Abstract Background: The strain parameters of Real-Time Three-Dimensional Spot Tracking Echocardiography (RT3D-STE) are GLS, GAS, GRS, and GCS, while each index can significantly diagnose Acute Myocardial Infarction (AMI) patients, but none of them can distinguish between NSTEMI and STEMI. MicroRNAs (miRNAs) play essential roles in Acute Myocardial Infarction (AMI), but little is known about the value of exosome miRNA combined with RealTime Three-Dimensional Spot Tracking Echocardiography (RT3D-STE) between ST-segment Elevation Myocardial Infarction (STEMI) and Non-ST-segment Elevation Myocardial Infarction (NSTEMI). Aim: To estimate the exosomal miRNAs related to strain parameters of RT3D-STE as biomarkers for early detection of STEMI and NSTEMI. Methods: The present study collected plasma samples from thirty-four (34) patients with AMI (including STEMI and NSTEMI) and employed high-throughput sequence technology and real-time quantitative polymerase chain reaction (RT-qPCR) to identify the differentially expressed miRNAs. The Pearson correlation coefficient is used to measure the strength of a linear association between differentially expressed miRNAs and strain parameters of RT3D-STE. Results: Twenty-eight (28) differentially expressed exosomal miRNAs were universally identified between STEMI, NSTEM, and normal groups. Among them, there are 10 miRNAs (miR-152-5p, miR-3681-5p, miR-193a-5p, miR-193b-5p miR-345-5p, miR-125a-5p, miR-365a-3p, miR-4520-2-3p, hsa-miR-193b-3p and hsa-miR-5579-5p) with a Pearson correlation greater than 0.6 with RT3D-STE strain parameters. Especially, miR-152-5p and miR-3681-5p showed the most significant correlation with RT3D-STE strain parameters. Target genes of these 10 miRNAs are analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment, and they were found to be mainly involved in the cellular metabolism processes and HIF-1 signaling pathway. RT-qPCR verified the significant differential expression of miR-152-5p and miR-3681-5p between STEMI and NSTEM groups. Conclusion: RT3D-STE and exosome miRNAs can be used as a hierarchical diagnostic system in AMI. If the RT3D-STE is abnormal, the exosome miRNAs can be detected again to obtain more detailed and accurate diagnostic results between STEMI and NSTEM groups. Exosomal miR-152-5p and miR-3681-5p may serve as potential biomarkers for ST-segment elevation myocardial infarction. HIGHLIGHTS RT3D-STE and exosome miRNAs can be used as a hierarchical diagnostic system in AMI. Exosomal miR-152-5p and miR-3681-5p function as potential biomarkers for ST-segment elevation myocardial infarction.

Abstract High heterogeneity of lung adenocarcinoma (LUAD) is a major clinical challenge. This study aims to characterize the molecular features of LUAD through classification based on metabolism-related genes. A total of 500 LUAD samples from The Cancer Genome Atlas (TCGA) and 612 from Gene Expression Omnibus (GEO) were integrated with 2,753 metabolism-related genes to determine the molecular classification. Systematic bioinformatics analysis was used to conduct correlation analysis between metabolism-related classification and molecular characteristics of LUAD. LUAD patients were divided into three molecular clusters (C1-C3). Survival analysis revealed that C1 and C2 showed good and poor prognoses, respectively. Associational analysis of classification and molecular characteristics revealed that C1 was associated with low pathological stage, metabolic pathways, high metabolic process, active immune process and checkpoint, sensitive drug response, as well as a low genetic mutation. Nevertheless, C2 was associated with high pathological stage, carcinogenic pathways, low metabolic process, inactive immune signatures, resistant drug response, and frequent genetic mutation. Eventually, a classifier with 60 metabolic genes was constructed, confirming the robustness of molecular classification on LUAD. Our findings promote the understanding of LUAD molecular characteristics, and the research data may be used for providing information be helpful for clinical diagnosis and treatment. (LUAD challenge metabolismrelated metabolism related 50 TCGA (TCGA 61 GEO (GEO 2753 2 753 2,75 C1C3. C1C3 CC C3 . C (C1-C3) prognoses respectively stage pathways checkpoint response mutation Nevertheless signatures Eventually 6 constructed treatment 5 275 75 2,7 C1C (C1-C3 27 7 2, (C1-C