes, demonstrates significant utility as a diagnostic prediction model for both IC and COVID-19. Multiple potential molecular medicines associated with common DEGs have been discovered. These pathways, hub genes, and models may provide new perspectives for future research into mechanisms and guide personalised and effective therapeutics for IC patients infected with COVID-19.", "claims": [{"id": "claim-method", "statement": "To unveil DEGs in the GSE147507 and GSE11783 datasets, we scrutinized COVID-19 vs. non-COVID-19 and IC vs. normal states using the \"limma\" package (version 4.1.1) by R18. The Benjamini–Hochberg False Discovery Rate (FDR) approach was employed to narrow down genes and maintain statistical significance.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38267482", "locator": "sections[4] \"Identification of DEGs and common DEGs between COVID-19 and IC\" paragraph 2", "quote": "To unveil DEGs in the GSE147507 and GSE11783 datasets, we scrutinized COVID-19 vs. non-COVID-19 and IC vs. normal states using the \"limma\" package (version 4.1.1) by R18. The Benjamini–Hochberg False Discovery Rate (FDR) approach was employed to narrow down genes and maintain statistical significance."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data analyzed in this study were obtained from publicly available databases. The GSE147507, GSE11783, GSE171110 and GSE57560 chips from the GEO database (https://www.ncbi.nlm.nih.gov/gds).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38267482", "locator": "sections[31] \"notes\" paragraph 1", "quote": "The data analyzed in this study were obtained from publicly available databases. The GSE147507, GSE11783, GSE171110 and GSE57560 chips from the GEO database (https://www.ncbi.nlm.nih.gov/gds)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 198 differentially expressed genes overlapped between COVID-19 and interstitial cystitis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38267482", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A grand total of 198 DEGs that overlapped were found and chosen for further research."}, {"asset": "source:pmid:38267482", "locator": "sections[14] \"Identification of the common DEGs of COVID-19 and IC\" paragraph 2", "quote": "A Venn diagram rendered the intersection of DEGs, yielding 198 common DEGs (Fig. 2E)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 198 differentially expressed genes overlapped between COVID-19 and interstitial cystitis.", "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:38267482"], "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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