Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39875572", "kind": "science-record", "revision": 4, "content_hash": "sha256:36a169b1582cb515c8ceb704af0277323013bcbb9fcc41c6321cac6731862c26", "created_at": "2026-11-01T20:30:09Z", "updated_at": "2026-11-01T22:56:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of biomarkers associated with coronary artery disease and non-alcoholic fatty liver disease by bioinformatics analysis and machine learning.", "doi": "10.1038/s41598-025-87923-7", "pmid": "39875572", "authors": ["Chuan Lu", "Mei Han", "Qiqi Ma", "Li Ying", "Yue Zhang"], "journal": "Scientific reports", "year": 2025, "abstract": "The constantly emerging evidence indicates a close association between coronary artery disease (CAD) and non-alcoholic fatty liver disease (NAFLD). However, the exact mechanisms underlying their mutual relationship remain undefined. This study aims to explore the common signature genes, potential mechanisms, diagnostic markers, and therapeutic targets for CAD and NAFLD. We downloaded CAD and NAFLD datasets from the Gene Expression Omnibus (GEO) database and analyzed the differentially expressed genes (DEGs) by limma. Protein-protein interaction (PPI) network was constructed with common DEGs (co-DEGs), and hub genes were screened by Maximal Clique Centrality (MCC) algorithm. Candidate biomarkers were selected from intersection of three machine learning algorithms. Expression levels, nomogram, the areas under the receiver operating characteristic curve (AUC) of candidate biomarkers were performed. CIBERSORT algorithm was used to assess the immune cell infiltration, and Spearman's correlations tests were used for calculating the correlation of biomarker genes. A total of 554 overlapping DEGs associated with CAD and NAFLD were obtained by analysis of GSE113079 and GSE89632 datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the co-DEGs were significantly enriched in immune effector process, inflammation response and lipid metabolism. The PPI network generated a 1245-edge network, and top 50 genes were selected using the MCC algorithm. The candidate biomarkers were screened from intersection of machine learning in GSE89632, including CEBPA, CXCL2, JUN and FOXO1. The ROC results showed that these four biomarker genes have good diagnostic value for patients with both CAD and NAFLD. Then we explored the immune landscape, immune infiltration and the correlation between biomarker gene expression in CAD and NAFLD samples. In this study, we predict that CEBPA, CXCL2, JUN and FOXO1 can be used to diagnose CAD and NAFLD. Our study provided new insights for potential biomarkers, molecular mechanism and therapeutic targets for both diseases.", "claims": [{"id": "claim-method", "statement": "“limma” package was used to identify differentially expressed genes (DEGs) in CAD and NAFLD datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39875572", "locator": "sections[4] \"Identification of differentially expressed genes in CAD and NAFLD\" paragraph 2", "quote": "“limma” package was used to identify differentially expressed genes (DEGs) in CAD and NAFLD datasets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). Data processing procedures are available from the Correspondence author (Yue Zhang) on a reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39875572", "locator": "sections[28] \"Availability of data and materials\" paragraph 2", "quote": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). Data processing procedures are available from the Correspondence author (Yue Zhang) on a reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The four biomarkers CEBPA, CXCL2, FOXO1 and JUN had AUCs of 0.776, 0.690, 0.820 and 0.691 for diagnosing CAD and 0.966, 0.982, 0.952 and 0.963, respectively, for diagnosing NAFLD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39875572", "locator": "sections[17] \"Construction of the nomogram and evaluation of the diagnostic efficacy for predicting CAD and NAFLD\" paragraph 2", "quote": "The AUC of CEBPA, CXCL2, FOXO1 and JUN for diagnosing CAD in GSE113079 were 0.776, 0.690, 0.820 and 0.691, respectively (Fig. 4A)."}, {"asset": "source:pmid:39875572", "locator": "sections[17] \"Construction of the nomogram and evaluation of the diagnostic efficacy for predicting CAD and NAFLD\" paragraph 2", "quote": "The AUC of the four biomarkers for diagnosing NAFLD in GSE89632 were 0.966, 0.982, 0.952 and 0.963, respectively (Fig. 4B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The four biomarkers CEBPA, CXCL2, FOXO1 and JUN had AUCs of 0.776, 0.690, 0.820 and 0.691 for diagnosing CAD and 0.966, 0.982, 0.952 and 0.963, respectively, for diagnosing NAFLD.", "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:39875572"], "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:GSE113079"], "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:GSE113079"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39875572", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-87923-7", "sha256": "sha256:9f1407fb9218e4d6c98ccc00bda6ccd38b751f6e51558e1b4e024118b39849cd", "bytes": 46727, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-87923-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11775188", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE113079", "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=GSE113079", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE113079", "version": null, "note": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). 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Data processing procedures are available from the Correspondence author (Yue Zhang) on a reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). Data processing procedures are available from the Correspondence author (Yue Zhang) on a reasonable request.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4837490", "GSM4837491", "GSM4837492", "GSM4837493", "GSM4837494", "GSM4837495", "GSM4837496", "GSM4837497", "GSM4837498", "GSM4837499", "GSM4837500", "GSM4837501", "GSM4837502", "GSM4837503", "GSM4837504", "GSM4837505", "GSM4837506", "GSM4837507", "GSM4837508", "GSM4837509", "GSM4837510", "GSM4837511", "GSM4837512", "GSM4837513", "GSM4837514", "GSM4837515", "GSM4837516", "GSM4837517", "GSM4837518", "GSM4837519", "GSM4837520", "GSM4837521", "GSM4837522"], "sample_count": 33, "group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Portal fibroblasts with mesenchymal stem cell features form a reservoir of proliferative myofibroblasts in liver fibrosis", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE42148", "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=GSE42148", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE42148", "version": null, "note": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). Data processing procedures are available from the Correspondence author (Yue Zhang) on a reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). 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Data processing procedures are available from the Correspondence author (Yue Zhang) on a reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All datasets supporting the conclusions were downloaded from the public GEO database, including GSE113079(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE113079), GSE42148(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE42148), GSE89632(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE89632) and GSE159676(https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc = GSE159676). 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