Identification of key genes in coronary artery disease: an integrative approach based on weighted gene co-expression network analysis and their correlation with immune infiltration

Yang, Yang; Xu, Xiangshan

doi:10.18632/aging.202638

“…We evaluate the type and fraction of immune cell in ltration between CAD patients and normal samples in the dataset using the CIBERSORT algorithm. Our results found CD8 T cells and NK cells share a decreased in ltration, and the in ltration of monocytes was increased in CAD patients, which was similar to the previous results [45][46][47]. In this GEO dataset, CD8 T cells and NK cells are favorable factors for preventing CAD, and monocytes likely promote the occurrence of CAD.…”

Section: Discussionsupporting

confidence: 90%

Epithelial-Mesenchymal Transition-Related lncRNAs And SNAI2 Are Potential Biomarkers in Coronary Artery Disease

Xu

¹

,

Zou

²

,

Liu

³

et al. 2021

Preprint

0

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BackgroundIncreasing evidence suggests that epithelial-mesenchymal transformation (EMT) is critical in the development of inflammatory response, atherosclerosis, and coronary artery disease (CAD). However, landscapes of EMT-related lncRNAs and their target genes have not been fully established in CAD.MethodsLncRNA and mRNA expression profiles obtained from Gene Expression Omnibus (GEO) database were used to identify the differentially expressed mRNAs (DEGs) and lncRNAs (DElncRNAs) between CAD and normal samples. Based on Pearson correlation analysis to identify the EMT-related lncRNAs, the optimal features were identified by receiver operating characteristic (ROC), the least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine Reverse Feature Elimination (SVM-RFE) algorithms, and logistic regression models were constructed aiming to distinguish CAD from normal samples. The cis and trans-regulatory networks were constructed based on EMT-related lncRNAs. We further estimated the infiltration of the immune cells in CAD patients with the CIBERSORT algorithm, and the correlation between key genes and infiltrating immune cells was analyzed.ResultsIn this study, a logistic regression model with powerful diagnostic capability was constructed based on a total of eight EMT-related lncRNAs identified by two machine learning methods. Then, results of the immune analysis revealed three significant immune cell subsets (CD8 T cells, monocytes, and NK cells) in CAD patients and found EMT-related lncRNAs were closely correlated with these immune cell subsets. By Pearson correlation analysis we got 34 “cis” and “trans” genes. Among them, SNAI2, an EMT-TF gene, was found in the trans-regulatory network of EMT-related lncRNAs. Further, through logistic regression and analysis of immune cell infiltration, we found SNAI2 was a potential biomarker for the diagnosis of CAD but also a close correlation between highly expressed SNAI2 and these three immune cell subsets in CAD patients.ConclusionIn conclusion, these biomarkers have important significance in the diagnosis of CAD patients. Eight EMT-related lncRNAs and SNAI2 can improve our understanding of the molecular mechanism between EMT and CAD.

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“…We evaluate the type and fraction of immune cell in ltration between CAD patients and normal samples in the dataset using the CIBERSORT algorithm. Our results found CD8 T cells and NK cells share a decreased in ltration, and the in ltration of monocytes was increased in CAD patients, which was similar to the previous results [46][47][48]. In this GEO dataset, CD8 T cells and NK cells are favorable factors for preventing CAD, and monocytes likely promote the occurrence of CAD.…”

Section: Discussionsupporting

confidence: 90%

Epithelial-Mesenchymal Transition-Related lncRNAs And SNAI2 Are Potential Biomarkers in Coronary Artery Disease

