Endothelial cell senescence: A systematic review and machine learning-based meta-analysis of transcriptomic studies.

Endothelial cell senescence: A systematic review and machine learning-based meta-analysis of transcriptomic studies. Ageing Res Rev. 2020 Nov 12;:101213 Authors: Park HS, Kim SY Abstract Numerous systemic vascular dysfunction that leads to age-related diseases is highly associated with endothelial cell senescence; thus, identifying consensus features of endothelial cell senescence is crucial in understanding the mechanisms and identifying potential therapeutic targets. Here, by utilizing a total of 8 screened studies from four different origins, we have successfully obtained common features in both gene and pathway level via sophisticated machine learning algorithms. A total of 400 differentially expressed genes (DEGs) were newly discovered with meta-analysis when compared to the usage of individual studies. The generated parsimonious model established 36 genes and 50 pathways features with non-zero coefficient, suggesting remarkable association of phosphoglycerate dehydrogenase and serine biosynthesis pathway with endothelial cellular senescence. For the cross-validation process to measure model performance of 36 deduced features, leave-one-study-out cross-validation (LOSOCV) was employed, resulting in an overall area under the receiver operating characteristic (AUROC) of 0.983 (95 % CI, 0.952-1.000) showing excellent discriminative performance. Moreover, pathway-level analysis was performed by Pathifier algorithm, obtaining a total...
Source: Ageing Research Reviews - Category: Genetics & Stem Cells Authors: Tags: Ageing Res Rev Source Type: research