Mining Potential Drug Targets and Constructing Diagnostic Models for Heart Failure Based on miRNA-mRNA Networks

Abstract

Heart failure (HF) is a globally prevalent cardiovascular disease, but effective drug targets and diagnostic models are still lacking. This study was designed to investigate effective drug targets and diagnostic models for HF in terms of miRNA targets, hoping to contribute to the understanding and treatment of HF. Using HF miRNA and gene expression profile data from the GEO database, we analyzed differentially expressed miRNAs/gene identification in HF using Limma and predicted miRNA targets by the online TargetScan database. Subsequently, gene set enrichment analysis and annotation were performed using WebGestaltR package. Protein-protein interactions were identified using the STRING database. The proximity of drugs to treat HF was also calculated and predicted for potential target therapeutic drug. In addition, further drug identification was performed by molecular docking. Finally, diagnostic models were constructed based on differential miRNAs. The GEO dataset was used to screen 66 differentially expressed miRNAs, incorporating 56 downregulated miRNAs and 10 upregulated miRNAs. The JAK-STAT signaling pathway, MAPK signaling pathway, p53 signaling pathway, Prolactin signaling pathway, and TGF-beta signaling pathway were enriched, as shown by KEGG enrichment analysis on the target genes. In addition, we found that 83 genes were upregulated and 92 genes were downregulated in HF patients vs. healthy individuals. Based on the inflammation-related score, hypoxia-related score, and energy metabolism-related score, we identified key miRNA-mRNA pairs and constructed an interaction network. Following that, TAP1, which had the highest expression and network connectivity in acute HF with crystal and molecular docking studies, was selected as a key candidate gene in the network. And the compound DB04847 was selected to produce a large number of favorable interactions with TAP1 protein. Finally, we constructed two diagnostic models based on the differential miRNAs hsa-miR-6785-5p and hsa-miR-4443. In conclusion, we identified TAP1, a key candidate gene in the diagnosis and treatment of HF, and determined that compound DB04847 is highly likely to be a potential inhibitor of TAP1. The TAP1 gene was also found to be regulated by hsa-miR-6785-5p and hsa-miR-4443, and a diagnostic model was constructed. This provides a new promising direction to improve the diagnosis, prognosis, and treatment outcome and guide more effective immunotherapy strategies of HF.

Figure 1

Differentially expressed miRNA analysis on the GSE104150 dataset: (a) volcano plot of miRNA differential analysis; (b) heat map of differential miRNA expression.

Figure 2

Differentially expressed gene analysis on the GSE21125 dataset: (a) volcano plot of differential analysis of acute heart failure patients vs. healthy individuals; (b) volcano plot of differential analysis of chronic heart failure patients vs. healthy individuals; (c) volcano plot of differential analysis of heart failure patients vs. healthy individuals.

Figure 3

Functional enrichment analysis: (a) KEGG enrichment analysis of differential genes; (b) network plot of miRNA interactions with differential target genes, where blue is miRNA, red is gene, and the size of the dot is degree, where the larger the dot, the closer the connectivity with other nodes at that point, and the more important the dot is.

Figure 4

Characterization of pathways abnormally regulated in heart failure: (a) heat map of enrichment scores of pathways significantly different in heart failure patients and healthy group by GSVA (P < 0.05); (b) heat map of correlation analysis between related pathways and differentially miRNA-regulated differential target genes; (c) heat map of correlation between energy metabolism, hypoxia score, and inflammation-related pathways and differentially miRNA-regulated; (d) heat map of correlation analysis between energy metabolism, hypoxia score, and inflammation-related pathways and differential miRNA-regulated target genes.

Figure 5

Screening analysis of correlated target genes: (a) box line plot of 24 genes expressed in acute, chronic heart failure and healthy groups (ANOVA, ^∗P < 0.05; ^∗∗P < 0.01; ^∗∗∗P < 0.001; and ^∗∗∗∗P < 0.0001); (b) construction of diagnostic models for 11 key genes.

Figure 6

miRNA-mRNA relationship analysis: (a) miRNA-mRNA interaction relationship network; (b) diagnostic model of miRNA; (c) distance density fractionation plot of drug to TAP1-related gene set.

Figure 7

Binding pattern map of TAP1 protein with compound DB04847: (a) 3D binding pattern map of compound DB04847 with TAP1 protein; (b) 2D analysis map of detailed interaction generated by compound DB04847 with TAP1 protein, in which the α-helix of the protein backbone is shown as a cyan band and the β-fold is shown as a magenta band. Compound DB04847 is shown as a plum-red stick, the amino acid residues that produce the interaction are shown as cyan sticks, and the colors of the heteroatoms in the compound and amino acid residues are shown by element type. Hydrogen bonds are shown as green dashed lines, π-π stacked interactions are shown as magenta dashed lines, and π-Alkyl interactions are shown as pink dashed lines.

Figure 8

Pathway analysis of aberrant regulation of TAP1 gene: (a) heat map of aberrantly regulated pathway enrichment scores; (b) difference in aberrantly regulated pathway enrichment scores between the high- and low-expression groups of the TAP1 gene; (c) point bar graph of correlation between aberrantly regulated pathway enrichment scores and TAP1 gene expression, where color is significance and size of point is strength of correlation; (d) correlation between TAP1 expression and hypoxia, energy metabolism, and inflammation-related pathways in a correlation heat map.