Low expression of PIK3C2A gene: A potential biomarker to predict the risk of acute myocardial infarction

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

Myocardial infarction complexity: A multi-omics approach

Myocardial infarction (MI), a prevalent cardiovascular disease, is fundamentally precipitated by thrombus formation in the coronary arteries, which subsequently decreases myocardial perfusion and leads to cellular necrosis. The intricacy of MI pathogenesis necessitates extensive research to elucidate the disease's root cause, thereby addressing the limitations present in its diagnosis and prognosis. With the continuous advancement of genomics technology, genomics, proteomics, metabolomics and transcriptomics are widely used in the study of MI, which provides an excellent way to identify new biomarkers that elucidate the complex mechanisms of MI. This paper provides a detailed review of various genomics studies of MI, including genomics, proteomics, transcriptomics, metabolomics and multi-omics studies. The metabolites and proteins involved in the pathogenesis of MI are investigated through integrated protein-protein interactions and multi-omics analysis by STRING and Metascape platforms. In conclusion, the future of omics research in myocardial infarction offers significant promise.

Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases

Cardiovascular disease (CVD) is caused by a multitude of complex and largely heritable conditions. Identifying key genes and understanding their susceptibility to CVD in the human genome can assist in early diagnosis and personalized treatment of the relevant patients. Heart failure (HF) is among those CVD phenotypes that has a high rate of mortality. In this study, we investigated genes primarily associated with HF and other CVDs. Achieving the goals of this study, we built a cohort of thirty-five consented patients, and sequenced their serum-based samples. We have generated and processed whole genome sequence (WGS) data, and performed functional mutation, splice, variant distribution, and divergence analysis to understand the relationships between each mutation type and its impact. Our variant and prevalence analysis found FLNA, CST3, LGALS3, and HBA1 linked to many enrichment pathways. Functional mutation analysis uncovered ACE, MME, LGALS3, NR3C2, PIK3C2A, CALD1, TEK, and TRPV1 to be notable and potentially significant genes. We discovered intron, 5' Flank, 3' UTR, and 3' Flank mutations to be the most common among HF and other CVD genes. Missense mutations were less common among HF and other CVD genes but had more of a functional impact. We reported HBA1, FADD, NPPC, ADRB2, ADBR1, MYH6, and PLN to be consequential based on our divergence analysis.

Role of serum CAP1 protein in the diagnosis of patients with first-time acute myocardial infarction

The dysregulation of adenylate cyclase-associated protein 1 (CAP1) is associated with a variety of inflammatory conditions. Here, we aimed to assess the role of serum CAP1 protein in predicting acute myocardial infarction (AMI), and to explore its effect and mechanism in vascular endothelial cells injury. ELISA was utilized to detected CAP1 protein expression in serum from 70 patients with first-time AMI at 0, 6, 12, 24, 48 hours and 7 days of the onset of chest pain. Receiver operating characteristic (ROC) curve analysis was administered to analyze the diagnostic power of CAP1 for AMI. The CCK-8 and 5-BrdU assays were applied to measure cell proliferation and inflammation in a model of oxidized low-density lipoprotein (ox-LDL) induced human umbilical vein endothelial cells (HUVEC). Luciferase reporter gene assay and Western blotting were used to assess the activity of NF-κB pathway. Results showed that serum CAP1 protein expression was upregulated in patients with first-time AMI, its expression was highest at 12 hours of the onset of chest pain. CAP1 protein was positively associated with the levels of cTnI and ox-LDL. CAP1 showed a relatively high diagnostic accuracy in patients with first-time AMI compared with cTnI, and CAP1 combined with cTnI had superior diagnostic value than CAP1 and cTnI alone. The expression of CAP1 protein was increased in supernatants of ox-LDL induced HUVEC in a dose- and time-dependent manner. CAP1 inhibited cell proliferation but promoted inflammation, and induced the activation of NF-κB pathway in vitro. To sum up, increased serum CAP1 expression might serve as a novel diagnostic biomarker for patients with first-time AMI, the mechanism might be related to its induction of NF-κB pathway activation causing abnormal proliferation and inflammation and thus mediating vascular endothelial cell injury.

Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine

Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.

Single-nucleus RNA sequencing in ischemic cardiomyopathy reveals common transcriptional profile underlying end-stage heart failure

Ischemic cardiomyopathy (ICM) is the leading cause of heart failure worldwide, yet the cellular and molecular signature of this disease is largely unclear. Using single-nucleus RNA sequencing (snRNA-seq) and integrated computational analyses, we profile the transcriptomes of over 99,000 human cardiac nuclei from the non-infarct region of the left ventricle of 7 ICM transplant recipients and 8 non-failing (NF) controls. We find the cellular composition of the ischemic heart is significantly altered, with decreased cardiomyocytes and increased proportions of lymphatic, angiogenic, and arterial endothelial cells in patients with ICM. We show that there is increased LAMININ signaling from endothelial cells to other cell types in ICM compared with NF. Finally, we find that the transcriptional changes that occur in ICM are similar to those in hypertrophic and dilated cardiomyopathies and that the mining of these combined datasets can identify druggable genes that could be used to target end-stage heart failure.

