Bioinformatics Analysis Reveals Diagnostic Markers and Vital Pathways Involved in Acute Coronary Syndrome

Abstract

Background: Acute coronary syndrome (ACS) has a high incidence and mortality rate. Early detection and intervention would provide clinical benefits. This study aimed to reveal hub genes, transcription factors (TFs), and microRNAs (miRNAs) that affect plaque stability and provide the possibility for the early diagnosis and treatment of ACS.

Methods: We obtained gene expression matrix GSE19339 for ACS patients and healthy subjects from public database. The differentially expressed genes (DEGs) were screened using Limma package in R software. The biological functions of DEGs were shown by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) network was mapped in Cytoscape, followed by screening of hub genes based on the Molecular Complex Detection (MCODE) plug-in. Functional Enrichment analysis tool (FunRich) and Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to predict miRNAs and TFs, respectively. Finally, GSE60993 expression matrix was chosen to plot receiver operating characteristic (ROC) curves with the aim of further assessing the reliability of our findings.

Results: We obtained 176 DEGs and further identified 16 hub genes by MCODE. The results of functional enrichment analysis showed that DEGs mediated inflammatory response and immune-related pathways. Among the predicted miRNAs, hsa-miR-4770, hsa-miR-5195, and hsa-miR-6088 all possessed two target genes, which might be closely related to the development of ACS. Moreover, we identified 11 TFs regulating hub gene transcriptional processes. Finally, ROC curves confirmed three genes with high confidence (area under the curve > 0.9), including VEGFA, SPP1, and VCAM1.

Conclusion: This study suggests that three genes (VEGFA, SPP1, and VCAM1) were involved in the molecular mechanisms of ACS pathogenesis and could serve as biomarkers of disease progression.

Figure 1

(a) The volcano plot was presented, in which green dots represented downregulated genes and red dots represented upregulated genes in ACS samples. (b) Heat map of gene expression. Each row represented one DEG, and the color gradually changed from green to red, indicating the shift of gene expression from low to high. ACS: acute coronary syndrome; DEG: differentially expressed gene.

Figure 2

(a) Top 8 items of BP, CC, and MF shown in the bar chart according to adjust p value. (b) GO chord plot and the order of genes arranged by logFC. (c, d) KEGG pathways enriched by DEGs presented by bubble plot and chord plot, respectively. BP, biological process; CC, cellular component; MF, molecular function; GO, Gene Ontology; logFC, log2 fold change; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 3

Gene set enrichment analysis of GSE19339. Significantly enriched gene sets were selected based on threshold values: | normalized enrichment score (NES)| > 1, nominal p value < 0.05, and FDR q value < 0.25.

Figure 4

(a) The PPI network analysis for DEGs. Different clusters identified by MCODE were marked with different colors. Sixteen dark blue dots were cluster 1, six purple dots were cluster 2, and three orange dots were cluster 3. (b) The PPI network of 16 hub genes. PPI: protein-protein interaction; MCODE: molecular complex detection.

Figure 5

(a) Interaction network between miRNAs and their target genes. Genes were indicated by purple arrows and miRNAs were represented by blue circles. Moreover, miRNAs targeting two genes were shown by green circles. (b) Interaction network between genes and TFs. The diamonds represented genes and circles indicated TFs. The smaller the p value, the darker the circle color. miRNAs: microRNAs; TFs: transcription factors.

Figure 6

ROC curves of three hub genes with excellent diagnostic value for ACS. The AUC was calculated for each plot. ROC: receiver operating characteristic; AUC: area under curve.