Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36299596", "kind": "science-record", "revision": 3, "content_hash": "sha256:4ec6dfb1b6ba3be5b559f61b4223de415cd9c6c790e450833134c9b96d41b062", "created_at": "2026-11-01T18:15:42Z", "updated_at": "2026-11-01T21:29:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of an immune-related signature for predicting the ischemic events in patients undergoing carotid endarterectomy.", "doi": "10.3389/fgene.2022.1014264", "pmid": "36299596", "authors": ["Shifu Li", "Qian Zhang", "Ling Weng", "Jian Li"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Inflammatory responses have drawn more attention to atherosclerosis; however, the immune-related genes (IRGs) as a prognostic factor in atherosclerotic plaque remain to be fully elucidated. Here, the purpose of this study was to investigate whether the IRGs could be identified as a reliable biomarker for predicting ischemic events in patients undergoing carotid endarterectomy (CEA). Methods: Two datasets GSE97210 and GSE21545 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE97210 was used to explore the significant pathways and differentially expressed IRGs (DEIRGs) between plaques and controls, which were further screened to identify the prognostic DEIRGs in the GSE21545 dataset. The identification of molecular subgroups with the prognostic gene expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. Functional analyses including GO, KEGG, GSVA, and GSEA analyses, and immune analyses including xCell and ssGSEA algorithms were conducted to elucidate the underlying mechanisms. The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis. Results: A total of 796 DEIRGs (including 588 upregulated and 208 downregulated) were identified. Nine prognostic DEIRGs were further screened with univariate Cox regression analysis. Two clusters with different prognosis were grouped based on the prognostic DEIRGs. Immune infiltration analysis shows that cluster 2 with a better prognosis presented with a higher immune response than cluster 1. A prognostic model based on seven IRGs (IL2RA, NR4A2, DES, ERAP2, SLPI, RASGRP1, and AGTR2) was developed and verified. Consistent with the immune analysis of the cluster, the immune infiltration in the low-risk group with a better prognosis was also more active than that in the high-risk group. Finally, a nomogram based on the seven genes was constructed, which might have future implications in clinical care. Conclusion: The expression of immune-related genes is correlated with the immune microenvironment of atherosclerotic patients and could be applied to predict the ischemic events in patients undergoing CEA accurately.", "claims": [{"id": "claim-method", "statement": "The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36299596", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The prognostic risk model was constructed using the LASSO algorithm and multivariate Cox regression analysis."}], "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 numbers can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36299596", "locator": "sections[27] \"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 numbers can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A seven-gene prognostic model achieved 1-, 3-, and 5-year AUCs of 0.91, 0.85, and 0.86, respectively, in the training cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36299596", "locator": "sections[19] \"Construction of the prognostic model based on prognostic IRGs\" paragraph 2", "quote": "Time-dependent ROC analysis was applied to further evaluate the prediction efficiency of the risk scores, with the areas under the curve (AUCs) of 1, 3, and 5 years being 0.91, 0.85, and 0.86, respectively, in the training cohort (Figure 7F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A seven-gene prognostic model achieved 1-, 3-, and 5-year AUCs of 0.91, 0.85, and 0.86, respectively, in the training cohort.", "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:36299596"], "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:GSE21545"], "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:GSE21545"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36299596", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.1014264", "sha256": "sha256:c3496d1106cf58bb25f60038fb1688b8acc7c60186e4d2a4b26238fc17113408", "bytes": 74348, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.1014264", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9592116", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21545", "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=GSE21545", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21545", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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