Identification and validation of novel gene markers of osteoporosis by weighted co expression analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35280365", "kind": "science-record", "revision": 5, "content_hash": "sha256:48f99d1b646085edc57d9bf9a7f9d1edffd42fb9dc343df348145ca5c47592df", "created_at": "2026-11-01T18:14:03Z", "updated_at": "2026-11-01T22:48:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of novel gene markers of osteoporosis by weighted co expression analysis.", "doi": "10.21037/atm-22-229", "pmid": "35280365", "authors": ["Yinan Chen", "Ling Zou", "Jiong Lu", "Minwei Hu", "Zeyu Yang", "Changhui Sun"], "journal": "Annals of translational medicine", "year": 2022, "abstract": "Background: Osteoporosis is a serious global public health concern. The present study identified specific osteoporosis biomarkers which were used to generate a predictive prognostic model for patients with osteoporosis.\nMethods: Datasets from the Gene Expression Omnibus (GEO) database were analyzed. Differentially expressed genes (DEGs) between osteoporosis and normal controls were identified by integrated microarray analysis of the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed gene function were performed. The best diagnostic gene biomarkers for Osteoporosis were identified using Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPIs) networks. Classification models including support vector machines (SVM) was developed to test the diagnostic value of the identified gene biomarkers for osteoporosis. Integrated microarray analysis of the GEO database (GSE62402, GSE7158 and GSE13850) was used for validation.\nResults: A total of 1,589 DEGs related to osteoporosis were identified in the GSE35959 dataset. These DEGs were enriched in various pathways including the negative regulation of the calcium ion transmembrane transport pathway. WGCNA identified 16 models, with the blue module showing a strong positive association with osteoporosis, and the turquoise module showing a considerable negative association with osteoporosis. Six hub genes were identified and used as features to build a predictive prognostic model for osteoporosis using the GSE35959 dataset. The model was verified using the GSE62402, GSE7158, and GSE13850 datasets.\nConclusions: These findings provide crucial insights into the mechanisms of the occurrence and progression of osteoporosis. Furthermore, the identification of novel potential biomarkers may contribute to the early diagnosis, prevention, and treatment of osteoporosis.", "claims": [{"id": "claim-method", "statement": "In the training dataset, the hub gene was used as the feature to obtain their corresponding expression spectrum and construct the classification model of support vector machine (26).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35280365", "locator": "sections[8] \"Model creation and verification\" paragraph 2", "quote": "In the training dataset, the hub gene was used as the feature to obtain their corresponding expression spectrum and construct the classification model of support vector machine (26)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA sequencing and methylation data of osteoporosis samples were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (17) public database under the data series accession number GSE35959 (18), GSE7158 (19), GSE13850 (14), and GSE7429 (20) on July 20, 2020.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35280365", "locator": "sections[3] \"Microarray profiles and data processing\" paragraph 2", "quote": "The RNA sequencing and methylation data of osteoporosis samples were obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) (17) public database under the data series accession number GSE35959 (18), GSE7158 (19), GSE13850 (14), and GSE7429 (20) on July 20, 2020."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A support vector machine model using six hub-gene expression features achieved 100% classification accuracy, sensitivity, specificity, and ROC AUC in the GSE35959 training dataset.", "kind": "quantitative", "target": {"value": 100.0, "unit": "per cent", "tolerance": null, "source": "The 6 hub genes identified above were used as features in the training dataset, and the corresponding expression spectra were acquired to build a support vector machine categorization model with 100% classification accuracy, and 14 out of 14 samples were correctly classified."}, "evidence": [{"asset": "source:pmid:35280365", "locator": "sections[16] \"Establishment and confirmation of the diagnostic models\" paragraph 2", "quote": "The 6 hub genes identified above were used as features in the training dataset, and the corresponding expression spectra were acquired to build a support vector machine categorization model with 100% classification accuracy, and 14 out of 14 samples were correctly classified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A support vector machine model using six hub-gene expression features achieved 100% classification accuracy, sensitivity, specificity, and ROC AUC in the GSE35959 training dataset.", "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:35280365"], "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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