Dataset Information

Identification of Featured Metabolism-Related Genes in Patients with Acute Myocardial Infarction

ABSTRACT:

Objective

A growing body of emerging evidence indicates that metabolic processes play a pivotal role in the biological processes underlying acute myocardial infarction (AMI). The aim of the current study was to identify featured metabolism-related genes in patients with AMI using a support vector machine (SVM) and to further explore the value of these genes in the diagnosis of AMI.

Methods

Gene microarray expression data related to AMI were downloaded from the GSE66360 dataset in the Gene Expression Omnibus (GEO) database. This data set consisted of 50 AMI samples and 49 normal controls that were randomly classified into a discovery cohort (21 AMI samples and 22 normal controls) and a validation cohort (28 AMI and 28 normal controls). We applied a machine learning method that combined SVM with recursive feature elimination (RFE) to discriminate AMI patients from normal controls. Based on this, an SVM classifier was constructed. Receiver operating characteristic (ROC) analysis was used to investigate the predictive value for the early diagnosis of AMI in the two cohorts and was then further verified in an independent external cohort.

Results

Three metabolism-related genes were identified based on SVM-RFE (AKR1C3, GLUL, and PDE4B). The SVM classifier based on the three genes allowed for excellent discrimination between AMI and healthy samples in both the discovery cohort (AUC = 0.989) and the validation cohort (AUC = 0.964), and this was further confirmed in the GSE68060 dataset (AUC = 0.839). Additionally, the SVM classifier allowed for perfect discrimination between recurrent AMI events and nonrecurrent events in the GSE68060 cohort (AUC = 0.992). GO and KEGG pathway enrichment analysis of the identified featured genes revealed significant enrichment of specific metabolic pathways.

Conclusion

The identified metabolism-related genes may play important roles in the development of AMI and may represent diagnostic and therapeutic biomarkers of AMI.

SUBMITTER: Xie H

PROVIDER: S-EPMC7737445 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveThis study aimed to analyze immune-related genes and immune cell components in the peripheral blood of patients with acute myocardial infarction (AMI).MethodsSix datasets were obtained from the GEO repository comprising 88 healthy samples and 215 AMI samples. We performed the weighted gene co-expression analysis (WGCNA) and five machine learning (ML) methods to identify immune-related genes and construct diagnostic models. CIBERSORT algorithm was adopted for the assessment of the degree of immune infiltration. Finally, RT-PCR, immunofluorescence double and immunohistochemistry were conducted to analyze the expression level of the identification of featured immune-related genes and localization relationship in heart tissue of AMI mouse model.ResultsA total of 496 immune-related DEGs were obtained between AMI and normal samples. WGCNA finally determined the co-expression modules that showed the most significantly positively associated with AMI (r=0.41; P<0.001). Among the five ML models, XGBoost had the highest AUC (0.849) and accuracy (0.812) to discriminate patients with AMI from normal in the validation sets. Furthermore, we found that the proportion of chemokine receptor (CCR), macrophages, neutrophils, and Treg cells in the AMI groups was significantly higher than that in the normal groups. In vitro RT-PCR verification revealed that SOCS3, MMP9, and AQP9 expression increased significantly in the AMI mouse model. Among the 22 immune cells, AQP9, MMP9, and SOCS3 displayed the strongest positive correlation with neutrophils. In MI-mice, MPO stained strongly along the lateral cardiomyocytes, whereas it was weaker in sham mice. Combined immunofluorescence was observed in same parts of the cytoplasm of cardiomyocytes in myocardial infarction area, indicating co-localization of MPO with MMP9 and SOCS3 in these areas, respectively.ConclusionImmune-related genes and immune cells are intimately related to AMI. Constructing different ML models based on these biomarkers could be a valuable approach to diagnosing AMI in clinical practice.

Project description:BackgroundAcute myocardial infarction (AMI) is the main clinical cause of death and cardiovascular disease and thus has high rates of morbidity and mortality. The increase in cardiovascular disease with aging is partly the result of vascular endothelial cell senescence and associated vascular dysfunction. This study was performed to identify potential key cellular senescence-related genes (SRGs) as biomarkers for the diagnosis of AMI using bioinformatics.MethodsUsing the CellAge database, we identified cellular SRGs. GSE66360 and GSE48060 for AMI patients and healthy controls and GSE19322 for mice were downloaded from the Gene Expression Omnibus (GEO) database. The GSE66360 dataset was divided into a training set and a validation set. The GSE48060 dataset was used as another validation set. The GSE19322 dataset was used to explore the evolution of the screened diagnostic markers in the dynamic process of AMI. Differentially expressed genes (DEGs) of AMI were identified from the GSE66360 training set. Differentially expressed senescence-related genes (DESRGs) selected from SRGs and DEGs were analyzed using Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in DESRGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed and targeted drug prediction was performed.ResultsA total of 520 DEGs were screened from the GSE66360 training set, and 279 SRGs were identified from the CellAge database. The overlapping DEGs and SRGs constituted 14 DESRGs, including 4 senescence suppressor genes and 10 senescence inducible genes. The top 10 hub genes, including FOS, MMP9, CEBPB, CDKN1A, CXCL1, ETS2, BCL6, SGK1, ZFP36, and IGFBP3, were screened. Furthermore, three diagnostic genes were identified: MMP9, ETS2, and BCL6. The ROC analysis showed that the respective area under the curves (AUCs) of MMP9, ETS2, and BCL6 were 0.786, 0.848, and 0.852 in the GSE66360 validation set and 0.708, 0.791, and 0.727 in the GSE48060 dataset. In the GSE19322 dataset, MMP9 (AUC, 0.888) and ETS2 (AUC, 0.929) had very high diagnostic values in the early stage of AMI. Finally, based on these three diagnostic genes, we found that drugs such as acetylcysteine and genistein may be targeted for the treatment of age-related AMI.ConclusionThe results of this study suggest that cellular SRGs might play an important role in AMI. MMP9, ETS2, and BCL6 have potential as specific biomarkers for the early diagnosis of AMI.

