Integrated analysis of cell-in-cell related genes and immune microenvironment in heart failure

Figures

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  FIGURE 1. Schematic overview of the study workflow.

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  FIGURE 2. Variant Landscape of CIC related genes in HF (A)Volcano plot illustrating CIC related DEGs between HF and normal tissues (green: downregulated DEGs; yellow: upregulated DEGs; grey: unchanged genes). Points with labels represent significant DEGs with FDR <0.05 and |log2FC| > 0.5 (B) Heatmap of differential analysis between HF and normal group. Blue indicates the normal cohort, yellow denotes the HF cohort, blue squares represent low expression, and yellow squares signify high expression (C) PPI network of CIC related DEGs (D,E) GO term and KEGG enrichment analyses were applied to the CIC related DEGs.

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  FIGURE 3. Selection of Signature Genes from CIC related DEGs using Machine Learning Algorithms (A) Significant features selected by the SVM-RFE algorithm (B) Optimal biomarkers screened by the RF algorithm (C) Variable selection in the LASSO model (D) Venn diagram showing the overlap of genes across three algorithms (E,F) ROC curves were utilized to evaluate the capability of diagnostic markers in distinguishing healthy from HF samples, with performance quantified by the AUC value (G) A nomogram was developed for HF prediction based on 10 genes (H) Boxplot illustrating the differential expression of CIC related features between HF tissues and normal tissues.

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  FIGURE 4. Validation analysis of nomogram model in HF. (A,B) Decision Curve Analysis was performed to evaluate the risk prediction nomogram for HF in the GSE141910 and GSE57338 datasets. (C,D) ROC curve analysis was conducted to validate the risk prediction nomogram for HF using data from GSE141910 and GSE57338. (E,F) Risk distribution analysis showed that HF samples consistently exhibited significantly higher risk scores than healthy samples in both GSE141910 and GSE57338.

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  FIGURE 5. Experimental validation of key CIC-related gene expression in a TAC-induced heart failure model (n = 6 rats per group). (A) qRT-PCR showing mRNA levels of 10 core CIC-related genes in sham and TAC hearts. LPAR2 and GZMB were upregulated, while IL-10 was downregulated in TAC rats. (B,C) Western blot analysis and quantification of LPAR2, GZMB, and IL-10 protein levels (normalized to GAPDH). (D) qRT-PCR of HF markers ANP, BNP, and β-MHC. (E,F) Western blot and quantification of ANP, BNP, and β-MHC. (G) qRT-PCR of fibrosis markers α-SMA, Collagen I, and Collagen III. (H,I) Western blot and quantification of α-SMA, Collagen I, and Collagen III. Protein levels were normalized to GAPDH. qRT-PCR and Western blotting data are shown as the mean ± SEM. Significance: *P < 0.05, **P < 0.01, ***P < 0.001.

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  FIGURE 6. HF related diagnostic biomarkers in the single-cell transcriptome (A) Quality control (B) A UMAP plot representing the 26 clusters across 20,161 cells from HF samples (C) Cell types identified by marker genes (D) Interaction analysis of cell types in the HF sample, showing interaction number and strength (E) Bubble plot of the average and percent expression of diagnostic biomarkers in different cell subtypes.

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  FIGURE 7. Identification of CIC-related molecular subgroups and immune microenvironment characteristics (A) Consensus clustering matrix indicating k = 2 as the optimal cluster number (B) PCA plot demonstrating distinct separation between the two subtypes (C) Expression profiles of HLA genes between subtypes (D) Comparison of immune cell infiltration abundance between subtypes. Cluster A shows higher infiltration of Tregs and M2 macrophages, while Cluster B is enriched in CD8+ T cells and NK cells (E) Differential expression of immune checkpoint genes (F,G) Gene Set Enrichment Analysis (GSEA) revealing distinct pathway activities. Cluster A is associated with immune regulation pathways, whereas Cluster B shows enrichment in metabolic and cardiac contraction pathways.