A novel prognostic 6-gene signature for osteoporosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36213260", "kind": "science-record", "revision": 5, "content_hash": "sha256:68588d1406b8c5b8aefefbff0bfd23988b3d355df4f45cd8eafd6f92e0d959ff", "created_at": "2026-11-01T18:15:33Z", "updated_at": "2026-11-01T22:50:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel prognostic 6-gene signature for osteoporosis.", "doi": "10.3389/fendo.2022.968397", "pmid": "36213260", "authors": ["Yu Zhao", "Jieping Yan", "Yimiao Zhu", "Zhenping Han", "Tingting Li", "Lijuan Wang"], "journal": "Frontiers in endocrinology", "year": 2022, "abstract": "Introduction: The incidence of osteoporosis (OP) keeps increasing due to global aging of the population. Therefore, identifying the diagnostic and prognostic biomarkers of OP is of great significance.\nMethods: mRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815). Gene modules most significantly related to OP were revealed using weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) between OP and normal samples in training set were identified using limma R package. Thereafter, above two gene sets were intersected to obtain the genes potentially related to OP. Protein-protein interaction (PPI) pairs were screened by STRING database and visualized using Cytoscape, while the plug-in cytoHubba was used to screen hub genes by determining their topological parameters. Afterwards, a diagnostic model was constructed using those hub genes, whose creditability was further evaluated by testing sets.\nResults: The results of WGCNA analysis found the Black module was most significantly related to OP, which included altogether 1286 genes. Meanwhile, 2771 DEGs were discovered between OP patients and the normal controls. After taking the intersection, 479 genes were identified potentially correlated with the development of OP. Subsequently, six hub genes were discovered through PPI network construction and node topological analysis. Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups.\nConclusion: Our current study constructed a six hub genes-based diagnostic model for OP. Our findings may shed some light on the research of the early diagnosis for OP and had certain practical significance.", "claims": [{"id": "claim-method", "statement": "First of all, we constructed the OP diagnostic model based on the SVM classification model according to the expression profiles of these six genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36213260", "locator": "sections[13] \"Performance of the diagnostic model in distinguishing normal samples and OP samples\" paragraph 2", "quote": "First of all, we constructed the OP diagnostic model based on the SVM classification model according to the expression profiles of these six genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE35959, GSE7158, GSE7429, GSE62402 and GSE56815.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36213260", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE35959, GSE7158, GSE7429, GSE62402 and GSE56815."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using mRNA expression data obtained from GEO, the study constructed a six-gene support vector machine model that accurately classified osteoporosis and normal samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36213260", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "mRNA data from OP and non-OP samples were obtained from GEO database, which were divided into training set (GSE35959) and testing sets (GSE7158, GSE62402, GSE7429 and GSE56815)."}, {"asset": "source:pmid:36213260", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Finally, we constructed a support vector machine model based on these six genes, which can accurately classified training and testing set samples into OP and normal groups."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using mRNA expression data obtained from GEO, the study constructed a six-gene support vector machine model that accurately classified osteoporosis and normal samples.", "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:36213260"], "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:36213260", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2022.968397", "sha256": "sha256:611ae4d44a84d77db81bff459b0a004387b5e5b6bb5b050967457a76a719bfe5", "bytes": 22214, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2022.968397", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9533022", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE35959", "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=GSE35959", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE35959", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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