Project description:BackgroundAcute myocardial infarction (AMI) is regarded as an urgent clinical entity, and identification of differentially expressed genes, lncRNAs, and altered pathways shall provide new insight into the molecular mechanisms behind AMI.Materials and methodsMicroarray data was collected to identify key genes and lncRNAs involved in AMI pathogenesis. The differential expression analysis and gene set enrichment analysis (GSEA) were employed to identify the upregulated and downregulated genes and pathways in AMI. The protein-protein interaction network and protein-RNA interaction analysis were utilized to reveal key long noncoding RNAs.ResultsIn the present study, we utilized gene expression profiles of circulating endothelial cells (CEC) from 49 patients of AMI and 50 controls and identified a total of 552 differentially expressed genes (DEGs). Based on these DEGs, we also observed that inflammatory response-related genes and pathways were highly upregulated in AMI. Mapping the DEGs to the protein-protein interaction (PPI) network and identifying the subnetworks, we found that OMD and WDFY3 were the hub nodes of two subnetworks with the highest connectivity, which were found to be involved in circadian rhythm and organ- or tissue-specific immune response. Furthermore, 23 lncRNAs were differentially expressed between AMI and control groups. Specifically, we identified some functional lncRNAs, including XIST and its antisense RNA, TSIX, and three lncRNAs (LINC00528, LINC00936, and LINC01001), which were predicted to be interacting with TLR2 and participate in Toll-like receptor signaling pathway. In addition, we also employed the MMPC algorithm to identify six gene signatures for AMI diagnosis. Particularly, the multivariable SVM model based on the six genes has achieved a satisfying performance (AUC = 0.97).ConclusionIn conclusion, we have identified key regulatory lncRNAs implicated in AMI, which not only deepens our understanding of the lncRNA-related molecular mechanism of AMI but also provides computationally predicted regulatory lncRNAs for AMI researchers.

Project description:BackgroundFerroptosis is a form of regulatory cell death (RCD) caused by iron-dependent lipid peroxidation. The role of ferroptosis in the process of acute myocardial infarction (AMI) is still unclear and requires further study. Therefore, it is helpful to identify ferroptosis related genes (FRGs) involved in AMI and explore their expression patterns and molecular mechanisms.MethodsThe AMI-related microarray datasets GSE66360 and GSE61144 were obtained using the Gene Expression Omnibus (GEO) online database. GO annotation, KEGG pathway enrichment analysis and Protein-protein interaction (PPI) analysis were performed for the common significant differential expression genes (CoDEGs) in these two datasets. The FRGs were obtained from the FerrDb V2 and the differentially expressed FRGs were used to identify potential biomarkers by receiver operating characteristic (ROC) analysis. The expression of these FRGs was verified using external dataset GSE60993 and GSE775. Finally, the expression of these FRGs was further verified in myocardial hypoxia model.ResultsA total of 131 CoDEGs were identified and these genes were mainly enriched in the pathways of "inflammatory response," "immune response," "plasma membrane," "receptor activity," "protein homodimerization activity," "calcium ion binding," "Phagosome," "Cytokine-cytokine receptor interaction," and "Toll-like receptor signaling pathway." The top 7 hub genes ITGAM, S100A12, S100A9, TLR2, TLR4, TLR8, and TREM1 were identified from the PPI network. 45 and 14 FRGs were identified in GSE66360 and GSE61144, respectively. FRGs ACSL1, ATG7, CAMKK2, GABARAPL1, KDM6B, LAMP2, PANX2, PGD, PTEN, SAT1, STAT3, TLR4, and ZFP36 were significantly differentially expressed in external dataset GSE60993 with AUC ≥ 0.7. Finally, ALOX5, CAMKK2, KDM6B, LAMP2, PTEN, PTGS2, and ULK1 were identified as biomarkers of AMI based on the time-gradient transcriptome dataset of AMI mice and the cellular hypoxia model.ConclusionIn this study, based on the existing datasets, we identified differentially expressed FRGs in blood samples from patients with AMI and further validated these FRGs in the mouse time-gradient transcriptome dataset of AMI and the cellular hypoxia model. This study explored the expression pattern and molecular mechanism of FRGs in AMI, providing a basis for the accurate diagnosis of AMI and the selection of new therapeutic targets.