Dataset Information

Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach.

ABSTRACT: Acute myocardial infarction (AMI) is a severe disease with elevated morbidity and mortality rate worldwide. This is attributed to great losses of cardiomyocytes, which can trigger the alteration of gene expression patterns. Although several attempts have been made to assess the AMI biomarkers, to date their role in rescuing myocardial injury remains unclear. Therefore, the current study investigated three independent microarray-based gene expression datasets from AMI patients (n = 85) and their age-sex-matched healthy controls (n = 70), to identify novel gene signatures that might be involved in cardioprotection. The differentially expressed genes (DEGs) were analyzed using 'GEO2R', and weighted gene correlation network analysis (WGCNA) was performed to identify biomarkers/modules. We found 91 DEGs, of which the number of upregulated and downregulated genes were 22 and 5, respectively. Specifically, we found that the deregulated genes such as ADOR-A3, BMP6, VPS8, and GPx3, may be associated with AMI. WGCNA revealed four highly preserved modules among all datasets. The 'Enrichr' unveiled the presence of miR-660 and STAT1, which is known to affect AMI severity. Conclusively, these genes and miRNA might play a crucial role the rescue of cardiomyocytes from severe damage, which could be helpful in developing appropriate therapeutic strategies for the management of AMI.

SUBMITTER: Kumar S

PROVIDER: S-EPMC9777571 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature

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Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach.

Kumar Sanjay S Shih Chun-Ming CM Tsai Lung-Wen LW Dubey Rajni R Gupta Deepika D Chakraborty Tanmoy T Sharma Naveen N Singh Abhishek Vikram AV Swarup Vishnu V Singh Himanshu Narayan HN

Genes 20221209 12

Acute myocardial infarction (AMI) is a severe disease with elevated morbidity and mortality rate worldwide. This is attributed to great losses of cardiomyocytes, which can trigger the alteration of gene expression patterns. Although several attempts have been made to assess the AMI biomarkers, to date their role in rescuing myocardial injury remains unclear. Therefore, the current study investigated three independent microarray-based gene expression datasets from AMI patients (n = 85) and their ...[more]

PMID: 36553589

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Project description:Myocardial infarction is a major cause of morbidity and mortality worldwide. Metabolomic investigations may be useful for understanding the pathogenesis of ST-segment elevation myocardial infarction (STEMI). STEMI patients were comprehensively examined via targeted metabolomic profiling, machine learning and weighted correlation network analysis. A total of 195 subjects, including 68 STEMI patients, 84 patients with stable angina pectoris (SAP) and 43 non-CVD patients, were enrolled in the study. Metabolomic profiling involving the quantitative analysis of 87 endogenous metabolites in plasma was conducted. This study is the first to perform targeted metabolomic profiling in patients with STEMI. We identified 36 significantly altered metabolites in STEMI patients. Increased levels of four amino acids, eight acylcarnitines, six metabolites of the NO-urea cycle and neurotransmitters, and three intermediates of tryptophan metabolism were detected. The following metabolites exhibited decreased levels: six amino acids, three acylcarnitines, three components of the NO-urea cycle and neurotransmitters, and three intermediates of tryptophan metabolism. We found that the significant changes in tryptophan metabolism observed in STEMI patients-the increase in anthranilic acid and tryptophol and decrease in xanthurenic acid and 3-OH-kynurenine-may play important roles in STEMI pathogenesis. On the basis of the differences in the constructed weighted correlation networks, new significant metabolite ratios were identified. Among the 22 significantly altered metabolite ratios identified, 13 were between STEMI patients and non-CVD patients, and 17 were between STEMI patients and SAP patients. Seven of these ratios were common to both comparisons (STEMI patients vs. non-CVD patients and STEMI patients vs. SAP patients). Additionally, two ratios were consistently observed among the STEMI, SAP and non-CVD groups (anthranilic acid: aspartic acid and GSG (glutamine: serine + glycine)). These findings provide new insight into the diagnosis and pathogenesis of STEMI.

Project description:Background: Acute myocardial infarction (AMI) is one of the main fatal diseases of cardiovascular diseases. Circular RNA (circRNA) is a non-coding RNA (ncRNA), which plays a role in cardiovascular disease as a competitive endogenous RNA (ceRNA). However, their role in AMI has not been fully clarified. This study aims to explore the mechanism of circRNA-related ceRNA network in AMI, and to identify the corresponding immune infiltration characteristics. Materials and Methods: The circRNA (GSE160717), miRNA (GSE24548), and mRNA (GSE60993) microarray datasets of AMI were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were identified by the "limma" package. After integrating the circRNA, miRNA and mRNA interaction, we constructed a circRNA-miRNA-mRNA network. The "clusterProfiler" package and String database were used for functional enrichment analysis and protein-protein interaction (PPI) analysis, respectively. After that, we constructed a circRNA-miRNA-hub gene network and validated the circRNAs and mRNAs using an independent dataset (GSE61144) as well as qRT-PCR. Finally, we used CIBERSORTx database to analyze the immune infiltration characteristics of AMI and the correlation between hub genes and immune cells. Results: Using the "limma" package of the R, 83 DEcircRNAs, 54 DEmiRNAs, and 754 DEmRNAs were identified in the microarray datasets of AMI. Among 83 DEcircRNAs, there are 55 exonic DEcircRNAs. Then, a circRNA-miRNA-mRNA network consists of 21 DEcircRNAs, 11 DEmiRNAs, and 106 DEmRNAs were predicted by the database. After that, 10 hub genes from the PPI network were identified. Then, a new circRNA-miRNA-hub gene network consists of 14 DEcircRNAs, 7 DEmiRNAs, and 9 DEmRNAs was constructed. After that, three key circRNAs (hsa_circ_0009018, hsa_circ_0030569 and hsa_circ_0031017) and three hub genes (BCL6, PTGS2 and PTEN) were identified from the network by qRT-PCR. Finally, immune infiltration analysis showed that hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. Conclusion: Our study constructed a circRNA-related ceRNA networks in AMI, consists of hsa_circ_0031017/hsa-miR-142-5p/PTEN axis, hsa_circ_0030569/hsa-miR-545/PTGS2 axis and hsa_circ_0009018/hsa-miR-139-3p/BCL6 axis. These three hub genes were significantly positively correlated with up-regulated immune cells (neutrophils, macrophages and plasma cells) in AMI. It helps improve understanding of AMI mechanism and provides future potential therapeutic targets.

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Dataset Information

Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach.

Publications

Transcriptomic Profiling Unravels Novel Deregulated Gene Signatures Associated with Acute Myocardial Infarction: A Bioinformatics Approach.

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