Predicting leukemic transformation in myelodysplastic syndrome using a transcriptomic signature.

ABSTRACT: Background: For prediction on leukemic transformation of MDS patients, emerging model based on transcriptomic datasets, exhibited superior predictive power to traditional prognostic systems. While these models were lack of external validation by independent cohorts, and the cell origin (CD34⁺ sorted cells) limited their feasibility in clinical practice. Methods: Transformation associated co-expressed gene cluster was derived based on GSE58831 ('WGCNA' package, R software). Accordingly, the least absolute shrinkage and selection operator algorithm was implemented to establish a scoring system (i.e., MDS15 score), using training set (GSE58831 originated from CD34⁺ cells) and testing set (GSE15061 originated from unsorted cells). Results: A total of

SUBMITTER: Guo C 

PROVIDER: S-EPMC10634373 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature