Identification of CALU and PALLD as Potential Biomarkers Associated With Immune Infiltration in Heart Failure

Screening of Genes Associated with Immune Infiltration of Discoid Lupus Erythematosus Based on Weighted Gene Co-expression Network Analysis

Discoid lupus erythematosus (DLE) is a disorder of the immune system commonly seen in women of childbearing age. The pathophysiology and aetiology are still poorly understood, and no cure is presently available. Therefore, there is an urgent need to explore the underlying molecular mechanisms, as well as search for new therapeutic targets. Gene expression data from skin biopsies samples of DLE patients and healthy controls were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between DLE and healthy control samples were identified by differential expression analysis. Samples were analysed using CIBERSORT to examine the proportion of immune infiltration. Weighted gene co-expression network analysis was used to screen for the module most relevant to immune infiltration. Candidate genes were uploaded to the TRRUST database to obtain the potential transcription factors regulating these genes. Protein-protein interaction (PPI) analysis was performed to obtain the hub genes most associated with immune infiltration among the candidate genes. A total of 273 DEGs were identified between the DLE and healthy control samples. The results of immunoinfiltration analysis showed that the abundances of resting memory CD4 T cells, activated memory CD4 T cells and M1 macrophages were significantly higher, while those of resting infiltration of plasma cells, regulatory T cells and dendritic cells were lower in DLE samples than in healthy control samples. Correlation analysis showed that ISG15, TRIM22, XAF1, IFIT2, OAS2, OAS3, OAS1, IFI44, IFI6, BST2, IFIT1 and MX2 were negatively correlated with the abundances of plasma cells, T-cell regulatory cells and resting dendritic cells and positively correlated with activated memory CD4 T cells and M1 macrophages. Our study shows that these hub genes may regulate DLE via immune-related pathways mediated by the infiltration of these immune cells.

Bioinformatics combining machine learning and single-cell sequencing analysis to identify common mechanisms and biomarkers of rheumatoid arthritis and ischemic heart failure

Patients with rheumatoid arthritis (RA) have an increased risk of ischemic heart failure (IHF), but the shared mechanisms are unclear. This study analyzed RNA sequencing data from five RA and IHF datasets to identify common biological mechanisms and significant biomarkers. One hundred and seventy-seven common differentially expressed genes (CDEGs) were identified, with enrichment analysis highlighting pathways related to sarcomere organization, ventricular myocardial tissue morphogenesis, chondrocyte differentiation, prolactin signaling, hematopoietic cell lineage, and protein methyltransferases. Five hub genes (CD2, CD3D, CCL5, IL7R, and SPATA18) were identified through protein-protein interaction (PPI) network analysis and machine learning. Co-expression and immune cell infiltration analyses underscored the importance of the inflammatory immune response, with hub genes showing significant correlations with plasma cells, activated CD4+ T memory cells, monocytes, and T regulatory cells. Single-cell RNA sequencing (scRNA-seq) confirmed hub gene expression primarily in T cells, activated T cells, monocytes, and NK cells. The findings underscore the critical roles of sarcomere organization, prolactin signaling, protein methyltransferase activity, and immune responses in the progression of IHF in RA patients. These insights not only identify valuable biomarkers and therapeutic targets but also offer promising directions for early diagnosis, personalized treatments, and preventive strategies for IHF in the context of RA. Moreover, the results highlight opportunities for repurposing existing drugs and developing new therapeutic interventions, which could reduce the risk of IHF in RA patients and improve their overall prognosis.

Correlation Between Prognostic Nutritional Index and Heart Failure in Adults with Diabetes in the United States: Study Results from NHANES (1999-2016)

Background

The relationship between diabetes and heart failure significantly impacts public health. This study assessed the prognostic nutritional index (PNI) as a predictor of heart failure risk in adult diabetic patients.

Methods

An analysis was performed on 1823 diabetic adults using data collected from the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2016. Serum albumin levels and lymphocyte counts were combined to calculate the PNI. We used descriptive statistics categorized by PNI quartiles and performed multivariate logistic regression to adjust for variables including age, gender, ethnicity, and coexisting medical conditions.

Results

The median age (mean ± SD) was 59.942 ± 12.171 years, and the mean value ± SD of the PNI was 52.412 ± 5.430. The prevalence of heart failure was 7.405%. In the fully adjusted model, for each 1-unit increase in PNI, the risk of heart failure decreased by 8.2% (odds ratio (OR), 0.918; 95% confidence interval (CI) 0.884, 0.953). Participants in the highest PNI quartile (Q4) had a 63% reduced risk of heart failure compared to those in the lowest quartile (Q1). Tests for interactions did not reveal any statistically significant differences among these stratified subgroups (p for interaction > 0.05).

Conclusions

This study demonstrated that a higher PNI was significantly associated with a decreased prevalence of heart failure in adults with diabetes.

