Background: Acute myeloid leukemia (AML) is biologically heterogeneous diseases with adverse prognosis. This study was conducted to find prognostic biomarkers that could effectively classify AML patients and provide guidance for treatment decision making.
Methods: Weighted gene co-expression network analysis was applied to detect co-expression modules and analyze their relationship with clinicopathologic characteristics using RNA sequencing data from The Cancer Genome Atlas database. The associations of gene expression with patients' mortality were investigated by a variety of statistical methods and validated in an independent dataset of 405 AML patients. A risk score formula was created based on a linear combination of five gene expression levels.
Results: The weighted gene co-expression network analysis detected 63 co-expression modules. The pink and darkred modules were negatively significantly correlated with overall survival of AML patients. High expression of FNDC3B, VSTM1 and CALR was associated with favourable overall survival, while high expression of PLA2G4A was associated with adverse overall survival. Hierarchical clustering analysis of FNDC3B, VSTM1, PLA2G4A, GOLGA3 and CALR uncovered four subgroups of AML patients. The cluster1 AML patients showed younger age, lower cytogenetics risk, higher frequency of NPM1 mutations and more favourable overall survival than cluster3 patients. The risk score was demonstrated to be an indicator of adverse prognosis in AML patients CONCLUSIONS: The FNDC3B, VSTM1, PLA2G4A, GOLGA3, CALR and risk score may serve as key prognostic biomarkers for the stratification and ultimately guide rational treatment of AML patients.
Keywords: Acute myeloid leukemia; Overall survival; Risk score; The cancer genome atlas database; Weighted gene co-expression network analysis.