Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37251077", "kind": "science-record", "revision": 4, "content_hash": "sha256:21eae84e20b181c50ad313c5d6a9b6ff00482fc2193a6619076cf2029aa964c0", "created_at": "2026-11-01T19:01:57Z", "updated_at": "2026-11-01T22:52:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms.", "doi": "10.3389/fmolb.2023.1204031", "pmid": "37251077", "authors": ["Haotian Wang", "Shaoshuo Li", "Baixing Chen", "Mao Wu", "Heng Yin", "Yang Shao", "Jianwei Wang"], "journal": "Frontiers in molecular biosciences", "year": 2023, "abstract": "Objectives: Cigarette smoking has been recognized as a predisposing factor for both osteoporosis (OP) and chronic obstructive pulmonary disease (COPD). This study aimed to investigate the shared gene signatures affected by cigarette smoking in OP and COPD through gene expression profiling. Materials and methods: Microarray datasets (GSE11784, GSE13850, GSE10006, and GSE103174) were obtained from Gene Expression Omnibus (GEO) and analyzed for differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) regression method and a random forest (RF) machine learning algorithm were used to identify candidate biomarkers. The diagnostic value of the method was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis. Finally, immune cell infiltration was analyzed to identify dysregulated immune cells in cigarette smoking-induced COPD. Results: In the smoking-related OP and COPD datasets, 2858 and 280 DEGs were identified, respectively. WGCNA revealed 982 genes strongly correlated with smoking-related OP, of which 32 overlapped with the hub genes of COPD. Gene Ontology (GO) enrichment analysis showed that the overlapping genes were enriched in the immune system category. Using LASSO regression and RF machine learning, six candidate genes were identified, and a logistic regression model was constructed, which had high diagnostic values for both the training set and external validation datasets. The area under the curves (AUCs) were 0.83 and 0.99, respectively. Immune cell infiltration analysis revealed dysregulation in several immune cells, and six immune-associated genes were identified for smoking-related OP and COPD, namely, mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1), tissue-type plasminogen activator (PLAT), sodium channel 1 subunit alpha (SCNN1A), sine oculis homeobox 3 (SIX3), sperm-associated antigen 9 (SPAG9), and vacuolar protein sorting 35 (VPS35). Conclusion: The findings suggest that immune cell infiltration profiles play a significant role in the shared pathogenesis of smoking-related OP and COPD. The results could provide valuable insights for developing novel therapeutic strategies for managing these disorders, as well as shedding light on their pathogenesis.", "claims": [{"id": "claim-method", "statement": "The DEGs were identified using the limma package in R (version 4.1.1; http://cran.r-project.org/) (Ritchie et al., 2015), and subsequently analyzed using different packages.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37251077", "locator": "sections[4] \"2.2 Data processing and analysis of differential gene expression\" paragraph 2", "quote": "The DEGs were identified using the limma package in R (version 4.1.1; http://cran.r-project.org/) (Ritchie et al., 2015), and subsequently analyzed using different packages."}], "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:37251077", "locator": "sections[20] \"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": "Using microarray data obtained from GEO and analyzed with differential gene expression and WGCNA approaches, the study identified six shared candidate genes and constructed a logistic regression model with reported AUCs of 0.83 and 0.99.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37251077", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Microarray datasets (GSE11784, GSE13850, GSE10006, and GSE103174) were obtained from Gene Expression Omnibus (GEO) and analyzed for differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA)."}, {"asset": "source:pmid:37251077", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Using LASSO regression and RF machine learning, six candidate genes were identified, and a logistic regression model was constructed, which had high diagnostic values for both the training set and external validation datasets."}, {"asset": "source:pmid:37251077", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The area under the curves (AUCs) were 0.83 and 0.99, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using microarray data obtained from GEO and analyzed with differential gene expression and WGCNA approaches, the study identified six shared candidate genes and constructed a logistic regression model with reported AUCs of 0.83 and 0.99.", "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:37251077"], "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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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:37251077", "data:geo:GSE10006"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE10006"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:42:14Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10007, "completion_tokens": 1778, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37251077", "type": "paper", "label": "pmid:37251077"}, {"id": "source:pmid:37251077", "type": "manuscript", "label": "PMID 37251077 full text"}, {"id": "data:geo:GSE10006", "type": "dataset", "label": "GEO GSE10006"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37251077", "type": "paper", "label": "pmid:37251077"}, {"id": "source:pmid:37251077", "type": "manuscript", "label": "PMID 37251077 full text"}, {"id": "data:geo:GSE10006", "type": "dataset", "label": "GEO GSE10006"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37251077", "type": "extraction", "used": ["source:pmid:37251077"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE10006", "type": "accession_resolution", "used": ["data:geo:GSE10006"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37251077", "parent_hash": "sha256:d1a24b078fea204672acc3e514befdc1b920e10da63804318cad0a18e5ca2a23", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE10006 licence not read", "asset:data:geo:GSE10006 not downloaded", "asset:data:geo:GSE103174 licence not read", "asset:data:geo:GSE103174 not downloaded", "asset:data:geo:GSE11784 licence not read", "asset:data:geo:GSE11784 not downloaded", "asset:data:geo:GSE13850 licence not read", "asset:data:geo:GSE13850 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["expression profiling. Materials and methods: Microarray datasets (GSE11784, GSE13850, GSE10006, and"], "order_basis": "paper_text_order:415", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The DEGs were identified using the limma package in R (version 4.1.1; http://cran.r-project.org/) (Ritchie et al., 2015), and subsequently analyzed using different packages."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}