Targeting CCR2 and PTPN22 Genes for Post-Infarct Heart Failure Therapy: A Network-based Repurposing of Kuanxiong Aerosol.

Figures

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  Figure 1. Network pharmacological analysis of KX formula. A) Illustration of interaction network between KX components (red nodes) and computationally predicted targets (blue circles), B).Comparative analysis of drug-target intersections through integrated Venn diagrams comparing KX targets and pathological targets in post-MI HF, C) GO enrichment of overlapping targets showing predominant associations with: (1) circulatory system process (GO:0003012); (2) muscle system process (GO:0003014); (3) protein kinase activity (GO:0016301), D) KEGG pathway mapping revealing three significantly enriched signaling mechanisms: Calcium signaling pathway (hsa04020), HIF-1 signaling pathway (hsa04066), and MAPK signaling pathway (hsa04010).

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  Figure 2. Integrated WGCNA analysis of post-MI HF. A) Co-expression network construction using GSE59867 dataset (n = 80: 46 controls, 34 post-MI HF cases) with soft threshold optimization (β = 13, R2 = 0.92), B) TOM-based hierarchical clustering identifying 12 gene modules (gene counts: black-238, brown-791, green-268, greenyellow-84, gray-1624, magenta-153, purple-106, red-256, salmon-64, tan-74, turquoise-1342), C) Module-trait correlation analysis demonstrating strongest negative association between brown module and disease status (correlation coefficient = -0.58, p = 2×10-8), D) Target prioritization through intersection of brown module genes (n = 789) with KX targets (n = 213), identifying dual-functional hub genes.

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  Figure 3. Immune cell infiltration analysis in post-MI HF. A) Heatmap showing differentially enriched immune cell phenotypes between control and disease groups (Wilcoxon rank-sum test, FDR-corrected p < 0.05), B) Correlation matrix of 29 immune cell types (Spearman correlation, p < 0.01 indicated by black stars), C) Boxplots demonstrating significantly elevated immune cell subsets in the disease group: APC_co_inhibition, CCR, iDCs, Macrophages, Neutrophils,Parainflammation, and T_helper_cells (p < 0.05, Wilcoxon test; n = 46 controls, 34 disease), D-E) Spearman correlation between CCR2/PTPN22 expression and immune cell infiltration scores: red indicates positive correlation, blue indicates negative correlation; only correlations with |r| > 0.3 and p < 0.05 are shown (n = 80 patients, GSE59867 dataset).

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  Figure 4. GSEA of CCR2 and PTPN22 expression signatures. A) Pathways enriched in CCR2-high vs. CCR2-low expression: Endocytosis pathway (KEGG hsa04144, NES = 1.82, nominal p = 0.034), Notch signaling pathway (KEGG hsa04330, NES = 1.89, nominal p = 0.02), B) Pathways enriched in PTPN22-high vs. PTPN22-low expression: Insulin signaling pathway (KEGG hsa04910, NES = 1.68, p = 0.049), PPAR signaling pathway (KEGG hsa03320, NES = 1.75, nominal p = 0.04).Enrichment score plots illustrating gene set distribution across expression stratifications (1,000 phenotype permutations, significance threshold nominal p < 0.05, GSE59867 cohort).

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  Figure 5. Transcriptional regulation, miRNA targeting, and exploratory diagnostic performance of key genes. A-B) Motif enrichment analysis using RcisTarget: top motifs (e.g., cisbp_M6416, NES=6.06) and associated transcription factors (TFs) for CCR2/PTPN22; FDR-corrected p < 0.05, C) miRNA-mRNA network: 156 miRNAs predicted to target CCR2/PTPN22 (miRDB database), with 162 interactions visualized in Cytoscape (circles: genes; triangles: miRNAs; edges: predicted binding), D–E) ROC curve analysis of CCR2 and PTPN22, showing moderate discriminatory ability for post-MI HF.

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  Figure 6. Correlation analysis of key genes and disease-regulating genes. A) Boxplots showing the expression levels of disease-regulated genes in control and post-MI HF samples from the GSE59867 dataset. Asterisks indicate significant differences (***p < 0.001, **p < 0.01), B) Scatter plot depicting the relationship between CCR2 and TTN, demonstrating a significant negative correlation (r = −0.481, p = 0.0039), C) Gene - gene interaction network of CCR2 and PTPN22 with disease - regulated genes (e.g., ACE, TTN, TNF). Nodes are colored according to the direction of the correlation (red for positive, blue for negative) and sized by significance (black dots for p < 0.05), D) Scatter plot showing a positive correlation between CCR2 and MIR21 (r = 0.59, p = 0.00024). E) Scatter plot indicating a significant negative correlation between PTPN22 and TNF (r = −0.459, p = 0.0063).