Novel prognostic genes and subclasses of acute myeloid leukemia revealed by survival analysis of gene expression data

• Published in: BMC medical genomics Vol. 14; no. 1; pp. 39 - 10
• Main Authors: Lai, Yanli, OuYang, Guifang, Sheng, Lixia, Zhang, Yanli, Lai, Binbin, Zhou, Miao
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 03.02.2021; BioMed Central; BMC
• Subjects: Acute myeloid leukemia; Adult; Aged; Biomarkers; Biomarkers, Tumor - genetics; Bone marrow; Cancer; Cytogenetics; Decision making; Female; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Leukemic; Gene Regulatory Networks; Genomes; Humans; Leukemia; Leukemia, Myeloid, Acute - genetics; Leukemia, Myeloid, Acute - mortality; Leukemia, Myeloid, Acute - pathology; Male; Medical prognosis; Middle Aged; Mortality; Mutation; Myeloid leukemia; Nucleophosmin; Oncology, Experimental; Overall survival; Prognosis; Ribonucleic acid; Risk score; RNA; RNA sequencing; Survival; Survival Analysis; The cancer genome atlas database; Weighted gene co-expression network analysis; China; The cancer genome atlas database; Risk score; Overall survival; Acute myeloid leukemia; Weighted gene co-expression network analysis
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-021-00888-0

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. 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. 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.