Identification and Verification of Feature Immune-Related Genes in Patients With Hypertrophic Cardiomyopathy Based on Bioinformatics Analyses

Necroptosis and immune infiltration in hypertrophic cardiomyopathy: novel insights from bioinformatics analyses

Background

Hypertrophic Cardiomyopathy (HCM), a widespread genetic heart disorder, is largely associated with sudden cardiac fatality. Necroptosis, an emerging type of programmed cell death, plays a fundamental role in several cardiovascular diseases.

Aim

This research utilized bioinformatics analysis to investigate necroptosis's implication in HCM.

Methods

The study retrieved RNA sequencing datasets GSE130036 and GSE141910 from the Gene Expression Omnibus (GEO) database. It detected necroptosis-linked differentially expressed genes (NRDEGs) by reviewing both the gene set for necroptosis and the differently expressed genes (DEGs). The enriched signaling pathway of HCM was assessed using GSEA, while common DEGs were studied through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Concurrently, the Protein-Protein Interaction network (PPI) proved useful for identifying central genes. CIBERSORT facilitated evaluating the correlation between distinct immune cell-type prevalence and NRDEGs by analyzing immune infiltration patterns. Lastly, GSE141910 dataset validated the expression ranks of NRDEGs and immune-cell penetration.

Results

The investigation disclosed significant enrichment and activation of the necroptosis pathway in HCM specimens. Seventeen diverse genes, including CYBB, BCL2, and JAK2 among others, were identified in the process. PPI network scrutiny classified nine of these genes as central genes. Results from GO and KEGG enrichment analyses showed substantial connections of these genes to pathways pertaining to the HIF-1 signaling track, necroptosis, and NOD-like receptor signaling process. Moreover, an imbalance in M2 macrophage cells in HCM samples was observed. Finally, CYBB, BCL2, and JAK2 emerged as vital genes and were validated using the GSE141910 dataset.

Conclusion

These results indicate necroptosis as a probable underlying factor in HCM, with immune cell infiltration playing a part. Additionally, CYBB, BCL2, JAK2 could act as potential biomarkers for recognizing HCM. This information forms crucial insights into the basic mechanisms of HCM and could enhance its diagnosis and management.

Identification of key programmed cell death-related genes and immune infiltration in extracorporeal membrane oxygenation treatment for acute myocardial infarction based on bioinformatics analysis

Background

Extracorporeal membrane oxygenation (ECMO) is an important clinical treatment for acute myocardial infarction (AMI) combined with cardiogenic shock, but the role of programmed cell death (PCD)-related genes in prognostication has not yet been investigated. Therefore, we explored the key prognostic biomarkers and immune infiltration in ECMO treatment in AMI combined with cardiogenic shock.

Methods

The GSE93101 dataset was analyzed from the Gene Expression Omnibus (GEO) database, and the expression levels of PCD-related genes in AMI under ECMO were identified. Differentially expressed PCD-related genes between successful and failed treatment samples were analyzed, and Least absolute shrinkage and selection operator (LASSO) logistic regression and random forest were used to screen PCD-related molecular markers for ECMO treatment in AMI combined with cardiogenic shock. Co-expressed regulatory network and enrichment functions of the hub PCD-related genes were performed. In addition, the single-sample gene set enrichment analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the ECMO treatment samples.

Results

A total of 115 differentially expressed genes were identified from the GSE93101 dataset, and 76 genes were associated with PCD. Then, two hub PCD-related genes, Cell division cycle associated 7 (CDCA7), ankyrin repeat and SOCS box containing 13 (ASB13) were identified as prognostic markers of ECMO treatment in AMI combined with cardiogenic shock. The most significant Gene Ontology (GO) enriched terms of the co-expressed protein of ASB13 are related to post-translational protein modification, cullin-RING ubiquitin ligase complex, and cullin family protein binding, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that ubiquitin mediated proteolysis is the most enriched pathway. The results of GO and KEGG analysis in CDCA7 were mainly involved in DNA and cell cycle related activities and pathways. Moreover, we found that the successful treatment samples contained a lower proportion of nature killer T cells using immune infiltration analysis. Immune cell infiltration analysis revealed that ASB13 was positively correlated with natural killer cell (r = 0.591, p = 0.026), monocyte (r = 0.586, p = 0.028), and gamma delta T cell (r = 0.562, p = 0.036).

Conclusion

The results of this study showed that ASB13 and CDCA7 may contribute to the occurrence and progression of AMI with cardiogenic shock under ECMO.

Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Background

Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors.

Methods

We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET.

Results

We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging.

Conclusions

Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.

Identification of Potential Diagnostic Biomarkers and Biological Pathways in Hypertrophic Cardiomyopathy Based on Bioinformatics Analysis

Hypertrophic cardiomyopathy (HCM) is a genetic heterogeneous disorder and the main cause of sudden cardiac death in adolescents and young adults. This study was aimed at identifying potential diagnostic biomarkers and biological pathways to help to diagnose and treat HCM through bioinformatics analysis. We selected the GSE36961 dataset from the Gene Expression Omnibus (GEO) database and identified 893 differentially expressed genes (DEGs). Subsequently, 12 modules were generated through weighted gene coexpression network analysis (WGCNA), and the turquoise module showed the highest negative correlation with HCM (cor = −0.9, p-value = 4 × 10−52). With the filtering standard gene significance (GS) < −0.7 and module membership (MM) > 0.9, 19 genes were then selected to establish the least absolute shrinkage and selection operator (LASSO) model, and LYVE1, MAFB, and MT1M were finally identified as key genes. The expression levels of these genes were additionally verified in the GSE130036 dataset. Gene Set Enrichment Analysis (GSEA) showed oxidative phosphorylation, tumor necrosis factor alpha-nuclear factor-κB (TNFα-NFκB), interferon-gamma (IFNγ) response, and inflammatory response were four pathways possibly related to HCM. In conclusion, LYVE1, MAFB, and MT1M were potential biomarkers of HCM, and oxidative stress, immune response as well as inflammatory response were likely to be associated with the pathogenesis of HCM.

Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

Cardiorenal syndrome (CRS), defined as acute or chronic damage to the heart or kidney triggering impairment of another organ, has a poor prognosis. However, the molecular mechanisms underlying CRS remain largely unknown. The RNA-sequencing data of the left ventricle tissue isolated from the sham-operated and CRS model rats at different time points were downloaded from the Gene Expression Omnibus (GEO) database. Genomic differences, protein–protein interaction networks, and short time-series analyses, revealed fibronectin 1 (FN1) and periostin (POSTN) as hub genes associated with CRS progression. The transcriptome sequencing data of humans obtained from the GEO revealed that FN1 and POSTN were both significantly associated with many different heart and kidney diseases. Peripheral blood samples from 20 control and 20 CRS patients were collected from the local hospital, and the gene expression levels of FN1 and POSTN were detected by real-time quantitative polymerase chain reaction. FN1 (area under the curve [AUC] = 0.807) and POSTN (AUC = 0.767) could distinguish CRS in the local cohort with high efficacy and were positively correlated with renal and heart damage markers, such as left ventricular ejection fraction. To improve the diagnostic ability, diagnosis models comprising FN1 and POSTN were constructed by logistic regression (F-Score = 0.718), classification tree (F-Score = 0.812), and random forest (F-Score = 1.000). Overall, the transcriptome data of CRS rat models were systematically analyzed, revealing that FN1 and POSTN were hub genes, which were validated in different public datasets and the local cohort.