Clinical value of ARG1 in acute myocardial infarction patients: Bioinformatics-based approach

Zhang, Rui; Ji, Zhenjun; Qu, Yi; Yang, Mingming; Su, Yamin; Zuo, Wenjie; Zhao, Qiong; Ma, Genshan; Li, Yongjun

doi:10.1016/j.biopha.2019.109590

Cited by 25 publications

(20 citation statements)

References 28 publications

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“…ARG1 in AMI patients than healthy controls, indicating the clinical significance of ARG1 in AMI [27]. In our study, the ARG1 expression in platelets represented the power (with AUC of 0.9, Figure 3a) in identifying patients with STEMI from SCAD, suggesting its potential as a biomarker for distinguishing cardiovascular disease.…”

Section: Discussionsupporting

confidence: 50%

“…This variety was caused by various preprocessing of the expression data: in our analysis, before DEGs analysis, we performed clustering with gene expression to identify experimentally caused outliers and discarded five samples including three patients with STEMI and 2 with SCAD ( Figure 1a ). A recent publication reported the significantly increased expression of ARG1 in AMI patients than healthy controls, indicating the clinical significance of ARG1 in AMI [ 27 ]. In our study, the ARG1 expression in platelets represented the power (with AUC of 0.9, Figure 3a ) in identifying patients with STEMI from SCAD, suggesting its potential as a biomarker for distinguishing cardiovascular disease.…”

Section: Discussionmentioning

confidence: 99%

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Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy

et al. 2021

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Acute myocardial infarction (AMI) was considered a fatal disease resulting in high morbidity and mortality; platelet activation or aggregation plays a critical role in participating in the pathogenesis of AMI. The current study aimed to reveal the underlying mechanisms of platelets in the confrontation of AMI and potential biomarkers that separate AMI from other cardiovascular diseases and healthy people with bioinformatic strategies. Immunity analysis revealed that the neutrophil was significantly decreased in patients with SCAD compared with patients with STsegment elevation myocardial infarction (STEMI) or healthy controls; monocytes and neutrophils showed potential in distinguishing patients with STEMI from patients with SCAD. Six differentially expressed genes (DEGs) showed great performances in differentiating STEMI patients from SCAD patients with AUC greater than 0.9. Correlation analysis showed that these six DEGs were significantly positively correlated with neutrophils; three genes were negatively correlated with monocytes. Weighted gene co-expression network analysis (WGCNA) found that module 'royalblue' had the highest correlation with STEMI; genes in STEMI-related module were enriched in cell-cell interactions, blood vessels' biological processes, and peroxisome proliferator-activated receptor (PPAR) signaling pathway; four genes (FN1, CD34, LPL, and WWTR1) represented the capability of identifying patients with STEMI from healthy controls and patients with SCAD; two genes (ARG1 and NAMPTL) were considered as novel biomarkers for identifying STEMI from SCAD; FN1 represented the potential as a novel biomarker for STEMI. Our findings indicated that the distribution of neutrophils could be considered as a potential molecular trait for separating patients with STEMI from SCAD.

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Section: Discussionsupporting

confidence: 50%

Section: Discussionmentioning

confidence: 99%

Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy

et al. 2021

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“…Recently, an increasing number of studies have shown that CCL20, IL1R2, and ITLN1 are correlated with MI [9,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. It was also revealed that the stimulation of peripheral blood mononuclear cells by transforming growth factor-β could lead to enhanced CCL20 expression [26].…”

Section: Discussionmentioning

confidence: 99%

Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction

Yao

Zhao²,

Zhou³

et al. 2021

Bioengineered

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In this study, we evaluated the diagnostic value of key genes in myocardial infarction (MI) based on data from the Gene Expression Omnibus (GEO) database. We used data from GSE66360 to identify a set of significant differentially expressed genes (DEGs) between MI and healthy controls. Logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and SignalP 3.0 server were used to identify the potential role of genes in predicting diagnosis in patients with MI. Principal component analysis (PCA), receiver operating characteristic (ROC) curve analyses, area under the curve (AUC) analyses, and C-index were used to estimate the diagnostic value of genes in patients with MI. The association was validated using six other independent data sets. Subsequently, bioinformatics analysis was conducted based on the aforementioned potential genes. A meta-analysis was performed to evaluate the diagnostic value of the genes in MI. Forty-four DEGs were selected from the GSE66360 dataset. A three-gene signature consisting of CCL20, IL1R2, and ITLN1 could effectively distinguish patients with MI. The three-gene signature was validated in seven independent cohorts. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to reveal the involvement of the three-gene signature in inflammationrelated biological processes and pathways. Moreover, diagnostic meta-analysis results of the three-gene signature showed that the pooled sensitivity, specificity, and AUC for MI were 0.80, 0.90, and 0.93, respectively. These results suggest that the three-gene signature is a novel candidate biomarker for distinguishing MI from healthy controls.

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“…Zhu et al (2015) found out that ARG1 can downregulate NO biosynthesis and promote the enrichment of inflammatory cells into infarcted heart areas in mice. In the latest research (Zhang et al, 2020), ARG1 was determined as a potential biomarker for AMI with the area under the receiver operating characteristic curve (AUC) up to 0.776. All together the application value of ARG1 in AMI is worth expecting.…”

Section: Discussionmentioning

confidence: 99%

Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identified Candidate Genes and Pathways Associated With Acute Myocardial Infarction

Chen

Liu

et al. 2021

Front. Genet.

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BackgroundAcute myocardial infarction (AMI), characterized by an event of myocardial necrosis, is a common cardiac emergency worldwide. However, the genetic mechanisms of AMI remain largely elusive.MethodsA genome-wide association study dataset of AMI was obtained from the CARDIoGRAMplusC4D project. A transcriptome-wide association study (TWAS) was conducted using the FUSION tool with gene expression references of the left ventricle and whole blood. Significant genes detected by TWAS were subjected to Gene Ontology (GO) enrichment analysis. Then the TWAS results of AMI were integrated with mRNA expression profiling to identify common genes and biological processes. Finally, the identified common genes were validated by RT-qPCR analysis.ResultsTWAS identified 1,050 genes for the left ventricle and 1,079 genes for whole blood. Upon comparison with the mRNA expression profile, 4 common genes were detected, including HP (PTWAS = 1.22 × 10–3, PGEO = 4.98 × 10–2); CAMP (PTWAS = 2.48 × 10–2, PGEO = 2.36 × 10–5); TNFAIP6 (PTWAS = 1.90 × 10–2, PGEO = 3.46 × 10–2); and ARG1 (PTWAS = 8.35 × 10–3, PGEO = 4.93 × 10–2). Functional enrichment analysis of the genes identified by TWAS detected multiple AMI-associated biological processes, including autophagy of mitochondrion (GO: 0000422) and mitochondrion disassembly (GO: 0061726).ConclusionThis integrative study of TWAS and mRNA expression profiling identified multiple candidate genes and biological processes for AMI. Our results may provide a fundamental clue for understanding the genetic mechanisms of AMI.

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Clinical value of ARG1 in acute myocardial infarction patients: Bioinformatics-based approach

Cited by 25 publications

References 28 publications

Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy

Identification of potential biomarkers and immune cell infiltration in acute myocardial infarction (AMI) using bioinformatics strategy

Potential role of a three-gene signature in predicting diagnosis in patients with myocardial infarction

Integrative Analysis of Transcriptome-Wide Association Study and mRNA Expression Profiles Identified Candidate Genes and Pathways Associated With Acute Myocardial Infarction

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