Bioinformatics analysis to screen for genes related to myocardial infarction.
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ABSTRACT:
SUBMITTER: Yang L
PROVIDER: S-EPMC9589498 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Bioinformatics analysis to screen for genes related to myocardial infarction.
Yang Liting L Pan Xuyang X Zhang Ying Y Zhao Dongsheng D Wang Liang L Yuan Guoliang G Zhou Changgao C Li Tao T Li Wei W
Frontiers in genetics 20221010
Myocardial infarction (MI) is an acute and persistent myocardial ischemia caused by coronary artery disease. This study screened potential genes related to MI. Three gene expression datasets related to MI were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened using the MetaDE package. Afterward, the modules and genes closely related to MI were screened and a gene co-expression network was constructed. A support vector machine (SVM) classifi ...[more]
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Project description:BackgroundCoronary heart disease is the leading cause of death worldwide. Myocardial infarction (MI) is a fatal manifestation of coronary heart disease, which can present as sudden death. Although the molecular mechanisms of coronary heart disease are still unknown, global gene expression profiling is regarded as a useful approach for deciphering the pathophysiology of this disease and subsequent diseases. This study used a bioinformatics analysis approach to better understand the molecular mechanisms underlying coronary heart disease.MethodsThis experimental study was conducted in the department of cardiology, Aja University of Medical Sciences (2021-2022), Tehran, Iran. To identify the key deregulated genes and pathways in coronary heart disease, an integrative approach was used by merging three gene expression datasets, including GSE19339, GSE66360, and GSE29111, into a single matrix. The t test was used for the statistical analysis, with a significance level of P<0.05.ResultsThe limma package in R was used to identify a total of 133 DEGs, consisting of 124 upregulated and nine downregulated genes. KDM5D, EIF1AY, and CCL20 are among the top upregulated genes. Moreover, the interleukin 17 (IL-17) signaling pathway and four other signaling pathways were identified as the potent underlying pathogenesis of both coronary artery disease (CAD) and MI using a systems biology approach. Accordingly, these findings can provide expression signatures and potential biomarkers in CAD and MI pathophysiology, which can contribute to both diagnosis and therapeutic purposes.ConclusionFive signaling pathways were introduced in MI and CAD that were primarily involved in inflammation, including the IL-17 signaling pathway, TNF signaling pathway, toll-like receptor signaling pathway, C-type lectin receptor signaling pathway, and rheumatoid arthritis signaling pathway.
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Project description:BackgroundInflammation and immune cell infiltration in infarcted myocardial tissue are critical to myocardial infarction (MI) prognosis, and alterations in sphingolipid metabolism (SM) have been shown to potentially influence the inflammatory response and induce cardioprotection, but the underlying mechanisms are unclear. We therefore performed bioinformatics analysis to screen for key genes of SM in MI immune cells.MethodsThree matrix files including GSE61145, GSE23294, and GSE71906 were downloaded from the Gene Expression Omnibus (GEO) database. GSE61145 was a human peripheral blood database, and GSE23294 and GSE71906 were 2 mouse myocardial tissue databases. R and annotation packages were used to screen for differentially-expressed genes (DEGs). Datasets of human and mouse cardiac tissues were downloaded from the GEO database for subsequent validation. The downloaded platform and matrix files were processed using R language and annotation packages. Key targets and enrichment pathways were identified using Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The Wilcoxon test was performed on the genes involved in SM pathways in neutrophils.ResultsA total of 261 DEGs were obtained from human peripheral blood datasets, among which 101 were immune-related. GO analysis revealed that neutrophil activation, T cell activation, and T cell differentiation were significantly enriched in the immune-related DEGs. Three types of immune cells were identified in infarcted myocardial tissues. In addition, 194 DEGs were obtained from mouse myocardial tissue data, among which 6 SM-related genes (Asah1, Degs1, Neu1, Sptlc2, Sphk1, and Gba2) were significantly associated with MI. Evaluation of the relationships between these DEGs and neutrophils showed that the expression of the Sptlc2 gene was significantly upregulated in neutrophils of the MI group, while the expression levels of the Asah1 and Degs1 genes were downregulated.ConclusionsWe identified 3 SM-related genes that were highly associated with neutrophils in MI, which may advance our understanding of SM in immune cells after MI.