Various Expressions of PIK3C2A and TXNIP Genes and Their Potential Role as Independent Risk Factors for Chronic Stable Angina and Acute Coronary Syndrome

BACKGROUND AND AIM

Genetic factors play a significant role in the onset and progression of coronary artery disease (CAD). PIK3C2A may contribute to the development of acute coronary syndrome (ACS) by affecting blood glucose levels and oxidative stress. The expression levels of TXNIP were significantly higher in patients with unstable angina pectoris. However, the situation is different in ACS. In the current study, we aim to investigate the role of PIK3C2A and TXNIP as independent risk factors for chronic stable angina (CSA) and ACS.

SUBJECTS AND METHODS

This study involved 215 subjects (60 patients with CSA, 55 patients with ACS, and 100 controls). All subjects were exposed for assaying gene expressions of PIK3C2A and TXNIP by quantitative real-time polymerase chain reaction.

RESULTS

It was found that TXNIP was upregulated, whereas PIK3C2A was downregulated in patients with CAD compared to the control group. PIK3C2A was significantly downregulated in patients with ACS compared to that in patients with CSA (p < 0.001), but TXNIP was not (p = 0.7). TXNIP was significantly upregulated in STEMI-ACS patients compared to CSA (p = 0.045) and NSTEMI ACS (p = 0.046), among non-diabetic (p = 0.023) smokers (p = 0.036) with hypertension (p = 0.005) and hypercholesterolemia (p = 0.001). ROC (receiver operating characteristic) curve analysis revealed that PIK3C2A (0.981; p < 0.001; 98.18) was the most sensitive mRNA for discriminating ACS from control, followed by TXNIP (0.775; p < 0.001; 70.91). However, for discriminating ACS from CSA combined mRNAs, (PIK3C2A + TXNIP) (0.893; p < 0.001; 98.18) and PIK3C2A (0.892; p < 0.001; 81.82) are promising biomarkers. On the other hand, the most sensitive mRNA for differentiating CSA from control is mRNAs (PIK3C2A + TXNIP) (0.963; p < 0.001; 95), then TXINP (81.3; p < 0.001; 93.33), and finally, PIK3C2A (0.782; p < 0.001; 81.67). In the multivariate regression model, PIK3C2A ((p = 0.002), 0.118 (0.031-0.445)) and smoking status ((p = 0.034); 0.151 (0.026-0.866)) were independent variables for ACS. Moreover, PIK3C2A ((p < 0.013); 0.706 (0.614-0.812)), Hb ((p = 0.013); 0.525 (0.317-0.871)), and total cholesterol ((p = 0.04); 0.865 (0.784-0.955)) were significantly (p < 0.05) and independently related to the prognosis of CSA. Furthermore, PIK3C2A ((p = 0.002), 0.923 (0.877-0.971)), TXNIP ((p = 0.001); 2.809 (1.558-5.064)) the body weight ((p = 0.033); 1.254 (1.018-1.544)) were independently associated with CSA.

CONCLUSIONS

Our study concluded that the dysregulated mRNA PIK3C2A and TXNIP gene expressions may be useful in diagnosis of CAD and prediction of ACS development.

Expression of Membrane Bound O-Acyltransferase Domain Containing 7 after Myocardial Infarction and its Role in Lipid Metabolism in vitro

Background: Previous microarray analysis on peripheral blood leukocytes from three patients with acute myocardial infarction (AMI) showed that elevated expression of membrane bound o-acyltransferase domain containing 7(MBOAT7) relative to control. To further verify these findings, we investigated more patients and explored the possible mechanisms in vitro. Objective: To study alterations in MBOAT7 expression in leukocytes after AMI, and to explore the relationship between MBOAT7 and lipid metabolism pathways in hepatocytes in vitro. Methods: Ninety patients with AMI and 90 controls were recruited from the Han population in Northeast China. RT-fluorescent PCR was used to measure MBOAT7 mRNA levels. MBOAT7 interference and overexpression vectors were constructed and transfected into L-02 hepatocytes and expression was examined by RT-qPCR and western blotting. The expression of SCAP, LDLR, HMGCR, ACAT1, ABCA1, SREBP1, ACC, FAS, SCD, and PPARγ in the lipid metabolism pathway were investigated by RT-qPCR. Triglyceride and cholesterol levels were measured by ELISA. Results: It was found that MBOAT7 mRNA levels were elevated in the leukocytes of patients with AMI. Hepatocytes were successfully transfected, shown by attenuated MBOAT7 mRNA levels in the silenced group (0.41±0.04 vs 1.01±0.07 for control, P=0.0019 <0.01) and raised levels in the overexpressing cells (23.29±0.39 vs 1.00±0.06 for control, P <0.0001). These results were confirmed by western blotting. Expression of the lipid metabolism-related genes was altered in response to MBOAT7 expression. Triglyceride levels increased after MBOAT7 silencing (118.40 ± 2.26 vs 70.54 ± 0.25 for control, P<0.0001), as did those of cholesterol (628.30 ± 8.89 vs 544.70 ± 11.04, P = 0.0041) but were not altered on MBOAT7 overexpression. Conclusion: MBOAT7 did not affect the metabolism of triglycerides in hepatocytes through fatty acid synthesis and decomposition pathways. The MBOAT7 level in the peripheral blood can be used as a marker for acute myocardial infarction but cannot be used as a single therapeutic target to regulate lipid metabolism.

