Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and osteoarthritis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37291167", "kind": "science-record", "revision": 4, "content_hash": "sha256:cb645aa6e2b42b386e60375dbb01fcb734ece4add56d3aa1ad095494269b3a3d", "created_at": "2026-11-01T19:02:01Z", "updated_at": "2026-11-01T22:52:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and osteoarthritis.", "doi": "10.1038/s41598-023-32555-y", "pmid": "37291167", "authors": ["Ziyi Chen", "Wenjuan Wang", "Hao Jue", "Yinghui Hua"], "journal": "Scientific reports", "year": 2023, "abstract": "A growing of evidence has showed that patients with osteoarthritis (OA) had a higher coronavirus 2019 (COVID-19) infection rate and a poorer prognosis after infected it. Additionally, scientists have also discovered that COVID-19 infection might cause pathological changes in the musculoskeletal system. However, its mechanism is still not fully elucidated. This study aims to further explore the sharing pathogenesis of patients with both OA and COVID-19 infection and find candidate drugs. Gene expression profiles of OA (GSE51588) and COVID-19 (GSE147507) were obtained from the Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) for both OA and COVID-19 were identified and several hub genes were extracted from them. Then gene and pathway enrichment analysis of the DEGs were performed; protein-protein interaction (PPI) network, transcription factor (TF)-gene regulatory network, TF-miRNA regulatory network and gene-disease association network were constructed based on the DEGs and hub genes. Finally, we predicted several candidate molecular drugs related to hub genes using DSigDB database. The receiver operating characteristic curve (ROC) was applied to evaluate the accuracy of hub genes in the diagnosis of both OA and COVID-19. In total, 83 overlapping DEGs were identified and selected for subsequent analyses. CXCR4, EGR2, ENO1, FASN, GATA6, HIST1H3H, HIST1H4H, HIST1H4I, HIST1H4K, MTHFD2, PDK1, TUBA4A, TUBB1 and TUBB3 were screened out as hub genes, and some showed preferable values as diagnostic markers for both OA and COVID-19. Several candidate molecular drugs, which are related to the hug genes, were identified. These sharing pathways and hub genes may provide new ideas for further mechanistic studies and guide more individual-based effective treatments for OA patients with COVID-19 infection.", "claims": [{"id": "claim-method", "statement": "We used “limma” package of R software (version 4.1.1) to select DEGs between COVID-19 and non-COVID-19, and between OA and normal21.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37291167", "locator": "sections[4] \"Identification of DEGs and shared DEGs between COVID-19 and OA\" paragraph 2", "quote": "We used “limma” package of R software (version 4.1.1) to select DEGs between COVID-19 and non-COVID-19, and between OA and normal21."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in GEO (https://www.ncbi.nlm.nih.gov/geo/), an online repository. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE147507"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE147507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37291167", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-023-32555-y", "sha256": "sha256:80cd9b92ff705c1ae3afb61214ccd879803783d32b1743f74ffa18d2ebc2f27e", "bytes": 40490, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-023-32555-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10248985", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE147507", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE147507", "version": null, "note": "The datasets presented in this study can be found in GEO (https://www.ncbi.nlm.nih.gov/geo/), an online repository. 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