Background: Heart failure (HF) remains a huge medical burden worldwide. Pathological cardiac hypertrophy is one of the most significant phenotypes of HF. Several studies have reported that the TGF-β pathway plays a double-sided role in HF. Therefore, TGF-β–related genes (TRGs) may be potential therapeutic targets for cardiac hypertrophy and HF. However, the roles of TRGs in HF at the single-cell level remain unclear. Method: In this study, to analyze the expression pattern of TRGs during the progress of cardiac hypertrophy and HF, we used three public single-cell RNA sequencing datasets for HF (GSE161470, GSE145154, and GSE161153), one HF transcriptome data (GSE57338), and one hypertrophic cardiomyopathy transcriptome data (GSE141910). Weighted gene co-expression network analysis (WGCNA), functional enrichment analysis and machine learning algorithms were used to filter hub genes. Transverse aortic constriction mice model, CCK-8, wound healing assay, quantitative real-time PCR and western blotting were used to validate bioinformatics results. Results: We observed that cardiac fibroblasts (CFs) and endothelial cells showed high TGF-β activity during the progress of HF. Three modules (royalblue, brown4, and darkturquoize) were identified to be significantly associated with TRGs in HF. Six hub genes (TANC2, ADAMTS2, DYNLL1, MRC2, EGR1, and OTUD1) showed anomaly trend in cardiac hypertrophy. We further validated the regulation of the TGF-β-MYC-ADAMTS2 axis on CFs activation in vitro. Conclusions: This study identified six hub genes (TANC2, ADAMTS2, DYNLL1, MRC2, EGR1, and OTUD1) by integrating scRNA and transcriptome data. These six hub genes might be therapeutic targets for cardiac hypertrophy and HF.

Background: Pathological cardiac hypertrophy, a condition that contributes to heart failure, is characterized by its intricate pathogenesis. The meticulous regulation of protein function, localization, and degradation is a crucial role played by deubiquitinating enzymes in cardiac pathophysiology. This study clarifies the participation and molecular mechanism of OTUD1 (OTU Deubiquitinase 1) in pathological cardiac hypertrophy.

Background:We sought to develop and validate a logistic model and a simple score system for prediction of significant coronary artery disease (CAD) in patients undergoing operations for rheumatic aortic valve disease.Hypothesis:The simple score model we established based on the logistic model was efficient and practical.Methods:A total of 669 rheumatic patients (mean age 51 ± 9 years), who underwent routine coronary angiography (CAG) before aortic valve surgery between 1998 and 2010, were analyzed. A bootstrap‐validated logistic regression model on the basis of clinical risk factors was developed to identify low‐risk (≤5%) patients, from which an additive model was derived. Receiver operating characteristic (ROC) curves were used to compare discrimination, and precision was quantified by the Hosmer‐Lemeshow statistic. Significant coronary atherosclerosis was defined as 50% or more luminal narrowing in 1 or more major epicardial vessels determined by means of coronary angiography.Results:Eighty‐eight (13.2%) patients had significant coronary atherosclerosis. Independent predictors of CAD include age, angina, diabetes mellitus, and hypertension. A total of 325 patients were designated as low risk according to the bootstrap logistic regression and additive models. Of these patients, only 4 (1.2%) had single‐vessel disease, and none had high‐risk CAD (ie, left main trunk, proximal left anterior descending, or multivessel disease). The bootstrap logistic regression and additive models show good discrimination, with an area under the ROC curve of 0.948 and 0.942, respectively.Conclusions:Our logistic regression model can reliably estimate the prevalence of significant CAD in rheumatic patients undergoing aortic valve operation, while the additive simple score system could reliably identify the low‐risk patients in whom routine preoperative angiography might be safely avoided. Clin. Cardiol. 2012 doi: 10.1002/clc.22033The authors have no funding, financial relationships, or conflicts of interest to disclose.Dr. Guan‐xin Zhang and Dr. Bai‐ling Li have contributed equally to the work. Dr. Lin‐han is co‐corresponding author (sh_hanlin@hotmail.com).

Background: Accumulative evidence has shown that systemic lupus erythematosus (SLE) increases the risk of various cardiovascular diseases including atrial fibrillation (AF). The study aimed to screen potential key genes underlying co-pathogenesis between SLE and AF, and to discover therapeutic targets for AF. Methods: Differentially expressed genes (DEGs) were identified, and co-expressed gene modules were obtained through weighted gene co-expression network analysis (WGCNA) based on the AF and SLE expression profiles from the GEO database. Subsequently, machine learning algorithms including LASSO regression and support vector machine (SVM) method were employed to identify the candidate therapeutic target for SLE-related AF. Furthermore, the therapeutic role of TMEM45A was validated both in vivo and vitro. Results: Totally, 26 DEGs were identified in SLE and AF. The PPI network combined with WGCNA identified 51 key genes in SLE and AF. Ultimately, Machine learning-based methods screened three hub genes in SLE combined with AF, including TMEM45A, ITGB2 and NFKBIA. The cMAP analysis exposed KI-8751 and YM-155 as potential drugs for AF treatment. Regarding TMEM45A, the aberrant expression was validated in blood of SLE patients. Additionally, TMEM45A expression was up-regulated in the atrial tissue of patients with AF. Furthermore, TMEM45A knockdown alleviated AF occurrence and atrial fibrosis in vivo and Ang II-induced NRCFs fibrosis in vitro. Conclusion: The crosstalk genes underlying co-pathogenesis between SLE and AF were unraveled. Furthermore, the pro-fibrotic role of TMEM45A was validated in vivo and vitro, highlighting its potential as a therapeutic target for AF.

Background: The accumulation of epicardial adipose tissue (EAT) is associated with cardiometabolic risks and adverse outcomes in heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF). This study aims to identify genes secreted by EAT that contribute to the shared pathogenesis of HFpEF and AF, potentially serving as biomarkers for diagnosis. Methods: Data sets from the GEO database for HFpEF-EAT, HFpEF-heart tissue, AF-EAT, AF-PBMC, and AF-heart tissue were analyzed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) identified key genes in EAT linked to HFpEF and AF. Functional enrichment and connectivity map analyses explored common pathways and therapeutic targets. Machine learning techniques, including LASSO regression, random forest, and support vector machine, identified shared biomarkers. CIBERSORT was used to assess immune cell infiltration, while gene set enrichment analysis identified pathways related to hub genes. Receiver operating characteristic (ROC) curve analysis and experimental validation assessed the bioinformatics findings. Results: In the HFpEF dataset, 200 key genes were identified by intersecting HFpEF-EAT, HFpEF-heart tissue, WGCNA analyses, and secretory proteins. For AF, 232 related genes were identified through similar methods. Thirteen genes were common between HFpEF and AF, with two central genes, ITPKA and WNT9B, selected as potential biomarkers through machine learning and ROC analysis. Immune cell infiltration and gene set enrichment analysis revealed pathways related to ITPKA/WNT9B. These patterns were confirmed in human samples. Conclusion: This study identified EAT-derived secretory proteins as potential biomarkers for HFpEF and AF, with ITPKA and WNT9B as central hub genes. These findings offer insights into potential diagnostic and therapeutic strategies for HFpEF and AF.