Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction.

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We used data from GSE66360 to identify a set of significant differentially expressed genes (DEGs) between MI and healthy controls. Logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and SignalP 3.0 server were used to identify the potential role of genes in predicting diagnosis in patients with MI. Principal component analysis (PCA), receiver operating characteristic (ROC) curve analyses, area under the curve (AUC) analyses, and C-index were used to estimate the diagnostic value of genes in patients with MI. The association was validated using six other independent data sets. Subsequently, bioinformatics analysis was conducted based on the aforementioned potential genes. A meta-analysis was performed to evaluate the diagnostic value of the genes in MI. Forty-four DEGs were selected from the GSE66360 dataset. A three-gene signature consisting of CCL20, IL1R2, and ITLN1 could effectively distinguish patients with MI. The three-gene signature was validated in seven independent cohorts. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to reveal the involvement of the three-gene signature in inflammation-related biological processes and pathways. Moreover, diagnostic meta-analysis results of the three-gene signature showed that the pooled sensitivity, specificity, and AUC for MI were 0.80, 0.90, and 0.93, respectively. These results suggest that the three-gene signature is a novel candidate biomarker for distinguishing MI from healthy controls.", "claims": [{"id": "claim-method", "statement": "Finally, multivariable logistic regression analysis was utilized to build a diagnosis-related gene signature by incorporating the detectable features selected from the peripheral blood of patients in the SignalP 3.0 server.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34130601", "locator": "sections[4] \"Identification of the diagnosis-related gene signature associated with MI\" paragraph 2", "quote": "Finally, multivariable logistic regression analysis was utilized to build a diagnosis-related gene signature by incorporating the detectable features selected from the peripheral blood of patients in the SignalP 3.0 server."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data supporting the findings of this study are available in the Gene Expression Omnibus (GEO) database (GSE66360, GSE141512, GSE24519, GSE34198, GSE48060, GSE60993, and GSE109048).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34130601", "locator": "sections[16] \"Data availability\" paragraph 2", "quote": "The data supporting the findings of this study are available in the Gene Expression Omnibus (GEO) database (GSE66360, GSE141512, GSE24519, GSE34198, GSE48060, GSE60993, and GSE109048)."}], "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:34130601"], "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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