Identification and subtype analysis of biomarkers associated with the solute carrier family in acute myocardial infarction.

The dysregulation of some solute carrier (SLC) proteins has been linked to a variety of diseases, including diabetes and chronic kidney disease. However, SLC-related genes (SLCs) has not been extensively studied in acute myocardial infarction (AMI). The GSE66360 and GSE60993 datasets, and SLCs geneset were enrolled in this study. Differentially expressed SLCs (DE-SLCs) were screened by overlapping DEGs between the AMI and control groups and SLCs. Next, functional enrichment analysis was carried out to research the function of DE-SLCs. Consistent clustering of samples from the GSE66360 dataset was accomplished based on DE-SLCs selected. Next, the gene set enrichment analysis (GSEA) was performed on the DEGs-cluster (cluster 1 vs cluster 2). Three machine learning models were performed to obtain key genes. Subsequently, biomarkers were obtained through receiver operating characteristic (ROC) curves and expression analysis. Then, the immune infiltration analysis was performed. Afterwards, single-gene GSEA was carried out, and the biomarker-drug network was established. Finally, quantitative real-time fluorescence PCR (qRT-PCR) was performed to verify the expression levels of biomarkers. In this study, 13 DE-SLCs were filtered by overlapping 366 SLCs and 448 DEGs. The functional enrichment results indicated that the genes were implicated with amino acid transport and TNF signaling pathway. After the consistency clustering analysis, the samples were classified into cluster 1 and cluster 2 subtypes. The functional enrichment results showed that DEGs-cluster were implicated with chemokine signaling pathway and so on. Further, SLC11A1 and SLC2A3 were identified as SLC-related biomarkers, which had the strongest negative relationship with resting memory CD4 T cells and the strongest positive association with activated mast cells. In addition, the single-gene GSEA results showed that cytosolic ribosome was enriched by the biomarkers. Five drugs targeting SLC2A3 were predicted as well. Lastly, the experimental results showed that the biomarkers expression trends were consistent with public database. In this study, 2 SLC-related biomarkers (SLC11A1 and SLC2A3) were screened and drug predictions were carried out to explore the prediction and treatment of AMI.

Development and validation of a multivariate model for predicting heart failure hospitalization and mortality in patients receiving maintenance hemodialysis.

BACKGROUND: Heart failure (HF) in patients undergoing maintenance hemodialysis (MHD) increases their hospitalization rates, mortality, and economic burden significantly. We aimed to develop and validate a predictive model utilizing contemporary deep phenotyping for individual risk assessment of all-cause mortality or HF hospitalization in patients on MHD. MATERIALS AND METHODS: A retrospective review was conducted from January 2017 to October 2022, including 348 patients receiving MHD from four centers. The variables were adjusted by Cox regression analysis, and the clinical prediction model was constructed and verified. RESULTS: The median follow-up durations were 14 months (interquartile range [IQR] 9-21) for the modeling set and 14 months (9-20) for the validation set. The composite outcome occurred in 72 (29.63%) of 243 patients in the modeling set and 39 (37.14%) of 105 patients in the validation set. The model predictors included age, albumin, history of cerebral hemorrhage, use of angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers/"sacubitril/valsartan", left ventricular ejection fraction, urea reduction ratio, N-terminal prohormone of brain natriuretic peptide, and right atrial size. The C-index was 0.834 (95% CI 0.784-0.883) for the modeling set and 0.853 (0.798, 0.908) for the validation set. The model exhibited excellent calibration across the complete risk profile, and the decision curve analysis (DCA) suggested its ability to maximize patient benefits. CONCLUSION: The developed prediction model offered an accurate and personalized assessment of HF hospitalization risk and all-cause mortality in patients with MHD. It can be employed to identify high-risk patients and guide treatment and follow-up.