BackgroundIschemic cardiomyopathy (ICM) induced heart failure (HF) is one of the most common causes of death worldwide. This study aimed to find candidate genes for ICM-HF and to identify relevant biomarkers by machine learning (ML).MethodsThe expression data of ICM-HF and normal samples were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between ICM-HF and normal group were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and gene ontology (GO) annotation analysis, protein–protein interaction (PPI) network, gene pathway enrichment analysis (GSEA), and single-sample gene set enrichment analysis (ssGSEA) were performed. Weighted gene co-expression network analysis (WGCNA) was applied to screen for disease-associated modules, and relevant genes were derived using four ML algorithms. The diagnostic values of candidate genes were assessed using receiver operating characteristic (ROC) curves. The immune cell infiltration analysis was performed between the ICM-HF and normal group. Validation was performed using another gene set.ResultsA total of 313 DEGs were identified between ICM-HF and normal group of GSE57345, which were mainly enriched in biological processes and pathways related to cell cycle regulation, lipid metabolism pathways, immune response pathways, and intrinsic organelle damage regulation. GSEA results showed positive correlations with pathways such as cholesterol metabolism in the ICM-HF group compared to normal group and lipid metabolism in adipocytes. GSEA results also showed a positive correlation with pathways such as cholesterol metabolism and a negative correlation with pathways such as lipolytic presentation in adipocytes compared to normal group. Combining multiple ML and cytohubba algorithms yielded 11 relevant genes. After validation using the GSE42955 validation sets, the 7 genes obtained by the machine learning algorithm were well verified. The immune cell infiltration analysis showed significant differences in mast cells, plasma cells, naive B cells, and NK cells.ConclusionCombined analysis using WGCNA and ML identified coiled-coil-helix-coiled-coil-helix domain containing 4 (CHCHD4), transmembrane protein 53 (TMEM53), acid phosphatase 3 (ACPP), aminoadipate-semialdehyde dehydrogenase (AASDH), purinergic receptor P2Y1 (P2RY1), caspase 3 (CASP3) and aquaporin 7 (AQP7) as potential biomarkers of ICM-HF. ICM-HF may be closely related to pathways such as mitochondrial damage and disorders of lipid metabolism, while the infiltration of multiple immune cells was identified to play a critical role in the progression of the disease.

Background Hypertrophic cardiomyopathy is a commonly inherited heart disease. In addition, single coronary artery (SCA) is a rare congenital anomaly of the coronary arteries. And SCA concomitant with severe hypertrophic obstructive cardiomyopathy (HOCM) has seldom been reported in the literature. However, such cases have not been reported to be treated with the Morrow procedure. Case presentation Herein, we presented a case of a 64-year-old female diagnosed with a single left coronary artery with severe HOCM. The HOCM was treated with the Morrow procedure. The patient was discharged on the seventh postoperative day and was asymptomatic during the follow-up. Conclusion To our knowledge, this is the first study reporting a single left coronary artery with severe HOCM treated with the Morrow procedure. In addition, myocardial protection by cardioplegia antegrade perfusion was safe for the patient with SCA and HOCM.

With a high prevalence of morbidity and mortality, myocardial infarction (MI) is a prevalent heart disease. The development and outcomes of post-MI heart failure (HF) continue to be a major factor in the poor post-MI prognosis despite the extensive medical treatment for MI. There are currently few indicators that can accurately predict post-MI heart failure. In this study, we re-examined single cell RNA-seq and bulk RNA-seq datasets collected from peripheral blood samples of myocardial infarction and dataset of patients who developed heart failure or heart failure did not occur after myocardial infarction. We discovered a subtype of immune-activation B cell which is associated with the discrimination of post-MI HF and nonHF patients. Such finding was further validated in independent corhorts using PCR. By incorporating the specific marker genes of the B subtype, we developed a prediction model with 13 markers that can predict the HF risk of myocardial infarction patients and provide useful clinical experience and tools.

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Over the years, bioinformatics tools have been used to identify functional genes. In the present study, bioinformatics analyses were conducted to explore the underlying molecular mechanisms of angiogenic factors in calcific aortic valve disease (CAVD). The raw gene expression profiles were from datasets GSE153555, GSE83453, and GSE51472, and the angiogenesis-related gene set was from the Gene Set Enrichment Analysis database (GSEA). In this study, R was used to screen for differentially expressed genes (DEGs) and co-expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) Pathway enrichment analysis were performed on DEGs and validated in clinical samples. DEGs in CAVD were significantly enriched in numerous immune response pathways, inflammatory response pathways and angiogenesis-related pathways. Nine highly expressed angiogenesis-related genes were identified, of which secretogranin II (SCG2) was the most critical gene. MiRNA and transcription factors (TFs) networks were established centered on five DEGs, and zinc finger E-box binding homeobox 1 (ZEB1) was the most important transcription factor, verified by PCR, immunohistochemical staining and western blotting experiments. Overall, this study identified key genes and TFs that may be involved in the pathogenesis of CAVD and may have promising applications in the treatment of CAVD.