Analysis and validation of biomarkers and immune cell infiltration profiles in unstable coronary atherosclerotic plaques using bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40027512", "kind": "science-record", "revision": 3, "content_hash": "sha256:b3c6048696172c54e90c3a08fee7b92e5639b290c67526a114e7acff930edfd1", "created_at": "2026-11-01T20:30:14Z", "updated_at": "2026-11-01T22:13:41Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis and validation of biomarkers and immune cell infiltration profiles in unstable coronary atherosclerotic plaques using bioinformatics and machine learning.", "doi": "10.3389/fcvm.2025.1451255", "pmid": "40027512", "authors": ["Pengyue Jin", "Shangyu Zhang", "Li Yang", "Yujie Zeng", "Yongguo Li", "Renkuan Tang"], "journal": "Frontiers in cardiovascular medicine", "year": 2025, "abstract": "Introduction: Decreased stability of coronary atherosclerotic plaques correlates with a heightened risk of acute coronary syndrome (ACS). Thus, early diagnosis and treatment of unstable plaques are imperative in averting adverse cardiovascular events. This study aims to identify diagnostic biomarkers for unstable coronary atherosclerotic plaques and investigate the role of immune cell infiltration in their formation.\nMethods: The datasets GSE163154 and GSE111782, obtained from the gene expression omnibus (GEO) database, were amalgamated for bioinformatics analysis, using the dataset GSE43292 as a test set. Sequentially, we performed principal component analysis (PCA), differential gene expression analysis, enrichment analysis, weighted gene co-expression network analysis (WGCNA), utilized a machine learning algorithm to screen key genes, conducted receiver operating characteristic (ROC) curve analysis and nomogram model to assess biomarker diagnostic efficacy, validated the biomarkers, and analyzed immune cell infiltration.\nResults: In conclusion, enrichment analyses demonstrate that genes are significantly enriched in inflammatory and immune-related pathways. We identified HSPA2 and GEM as key genes and validated them experimentally. Significant differences existed in immune cell infiltration between subgroups. Additionally, HSPA2 and GEM showed significant associations with a wide range of immune cells.\nDiscussion: HSPA2 and GEM can function as diagnostic biomarkers for unstable coronary atherosclerotic plaques. In combination with immune cell infiltration analyses, our study provides new insights into the future study of unstable plaque occurrence and molecular mechanisms.", "claims": [{"id": "claim-method", "statement": "Subsequently, two machine learning algorithms, LASSO regression analysis and RF, were employed to identify diagnostic biomarkers associated with unstable plaques.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40027512", "locator": "sections[17] \"Screening for diagnostic markers using machine learning algorithms\" paragraph 2", "quote": "Subsequently, two machine learning algorithms, LASSO regression analysis and RF, were employed to identify diagnostic biomarkers associated with unstable plaques."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be in the Gene Expression Omnibus database at https://www.ncbi.nlm.nih.gov/geo/, accession numbers: GSE111782, GSE163154 and GSE43292.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40027512", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be in the Gene Expression Omnibus database at https://www.ncbi.nlm.nih.gov/geo/, accession numbers: GSE111782, GSE163154 and GSE43292."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the training cohort, HSPA2 and GEM exhibited promising diagnostic efficacy for unstable plaques, with AUCs of 0.934 and 0.913, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40027512", "locator": "sections[18] \"Assessment of diagnostic efficacy of diagnostic biomarkers\" paragraph 2", "quote": "Within the training cohort, HSPA2 (AUC = 0.934) and GEM (AUC = 0.913) exhibited promising diagnostic efficacy for unstable plaques."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In the training cohort, HSPA2 and GEM exhibited promising diagnostic efficacy for unstable plaques, with AUCs of 0.934 and 0.913, respectively.", "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:40027512"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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