Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction

Abstract

Acute myocardial infarction (AMI) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for AMI have not been identified. In order to explore the potential diagnostic biomarkers and possible regulatory targets of AMI, we used a network analysis-based approach to analyze microarray expression profiling of peripheral blood in patients with AMI. The significant differentially-expressed genes (DEGs) were screened by Limma and constructed a gene function regulatory network (GO-Tree) to obtain the inherent affiliation of significant function terms. The pathway action network was constructed, and the signal transfer relationship between pathway terms was mined in order to investigate the impact of core pathway terms in AMI. Subsequently, constructed the transcription regulatory network of DEGs. Weighted gene co-expression network analysis (WGCNA) was employed to identify significantly altered gene modules and hub genes in two groups. Subsequently, the transcription regulation network of DEGs was constructed. We found that specific gene modules may provide a better insight into the potential diagnostic biomarkers of AMI. Our findings revealed and verified that NCF4, AQP9, NFIL3, DYSF, GZMA, TBX21, PRF1 and PTGDR genes by RT-qPCR. TBX21 and PRF1 may be potential candidates for diagnostic biomarker and possible regulatory targets in AMI.

Transcriptomic Analysis of the Association Between Diabetes Mellitus and Myocardial Infarction

“…NFIL3 mRNA expression level is highly increased in the diabetic mice which is consistent with our results, and the up-regulation contributes to direct suppression of Aryl hydrocarbon receptor nuclear translocator (ARNT), a potential determinant of pancreatic β-cell dysfunction and T2DM [24]. Similarly, NFIL3 is also up-regulated in acute MI and may be a potential candidate for diagnostic biomarker and possible regulatory target in acute MI [37]. SLPI (secretory leukocyte protease inhibitor), an alarm antiprotease secreted by neutrophils, macrophages and mucous membrane epithelial cells, potently inhibits the inflammatory cascade [25].…”

Weighted Gene Co-expression Network Analysis Identifies FKBP11 as a Key Regulator in Acute Aortic Dissection through a NF-kB Dependent Pathway

“…WGCNA was used to cluster groups of strongly co-expressed genes into co-expression networks among DEGs (Zhang and Horvath, 2005 ; Langfelder and Horvath, 2008 ). In data processing, the genome-wide gene expression data was preliminarily filtered, followed by measuring the consistency of gene expression profiles by Pearson correlation, and finally using the power adjacent function to Pearson correlation matrix, data was transformed into weighted gene co-expression networks (Chen et al, 2016 ).…”

“…Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been widely used in various biological aspects, e.g., cancer, mouse genetics, analysis of acute myocardial infarction, etc (Langfelder and Horvath, 2008 ; Chen et al, 2016 ; Liu W. et al, 2017 ).…”

Identification of differentially expressed genes, lncRNAs and miRNAs which are associated with tumor malignant phenotypes in hepatoblastoma patients

“…WGCNA is an algorithm for constructing a co-expression network, defined by the similarity of gene co-expression [ 42 ]. In data processing, the genome-wide gene expression data was initially filtrated with measuring the consistency of gene expression profiles by Pearson correlation, then we utilized the power adjacent function to Pearson correlation matrix to transform data into weighted gene co-expression networks.…”