Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38311613", "kind": "science-record", "revision": 4, "content_hash": "sha256:b9f9945233fc0b580960d289f040139e568a508cba0212c022063b284d612e91", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T22:53:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females.", "doi": "10.1038/s41598-024-52429-1", "pmid": "38311613", "authors": ["Jinzhi Lai", "Hainan Yang", "Jingshan Huang", "Lijiang He"], "journal": "Scientific reports", "year": 2024, "abstract": "The Wnt signaling pathway is essential for bone development and maintaining skeletal homeostasis, making it particularly relevant in osteoporosis patients. Our study aimed to identify distinct molecular clusters associated with the Wnt pathway and develop a diagnostic model for osteoporosis in postmenopausal Caucasian women. We downloaded three datasets (GSE56814, GSE56815 and GSE2208) related to osteoporosis from the GEO database. Our analysis identified a total of 371 differentially expressed genes (DEGs) between low and high bone mineral density (BMD) groups, with 12 genes associated with the Wnt signaling pathway, referred to as osteoporosis-associated Wnt pathway-related genes. Employing four independent machine learning models, we established a diagnostic model using the 12 osteoporosis-associated Wnt pathway-related genes in the training set. The XGB model showed the most promising discriminative potential. We further validate the predictive capability of our diagnostic model by applying it to three external datasets specifically related to osteoporosis. Subsequently, we constructed a diagnostic nomogram based on the five crucial genes identified from the XGB model. In addition, through the utilization of DGIdb, we identified a total of 30 molecular compounds or medications that exhibit potential as promising therapeutic targets for osteoporosis. In summary, our comprehensive analysis provides valuable insights into the relationship between the osteoporosis and Wnt signaling pathway.", "claims": [{"id": "claim-method", "statement": "We then utilized the 'Linear Models for Microarray Data (limma)' R package to screen for differentially expressed genes (DEGs) in the training set (https://bioconductor.org/packages/release/bioc/html/limma.html), using the inclusion criteria of |log2-fold change (FC)| > 0.1 and adjusted p-value < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38311613", "locator": "sections[3] \"Data acquisition and BMD measurements\" paragraph 3", "quote": "We then utilized the 'Linear Models for Microarray Data (limma)' R package to screen for differentially expressed genes (DEGs) in the training set (https://bioconductor.org/packages/release/bioc/html/limma.html), using the inclusion criteria of |log2-fold change (FC)| > 0.1 and adjusted p-value < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets involved in this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), accession numbers: GSE56814, GSE56815, GSE2208, GSE7158, GSE7429, and GSE13850.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38311613", "locator": "sections[25] \"notes\" paragraph 1", "quote": "The datasets involved in this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), accession numbers: GSE56814, GSE56815, GSE2208, GSE7158, GSE7429, and GSE13850."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma to analyze publicly available GEO gene-expression data, the study identified 371 differentially expressed genes between low- and high-BMD groups and developed a five-gene XGB diagnostic model for osteoporosis.", "kind": "quantitative", "target": {"value": 2.0, "unit": "fold change", "tolerance": null, "source": "We then utilized the 'Linear Models for Microarray Data (limma)' R package to screen for differentially expressed genes (DEGs) in the training set (https://bioconductor.org/packages/release/bioc/html/limma.html), using the inclusion criteria of |log2-fold change (FC)| > 0.1 and adjusted p-value < 0.05."}, "evidence": [{"asset": "source:pmid:38311613", "locator": "sections[3] \"Data acquisition and BMD measurements\" paragraph 2", "quote": "In this study, we utilized three microarray datasets (GSE56814, GSE56815, GSE2208) from the GEO website database (www.ncbi.nlm.nih.gov/geo) as training sets to investigate the relevance of osteoporosis to gene expression profiles15–17."}, {"asset": "source:pmid:38311613", "locator": "sections[3] \"Data acquisition and BMD measurements\" paragraph 3", "quote": "We then utilized the 'Linear Models for Microarray Data (limma)' R package to screen for differentially expressed genes (DEGs) in the training set (https://bioconductor.org/packages/release/bioc/html/limma.html), using the inclusion criteria of |log2-fold change (FC)| > 0.1 and adjusted p-value < 0.05."}, {"asset": "source:pmid:38311613", "locator": "sections[11] \"Identifying Wnt pathway-related genes associated with osteoporosis in postmenopausal females\" paragraph 2", "quote": "As a result, we identified a total of 371 DEGs between low and high BMD groups, including 138 up-regulated genes and 233 down-regulated genes (Fig. 1A)."}, {"asset": "source:pmid:38311613", "locator": "sections[14] \"Machine learning model development for osteoporosis prediction\" paragraph 2", "quote": "The XGB machine learning model demonstrated the highest area under the ROC curve (Fig. 5D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using limma to analyze publicly available GEO gene-expression data, the study identified 371 differentially expressed genes between low- and high-BMD groups and developed a five-gene XGB diagnostic model for osteoporosis.", "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:38311613"], "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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A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE56814", "GSE56815", "GSE2208", "GSE7158", "GSE7429", "GSE13850"], "resolved_accessions": ["GSE56814", "GSE56815", "GSE2208", "GSE7158", "GSE7429", "GSE13850"], "statements": ["The datasets involved in this study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), accession numbers: GSE56814, GSE56815, GSE2208, GSE7158, GSE7429, and GSE13850."], "basis": "The paper names GSE56814, GSE56815, GSE2208, GSE7158, GSE7429, GSE13850 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:38311613", "data:geo:GSE13850"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE13850"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:07:36Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9287, "completion_tokens": 2152, "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:38311613", "type": "paper", "label": "pmid:38311613"}, {"id": "source:pmid:38311613", "type": "manuscript", "label": "PMID 38311613 full text"}, {"id": "data:geo:GSE13850", "type": "dataset", "label": "GEO GSE13850"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38311613", "type": "paper", "label": "pmid:38311613"}, {"id": "source:pmid:38311613", "type": "manuscript", "label": "PMID 38311613 full text"}, {"id": "data:geo:GSE13850", "type": "dataset", "label": "GEO GSE13850"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38311613", "type": "extraction", "used": ["source:pmid:38311613"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE13850", "type": "accession_resolution", "used": ["data:geo:GSE13850"], "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:38311613", "parent_hash": "sha256:2ed876ec98e0a6b9966493df61865459ca75276945acddd1f0c44d9401a5d9e9", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE13850 licence not read", "asset:data:geo:GSE13850 not downloaded", "asset:data:geo:GSE2208 licence not read", "asset:data:geo:GSE2208 not downloaded", "asset:data:geo:GSE56814 licence not read", "asset:data:geo:GSE56814 not downloaded", "asset:data:geo:GSE56815 licence not read", "asset:data:geo:GSE56815 not downloaded", "asset:data:geo:GSE7158 licence not read", "asset:data:geo:GSE7158 not downloaded", "asset:data:geo:GSE7429 licence not read", "asset:data:geo:GSE7429 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["We then utilized the 'Linear Models for Microarray Data (limma)' R package to screen for differentially expressed genes (DEGs) in the training set (https://bioconductor.org/packages/release/bioc/html/limma.html), using the inclusion criteria of |log2-fold change (FC)| > 0.1 and adjusted p-value < 0.05."], "order_basis": "analysis_only", "id": "assay:1"}]}