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. 2023 Oct;28(9-10):1406-1421.
doi: 10.1007/s10495-023-01871-z. Epub 2023 Jul 18.

Integrative analysis of genes reveals endoplasmic reticulum stress-related immune responses involved in dilated cardiomyopathy with fibrosis

Wanpeng Li  1 , Peiling Liu  2 , Huilin Liu  3 , Fuchun Zhang  3 , Yicheng Fu  4
Affiliations

Integrative analysis of genes reveals endoplasmic reticulum stress-related immune responses involved in dilated cardiomyopathy with fibrosis

Wanpeng Li et al. Apoptosis. 2023 Oct.

Erratum in

Abstract

Endoplasmic reticulum (ER) stress has been implicated in the mechanisms underlying the fibrotic process in dilated cardiomyopathy (DCM) and results in disease exacerbation; however, the molecular details of this mechanism remain unclear. Through microarray and bioinformatic analyses, we explored genetic alterations in myocardial fibrosis (MF) and identified potential biomarkers related to ER stress. We integrated two public microarray datasets, including 19 DCM and 16 control samples, and comprehensively analyzed differential expression, biological functions, molecular interactions, and immune infiltration levels. The immune cell signatures suggest that inflammatory immune imbalance may promote MF progression. Both innate and adaptive immunity are involved in MF development, and T-cell subsets account for a considerable proportion of immune infiltration. The immune subtypes were further compared, and 103 differentially expressed ER stress-related genes were identified. These genes were mainly enriched in neuronal apoptosis, protein modification, oxidative stress reaction, glycolysis and gluconeogenesis, and NOD-like receptor signaling pathways. Furthermore, the 15 highest-scoring core genes were identified. Seven hub genes (AK1, ARPC3, GSN, KPNA2, PARP1, PFKL, and PRKC) might participate in immune-related mechanisms. Our results offer a new integrative view of the pathways and interaction networks of ER stress-related genes and provide guidance for developing novel therapeutic strategies for MF.

Keywords: Bioinformatics; Dilated cardiomyopathy; Endoplasmic reticulum stress; Immune cells; Myocardial fibrosis.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart and boxplots of consolidated datasets before and after removing batch effects. A Flow chart of methodologies applied in the current study. B Boxplots of consolidated datasets (GSE3585 and GSE42955) before removing the batch effects. C Boxplots of consolidated datasets (GSE3585 and GSE42955) after removing the batch effects
Fig. 2
Fig. 2
Assessment and visualization of immune cell infiltrates. A Bar plot of overall immune cell proportions. B Correlation heatmap depicting the correlations between infiltrating immune cells in MF tissues. The numbers in the plots represent the Pearson’s correlation coefficient. C Correlation between sample infiltration levels and 28 immune cell types in MF tissues. The blue shading denotes negative genetic correlations, and the red shading denotes positive correlations, with gradations of color intensity reflecting an increasing strength in the correlation. MF: Myocardial Fibrosis
Fig. 3
Fig. 3
Construction of characteristic immune subtypes, screening of differential genes, and visualization. A Construction of subtypes with different immune signatures. B Differential gene expression between subtypes is shown in a volcano map. Here, red represents upregulated genes and blue represents downregulated genes. C Heat map depicting a series of differential genes between two subtypes. D Venn diagram demonstrating the intersections of DEGs between ERSRGs. DEGs: ERSRGs: ER stress-related genes
Fig. 4
Fig. 4
Functional enrichment analyses of the genes modified by DEERSRGs. A GO analysis revealed the most enriched categories for biological processes (BP), cellular components (CC), and molecular functions (MF). B A meshwork of GO clusters for the BP. C A meshwork of GO clusters for the CC. D A meshwork of GO clusters for the MF. E Sankey diagram of the significant KEGG pathways. F Histogram of the DO analysis. BP: Biological Processes; CC: Cellular Components; MF: Molecular Functions; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; DO: Disease Ontology
Fig. 5
Fig. 5
GSEA and GSVA enrichment analysis. A–E Five pathways obtained via GSEA enrichment analysis. F Heat maps of nine pathways obtained via GSVA enrichment analysis. GSEA: Gene Set Enrichment Analysis; GSVA: Gene Set Variation Analysis
Fig. 6
Fig. 6
Protein-protein interaction (PPI) network analysis and hub gene screening. A The STRING database was used to generate the PPI interaction network of 103 DEERSRGs. B Hub genes screened using cytoHubba plug-in. C Differential expression analysis of hub genes between two immune subtypes. DEERSRGs: Differentially Expressed ER Stress-related Genes; PPI: Protein-protein Interaction
Fig. 7
Fig. 7
Correlation between hub genes and immune infiltrating cells. A–I Scatter plots of the correlation analysis between hub genes and immune cell types
Fig. 8
Fig. 8
Correlation between immune infiltrating cells and immune subtypes. A–F Violin plots of differential expression analysis between the six immune cell types and subtypes associated with different immune signatures
Fig. 9
Fig. 9
Gene network analysis. A Transcriptional network relationships with hub genes, where red represents hub genes and green represents transcription factors. B Network relationships of hub genes with miRNA, where red represents hub genes and purple represents miRNAs. C Network relationships between hub genes and drugs, where red represents hub genes and gray represents drugs
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