Typing characteristics of metabolism-related genes in osteoporosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36188607", "kind": "science-record", "revision": 5, "content_hash": "sha256:3a7e5b64753f7579c036372e409e5d2ee19ecb262134fa29c041925429f2d763", "created_at": "2026-11-01T18:15:29Z", "updated_at": "2026-11-01T22:50:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Typing characteristics of metabolism-related genes in osteoporosis.", "doi": "10.3389/fphar.2022.999157", "pmid": "36188607", "authors": ["Jiandong Guo", "Qinghua Huang", "Yundong Zhou", "Yining Xu", "Chenyu Zong", "Panyang Shen", "Yan Ma", "Jinxi Zhang", "Yongfeng Cui", "Liuqian Yu", "Jiawei Gao", "Gang Liu", "Kangmao Huang", "Wenbin Xu"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning. Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method. Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9. Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.", "claims": [{"id": "claim-method", "statement": "We constructed an SVM classifier using the top-ranked mrmr genes and applied an incremental feature selection (IFS) approach to determine the optimal number of genes as signature genes. The SVM function from package e10171 in the R language was used to implement the SVM method. We used to leave one out cross validation (loocv) to evaluate the predictive performance of each SVM classifier.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36188607", "locator": "sections[7] \"Screening of characteristic genes related to metabolism in osteoporosis\" paragraph 2", "quote": "We constructed an SVM classifier using the top-ranked mrmr genes and applied an incremental feature selection (IFS) approach to determine the optimal number of genes as signature genes. The SVM function from package e10171 in the R language was used to implement the SVM method. We used to leave one out cross validation (loocv) to evaluate the predictive performance of each SVM classifier."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36188607", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Unsupervised clustering identified three distinct metabolic subtypes of osteoporosis, and machine learning with SVM identified 10 potentially metabolically related genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36188607", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes."}, {"asset": "source:pmid:36188607", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Unsupervised clustering identified three distinct metabolic subtypes of osteoporosis, and machine learning with SVM identified 10 potentially metabolically related genes.", "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:36188607"], "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:GSE35959"], "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:GSE35959"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36188607", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2022.999157", "sha256": 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