Identification and subtype analysis of biomarkers associated with the solute carrier family in acute myocardial infarction

Abstract

The dysregulation of some solute carrier (SLC) proteins has been linked to a variety of diseases, including diabetes and chronic kidney disease. However, SLC-related genes (SLCs) has not been extensively studied in acute myocardial infarction (AMI). The GSE66360 and GSE60993 datasets, and SLCs geneset were enrolled in this study. Differentially expressed SLCs (DE-SLCs) were screened by overlapping DEGs between the AMI and control groups and SLCs. Next, functional enrichment analysis was carried out to research the function of DE-SLCs. Consistent clustering of samples from the GSE66360 dataset was accomplished based on DE-SLCs selected. Next, the gene set enrichment analysis (GSEA) was performed on the DEGs-cluster (cluster 1 vs cluster 2). Three machine learning models were performed to obtain key genes. Subsequently, biomarkers were obtained through receiver operating characteristic (ROC) curves and expression analysis. Then, the immune infiltration analysis was performed. Afterwards, single-gene GSEA was carried out, and the biomarker-drug network was established. Finally, quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of biomarkers. In this study, 13 DE-SLCs were filtered by overlapping 366 SLCs and 448 DEGs. The functional enrichment results indicated that the genes were implicated with amino acid transport and TNF signaling pathway. After the consistency clustering analysis, the samples were classified into cluster 1 and cluster 2 subtypes. The functional enrichment results showed that DEGs-cluster were implicated with chemokine signaling pathway and so on. Further, SLC11A1 and SLC2A3 were identified as SLC-related biomarkers, which had the strongest negative relationship with resting memory CD4 T cells and the strongest positive association with activated mast cells. In addition, the single-gene GSEA results showed that cytosolic ribosome was enriched by the biomarkers. Five drugs targeting SLC2A3 were predicted as well. Lastly, the experimental results showed that the biomarkers expression trends were consistent with public database. In this study, 2 SLC-related biomarkers (SLC11A1 and SLC2A3) were screened and drug predictions were carried out to explore the prediction and treatment of AMI.