Unknown

Dataset Information

The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML.


ABSTRACT:

Background

The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA.

Methods

We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and 'WGCNA' package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the pre

SUBMITTER: Guo C 

PROVIDER: S-EPMC8164775 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature