Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis

Zhang, Shu; Liu, Weixia; Liu, Xiaoyan; Qi, Jiaxin; Deng, Chonghai

doi:10.1097/md.0000000000008375

Cited by 22 publications

(22 citation statements)

References 44 publications

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“…Furthermore, STAT1 and AKT3 were associated with the diagnosis and prognosis of patients with MI. Activation of STAT1 has been demonstrated to induce cardiomyocyte apoptosis following ischemia/reperfusion injury by enhancing the expression of pro-apoptotic proteins, which suggests that STAT1 plays a key role in the pathology of MI ( 68 – 70 ). AKT3 is involved in cardiomyocyte cell death ( 71 ).…”

Section: Discussionmentioning

confidence: 99%

Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis

Wang¹,

Cao²

2020

Mol Med Rep

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Myocardial infarction (Mi) is one of the leading causes of death globally. The aim of the present study was to find valuable micrornas (mirnas/mirs) and target mrnas in order to contribute to our understanding of the pathology of Mi. mirna and mrna data were downloaded for differential expression analysis. Then, a regulatory network between mirnas and mrnas was established, followed by function annotation of target mrnas. Thirdly, prognosis and diagnostic analysis of differentially methylated target mrnas were performed. Finally, an in vitro experiment was used to validate the expression of selected mirnas and target mrnas. a total of 19 differentially expressed mirnas and 1,007 differentially expressed mRNAs were identified. Several regulatory interaction pairs between mirna and mrnas were identified, such as hsa-mir-142-2p-long-chain-fattyacid-coa ligase 1 (acSl1), hsa-mir-15a-3p-nicotinamide phosphoribosyltransferase (naMPT), hsa-mir-33b-5p-regulator of G-protein signaling 2 (rGS2), hsa-mir-17-3p-Jun dimerization protein 2 (JdP2), hsa-mir-24-1-5p-aquaporin-9 (aQP9) and hsa-mir-34a-5p-STaT1/aKT3. of note, it was demonstrated that acSl1, naMPT, rGS2, JdP2, aQP9, STaT1 and aKT3 had diagnostic and prognostic values for patients with Mi. in addition, STaT1 was involved in the 'chemokine signaling pathway' and 'Jak-STaT signaling pathway'. aKT3 was involved in both the 'MaPK signaling pathway' and 'T cell receptor signaling pathway'. reverse transcription-quantitative Pcr validation of hsa-mir-142-3p, hsa-mir-15a-3p, hsa-mir-33b-5p, acSl1, naMPT, rGS2 and JdP2 expression was consistent with the bioinformatics analysis. In conclusion, the identified miRNAs and mRNAs may be involved in the pathology of Mi.

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Section: Discussionmentioning

confidence: 99%

Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis

Wang¹,

Cao²

2020

Mol Med Rep

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“…EDN was first identified as a neurotoxin, and recent studies suggest that EDN plays a role in antiviral host defense, as a chemoattractant for human dendritic cells, and most recently, as an endogenous ligand for toll-like receptor (TLR) 2 [27]. TLR2 is reported to regulate myocardial ischemia, and sTLR2 may involve in the innate immune response in the pathogenesis of heart failure after acute MI [28]. Thus, we hypothesize that EDN/RNASE2 is likely to be associated with recurrent MI via its direct interactions with TLR2 and dendritic cells.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes involved in myocardial infarction

Qiu

Liu

2019

Eur J Med Res

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Background This study focuses on the identification of conserved genes involved in myocardial infarction (MI), and then analyzed the differentially expressed genes (DEGs) between the incident and recurrent events to identify MI-recurrent biomarkers. Methods Gene expression data of MI peripheral blood were downloaded from GSE97320 and GSE66360 datasets. We identified the common DEGs in these two datasets by functional enrichment analysis and protein–protein interaction (PPI) network analysis. GSE48060 was further analyzed to validate the conserved genes in MI and to compare the DEGs between the incident and recurrent MI. Results A total of 477 conserved genes were identified in the comparison between MI and control. Protein–protein interaction (PPI) network showed hub genes, such as MAPK14 , STAT3 , and MAPKAPK2 . Part of those conserved genes was validated in the analysis of GSE48060. The DEGs in the incident and recurrent MI showed significant differences, including RNASE2 and A2M - AS1 as the potential biomarkers of MI recurrence. Conclusions The conserved genes in the pathogenesis of MI were identified, benefit for target therapy. Meanwhile, some specific genes may be used as markers for the prediction of recurrent MI. Electronic supplementary material The online version of this article (10.1186/s40001-019-0381-x) contains supplementary material, which is available to authorized users.

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“…They revealed that TBX21 and PRF1 may be potential candidates for diagnostic biomarker in AMI. Similarly, Zhang et al used the dataset GSE48060 to screen potential biomarkers for AMI development (Zhang et al, 2017b). Huan et al analyzed coronary heart disease-related genes by constructing co-expression networks (Huan et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction

Niu

Zhang²,

Zhang

et al. 2019

Front. Genet.

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Background: The development of heart failure (HF) remains a common complication following an acute myocardial infarction (AMI), and is associated with substantial adverse outcomes. However, the specific predictive biomarkers and candidate therapeutic targets for post-infarction HF have not been fully established. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify key modules, hub genes, and possible regulatory targets involved in the development of HF following AMI. Methods: Genes exhibiting the most (top 50%) variation in expression levels across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology and pathway enrichment analyses were performed on genes identified in the key module by Metascape. Gene regulatory networks were constructed using the microarray probe reannotation and bioinformatics database. Hub genes were screened out from the key module and validated using other datasets. Results: A total of 10,265 most varied genes and six modules were identified between AMI patients who developed HF within 6 months of follow-up and those who did not. Specifically, the blue module was found to be the most significantly related to the development of post-infarction HF. Functional enrichment analysis revealed that the blue module was primarily associated with the inflammatory response, immune system, and apoptosis. Seven transcriptional factors, including SPI1, ZBTB7A, IRF8, PPARG, P65, KLF4, and Fos, were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-142-3p and LINC00537, were identified as having close interactions with genes from the blue module. A total of six hub genes (BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B) were identified and validated for their predictive value in identifying future HFs. Conclusions: By using the WGCNA, we provide new insights into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions, and prognosis prediction in AMI patients.

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Biomarkers identification for acute myocardial infarction detection via weighted gene co-expression network analysis

Cited by 22 publications

References 44 publications

Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis

Uncovering potential differentially expressed miRNAs and targeted mRNAs in myocardial infarction based on integrating analysis

Identification of key genes involved in myocardial infarction

Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction

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