Xu

¹

,

Zou

²

,

Liu

³

et al. 2021

Preprint

1

0

View full text Add to dashboard Cite

BackgroundIncreasing evidence suggests that epithelial-mesenchymal transformation (EMT) is critical in the development of inflammatory response, atherosclerosis, and coronary artery disease (CAD). However, landscapes of EMT-related lncRNAs and their target genes have not been fully established in CAD.MethodsLncRNA and mRNA expression profiles obtained from Gene Expression Omnibus (GEO) database were used to identify the differentially expressed mRNAs (DEGs) and lncRNAs (DElncRNAs) between CAD and normal samples. Based on Pearson correlation analysis to identify the EMT-related lncRNAs, the optimal features were identified by receiver operating characteristic (ROC), the least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine Reverse Feature Elimination (SVM-RFE) algorithms, and logistic regression models were constructed aiming to distinguish CAD from normal samples. The cis and trans-regulatory networks were constructed based on EMT-related lncRNAs. We further estimated the infiltration of the immune cells in CAD patients with the CIBERSORT algorithm, and the correlation between key genes and infiltrating immune cells was analyzed.ResultsIn this study, a logistic regression model with powerful diagnostic capability was constructed based on a total of eight EMT-related lncRNAs identified by two machine learning methods. Then, results of the immune analysis revealed three significant immune cell subsets (CD8 T cells, monocytes, and NK cells) in CAD patients and found EMT-related lncRNAs were closely correlated with these immune cell subsets. By Pearson correlation analysis we got 34 “cis” and “trans” genes. Among them, SNAI2, an EMT-TF gene, was found in the trans-regulatory network of EMT-related lncRNAs. Further, through logistic regression and analysis of immune cell infiltration, we found SNAI2 was a potential biomarker for the diagnosis of CAD but also a close correlation between highly expressed SNAI2 and these three immune cell subsets in CAD patients.ConclusionIn conclusion, these biomarkers have important significance in the diagnosis of CAD patients. Eight EMT-related lncRNAs and SNAI2 can improve our understanding of the molecular mechanism between EMT and CAD.

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“…Its transcriptional expression also is more downregulated in smokers than nonsmokers at altitude, which might be related to the inhibition of the hypoxic response [ 43 ]. LRRC18 was also identified as a critical gene in coronary artery disease diagnosis [ 44 ], and its single-nucleotide polymorphisms are associated with systemic lupus erythematosus [ 45 ]. However, the relationship between these genes and the HAPC remains unclear.…”

Section: Discussionmentioning

confidence: 99%

Microarray-Based Prediction of Polycythemia after Exposure to High Altitudes

Wang

¹

,

Liu

²

,

Song

³

et al. 2022

Genes

2

0

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In high-altitude environments, the prevalence of high-altitude polycythemia (HAPC) ranges between 5 and 18 percent. However, there is currently no effective treatment for this condition. Therefore, disease prevention has emerged as a critical strategy against this disease. Here, we looked into the microarray profiles of GSE135109 and GSE29977, linked to either short- or long-term exposure to the Qinghai Tibet Plateau (QTP). The results revealed inhibition in the adaptive immune response during 30 days of exposure to QTP. Following a gene set enrichment analysis (GSEA) discovered that genes associated with HAPC were enriched in Cluster1, which showed a dramatic upregulation on the third day after arriving at the QTP. We then used GeneLogit to construct a logistic prediction model, which allowed us to identify 50 genes that classify HAPC patients. In these genes, LRRC18 and HCAR3 were also significantly altered following early QTP exposure, suggesting that they may serve as hub genes for HAPC development. The in-depth study of a combination of the datasets of transcriptomic changes during exposure to a high altitude and whether diseases occur after long-term exposure in Hans can give us some inspiration about genes associated with HAPC development during adaption to high altitudes.

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Identification of key genes in coronary artery disease: an integrative approach based on weighted gene co-expression network analysis and their correlation with immune infiltration

Cited by 15 publications

References 32 publications

Epithelial-Mesenchymal Transition-Related lncRNAs And SNAI2 Are Potential Biomarkers in Coronary Artery Disease

Epithelial-Mesenchymal Transition-Related lncRNAs And SNAI2 Are Potential Biomarkers in Coronary Artery Disease

Epithelial-Mesenchymal Transition-Related lncRNAs And SNAI2 Are Potential Biomarkers in Coronary Artery Disease

Microarray-Based Prediction of Polycythemia after Exposure to High Altitudes

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