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Purpose <p>This study aimed to identify novel candidates that regulate Endothelial to mesenchymal transition(EndMT) in atherosclerosis by integrating multi-omics data.</p> Methods <p>The single-cell RNA sequencing (scRNA-seq) dataset GSE159677, bulk RNA-seq dataset GSE118446 and microarray dataset GSE56309 were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Differentially expressed genes (DEGs) from GSE118446 and GSE56309 were analyzed using limma package. Functional enrichment analysis was applied by DAVID functional annotation tool. Quantitative real-time polymerase chain reaction (qPCR) and western blotting were used for further validation.</p> Results <p>Nine endothelial cell (EC) clusters were identified in human plaques, with EC cluster 5 exhibiting an EndMT phenotype. The intersection of genes from EC cluster 5 and common DEGs in vitro EndMT models revealed seven mesenchymal candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1. Validation of these findings was carried out through qPCR analysis.</p> Conclusion <p>Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1.</p>

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Uncovering endothelial to mesenchymal transition drivers in atherosclerosis via multi-omics analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月17日

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  • Qingyan Huang,
  • Yuhong Gan,
  • Xiaoqi Zheng,
  • Zhikang Yu,
  • Qionghui Huang,
  • Mingfeng Huang

摘要

Purpose

This study aimed to identify novel candidates that regulate Endothelial to mesenchymal transition(EndMT) in atherosclerosis by integrating multi-omics data.

Methods

The single-cell RNA sequencing (scRNA-seq) dataset GSE159677, bulk RNA-seq dataset GSE118446 and microarray dataset GSE56309 were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Differentially expressed genes (DEGs) from GSE118446 and GSE56309 were analyzed using limma package. Functional enrichment analysis was applied by DAVID functional annotation tool. Quantitative real-time polymerase chain reaction (qPCR) and western blotting were used for further validation.

Results

Nine endothelial cell (EC) clusters were identified in human plaques, with EC cluster 5 exhibiting an EndMT phenotype. The intersection of genes from EC cluster 5 and common DEGs in vitro EndMT models revealed seven mesenchymal candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1. Validation of these findings was carried out through qPCR analysis.

Conclusion

Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1.