COL3A1, COL5A1 and COL6A2 serve as potential molecular biomarkers for osteoarthritis based on weighted gene co‑expression network analysis bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37869636", "kind": "science-record", "revision": 4, "content_hash": "sha256:7fa4649b65070a4372c3980a70db74198cbf5174922718e49b1962de1ce44446", "created_at": "2026-11-01T19:02:43Z", "updated_at": "2026-11-01T22:52:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "COL3A1, COL5A1 and COL6A2 serve as potential molecular biomarkers for osteoarthritis based on weighted gene co‑expression network analysis bioinformatics analysis.", "doi": "10.3892/etm.2023.12239", "pmid": "37869636", "authors": ["Yufeng Zhang", "Yingzhen Niu", "Yonggang Peng", "Xueyang Pan", "Fei Wang"], "journal": "Experimental and therapeutic medicine", "year": 2023, "abstract": "Osteoarthritis (OA) is a non-inflammatory degenerative joint disease, characterized by joint pain and stiffness. The prevalence of OA increases with age. However, the relationship between biomarkers [collagen type III α1 (COL3A1), COL5A1, COL6A2, COL12A1] and OA remains unclear. The OA subchondral bone dataset GSE51588 was downloaded from the GEO database, and the differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis was performed, and a protein-protein interaction network was constructed and further analyzed using Cytoscape and STRING. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and then Gene Set Enrichment Analysis (GSEA) was used to formulate the molecular functions and pathways based on the results of GO and KEGG analyses. Comparative Toxicogenomics Database and TargetScan were used to identify the hub-gene-related diseases and the microRNAs that regulated the central hub genes. Immunohistochemical staining was performed to confirm the expression of related proteins in OA and non-OA tissue samples. A total of 1,679 DEGs were identified. GO analysis showed that the DEGs were primarily enriched in the process of 'immune system', 'extracellular region', 'secretory granule', 'collagen-containing extracellular matrix', 'ECM-receptor, glycosaminoglycan binding' and 'systemic lupus erythematosus'. The results of GSEA were similar to those of GO and KEGG enrichment terms for DEGs. A total of 25 important modules were generated, and two core gene clusters and seven core genes were obtained (COL6A2, COL5A2, COL12A1, COL5A1, COL6A1, LUM and COL3A1). Core genes were expressed differentially between OA subchondral bone and normal tissue samples. The expression levels of COL3A1, COL5A1 and COL6A2 in OA subchondral bone tissue were higher compared with those in normal tissues, but COL12A1 expression was not significantly increased; all stained markers were highly expressed in surrounding tissues of immunohistochemical staining. In conclusion, COL3A1, COL5A1 and COL6A2 may be potential molecular biomarkers for OA.", "claims": [{"id": "claim-method", "statement": "The R package limma (version 3.40.6) (13) was used to explore the DEGs between OA and non-OA subchondral bone tissues. The GSE51588 dataset was analyzed using the lmFit method for multivariate regression (13). The Bayes method was used to calculate the moderated t-statistics, moderated f-statistics and log ratios of differential expression by empirical Bayesian adjustment, which aims to shrink the standard errors towards a common value (13).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37869636", "locator": "sections[4] \"DEGs\" paragraph 2", "quote": "The R package limma (version 3.40.6) (13) was used to explore the DEGs between OA and non-OA subchondral bone tissues. The GSE51588 dataset was analyzed using the lmFit method for multivariate regression (13). The Bayes method was used to calculate the moderated t-statistics, moderated f-statistics and log ratios of differential expression by empirical Bayesian adjustment, which aims to shrink the standard errors towards a common value (13)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37869636", "locator": "sections[27] \"Availability of data and materials\" paragraph 2", "quote": "The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 1,679 DEGs were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37869636", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 1,679 DEGs were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 1,679 DEGs were identified.", "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:37869636"], "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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