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. 2016 Dec 9:7:615.
doi: 10.3389/fphys.2016.00615. eCollection 2016.

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

Jiaqi Chen  1 , Ling Yu  2 , Siwei Zhang  1 , Xia Chen  1
Affiliations

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

Jiaqi Chen et al. Front Physiol. .

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.

Keywords: acute myocardial infarction; biomarkers; hub genes; inflammation; systems biology.

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Figures

Figure 1
Figure 1
DEGs selection and hierarchical clustering analysis. (A) The Limma algorithm was applied to filter the DEGs; we selected the DEGs according to P <0.05 and Fold change > 1.2. (B) DEGs can be effectively divided into AMI and control groups. Red indicates that the gene that is upregulated and green represents down-regulated genes.
Figure 2
Figure 2
GO enrichment analysis and GO-Tree. (A) The significant GO terms that conformed to a P <0.05 were screened. (B) GO-Tree: red circles represent the upregulated genes involved in GO terms, and green circles represent the down-regulated genes involved in GO terms. (C) We used the Fisher's exact test to select the significant pathway, identified by a P <0.05. (D) Pathway-act-network. P <0.05. Red circles represent the upregulated genes involved in pathway terms, and green circles represent the down-regulated genes involved in pathway terms.
Figure 3
Figure 3
Gene co-expression network analyses. The Pearson correlation was calculated for each pair of genes, and the significantly correlated pairs were used to construct the co-expression network (P <0.05). (A) AMI. (B) Control. Red nodes represent the key regulatory genes with the highest K-Core. The node color represents the K-Core. The node size represents the K-Core power, and the edges between two nodes represent the interactions between the genes.
Figure 4
Figure 4
(A) Network analysis of gene expression in AMI identifies distinct modules of co-expression genes. (B) Correlation between the gene modules and immune and inflammatory responses. Scatter plot of MM vs. GS in (C) blue and (D) brown modules. Cor represents an absolute correlation coefficient of GS and MM; P-value for significance assessment. It follows that in both modules, GS and MM have a high correlation. In the high correlation of image: upper right node (gene) and immune and inflammatory responses, on the other hand in the module also has an important significance.
Figure 5
Figure 5
Construction of a co-expression network of module genes. (A) Blue module; (B) brown module. Red nodes represent the hub genes with the highest K-Core and MM. The node color represents the K-Core. The node size represents the K-Core power, and the edges between two nodes represent the interactions between the genes.
Figure 6
Figure 6
Transcription regulatory network of DEGs. Based on JASPAR database, the transcription regulatory network constructed by DEGs included 66 nodes (A). (B) Construction of transcription regulatory network centered around TBX21. Red V-type represents up-regulated TF, green V-type represents down-regulated TF; red circles represent up-regulated TG, green circles represent the down-regulated TG.
Figure 7
Figure 7
Validation of microarray data with RT-qPCR. Several hub genes identified in microarray data are dysregulation in AMI patients. mRNA expression of hub genes identified in microarray data validated by RT-qPCR is shown. Total RNAs were isolated from PBMCs or AMI patients and healthy donors. Reverse-transcribed to cDNA and used as template for RT-qPCR analysis. Relative Expression of each gene in PBMCs from healthy donors were considered as 1.
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