Comparative Proteomic and Phosphoproteomic Analyses Reveal Molecular Signatures of Myocardial Infarction and Transverse Aortic Constriction in Aged Mouse Models

In the elderly population, coronary heart disease (CHD) often coexists with hypertension. However, excessive blood pressure reduction can paradoxically increase the incidence of adverse events. Understanding the molecular mechanisms underlying hypertension and CHD in aged populations is crucial for developing targeted therapies and improving clinical outcomes. In this study, we constructed myocardial infarction (MI) and transverse aortic constriction (TAC) modelsY in aged mice to simulate the disease states of CHD and hypertension, respectively. Using integrated proteomic and phosphoproteomic analyses, we investigated the molecular signatures associated with MI and TAC in these models. Our aim was to identify key molecules involved in these conditions and to understand their unique and shared characteristics. Through our comprehensive proteomic and phosphoproteomic analysis, we identified a total of 1583 proteins and 232 phosphorylated proteins. We observed significant upregulation of heart disease markers such as Myh7, Xirp2, and Acta1, indicating the successful establishment of the MI and TAC models. The overlapped differentially expressed proteins (DEPs) and differentially phosphorylated proteins (DPPs) in MI and TAC were involved in heart failure-related processes including cardiac muscle contraction and hypertrophic cardiomyopathy, further supporting the validity of the models. Among the DEPs, Ppme1 was upregulated in the TAC model but downregulated in the MI model, while Sec31a and Gm56451 displayed the opposite expression patterns. Among the DPPs, Ablim1 and Atp2a2 were found to be significantly upregulated in the TAC model, whereas their expression was markedly reduced in the MI model. In addition, five other DPPs, including REV_Q3TAY5, Cbx3, PITPNB, Eif4b, and A0A1Y7VP73, were elevated in the MI model but decreased in the TAC model. In conclusion, these findings suggest that MI and TAC not only share certain molecular features but also retain their unique characteristics, providing potential biomarkers and therapeutic targets.

CENPA and BRCA1 are potential biomarkers associated with immune infiltration in heart failure and pan-cancer

Heart failure (HF) and cancer are the two leading causes of death worldwide and affect one another in a bidirectional way. We aimed to identify hub therapeutic genes as potential biomarkers for the identification and treatment of HF and cancer. Gene expression data of heart samples from patients with ischemic HF (IHF) and healthy controls were retrieved from the GSE42955 and GSE57338 databases. Difference analysis and weighted gene co-expression network analysis (WGCNA) were used to identify key modules associated with IHF. The overlapping genes were subjected to gene and protein enrichment analyses to construct a protein-protein interaction (PPI) network, which was screened for hub genes among the overlapping genes. A total of eight hub genes were subjected to correlation, immune cell infiltration, and ROC analyses. Then we analyzed the roles of two significant genes in 33 tumor types to explore their potential as common targets in HF and cancer. A total of 85 genes were identified by WGCNA and differentially expressed gene (DEG) analyses. BRCA1, MED17, CENPA, RXRA, RXRB, SMARCA2, CDCA2, and PMS2 were identified as the hub genes with IHF. Finally, CENPA and BRCA1 were identified as potential common targets for IHF and cancer. These findings provide new perspectives for expanding our understanding of the etiology and underlying mechanisms of HF and cancer.

Single-cell RNA sequencing and ATAC sequencing identify novel biomarkers for bicuspid aortic valve-associated thoracic aortic aneurysm

Introduction

Bicuspid aortic valve (BAV) is the most prevalent congenital cardiovascular defect and known to cause thoracic aortic aneurysms (TAAs). To improve our understanding of BAV pathogenesis, we characterized the cellular composition of BAV tissues and identified molecular changes in each cell population.

Methods

Tissue samples from two patients with BAV and two heart transplant donors were analyzed using single-cell RNA sequencing, assay for transposase-accessible chromatin using sequencing, and weighted gene coexpression network analysis for differential gene analysis. TAA-related changes were evaluated by comparing the proportion of each cell type and gene expression profiles between TAA and control tissues. Further, by combining our single-cell RNA sequencing data with publicly available data from genome-wide association studies, we determined critical genes for BAV.

Results

We found 20 cell subpopulations in TAA tissues, including multiple subtypes of smooth muscle cells, fibroblasts, macrophages, and T lymphocytes. This result suggested that these cells play multiple functional roles in BAV development. Several differentially expressed genes, including CD9, FHL1y, HSP90AA1, GAS6, PALLD, and ACTA2, were identified.

Discussion

We believe that this comprehensive assessment of the cellular composition of TAA tissues and the insights into altered gene expression patterns can facilitate identification of novel diagnostic biomarkers and therapeutic targets for BAV-associated TAA.

