Immune-Associated Gene Signatures and Subtypes to Predict the Progression of Atherosclerotic Plaques Based on Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35559253", "kind": "science-record", "revision": 5, "content_hash": "sha256:3fadb7a2406cde9e67db6a48dc90d66df9156e1017f22a0b0de4597da69cf01e", "created_at": "2026-11-01T18:14:27Z", "updated_at": "2026-11-01T22:49:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune-Associated Gene Signatures and Subtypes to Predict the Progression of Atherosclerotic Plaques Based on Machine Learning.", "doi": "10.3389/fphar.2022.865624", "pmid": "35559253", "authors": ["Yujia Yang", "Xu Yi", "Yue Cai", "Yuan Zhang", "Zhiqiang Xu"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Objective: Experimental and clinical evidence suggests that atherosclerosis is a chronic inflammatory disease. Our study was conducted for uncovering the roles of immune-associated genes during atherosclerotic plaque progression. Methods: Gene expression profiling of GSE28829, GSE43292, GSE41571, and GSE120521 datasets was retrieved from the GEO database. Three machine learning algorithms, least absolute shrinkage, and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE) were utilized for screening characteristic genes among atherosclerotic plaque progression- and immune-associated genes. ROC curves were generated for estimating the diagnostic efficacy. Immune cell infiltrations were estimated via ssGSEA, and immune checkpoints were quantified. CMap analysis was implemented to screen potential small-molecule compounds. Atherosclerotic plaque specimens were classified using a consensus clustering approach. Results: Seven characteristic genes (TNFSF13B, CCL5, CCL19, ITGAL, CD14, GZMB, and BTK) were identified, which enabled the prediction of progression of atherosclerotic plaques. Higher immune cell infiltrations and immune checkpoint expressions were found in advanced-stage than in early-stage atherosclerotic plaques and were positively linked to characteristic genes. Patients could clinically benefit from the characteristic gene-based nomogram. Several small molecular compounds were predicted based on the characteristic genes. Two subtypes, namely, C1 immune subtype and C2 non-immune subtype, were classified across atherosclerotic plaques. The characteristic genes presented higher expression in C1 than in C2 subtypes. Conclusion: Our findings provide several promising atherosclerotic plaque progression- and immune-associated genes as well as immune subtypes, which might enable to assist the design of more accurately tailored cardiovascular immunotherapy.", "claims": [{"id": "claim-method", "statement": "Three algorithms were applied for selecting characteristic genes among key atherosclerotic plaque progression- and immune-associated genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35559253", "locator": "sections[18] \"Selection of Characteristic Genes via LASSO, Random Forest and SVM-RFE Algorithms\" paragraph 2", "quote": "Three algorithms were applied for selecting characteristic genes among key atherosclerotic plaque progression- and immune-associated genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35559253", "locator": "sections[29] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "When all seven characteristic genes were fitted into one variable, the AUC of the ROC curve was 0.918, demonstrating favorable diagnostic efficiency in predicting atherosclerotic plaque progression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35559253", "locator": "sections[19] \"Diagnostic Efficacy of Characteristic Genes in Predicting Atherosclerotic Plaque Progression\" paragraph 2", "quote": "When all of them were fitted into one variable, the AUC of the ROC curve was 0.918, demonstrating the favorable diagnostic efficiency in predicting atherosclerotic plaque progression (Figure 3H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "When all seven characteristic genes were fitted into one variable, the AUC of the ROC curve was 0.918, demonstrating favorable diagnostic efficiency in predicting atherosclerotic plaque progression.", "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:35559253"], "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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