Identification of Biomarkers Associated with Diagnosis of Osteoarthritis Patients Based on Bioinformatics and Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35733917", "kind": "science-record", "revision": 4, "content_hash": "sha256:02d5847cea88e6137835ba5ea6cd9f1a97fa868f495693a836603dac1c57344d", "created_at": "2026-11-01T18:14:45Z", "updated_at": "2026-11-01T22:06:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Biomarkers Associated with Diagnosis of Osteoarthritis Patients Based on Bioinformatics and Machine Learning.", "doi": "10.1155/2022/5600190", "pmid": "35733917", "authors": ["Yihao Liang", "Fangzheng Lin", "Yunfei Huang"], "journal": "Journal of immunology research", "year": 2022, "abstract": "Osteoarthritis (OA) is thought to be the most prevalent chronic joint disease. The incidence of OA is rising because of the ageing population and the epidemic of obesity. This research was designed for the identification of novel diagnostic biomarkers for OA and analyzing the possible association between critical genes and infiltrated immune cells. 10 OA samples from patients with spinal OA and 10 normal samples were collected. GSE55235 and GSE55457 datasets including human OA and normal samples were downloaded from the GEO datasets. Differentially expressed genes (DEGs) were identified between 20 OA and 20 controls. SVM-RFE analysis and LASSO regression model were carried out to screen possible markers. The compositional patterns of the 22 types of immune cell fraction in OA were determined by the use of CIBERSORT. The expression level of the biomarkers in OA was examined by the use of RT-PCR. In this study, an overall 44 DEGs were identified: 18 genes were remarkably upregulated and 26 genes were distinctly downregulated. KEGG pathway analyses revealed that pathways were significantly enriched including IL-17 signal path, rheumatoid arthritis, TNF signal path, and lipid and atherosclerosis. Based on the results of machine learning, we identified APOLD1 and EPYC as critical diagnostic genes in OA, which were further confirmed using ROC assays. Immune cell infiltration analysis revealed that APOLD1 was correlated with mastocytes stimulated, NK cells resting, T cells CD4 memory resting, DCs stimulated, T cells gamma delta, macrophages M0, NK cells stimulated, and mastocytes resting. Moreover, we found that EPYC was correlated with mastocytes stimulated, NK cells resting, T cells CD4 memory resting, DCs stimulated, T cells gamma delta, macrophages M0, NK cells stimulated, and mastocytes resting. Overall, our findings might provide some novel clue for the exploration of novel markers for OA diagnosis. The critical genes and their associations with immune infiltration may offer new insight into understanding OA developments.", "claims": [{"id": "claim-method", "statement": "Student's t-tests were utilized to contrast the genetic expressions between OA specimens and neighboring healthy specimens. For the sake of examining the categorization effects of critical genes on OA and healthy specimens, ROC curves and AUC were computed via the R package “pROC.”", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35733917", "locator": "sections[10] \"2.8. Statistical Analysis\" paragraph 2", "quote": "Student's t-tests were utilized to contrast the genetic expressions between OA specimens and neighboring healthy specimens. For the sake of examining the categorization effects of critical genes on OA and healthy specimens, ROC curves and AUC were computed via the R package “pROC.”"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35733917", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "APOLD1 and EPYC showed strong diagnostic ability for distinguishing OA samples from normal samples, with AUC values of 0.992 and 0.995, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35733917", "locator": "sections[15] \"3.4. The Expression and Diagnosis Significance of APOLD1 and EPYC in OA\" paragraph 2", "quote": "We found that four genes exhibited a strong ability in screening OA samples from normal samples, including APOLD1 (Figure 4(c), AUC = 0.992) and EPYC (Figure 4(d), AUC = 0.995)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "APOLD1 and EPYC showed strong diagnostic ability for distinguishing OA samples from normal samples, with AUC values of 0.992 and 0.995, respectively.", "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:35733917"], "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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