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Project description:BackgroundAccurate myocardial infarction (AMI) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) modification plays an important role in the development of cardiac remodeling and the cardiomyocyte contractile function. The aim of this study is to analyze the m6A-related molecular biological mechanisms of AMI in terms of accurate diagnosis and prognosis.MethodsThe platform data and probe data of the GSE66360 data set were downloaded. The differential analysis was conducted by combining the m6A-related gene expression. Thereafter, a diagnostic model was established using the random-forest method. The diagnostic accuracy of the diagnostic models was assessed by using the area under the receiver operating characteristic (ROC) curve (AUC). Next, the patients with AMI were clustered by unsupervised machine learning using the R software. Finally, an immune cell clustering analysis for each cluster was conducted to determine the correlations between m6A-related gene expression and the infiltration amount of the immune cells. The case and control groups were not matched in terms of demographics.ResultsThe GSE6636 data set comprised 99 participants (49 patients with AMI and 50 without in control group). The differential analysis identified 10 m6A-related genes: 5 writers [Methyltransferase-like 3 (METTL3), Methyltransferase-like 14 (METTL14), Wilms tumor 1-associated protein (WTAP), Zinc Finger CCCH-Type Containing 13 (ZC3H13), and Casitas B-lineage proto-oncogene like 1 (CBLL1)], 4 readers [YT521-B homology domain-containing family 3 (YTHDF3), Fragile X mental retardation type 1 (FMR1), YT521-B homology-domain-containing protein 1 (YTHDC1), and insulin-like growth factor binding protein 3 (IGFBP3)] and 1 eraser [fat mass and obesity associated (FTO) gene]. The Mean Decrease Gini (MDG) values of these 10 genes were greater than 2. The FTO, WTAP, YTHDC1, IGFBP3, and CBLL1 were included in the model with a C index of 0.842. METTL3, ZC3H13, WTAP, and CBLL1 were highly expressed in Type A, and YTHDF3 was highly expressed in Type B.ConclusionsA diagnostic model of AMI was established based on the genes of FTO, WTAP, YTHDC1, IGFBP3, and CBLL1. Additionally, 2 molecular subtypes were successfully identified from the above-mentioned gene. Our findings could provide a novel method for the accurate diagnosis of AMI.
| S-EPMC9186242 | biostudies-literature
Project description:BackgroundMyocardial infarction (MI) is a common cause of death worldwide. It is characterized by coronary artery occlusion that causes ischemia and hypoxia of myocardial cells, leading to irreversible myocardial damage.Materials and methodsTo explore potential targets for treatment of MI, we reorganized and analyzed two microarray datasets (GSE4648 and GSE775). The GEO2R tool was used to screen for differentially expressed genes (DEGs) between infarcted and normal myocardium. We used the Database for Annotation, Visualization and Integrated Discovery (DAVID) to perform Gene Ontology functional annotation analysis (GO analysis) and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analysis (KEGG analysis). We examined protein-protein interactions to characterize the relationship between differentially expressed genes, and we screened potential hub genes according to the degree of connection. PCR and Western blotting were used to identify the core genes.ResultsAt different times of infarction, a total of 35 genes showed upregulation at all times; however, none of the genes showed downregulation at all 3 times. Similarly, 10 hub genes with high degrees of connectivity were identified. In vivo and in vitro experiments suggested that expression levels of MMP-9 increased at various times after myocardial infarction and that expression increased in a variety of cells simultaneously.ConclusionExpression levels of MMP-9 increase throughout the course of acute myocardial infarction, and this expression has both positive and negative sides. Further studies are needed to explore the role of MMP-9 in MI treatment. The potential values of Il6, Spp1, Ptgs2, Serpine1, Plaur, Cxcl5, Lgals3, Serpinb2, and Cd14 are also worth exploring.
