Targeting ERBB2 and PIK3R1 as a therapeutic strategy for dilated cardiomyopathy: A single-cell sequencing and mendelian randomization analysis.

Background: Dilated cardiomyopathy (DCM) is widely recognized as a significant contributor to heart failure. Nevertheless, the absence of pharmaceutical interventions capable of reversing disease progression and improving prognosis underscores the imperative for additional research in this area. Methods: First, we identified and evaluated three gene sets, namely "SC-DCM", "EP-DCM" and "Drug", using big data and multiple bioinformatics analysis methods. Accordingly, drug-treatable ("Hub") genes in DCM were identified. Following this, four microarray expression profile datasets were employed to authenticate the expression levels and discriminatory efficacy of "Hub" genes. Additionally, mendelian randomization analysis was conducted to ascertain the causal association between the "Hub genes" and heart failure. Finally, the "DGIdb" was applied to identify "Hub" genes-targeted drugs. The "ssGSEA" algorithm assessed the level of immune cell infiltration in DCM. Results: Enrichment analysis showed that the "SC-DCM" and "EP-DCM" gene sets were closely associated with DCM. PIK3R1 and ERBB2 were identified as drug-treatable genes in DCM. Additional analysis using MR supported a causal relationship between ERBB2 and heart failure, but not PIK3R1. Moreover, PIK3R1 was positively correlated with immune activation, while ERBB2 was negatively correlated. We found that everolimus was a pharmacological inhibitor for both PIK3R1 and ERBB2. However, no pharmacological agonist was found for ERBB2. Conclusion: PIK3R1 and ERBB2 are drug-treatable genes in DCM. ERBB2 has a causal effect on heart failure, and its normal expression may play a role in preventing the progression of DCM to heart failure. In addition, there is a cross-expression of PIK3R1 and ERBB2 genes in both DCM and tumors. The adaptive immune system and PIK3R1 may be involved in DCM disease progression, while ERBB2 exerts a protective effect against DCM.

Common molecular mechanism and immune infiltration patterns of thoracic and abdominal aortic aneurysms.

Background: Aortic disease (aortic aneurysm (AA), dissection (AD)) is a serious threat to patient lives. Little is currently known about the molecular mechanisms and immune infiltration patterns underlying the development and progression of thoracic and abdominal aortic aneurysms (TAA and AAA), warranting further research. Methods: We downloaded AA (includes TAA and AAA) datasets from the GEO database. The potential biomarkers in TAA and AAA were identified using differential expression analysis and two machine-learning algorithms. The discrimination power of the potential biomarkers and their diagnostic accuracy was assessed in validation datasets using ROC curve analysis. Then, GSEA, KEGG, GO and DO analyses were conducted. Furthermore, two immuno-infiltration analysis algorithms were utilized to analyze the common immune infiltration patterns in TAA and AAA. Finally, a retrospective clinical study was performed on 78 patients with AD, and the serum from 6 patients was used for whole exome sequencing (WES). Results: The intersection of TAA and AAA datasets yielded 82 differentially expressed genes (DEGs). Subsequently, the biomarkers (CX3CR1 and HBB) were acquired by screening using two machine-learning algorithms and ROC curve analysis. The functional analysis of DEGs showed significant enrichment in inflammation and regulation of angiogenic pathways. Immune cell infiltration analysis revealed that adaptive and innate immune responses were closely linked to AA progression. However, neither CX3CR1 nor HBB was associated with B cell-mediated humoral immunity. CX3CR1 expression was correlated with macrophages and HBB with eosinophils. Finally, our retrospective clinical study revealed a hyperinflammatory environment in aortic disease. The WES study identified disease biomarkers and gene variants, some of which may be druggable. Conclusion: The genes CX3CR1 and HBB can be used as common biomarkers in TAA and AAA. Large numbers of innate and adaptive immune cells are infiltrated in AA and are closely linked to the development and progression of AA. Moreover, CX3CR1 and HBB are highly correlated with the infiltration of immune cells and may be potential targets of immunotherapeutic drugs. Gene mutation research is a promising direction for the treatment of aortic disease.

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

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.