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

ABSTRACT

In this study, we evaluated the diagnostic value of key genes in myocardial infarction (MI) based on data from the Gene Expression Omnibus (GEO) database. 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.

Graphical abstract

KEYWORDS:

• Gene signature
• myocardial infarction
• diagnosis
• meta-analysis
• biomarker

Highlights

1.CCL20, IL1R2, and ITLN1 were selected as hub gene in MI by bioinformatics analysis.

2.CCL20, IL1R2, and ITLN1 were significantly upregulated in MI patients.

3.A three-gene signature (CCL20, IL1R2, and ITLN1) may be a novel candidate biomarker for distinguishing MI from healthy controls.

Data availability

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).

Disclosure statement

The authors report no conflict of interest.

Additional information

Funding

This project was sponsored by the Key Project of the Education Department of Hebei Province [Grant no. ZD20161011], The Hebei Provincial Natural Science Foundation [Grant no. H2018406061], and The Key Project of the Health Commission of Hebei Province [Grant no. 20181153].