| S-EPMC7718865 | biostudies-literature
Project description:BackgroundAcute myocardial infarction (AMI) is one of the most serious cardiovascular diseases. Apoptosis is a type of programmed cell death that causes DNA degradation and chromatin condensation. The role of apoptosis in AMI progression remains unclear.MethodsThree AMI-related microarray datasets (GSE48060, GSE66360 and GSE97320) were obtained from the Gene Expression Omnibus database and combined for further analysis. Differential expression analysis and enrichment analysis were performed on the combined dataset to identify differentially expressed genes (DEGs). Apoptosis-related genes (ARGs) were screened through the intersection of genes associated with apoptosis in previous studies and DEGs. The expression pattern of ARGs was studied on the basis of their raw expression data. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and Random Forest (RF) were utilized to screen crucial genes in these ARGs. Immune infiltration was estimated by single sample gene set enrichment analysis (ssGSEA). Corresponding online databases were used to predict miRNAs, transcription factors (TFs) and therapeutic agents of crucial genes. A nomogram clinical prediction model of the crucial genes was constructed and evaluated. The Mendelian randomization analysis was employed to investigate whether there is a causal relationship between apoptosis and AMI. Finally, an AMI mouse model was established, and apoptosis in the hearts of AMI mice was assessed via TUNEL staining. qRT-PCR was employed to validate these crucial genes in the hearts of AMI mice. The external dataset GSE59867 was used for further validating the crucial genes.ResultsFifteen ARGs (GADD45A, DDIT3, FEZ1, PMAIP1, IER3, IFNGR1, CDKN1A, GNA15, IL1B, EREG, BCL10, JUN, EGR3, GADD45B, and CD14) were identified. Six crucial genes (CDKN1A, BCL10, PMAIP1, IL1B, GNA15, and CD14) were screened from ARGs by machine learning. A total of 102 miRNAs, 13 TFs and 23 therapeutic drugs were predicted targeting these crucial genes. The clinical prediction model of the crucial genes has shown good predictive capability. The Mendelian randomization analysis demonstrated that apoptosis is a risk factor for AMI. Lastly, the expression of CDKN1A, CD14 and IL1B was verified in the AMI mouse model and external dataset.ConclusionsIn this study, ARGs were screened by machine learning algorithms, and verified by qRT-PCR in the AMI mouse model. Finally, we demonstrated that CDKN1A, CD14 and IL1B were the crucial genes involved in apoptosis in AMI. These genes may provide new target for the recognition and intervention of apoptosis in AMI.
