Single-Cell Sequencing Revealed Pivotal Genes Related to Prognosis of Myocardial Infarction Patients

The roles of Th cells in myocardial infarction

Myocardial infarction, commonly known as a heart attack, is a serious condition caused by the abrupt stoppage of blood flow to a part of the heart, leading to tissue damage. A significant aspect of this condition is reperfusion injury, which occurs when blood flow is restored but exacerbates the damage. This review first addresses the role of the innate immune system, including neutrophils and macrophages, in the cascade of events leading to myocardial infarction and reperfusion injury. It then shifts focus to the critical involvement of CD4+ T helper cells in these processes. These cells, pivotal in regulating the immune response and tissue recovery, include various subpopulations such as Th1, Th2, Th9, Th17, and Th22, each playing a unique role in the pathophysiology of myocardial infarction and reperfusion injury. These subpopulations contribute to the injury process through diverse mechanisms, with cytokines such as IFN-γ and IL-4 influencing the balance between tissue repair and injury exacerbation. Understanding the interplay between the innate immune system and CD4+ T helper cells, along with their cytokines, is crucial for developing targeted therapies to mitigate myocardial infarction and reperfusion injury, ultimately improving outcomes for cardiac patients.

Retracted: Single-Cell Sequencing Revealed Pivotal Genes Related to Prognosis of Myocardial Infarction Patients

[This retracts the article DOI: 10.1155/2022/6534126.].

Post-myocardial infarction fibrosis: Pathophysiology, examination, and intervention

Cardiac fibrosis plays an indispensable role in cardiac tissue homeostasis and repair after myocardial infarction (MI). The cardiac fibroblast-to-myofibroblast differentiation and extracellular matrix collagen deposition are the hallmarks of cardiac fibrosis, which are modulated by multiple signaling pathways and various types of cells in time-dependent manners. Our understanding of the development of cardiac fibrosis after MI has evolved in basic and clinical researches, and the regulation of fibrotic remodeling may facilitate novel diagnostic and therapeutic strategies, and finally improve outcomes. Here, we aim to elaborate pathophysiology, examination and intervention of cardiac fibrosis after MI.

Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.

Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction

Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases with high morbidity and mortality. Numerous studies have indicated that S100A12 may has an essential role in the occurrence and development of AMI, and in-depth studies are currently lacking. The purpose of this study is to investigate the effect of S100A12 on inflammation and oxidative stress and to determine its clinical applicability in AMI. Here, AMI datasets used to explore the expression pattern of S100A12 in AMI were derived from the Gene Expression Omnibus (GEO) database. The pooled standard average deviation (SMD) was calculated to further determine S100A12 expression. The overlapping differentially expressed genes (DEGs) contained in all included datasets were recognized by the GEO2R tool. Then, functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were carried out to determine the molecular function of overlapping DEGs. Gene set enrichment analysis (GSEA) was conducted to determine unrevealed mechanisms of S100A12. Summary receiver operating characteristic (SROC) curve analysis and receiver operating characteristic (ROC) curve analysis were carried out to identify the diagnostic capabilities of S100A12. Moreover, we screened miRNAs targeting S100A12 using three online databases (miRWalk, TargetScan, and miRDB). In addition, by comprehensively using enzyme-linked immunosorbent assay (ELISA), real-time quantitative PCR (RT-qPCR), Western blotting (WB) methods, etc., we used the AC16 cells to validate the expression and underlying mechanism of S100A12. In our study, five datasets related to AMI, GSE24519, GSE60993, GSE66360, GSE97320, and GSE48060 were included; 412 overlapping DEGs were identified. Protein-protein interaction (PPI) network and functional analyses showed that S100A12 was a pivotal gene related to inflammation and oxidative stress. Then, S100A12 overexpression was identified based on the included datasets. The pooled standard average deviation (SMD) also showed that S100A12 was upregulated in AMI (SMD = 1.36, 95% CI: 0.70-2.03, p = 0.024). The SROC curve analysis result suggested that S100A12 had remarkable diagnostic ability in AMI (AUC = 0.90, 95% CI: 0.87-0.92). And nine miRNAs targeting S100A12 were also identified. Additionally, the overexpression of S100A12 was further confirmed that it maybe promote inflammation and oxidative stress in AMI through comprehensive in vitro experiments. In summary, our study suggests that overexpressed S100A12 may be a latent diagnostic biomarker and therapeutic target of AMI that induces excessive inflammation and oxidative stress. Nine miRNAs targeting S100A12 may play a crucial role in AMI, but further studies are still needed. Our work provides a positive inspiration for the in-depth study of S100A12 in AMI.