Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34555052", "kind": "science-record", "revision": 7, "content_hash": "sha256:2edfe1884c6234ce61f5b10658940c646565c9726ba77bb8341a04f245e7a180", "created_at": "2026-11-01T15:41:53Z", "updated_at": "2026-11-01T22:04:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning.", "doi": "10.1371/journal.pone.0257343", "pmid": "34555052", "authors": ["Shaoshuo Li", "Baixing Chen", "Hao Chen", "Zhen Hua", "Yang Shao", "Heng Yin", "Jianwei Wang"], "journal": "PloS one", "year": 2021, "abstract": "OBJECTIVES: Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO).\nMATERIALS AND METHODS: The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve.\nRESULTS: Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF.\nCONCLUSION: The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.", "claims": [{"id": "claim-data-access", "statement": "All relevant data are within the manuscript and its Supporting Information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34555052", "locator": "sections[19] \"Data Availability\" paragraph 2", "quote": "All relevant data are within the manuscript and its Supporting Information files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:34555052"], "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:GSE13850"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE13850"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34555052", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0257343", "sha256": "sha256:7b90b9d4a2819118e78207620049c19e23fde17d02d7e30acc01a3b3202487b7", "bytes": 44888, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0257343", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8459994", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13850", "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=GSE13850", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13850", "version": null, "note": "All relevant data are within the manuscript and its Supporting Information files.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All relevant data are within the manuscript and its Supporting Information files.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM179758", "GSM179759", "GSM179760", "GSM179761", "GSM179762", "GSM179763", "GSM179764", "GSM179765", "GSM179766", "GSM179767", "GSM179768", "GSM179769", "GSM179770", "GSM179771", "GSM179772", "GSM179773", "GSM179774", "GSM179775", "GSM179776", "GSM179777", "GSM348199", "GSM348200", "GSM348201", "GSM348202", "GSM348203", "GSM348204", "GSM348205", "GSM348206", "GSM348207", "GSM348208", "GSM348209", "GSM348210", "GSM348211", "GSM348212", "GSM348213", "GSM348214", "GSM348215", "GSM348216", "GSM348217", "GSM348218"], "sample_count": 40, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Gene Expression of Circulating B Lymphocytes for Smoking-related Osteoporosis in Postmenopausal Females", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "asset:table:pmid:34555052:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:d337fddb7efc977c981ef38788c4ee2fe548732da52f71ab2a9e2eb1bc5425ac", "bytes": 532, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#pone.0257343.t001", "version": null, "note": "Correlation between modules and smoking-related postmenopausal osteoporosis.", "structure": {"pmcid": "PMC8459994", "section_index": 11, "section_title": "3.1. 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