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Project description:BackgroundAcute myocardial infarction (AMI), a subset of acute coronary syndrome, remains the major cause of mortality worldwide. Mitochondrial dysfunction is critically involved in AMI progression, and mitophagy plays a vital role in eliminating damaged mitochondria. This study aimed to explore mitophagy-related biomarkers and their potential molecular basis in AMI.MethodsAMI datasets (GSE24519 and GSE34198) from the Gene Expression Omnibus database were combined and the batch effects were removed. Differentially expressed genes (DEGs) in AMI were selected, intersected with mitophagy-related genes for mitophagy-related DEGs (MRDEGs), and then subjected to enrichment analyses. Next, the MRDEGs were screened using machine learning methods (logistic regression analysis, RandomForest, least absolute shrinkage and selection operator) to construct a diagnostic risk model and select the key genes in AMI. The diagnostic efficacy of the model was evaluated using a nomogram. Moreover, the infiltration patterns of different immune cells in two risk groups were compared. We also explored the interactions between the key genes themselves or with miRNAs/transcription factors (TFs) and drug compounds and visualized the protein structure of the key genes. Finally, we explored and validated the expression of key genes in plasma samples of patients with an AMI and healthy individuals.ResultsWe screened 28 MRDEGs in AMI. Based on machine learning methods, 12 key genes were screened for the diagnostic risk model, including AGPS, CA2, CAT, LTA4H, MYO9B, PRDX6, PYGB, SIRT3, TFEB, TOM1, UBA52, and UBB. The nomogram further revealed the accuracy of the model for AMI diagnosis. Moreover, we found a lower abundance of immune cells such as gamma delta T and natural killer cells in the high-risk group, and the expression of key genes showed a significant correlation with immune infiltration levels in both groups. Finally, 64 miRNA-mRNA pairs, 75 TF-mRNA pairs, 119 RNA-binding protein-mRNA pairs, and 32 drug-mRNA pairs were obtained in the interaction networks.ConclusionsIn total, 12 key MRDEGs were identified and a risk model was constructed for AMI diagnosis. The findings of this study might provide novel biomarkers for improving the detection of AMI.
| S-EPMC11770045 | biostudies-literature
Project description:BackgroundMyocardial infarction (MI) caused by severe coronary artery disease has high incidence and mortality rates, making its prevention and treatment a central and challenging aspect of clinical work for cardiovascular practitioners. Recently, researchers have turned their attention to a novel mechanism of cell death caused by Cu2+, cuproptosis.MethodsThis study integrated data from three MI-related bulk datasets downloaded from the Gene Expression Omnibus (GEO) database, and identified 16 differentially expressed genes (DEGs) related to cuproptosis by taking intersection of the 6378 DEGs obtained by differential analysis with 49 cuproptosis-related genes. Four hub genes, Dbt, Dlat, Ube2d1 and Ube2d3, were screened out through random forest analysis and Lasso analysis. In the disease group, Dbt, Dlat, and Ube2d1 showed low expression, while Ube2d3 exhibited high expression.ResultsFocusing on Ube2d3 for subsequent functional studies, we confirmed its high expression in the MI group through qRT-PCR and Western Blot detection after successful construction of a MI mouse model by left anterior descending (LAD) coronary artery ligation, and further clarified the correlation of cuproptosis with MI development by detecting the levels of cuproptosis-related proteins. Moreover, through in vitro experiments, Ube2d3 was confirmed to be highly expressed in oxygen-glucose deprivation (OGD)-treated cardiomyocytes AC16. In order to further clarify the role of Ube2d3, we knocked down Ube2d3 expression in OGD-treated AC16 cells, and confirmed Ube2d3's promoting role in the hypoxia damage of AC16 cells by inducing cuproptosis, as evidenced by the detection of MTT, TUNEL, LDH release and cuproptosis-related proteins.ConclusionIn summary, our findings indicate that Ube2d3 regulates cuproptosis to affect the progression of MI.
| S-EPMC10909922 | biostudies-literature
Project description:BackgroundThe present study was to investigated differential expressed genes (GEGs) in ischemic cardiomyopathy (ICM), and to construct regulation networks, and to study the correlation between myocardial infarction risk.MethodsData sets were downloaded from the Gene Expression Omnibus (GEO) to screen out messenger RNA (mRNA) and long non-coding RNA (lncRNA) differentially expressed between ICM samples and normal samples. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Differentially expressed mRNA and lncRNA were analyzed, and bioinformatics methods were used to predict and analyze microRNA (miRNA), and a competing endogenous RNA (Hub gene) regulatory network was constructed. Using the Limma software package in R language, DEGs of ICM were screened with non-heart failure donors as the control group under the conditions that the differential expression ratio was not less than 2 times, and the corrected P value was <0.05. The ClusterProfiler software package was used for GO enrichment analysis and KEGG enrichment analysis. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.0 online database was used to screen key genes for protein-protein interaction (PPI) network analysis.ResultsThe GO function analysis and KEGG pathway analysis showed that the DEGs were significantly enriched in metabolic pathways, oxidative phosphorylation, extracellular matrix receptor interactions, and other pathways, and were closely related to fibrosis, collagen catabolic process, and inflammatory response function, and a Hub gene regulatory network related to ICM lncRNA was constructed. Bioinformatics methods were used to effectively analyze the DEGs of ICM, and the Hub gene regulatory network of ICM was successfully constructed.ConclusionsThis study identified a certain risk correlation between ICM susceptibility genes and myocardial infarction.
