Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34552349", "kind": "science-record", "revision": 7, "content_hash": "sha256:9c14eab890d1001bef165172e759fa267741bb6bea1f97564ab6e5aa0ae1f398", "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": "Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells.", "doi": "10.2147/IJGM.S329005", "pmid": "34552349", "authors": ["Jie Liu", "Xiaodong Wang", "Junhua Lin", "Shaohua Li", "Guoxiong Deng", "Jinru Wei"], "journal": "International journal of general medicine", "year": 2021, "abstract": "OBJECTIVE: Coronary artery disease (CAD) is a serious global health concern. Current diagnostic methods for CAD involve risk to the patient and are costly, so better diagnostic tools are needed. We defined four classifiers based on gene expression profiles in peripheral blood mononuclear cells and determined their potential for CAD detection.\nMETHODS: We downloaded a CAD-related data set (GSE113079) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in peripheral blood mononuclear cells between CAD samples and healthy controls. DEGs were analyzed for functional enrichment. To create a robust CAD classifier, DEGs were identified by feature selection using the principal component analysis. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and support vector machine (SVM) models were created. Gene set variation analysis (GSVA) score and gene set enrichment analysis (GSEA) were also conducted. The performance of the models was evaluated in terms of the area under receiver operating characteristic curves (AUC).\nRESULTS: In the training set, we found 135 up-regulated genes and 104 down-regulated genes in CAD patients compared with controls. The DEGs were involved in some pathways associated with CAD, such as pathways involving calcium and interleukin-17 signaling. Twenty genes were identified as optimal features and used to generate the logistic classifier based on LASSO. The AUC for the classifier was 1.00 in the training set and 0.997 in the test set. Using the 20 DEGs, SVM and random forest classifiers were also generated and showed high diagnostic efficacy, with respective AUCs of 0.997 and 1.00 against the training set. A GSVA score was also established using the top 20 significant DEGs, which showed an AUC of 0.971 in the training set and 0.989 in the test set. Furthermore, GSEA showed autophagy and the proteasome to be major pathways involving the DEGs.\nCONCLUSION: We identified a set of genes specific for CAD whose expression can be measured non-invasively. Using these genes, we defined four diagnostic classifiers using multiple methods.", "claims": [{"id": "claim-method", "statement": "Using the LASSO method, we selected the optimal features from DEGs to construct a logistic regression model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34552349", "locator": "sections[6] \"Feature Selection Using the LASSO Method and Principal Component Analysis (PCA)\" paragraph 2", "quote": "Using the LASSO method, we selected the optimal features from DEGs to construct a logistic regression model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The dataset supporting the conclusions of this article is available in the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database. The persistent identifier and hyperlink to the dataset is https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113079.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34552349", "locator": "sections[21] \"Data Sharing Statement\" paragraph 2", "quote": "The dataset supporting the conclusions of this article is available in the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database. The persistent identifier and hyperlink to the dataset is https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE113079."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "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:34552349"], "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:34552349", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S329005", "sha256": "sha256:e1371a2a3ccc14aeb550cdabef9c9fa0eca1aaea9e152cd21128c7695c1f1041", "bytes": 30717, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/IJGM.S329005", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8450378", "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": "The dataset supporting the conclusions of this article is available in the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database. 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