European journal of medical research · 2025
Identification of key genes and regulatory networks associated with atherosclerotic carotid artery stenosis through comprehensive bioinformatics analysis and machine learning.
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Yang Lei, He Peidong, Lei Wenkang, Wang Zhen, Liang Jingjing
Аннотация
OBJECTIVE: To identify the potential diagnostic biomarkers and therapeutic targets of atherosclerotic carotid artery stenosis (ACAS), a comprehensive bioinformatics analysis was conducted to identify its related key genes and regulatory networks.
METHODS: Four datasets, including GSE43292, GSE100927, GSE28829, and GSE198600, were integrated to form the training set, with the GSE163154 dataset serving as the validation set. Subsequently, differential expression and functional enrichment analysis were performed on the training set. In addition, key pathogenic genes were identified using the protein-protein interaction networks, molecular complex detection technique, and three machine learning (ML) algorithms. These identified genes were validated through inter-group differences and receiver operating characteristic (ROC) curve analyses. Immune-related functions and immune cell correlations were analyzed and verified using ACAS plaque tissue samples.
RESULTS: Following the analysis, a total of 33 downregulated and 52 upregulated genes were identified. Furthermore, enrichment analysis of gene sets demonstrated that the highly expressed group was involved in cellular receptor signaling, leishmaniasis infection, lysosome, PPAR-signaling, and Toll-like receptor pathways. In contrast, the low-expressed group was involved in mechanisms involving dilated cardiomyopathy, pyruvate metabolism, hypertrophic cardiomyopathy, spliceosome, and TGF-β signaling pathways. Notably, ANPEP, CSF1R, MMP9, and CASQ2 were found to differ significantly between groups. Correlation analysis revealed positive associations between MMP9 expression and neutrophil infiltration, CASQ2 expression and M2 macrophage abundance, and CSF1R expression and M1 macrophage levels.
CONCLUSION: Consequently, these genes (ANPEP, CSF1R, MMP9, and CASQ2) may serve as potential biomarkers and therapeutic targets in the diagnosis and treatment of ACAS.
METHODS: Four datasets, including GSE43292, GSE100927, GSE28829, and GSE198600, were integrated to form the training set, with the GSE163154 dataset serving as the validation set. Subsequently, differential expression and functional enrichment analysis were performed on the training set. In addition, key pathogenic genes were identified using the protein-protein interaction networks, molecular complex detection technique, and three machine learning (ML) algorithms. These identified genes were validated through inter-group differences and receiver operating characteristic (ROC) curve analyses. Immune-related functions and immune cell correlations were analyzed and verified using ACAS plaque tissue samples.
RESULTS: Following the analysis, a total of 33 downregulated and 52 upregulated genes were identified. Furthermore, enrichment analysis of gene sets demonstrated that the highly expressed group was involved in cellular receptor signaling, leishmaniasis infection, lysosome, PPAR-signaling, and Toll-like receptor pathways. In contrast, the low-expressed group was involved in mechanisms involving dilated cardiomyopathy, pyruvate metabolism, hypertrophic cardiomyopathy, spliceosome, and TGF-β signaling pathways. Notably, ANPEP, CSF1R, MMP9, and CASQ2 were found to differ significantly between groups. Correlation analysis revealed positive associations between MMP9 expression and neutrophil infiltration, CASQ2 expression and M2 macrophage abundance, and CSF1R expression and M1 macrophage levels.
CONCLUSION: Consequently, these genes (ANPEP, CSF1R, MMP9, and CASQ2) may serve as potential biomarkers and therapeutic targets in the diagnosis and treatment of ACAS.