| S-EPMC9562554 | biostudies-literature
Project description:BackgroundAcute myocardial infarction (AMI) is a common disease leading threat to human health around the world. Here we aimed to explore new biomarkers and potential therapeutic targets in AMI through adopting integrated bioinformatics tools.MethodsThe gene expression Omnibus (GEO) database was used to obtain genes data of AMI and no-AMI whole blood. Furthermore, differentially expressed genes (DEGs) were screened using the "Limma" package in R 3.6.1 software. Functional and pathway enrichment analyses of DEGs were performed via "Bioconductor" and "GOplot" package in R 3.6.1 software. In order to screen hub DEGs, the STRING version 11.0 database, Cytoscape and molecular complex detection (MCODE) were applied. Correlation among the hub DEGs was evaluated using Pearson's correlation analysis.ResultsBy performing DEGs analysis, 289 upregulated and 62 downregulated DEGs were successfully identified from GSE66360, respectively. And they were mainly enriched in the terms of neutrophil activation, immune response, cytokine, nuclear factor kappa-B (NF-κB) signaling pathway, IL-17 signaling pathway, and tumor necrosis factor (TNF) signaling pathway. Based on the data of protein-protein interaction (PPI), the top 10 hub genes were ranked, including interleukin-8 (CXCL8), TNF, N-formyl peptide receptor 2 (FPR2), growth-regulated alpha protein (CXCL1), transcription factor AP-1 (JUN), interleukin-1 beta (IL1B), platelet basic protein (PPBP), matrix metalloproteinase-9 (MMP9), toll-like receptor 2 (TLR2), and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G). What's more, the results of correlation analysis demonstrated that there was positive correlation between the 10 hub DEGs.ConclusionTen DEGs were identified as potential candidate diagnostic biomarkers for patients with AMI in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with AMI.
| S-EPMC8052032 | biostudies-literature
Project description:Ferroptosis is closely associated with the development of disease in the body. However, there are few studies on ferroptosis-related genes (FRGs) in obesity. Therefore, key genes and signalling pathways related to ferroptosis in obesity were screened. Briefly, the RNA sequencing data of obesity and the non-obesity human samples and 259 FRGs were downloaded from GEO database and FerrDb database, respectively. The obesity-related module genes were firstly screened by weighted gene co-expression network analysis (WGCNA) and crossed with differentially expressed genes (DEGs) of obesity/normal samples and FRGs to obtain obesity-ferroptosis related (OFR) DEGs. Then, key genes were screened by PPI network. Next, the correlation of key genes and differential immune cells between obesity and normal samples were further explored by immune infiltration analysis. Finally, microRNA (miRNA)-messenger RNA (mRNA), transcription factor (TF)-mRNA networks and drug-gene interaction networks were constructed. As a result, 17 OFR DEGs were obtained, which mainly participated in processes such as lipid metabolism or adipocyte differentiation. The 4 key genes, STAT3, IL-6, PTGS2, and VEGFA, constituted the network. M2 macrophages, T cells CD8, mast cells activated, and T cells CD4 memory resting had significant differences between obesity and normal samples. Moreover, 51 miRNAs and 164 drugs were predicted for 4 key genes. All in all, this study has screened 4 FRGs, including IL-6, VEGFA, STAT3, and PTGS2, in obesity patients.
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