Identification of diagnostic model in heart failure with myocardial fibrosis and conduction block by integrated gene co-expression network analysis

BACKGROUND

Despite the advancements in heart failure(HF) research, the early diagnosis of HF continues to be a challenging issue in clinical practice. This study aims to investigate the genes related to myocardial fibrosis and conduction block, with the goal of developing a diagnostic model for early treatment of HF in patients.

METHOD

The gene expression profiles of GSE57345, GSE16499, and GSE9128 were obtained from the Gene Expression Omnibus (GEO) database. After merging the expression profile data and adjusting for batch effects, differentially expressed genes (DEGs) associated with conduction block and myocardial fibrosis were identified. Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, and gene set enrichment analysis (GSEA) were utilized for functional enrichment analysis. A protein-protein interaction network (PPI) was constructed using a string database. Potential key genes were selected based on the bioinformatics information mentioned above. SVM and LASSO were employed to identify hub genes and construct the module associated with HF. The mRNA levels of TAC mice and external datasets (GSE141910 and GSE59867) are utilized for validating the diagnostic model. Additionally, the study explores the relationship between the diagnostic model and immune cell infiltration.

RESULTS

A total of 395 genes exhibiting differential expression were identified. Functional enrichment analysis revealed that these specific genes primarily participate in biological processes and pathways associated with the constituents of the extracellular matrix (ECM), immune system processes, and inflammatory responses. We identified a diagnostic model consisting of 16 hub genes, and its predictive performance was validated using external data sets and a transverse aortic coarctation (TAC) mouse model. In addition, we observed significant differences in mRNA expression of 7 genes in the TAC mouse model. Interestingly, our study also unveiled a correlation between these model genes and immune cell infiltration.

CONCLUSIONS

We identified sixteen key genes associated with myocardial fibrosis and conduction block, as well as diagnostic models for heart failure. Our findings have significant implications for the intensive management of individuals with potential genetic variants associated with heart failure, especially in the context of advancing cell-targeted therapy for myocardial fibrosis.

Cytotoxic CD8+ Temra cells show loss of chromatin accessibility at genes associated with T cell activation

As humans age, their memory T cell compartment expands due to the lifelong exposure to antigens. This expansion is characterized by terminally differentiated CD8+ T cells (Temra), which possess NK cell-like phenotype and are associated with chronic inflammatory conditions. Temra cells are predominantly driven by the sporadic reactivation of cytomegalovirus (CMV), yet their epigenomic patterns and cellular heterogeneity remain understudied. To address this gap, we correlated their gene expression profiles with chromatin openness and conducted single-cell transcriptome analysis, comparing them to other CD8+ subsets and CMV-responses. We confirmed that Temra cells exhibit high expression of genes associated with cytotoxicity and lower expression of costimulatory and chemokine genes. The data revealed that CMV-responsive CD8+ T cells (Tcmv) were predominantly derived from a mixed population of Temra and memory cells (Tcm/em) and shared their transcriptomic profiles. Using ATAC-seq analysis, we identified 1449 differentially accessible chromatin regions between CD8+ Temra and Tcm/em cells, of which only 127 sites gained chromatin accessibility in Temra cells. We further identified 51 gene loci, including costimulatory CD27, CD28, and ICOS genes, whose chromatin accessibility correlated with their gene expression. The differential chromatin regions Tcm/em cells were enriched in motifs that bind multiple transcriptional activators, such as Jun/Fos, NFkappaB, and STAT, whereas the open regions in Temra cells mainly contained binding sites of T-box transcription factors. Our single-cell analysis of CD8+CCR7loCD45RAhi sorted Temra population showed several subsets of Temra and NKT-like cells and CMC1+ Temra populations in older individuals that were shifted towards decreased cytotoxicity. Among CD8+CCR7loCD45RAhi sorted cells, we found a decreased proportion of IL7R+ Tcm/em-like and MAIT cells in individuals with high levels of CMV antibodies (CMVhi). These results shed new light on the molecular and cellular heterogeneity of CD8+ Temra cells and their relationship to aging and CMV infection.

Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis

BACKGROUND

Kawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood.

MATERIAL/METHODS

Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers.

RESULTS

WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells.

CONCLUSION

Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.

[High expression of Circ-PALLD in heart failure is transcriptionally regulated by the transcription factor GATA4]

OBJECTIVE

To determine the changes in the expression of circular RNA Circ-PALLD in heart failure and explore the biogenesis of Circ-PALLD.

METHODS

We analyzed second-generation sequencing results of human and murine heart failure samples to identify the candidate CircRNAs. Sanger generation sequencing was performed after PCR amplification, and the sequencing results were compared to determine the reverse splicing pattern of the corresponding CircRNAs. We further examined the expressions of CircRNAs and linear RNAs in 8 patients with heart failure admitted in our hospital, and RT-qPCR was performed to detect the expression levels of Circ-PALLD and PALLD in the failing myocardium. Bioinformatic analysis was performed to predict the transcription factors that may regulate PALLD. Small interfering RNAs (siRNAs) against GATA4 were used to determine the regulatory effect of the transcription factor GATA4 on PALLD.

