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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level
Published on: March 14, 2022
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Endothelial cell senescence: A machine learning-based meta-analysis of transcriptomic studies
Hyun Suk Park1, Sung Young Kim1
1Department of Biochemistry, Konkuk University School of Medicine, Seoul, Republic of Korea.
Ageing Research Reviews
|November 15, 2020
Summary
This study identified key genetic and pathway features of endothelial cell senescence using meta-analysis. These findings, including phosphoglycerate dehydrogenase and serine biosynthesis, offer new therapeutic targets for age-related vascular dysfunction.
Area of Science:
- Vascular Biology
- Cellular Senescence
- Bioinformatics
Background:
- Endothelial cell (EC) senescence is linked to age-related vascular dysfunction.
- Identifying consensus features of EC senescence is vital for understanding mechanisms and developing therapies.
Purpose of the Study:
- To identify common gene and pathway features of endothelial cell senescence through meta-analysis.
- To discover novel therapeutic targets for vascular dysfunction.
Main Methods:
- Meta-analysis of 8 screened studies on EC senescence.
- Application of machine learning algorithms to identify differentially expressed genes (DEGs) and pathways.
- Leave-one-study-out cross-validation (LOSOCV) and Pathifier algorithm for analysis.
Main Results:
- Identified 400 novel DEGs and 36 core gene features.
- Discovered 57 core pathways, notably phosphoglycerate dehydrogenase and serine biosynthesis pathway.
- Achieved high discriminative performance with AUROC values of 0.983 for genes and 0.982 for pathways.
Conclusions:
- Sophisticated meta-analysis effectively identifies consensus features of EC senescence.
- The findings provide a robust model for understanding vascular dysfunction pathogenesis.
- Identified features represent potential therapeutic targets for age-related diseases.
