BACKGROUND: Pericoronary adipose tissue (PCAT) and Coronary Artery Disease Reporting and Data System (CAD-RADS) category had prognostic values for major adverse cardiovascular events (MACEs). However, little is known about the difference between CAD-RADS and PCAT computed tomography (CT) attenuation for predicting MACEs. This study was to compare the prognostic value of PCAT and CAD-RADS for MACEs in patients with acute chest pain. METHODS: Between January 2010 and December 2021, all consecutive emergency patients with acute chest pain referred for coronary computed tomography angiography were enrolled in this retrospective study. MACEs included unstable angina requiring hospitalization, coronary revascularization, nonfatal myocardial infarction, and all-cause death. Patients’ clinical characteristics, CAD-RADS, and PCAT CT attenuation were used to evaluate risk factors of MACEs using multivariable Cox regression analysis. RESULTS: A total of 1313 patients were evaluated (mean age, 57.13±12.57 years; 782 men). During a median follow-up of 38 months, 142 of the 1313 patients (10.81%) experienced MACEs. Multivariable Cox regression analysis showed that CAD-RADS categories 2, 3, 4, 5 (hazard ratio range, 2.286–8.325; all P <0.005) and right coronary artery PCAT CT attenuation (hazard ratio, 1.033; P =0.006) were independent predictors of MACEs after adjusting for clinical risk factors. The C statistics revealed that CAD-RADS improved risk stratification compared with PCAT CT alone (C-index, 0.760 versus 0.712; P =0.036). However, the benefit of right coronary artery PCAT CT attenuation combined with CAD-RADS was not significant compared with CAD-RADS alone (0.777 versus 0.760; P =0.129). CONCLUSIONS: Right coronary artery PCAT CT attenuation and CAD-RADS were independent predictors of MACEs. However, no incremental prognostic value of right coronary artery PCAT CT attenuation beyond CAD-RADS was detected for MACEs in patients with acute chest pain.

Introduction: Autophagy refers to the process of breaking down and recycling damaged or unnecessary components within a cell to maintain cellular homeostasis. Heart failure (HF) is a severe medical condition that poses a serious threat to the patient's life. Autophagy is known to play a pivotal role in the pathogenesis of HF. However, our understanding of the specific mechanisms involved remains incomplete. Here, we identify autophagy-related genes (ARGs) associated with HF, which we believe will contribute to further comprehending the pathogenesis of HF.Methods: By searching the GEO (Gene Expression Omnibus) database, we found the GSE57338 dataset, which was related to HF. ARGs were obtained from the HADb and HAMdb databases. Annotation of GO and enrichment analysis of KEGG pathway were carried out on the differentially expressed ARGs (AR-DEGs). We employed machine learning algorithms to conduct a thorough screening of significant genes and validated these genes by analyzing external dataset GSE76701 and conducting mouse models experimentation. At last, immune infiltration analysis was conducted, target drugs were screened and a TF regulatory network was constructed.Results: Through processing the dataset with R language, we obtained a total of 442 DEGs. Additionally, we retrieved 803 ARGs from the database. The intersection of these two sets resulted in 15 AR-DEGs. Upon performing functional enrichment analysis, it was discovered that these genes exhibited significant enrichment in domains related to “regulation of cell growth”, “icosatetraenoic acid binding”, and “IL-17 signaling pathway”. After screening and verification, we ultimately identified 4 key genes. Finally, an analysis of immune infiltration illustrated significant discrepancies in 16 distinct types of immune cells between the HF and control group and up to 194 potential drugs and 16 TFs were identified based on the key genes.Discussion: In this study, TPCN1, MAP2K1, S100A9, and CD38 were considered as key autophagy-related genes in HF. With these relevant data, further exploration of the molecular mechanisms of autophagy in HF can be carried out.

The regulator of calcineurin 1 (RCAN1) is expressed in multiple organs, including the heart, liver, brain, and kidney, and is closely linked to the pathogenesis of cardiovascular diseases, Down syndrome, and Alzheimer’s disease. It is also implicated in the development of various organ tumors; however, its potential role in hepatocellular carcinoma (HCC) remains poorly understood. Therefore, the objective of this study was to investigate the potential mechanisms of RCAN1 in HCC through bioinformatics analysis. We conducted a joint analysis based on the NCBI and TCGA databases, integrating both bulk transcriptome and single-cell analyses to examine the principal biological functions of RCAN1 in HCC, as well as its roles related to phenotype, metabolism, and cell communication. Subsequently, an RCAN1-overexpressing cell line was established, and the effects of RCAN1 on tumor cells were validated through in vitro experiments. Moreover, we endeavored to identify potential related drugs using molecular docking and molecular dynamics simulations. The expression of RCAN1 was found to be downregulated in 19 types of cancer tissues and upregulated in 11 types of cancer tissues. Higher levels of RCAN1 expression were associated with improved patient survival. RCAN1 was predominantly expressed in hepatocytes, macrophages, endothelial cells, and monocytes, and its high expression not only closely correlated with the distribution of cells related to the HCC phenotype but also with the distribution of HCC cells themselves. Additionally, Rcan1 may directly or indirectly participate in metabolic pathways such as alanine, aspartate, and glutamate metabolism, as well as butanoate metabolism, thereby influencing tumor cell proliferation and migration. In vitro experiments confirmed that RCAN1 overexpression promoted apoptosis while inhibiting proliferation and invasion of HCC cells. Through molecular docking of 1615 drugs, we screened brompheniramine as a potential target drug and verified our results by molecular dynamics. In this study, we revealed the relationship between RCAN1 and HCC through bioinformatics methods, verified that RCAN1 can affect the progress of the disease through experiments, and finally identified potential therapeutic drugs through drug molecular docking and molecular dynamics.