Identification of key immune genes of osteoporosis based on bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37361541", "kind": "science-record", "revision": 4, "content_hash": "sha256:ad0a5d0381d301af5815e0872734d8952943d4a32747f8867a7a916c8fad4d27", "created_at": "2026-11-01T19:02:06Z", "updated_at": "2026-11-01T22:52:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key immune genes of osteoporosis based on bioinformatics and machine learning.", "doi": "10.3389/fendo.2023.1118886", "pmid": "37361541", "authors": ["Song Hao", "Mao Xinqi", "Xu Weicheng", "Yang Shiwei", "Cao Lumin", "Wang Xiao", "Liu Dong", "Hua Jun"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Introduction: Immunity is involved in a variety of bone metabolic processes, especially osteoporosis. The aim of this study is to explore new bone immune-related markers by bioinformatics method and evaluate their ability to predict osteoporosis.\nMethods: The mRNA expression profiles were obtained from GSE7158 in Gene expression Omnibus (GEO), and immune-related genes were obtained from ImmPort database (https://www.immport.org/shared/). immune genes related to bone mineral density(BMD) were screened out for differential analysis. protein-protein interaction (PPIs) networks were used to analyze the interrelationships between different immune-related genes (DIRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DIRGs function were performed. A least absolute shrinkage and selection operation (LASSO) regression model and multiple Support Vector Machine-Recursive Feature Elimination (mSVM-RFE) model were constructed to identify the candidate genes for osteoporosis prediction The receiver operator characteristic (ROC) curves were used to validate the performances of predictive models and candidate genes in GEO database (GSE7158,GSE13850).Through the RT - qPCR verify the key genes differentially expressed in peripheral blood mononuclear cells Finally, we constructed a nomogram model for predicting osteoporosis based on five immune-related genes. CIBERSORT algorithm was used to calculate the relative proportion of 22 immune cells.\nResults: A total of 1158 DEGs and 66 DIRGs were identified between high-BMD and low-BMD women. These DIRGs were mainly enriched in cytokine-mediated signaling pathway, positive regulation of response to external stimulus and the cellular components of genes are mostly localized to external side of plasma membrane. And the KEGG enrichment analysis were mainly involved in Cytokine-cytokine receptor interaction, PI3K-Akt signaling pathway, Neuroactive ligand-receptor interaction,Natural killer cell mediated cytotoxicity. Then five key genes (CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1) were identified and used as features to construct a predictive prognostic model for osteoporosis using the GSE7158 dataset.\nConclusion: Immunity plays an important role in the development of osteoporosis.CCR5, IAPP, IFNA4, IGHV3-73 and PTGER1were play an important role in the occurrences and diagnosis of OP.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) and multiple support vector machine-recursive feature elimination (mSVM-RFE) algorithms were used to identify significant prognostic biomarkers in OP.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37361541", "locator": "sections[7] \"Construction of an IRG prediction model for OP\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) and multiple support vector machine-recursive feature elimination (mSVM-RFE) algorithms were used to identify significant prognostic biomarkers in OP."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: Data generated or analyzed during this study are available in the following repositories: [GEO : GSE7158, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7158]; [GEO : GSE13850, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13850]; [ImmPort, https://s3.immport.org/release/genelists/GeneList.txt?download=true].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37361541", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: Data generated or analyzed during this study are available in the following repositories: [GEO : GSE7158, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7158]; [GEO : GSE13850, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13850]; [ImmPort, https://s3.immport.org/release/genelists/GeneList.txt?download=true]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:37361541"], "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": "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:GSE13850"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37361541", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2023.1118886", "sha256": "sha256:6957a6084b0ec8c3b1c47fd16923fb4e1531d6873c966f1c02c0bec86fea0cca", "bytes": 33348, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2023.1118886", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10289263", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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": "Publicly available datasets were analyzed in this study. This data can be found here: Data generated or analyzed during this study are available in the following repositories: [GEO : GSE7158, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7158]; [GEO : GSE13850, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13850]; [ImmPort, https://s3.immport.org/release/genelists/GeneList.txt?download=true].", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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This data can be found here: Data generated or analyzed during this study are available in the following repositories: [GEO : GSE7158, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7158]; [GEO : GSE13850, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13850]; [ImmPort, https://s3.immport.org/release/genelists/GeneList.txt?download=true].", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM172405", "GSM172406", "GSM172407", "GSM172408", "GSM172409", "GSM172410", "GSM172411", "GSM172412", "GSM172413", "GSM172414", "GSM172415", "GSM172416", "GSM172417", "GSM172418", "GSM172419", "GSM172420", "GSM172421", "GSM172422", "GSM172423", "GSM172424", "GSM172425", "GSM172426", "GSM172427", "GSM172428", "GSM172429", "GSM172430"], "sample_count": 26, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Genome Wide Gene Expression Study of Circulating Monocytes in human with extremely high vs. low bone mass", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE13850 not downloaded", "asset:data:geo:GSE13850 licence not read", "asset:data:geo:GSE7158 not downloaded", "asset:data:geo:GSE7158 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The least absolute shrinkage and selection operator (LASSO) and multiple support vector machine-recursive feature elimination (mSVM-RFE) algorithms were used to identify significant prognostic biomarkers in OP."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "principal component analysis"}}, {"id": "method-3", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-4", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC curve"}}], "targets": [], "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": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: support_vector_machine."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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