Low ZCCHC9 Gene Expression in Peripheral Blood May Be an Acute Myocardial Infarction Genetic Molecular Marker in Patients with Stable Coronary Atherosclerotic Disease

Purpose

Patients and Methods

Results

Conclusion

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

INTRODUCTION

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules.

METHODS

The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed.

RESULTS

A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene-miRNA regulatory network and target gene-TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed.

CONCLUSION

The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

Diagnostic and Prognostic Biomarkers for Myocardial Infarction

The incidence of myocardial infarction (MI) increases every year worldwide. Better diagnostic and prognostic biomarkers for clinical applications are the consistent pursuit of MI research. In addition to electrocardiogram, echocardiography, coronary angiography, etc., circulating biomarkers are essential for the diagnosis, prognosis, and treatment effect monitoring of MI patients. In this review, we assessed both strength and weakness of MI circulating biomarkers including: (1) originated from damaged myocardial tissues including current golden standard cardiac troponin, (2) released from non-myocardial tissues due to MI-induced systems reactions, and (3) preexisted in blood circulation before the occurrence of MI event. We also summarized newly reported MI biomarkers. We proposed that the biomarkers preexisting in blood circulation before MI incidents should be emphasized in research and development for MI prevention in near future.

Low G0S2 gene expression levels in peripheral blood may be a genetic marker of acute myocardial infarction in patients with stable coronary atherosclerotic disease: A retrospective clinical study

BACKGROUND

The G0/G1 switch 2 (G0S2) gene is closely related to lipolysis, cell proliferation, apoptosis, oxidative phosphorylation, and the development of a variety of tumors. The aim of the present study was to expand the sample size to confirm the relationship between the expression of the G0S2 gene in peripheral blood and acute myocardial infarction (AMI) based on previous gene chip results.

METHODS

Three hundred patients were initially selected, of which 133 were excluded in accordance with the exclusion criteria. Peripheral blood leukocytes were collected from 92 patients with AMI and 75 patients with stable coronary atherosclerotic disease (CAD). mRNA expression levels of G0S2 in peripheral blood leukocytes was measured by RT-PCR, and protein expression levels by Western blot analysis. The results of these assays in the 2 groups were compared.

RESULTS

mRNA expression levels of GOS2 in the peripheral blood leukocytes of patients with AMI were 0.41-fold lower than those of patients with stable CAD (P < .05), and GOS2 protein expression levels were 0.45-fold lower. Multivariate logistic regression analysis indicated that low expression levels of the G0S2 gene increased the risk of AMI by 2.08-fold in stable CAD patients.

CONCLUSIONS

G0S2 gene expression in the peripheral blood leukocytes of AMI patients was lower than that of stable CAD patients. Low G0S2 gene expression in peripheral blood leukocytes is an independent risk factor for AMI in stable CAD patients.

The Biomarkers for Acute Myocardial Infarction and Heart Failure

The use of a large number of cardiovascular biomarkers, meant to complement the use of the electrocardiogram, echocardiography cardiac imaging, and clinical symptom assessment, has become a routine in clinical diagnosis, differential diagnosis, risk stratification, and prognosis and guides the management of patients with suspected cardiovascular diseases. There is a broad consensus that cardiac troponin and natriuretic peptides are the preferred biomarkers in clinical practice for the diagnosis of the acute coronary syndrome and heart failure, respectively, while the roles and possible clinical applications of several other potential biomarkers are still under study. This review mainly focuses on the recent studies of the roles and clinical applications of troponin and natriuretic peptides, which seem to be the best-validated markers in distinguishing and predicting the future cardiac events of patients with suspected cardiovascular diseases. Additionally, the review briefly discusses some of the large number of potential markers that may play a more prominent role in the future.