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

Abstract

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.

Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis

“…This hypothesis has been experimentally substantiated in various types of cancers, including hematological malignancies [40][41][42][43][44]. Nowadays, there are more and more studies involving applying WGCNA in AML-related analysis published in journals of different levels [45][46][47][48][49][50], which proves the recognition of this algorithm to some extent. However, there is no study aimed at finding AML survival-specific biomarkers using the WGCNA methodology based on adult CN-AML data by far.…”

m6A regulator-based methylation modification patterns and characterization of tumor microenvironment in acute myeloid leukemia

“…Interestingly, VSTM1 showed its highest expression level in the m6A gene cluster C, whereas M1AP was expressed lowest in the m6A gene cluster B ( Figure 4C ). High expression of VSTM1 was reported to be associated with a more favorable clinical outcome ( Lai et al, 2021 ). The three m6A gene clusters indicated significant differences in the expression of VIRMA, IGF2BP2, FMR1, IGF2BP3, ELAVL1, and YTHDC2 ( Figure 4D ).…”

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

“…The cytoplasmic phospholipase A2-α (cPLA2α) encoded by the PLA2G4A gene acts as an upstream regulator of the eicosanoid signaling pathway by providing intracellular AA ( Bazhan and Khaniani, 2018 ). Studies have shown that the PLA2G4A gene can be used as a biomarker in various diseases such as gastric cancer ( Bazhan and Khaniani, 2018 ), acute myeloid leukemia ( Hassan et al, 2021 ; Lai et al, 2021 ), cholangiocarcinoma ( Sun et al, 2019 ), and colorectal cancer ( Zhan et al, 2021a ). PLA2G4A activates the colorectal cancer microenvironment to produce pro-cytokines IL-17A and adenosine, thereby establishing an effective immunosuppressive microenvironment and promoting immune evasion and tumor metastasis ( Zhan et al, 2021b ).…”