High expression of LOC541471, GDAP1, SOD1, and STK25 is associated with poor overall survival of patients with acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive heterogeneous hematological malignancy with remarkably heterogeneous outcomes. This study aimed to identify potential biomarkers for AML risk stratification via analysis of gene expression profiles.RNA sequencing data from 167 adult AML patients in the Cancer Genome Atlas (TCGA) database were obtained for overall survival (OS) analysis, and 52 bone marrow (BM) samples from our clinical center were used for validation. Additionally, siRNA was used to investigate the role of prognostic genes in the apoptosis and proliferation of AML cells.Co-expression of 103 long non-coding RNAs (lncRNAs) and mRNAs in the red module that were positively correlated with European Leukemia Network (ELN) risk stratification and age was identified by weighted gene co-expression network analysis (WGCNA). After screening by uni- and multivariate Cox regression, Kaplan-Meier survival, and protein-protein interaction analysis, four genes including the lncRNA LOC541471, GDAP1, SOD1, and STK25 were incorporated into calculating a risk score from coefficients of the multivariate Cox regression model. Notably, GDAP1 expression was the greatest contributor to OS among the four genes. Interestingly, the risk score, ELN risk stratification, and age were independent prognostic factors for AML patients, and a nomogram model constructed with these factors could illustrate and personalize the 1-, 3-, and 5-year OS rates of AML patients. The calibration and time-dependent receiver operating characteristic curves (ROCs) suggested that the nomogram had a good predictive performance. Furthermore, new risk stratification was developed for AML patients based on the nomogram model. Importantly, knockdown of LOC541471, GDPA1, SOD1, or STK25 promoted apoptosis and inhibited the proliferation of THP-1 cells compared to controls.High expression of LOC541471, GDAP1, SOD1, and STK25 may be biomarkers for risk stratification of AML patients, which may provide novel insight into evaluating prognosis, monitoring progression, and designing combinational targeted therapies.