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

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Keywords: acute myeloid leukemia; apoptosis; biomarker; long non-coding RNA; nomogram; risk stratification.

FIGURE 1

Identification of a module related to the clinical characteristics of AML by WGCNA. (A) A soft threshold (power) was obtained. The red line indicates that the scale‐free topology model was fitted to 0.85. (B) The gene dendrogram was constructed by hierarchical clustering. The different colors below the dendrogram display the modules corresponding to the co‐expressed genes. (C) Module–characteristics relationships. The rows represent the module eigengene (ME) and its color, and the column represents the clinical characteristics. The correlation coefficient and p‐value are shown in each cell. The red box shows the correlation between the red module, risk stratification, and age. (D) Heatmap of module adjacencies. Darker colors indicate a stronger correlation. (E) A topological overlapping heatmap of genes within the module. In the heatmap, darker colors indicate higher topological overlap.

FIGURE 2

Kaplan–Meier survival analysis of co‐expressed genes. (A and B): Kaplan–Meier survival analysis of LOC541471, STK25, SOD1, GDAP1, IL7, PLA2G6, RDH10, and SDPR in the training (A) and validation (B) cohorts. (C and D): The bubble plots show the Spearman correlation coefficient and P values between LOC541471 and STK25, SOD1, GDAP1, IL7, PLA2G6, RDH10, and SDPR in the training (C) and validation (D) cohorts.

FIGURE 3

Weighted combination analysis of LOC541471 and co‐expressed mRNAs. (A) The multivariate COX regression coefficients for LOC541471 and co‐expressed mRNAs before (left) and after (right) adjustment in the training cohort. (B) The Kaplan–Meier for AML patients with low‐, intermediate‐, and high‐risk scores were plotted based on the risk scores 1.3 and 1.9 in training (left) and validation (right) cohorts.

FIGURE 4

Nomogram to visualize and personalize the OS rate for AML patients. (A) Construction of a nomogram model. In general, each covariate of an individual contributes a point based on the evaluation of the nomogram model. Total points are obtained by adding the given points for all covariates. Then, the total points corresponding to the 1‐, 3‐, and 5‐year OS rates can be represented by the nomogram model. A higher total point usually indicates a lower expected OS rate. B and D: 1‐ (left), 3‐ (middle), and 5‐year (right) calibration curves for the nomogram model in training (B) and validation (D) cohorts. (C and E): Time‐dependent receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of the nomogram model in the training (C) and validation (E) cohorts.

FIGURE 5

Establishment of risk stratification for AML patients. A and C: The Kaplan–Meier (left) and time‐dependent ROC curves (right) for AML patients with low, intermediate, and high risk were plotted according to the total points 48 and 94 in the training (A) and validation (C) cohorts. B and D: Kaplan–Meier (left) and time‐dependent ROC curves (right) of AML patients were constructed based on existing European Leukemia Network (ELN) risk stratification in the training (B) and validation (D) cohorts.

FIGURE 6

Knockdown of LOC541471 or GDAP1 promotes apoptosis and inhibits proliferation in AML cells. (A) Representative plots of the apoptosis of THP‐1 cells detected by flow cytometry. THP‐1 cells were transfected with control siRNA, LOC541471 siRNA, or GDAP1 siRNA. (B) Quantitative real‐time RT‐PCR (qRT‐PCR) was used to detect the expression level of LOC541471 or GDAP1 in THP‐1 cells after knocking down LOC541471 or GDAP1. 18 S rRNA served as an internal control. (C) The apoptosis rate and cell viability of THP‐1 cells transfected with corresponding siRNAs in three repeated experiments were analyzed. (D) Representative plots of the apoptosis of THP‐1 cells transfected with corresponding siRNAs followed by a 24 h AraC (1 μM) treatment. (E) The apoptosis rate and cell viability of THP‐1 cells transfected with corresponding siRNAs followed by a 24 h AraC (1 μM) treatment.