RESULTS

Sanger sequencing and sequence alignment verified the reverse splicing of Circ-VWA8, Circ-VMP1, Circ-PRDM5, Circ-PLCL2, Circ-PALLD, Circ-NFATC3, Circ-MLIP, Circ-FAM208A, Circ-ANKIB1, and Circ-AGTPBP1, demonstrated their loop-forming nature and determined the exon arrangement of reverse splicing. Semi-quantitative PCR results showed that the expression levels of CircPALLD, Circ-NFATC3 and Circ-AGTPBP1 were significantly increased while the expression level of linear PALLD was significantly decreased in the myocardial tissues of heart failure patients. Bioinformatic analysis suggested that the transcription of PALLD was regulated possibly by the transcription factor GATA4. RT-qPCR showed that the expression level of Circ-PALLD was significantly increased, while PALLD expression was significantly decreased in the failing myocardium, which was consistent with the results of semi-quantitative PCR. In primary mammary rat cardiomyocytes, GATA4 knockdown resulted in lowered expressions of both Circ-PALLD and PALLD.

CONCLUSION

Circ-PALLD is highly expressed in heart failure and can be used as a novel molecular marker for chronic heart failure, and GATA4 may play important role in regulating its transcription. Circ-PALLD points a new direction for investigating the molecular mechanism of heart failure and may also serve as a potential therapeutic target for heart failure.

WGCNA and molecular docking identify hub genes for cardiac aging

Background

Cardiac aging and ageing-related cardiovascular diseases remain increase medical and social burden. Discovering the molecular mechanisms associated with cardiac aging is expected to provide new perspectives for delaying aging and related disease treatment.

Methods

The samples in GEO database were divided into older group and younger group based on age. Age-associated differentially expressed genes (DEGs) were identified by limma package. Gene modules significantly associated with age were mined using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction networks (PPI) networks were developed using genes within modules, and topological analysis on the networks was performed to identify hub genes in cardiac aging. Pearson correlation was used to analyze the association among hub genes and immune and immune-related pathways. Molecular docking of hub genes and the anti-aging drug Sirolimus was performed to explore the potential role of hub genes in treating cardiac aging.

Results

We found a generally negative correlation between age and immunity, with a significant negative correlation between age and b_cell_receptor_signaling_pathway, fc_gamma_r_mediated_phagocytosis, chemokine signaling pathway, t-cell receptor signaling pathway, toll_like_receptor_signaling_pathway, and jak_stat_signaling_pathway, respectively. Finally, 10 cardiac aging-related hub genes including LCP2, PTPRC, RAC2, CD48, CD68, CCR2, CCL2, IL10, CCL5 and IGF1 were identified. 10-hub genes were closely associated with age and immune-related pathways. There was a strong binding interaction between Sirolimus-CCR2. CCR2 may be a key target for Sirolimus in the treatment of cardiac aging.

Conclusion

The 10 hub genes may be potential therapeutic targets for cardiac aging, and our study provided new ideas for the treatment of cardiac aging.

Bioinformatics and systems biology approaches to identify molecular targeting mechanism influenced by COVID-19 on heart failure

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged as a contemporary hazard to people. It has been known that COVID-19 can both induce heart failure (HF) and raise the risk of patient mortality. However, the mechanism underlying the association between COVID-19 and HF remains unclear. The common molecular pathways between COVID-19 and HF were identified using bioinformatic and systems biology techniques. Transcriptome analysis was performed to identify differentially expressed genes (DEGs). To identify gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, common DEGs were used for enrichment analysis. The results showed that COVID-19 and HF have several common immune mechanisms, including differentiation of T helper (Th) 1, Th 2, Th 17 cells; activation of lymphocytes; and binding of major histocompatibility complex class I and II protein complexes. Furthermore, a protein-protein interaction network was constructed to identify hub genes, and immune cell infiltration analysis was performed. Six hub genes (FCGR3A, CD69, IFNG, CCR7, CCL5, and CCL4) were closely associated with COVID-19 and HF. These targets were associated with immune cells (central memory CD8 T cells, T follicular helper cells, regulatory T cells, myeloid-derived suppressor cells, plasmacytoid dendritic cells, macrophages, eosinophils, and neutrophils). Additionally, transcription factors, microRNAs, drugs, and chemicals that are closely associated with COVID-19 and HF were identified through the interaction network.

Identification of potential biomarkers of inflammation-related genes for ischemic cardiomyopathy

Objective

Inflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.

Materials and methods

The microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).

Results

64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.

Conclusion

SERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.