Dysregulation and imbalance of innate and adaptive immunity are involved in the cardiomyopathy progression

Abstract

Background: Cardiomyopathy is known to be a heterogeneous disease with numerous etiologies. They all have varying degrees and types of myocardial pathological changes, resulting in impaired contractility, ventricle relaxation, and heart failure. The purpose of this study was to determine the pathogenesis, immune-related pathways and important biomarkers engaged in the progression of cardiomyopathy from various etiologies.

Methods: We downloaded the gene microarray data from the Gene Expression Omnibus (GEO). The hub genes between cardiomyopathy and non-cardiomyopathy control groups were identified using differential expression analysis, least absolute shrinkage and selection operator (LASSO) regression and weighted gene co-expression network analysis (WGCNA). To assess the diagnostic precision of hub genes, receiver-operating characteristic (ROC) curves as well as the area under the ROC curve (AUC) were utilized. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis and Gene Ontology (GO) analysis were conducted on the obtained differential genes. Finally, single-sample GSEA (ssGSEA) and Gene Set Enrichment Analysis (GSEA) were utilized to analyze the infiltration level of 28 immune cells and their relationship with hub genes based on gene expression profile data and all differential gene files.

Results: A total of 82 differentially expressed genes (DEGs) were screened after the training datasets were merged and intersected. The WGCNA analysis clustered the expression profile data into four co-expression modules, The turquoise module exhibited the strongest relationship with clinical traits, and nine candidate key genes were obtained from the module. Then we intersected DEGs with nine candidate genes. LASSO regression analysis identified the last three hub genes as promising biomarkers to distinguish the cardiomyopathy group from the non-cardiomyopathy control group. ROC curve analysis in the validation dataset revealed the sensitivity and accuracy of three hub genes as marker genes. The majority of the functional enrichment analysis results were concentrated on immunological and inflammatory pathways. Immune infiltration analysis revealed a significant correlation between regulatory T cells, type I helper T cells, macrophages, myeloid-derived suppressor cells, natural killer cells, activated dendritic cells and the abundance of immune infiltration in hub genes.

Conclusion: The hub genes (CD14, CCL2, and SERPINA3) can be used as markers to distinguish cardiomyopathy from non-cardiomyopathy individuals. Among them, SERPINA3 has the best diagnostic performance. T cell immunity (adaptive immune response) is closely linked to cardiomyopathy progression. Hub genes may protect the myocardium from injury through myeloid-derived suppressor cells, regulatory T cells, helper T cells, monocytes/macrophages, natural killer cells and activated dendritic cells. The innate immune response is crucial to this process. Dysregulation and imbalance of innate immune cells or activation of adaptive immune responses are involved in cardiomyopathy disease progression in patients.

Keywords: LASSO regression; biomarkers; cardiomyopathy; immune cell infiltration; weight gene co-expression network analysis (WGCNA).

Figure 1

Determining optimal soft thresholds (β) in WGCNA. (A) Examination of the average connectivity under various β and scale-free fitting index. The red line implies that the corresponding soft threshold is 10 when the correlation coefficient is 0.9. (B) Connectivity distribution histogram and a scale-free network correlation coefficient of 0.96 checked at β = 3.

Figure 2

Constructing WGCNA modules and screening candidate key genes. (A) Gene clustering dendrogram: each branch represents a gene, and each color below represents a co-expression module. (B) Module-trait relationships heatmap, with turquoise modules significantly associated with controls. (C) Distributing gene significance in each module. (D) In the turquoise module, a scatter plot of gene module members vs. gene significance is shown, GS > 0.5 and MM > 0.8 are candidate key genes.

Figure 3

Screening of DEGs and identification of Hub genes. (A) Volcano plots of DEGs expression in control and cardiomyopathy groups. (B) Heatmap of DEGs expression in cardiomyopathy and control groups. (C) Venn diagram of the DEGs' intersection and candidate genes of the turquoise module. (D) Map of the regression coefficients of the eight genes in LASSO model. (E) Three hub genes screened by 10-fold cross-validation in the LASSO regression model.

Figure 4

Identification of the expression level of the Hub gene. (A) The expression levels of hub genes, CD14, CCL2 and SERPINA3 in the training datasets were significantly lower in the cardiomyopathy group than in the control group. (B) The hub gene expression was verified in the large sample validation dataset (GSE5406), and the expressions of CD14, CCL2 and SERPINA3 in the cardiomyopathy group were significantly lower than those in the control group, of which SERPINA3 had the most significant difference. “***”, “**”, “*” represent P < (0.001, 0.01, 0.05).

Figure 5

Validation hub genes are used as marker genes (A) Diagnostic ability of hub genes in the training datasets. The area under ROC curve (AUC) was used to evaluate the discriminating ability of hub gene in cardiomyopathy and control groups. (B) The validation results of hub gene in the large sample validation dataset (GSE5406) were similar to those of the training datasets.

Figure 6

GO enrichment analysis of DEGs. (A) The histogram of GO enrichment analysis; the redder the color, the more significant the enrichment. (B) Bubble diagram representing GO enrichment analysis; the size of bubbles represents the number of enriched genes, and the redder the color of the bubbles, the more significant the enrichment is. (C) The first circle indicates that BP, CC, MF are represented by different colors and the top six enriched GO:ID are taken. The second circle represents the number of genes in different GO:ID genome backgrounds, where different colors represent the significant degree of DEGs enrichment. The third circle represents the number of genes enriched by DEGs. The fourth circle represents the proportion of genes.

Figure 7

KEGG enrichment analysis of DEGs. (A) The histogram of KEGG enrichment analysis; the redder the color, the more significant the enrichment. (B) Bubble diagram representing KEGG enrichment analysis; the size of bubbles indicates the number of enriched genes, and the redder the color of bubbles, the more significant the enrichment is. (C) The first circle represents the enriched KEGG:ID. The second circle represents the number of genes in different KEGG:ID pathway backgrounds, where different colors represent the significant degree of DEGs enrichment. The third circle represents the number of genes for which DEGs are enriched in the pathway. The fourth circle represents the proportion of genes.

Figure 8

Enrichment of GSEA immune signature gene set. (A) Immune gene set scores in the control group. (B) Immune gene set scores in the cardiomyopathy group.

Figure 9

ssGSEA analysis of immune cell infiltration and its correlation with hub genes. (A,B) Heatmaps and violin plots showing the differences and distribution of 28 immune cells in cardiomyopathy and control groups. (C) The relation between immune cell infiltration and three hub genes; the redder the color, the more significant the difference. “***”, “**”, “*” represent P < (0.001, 0.01, 0.05).