A long non-coding RNAs expression signature to improve prognostic prediction of Wilms tumor in children

Abstract

Background: Wilms tumor (WT) is the most frequent malignancy of the kidney in children, and a subset of patients remains with a poor prognosis. This study aimed to identify key long non-coding RNAs (lncRNAs) related to prognosis and establish a genomic-clinicopathologic nomogram to predict survival in children with WT.

Methods: Clinical data of 124 WT patients and the relevant RNA sequencing data including lncRNAs expression signature of primary WT samples were obtained from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) Data Matrix. Then, lncRNAs associated with overall survival (OS) were identified through univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. The risk scores of 124 participants were calculated, and survival analyses were performed between low- and high-risk groups. A genomic-clinicopathologic nomogram was then developed and evaluated by time-dependent receiver operating characteristic (ROC) curves, including the area under the curve (AUC), calibration curve, and decision curve analysis. Subsequently, bioinformatics analyses were performed to explore the potential molecular mechanisms that affect the prognosis of WT. The package "DESeq2" was used to identify differentially expressed protein-coding genes (DEPCGs) between groups. Gene Set Enrichment Analysis (GSEA) was applied to explore the differences in pathways enrichment. The analytical tools CIBERSORTx and ESTIMATE were used to investigate the discrepancies of the immune microenvironment.

Results: A total of 10 lncRNAs were selected as independent predictors associated with OS (P<0.05). Participants in the high-risk group had a significantly worse OS and event-free survival (EFS) than those in the low-risk group (P<2E-16 and P=2.03E-04, respectively). The risk score and 3 clinicopathological features (gender, cooperative group protocol, and stage) were identified to construct the nomogram (combined model) (P=5.11E-17). The combined model (1-year AUC: 0.9272, 3-year AUC: 0.9428, 5-year AUC: 0.9259) and risk score model (1-year AUC: 0.9285, 3-year AUC: 0.9399, 5-year AUC: 0.9266) displayed higher predictive accuracy than that of the other models. Subsequently, 105 DEPCGs were identified. The GSEA revealed 4 significant pathways. Analysis with CIBERSORTx demonstrated that monocytes, macrophages M1, activated dendritic cells, and resting mast cells had significant infiltration differences between groups.

Conclusions: This study constructed a genomic-clinicopathologic nomogram, which might present a novel and efficient method for treating patients with WT.

Keywords: Wilms tumor (WT); bioinformatics analysis; long non-coding RNA (lncRNA); nomograms; prognosis.

Figure 1

Candidate lncRNAs selection through LASSO-penalized Cox regression. (A) 10-fold cross-validation with lambda.min. (B) Coefficient profiles of the candidate lncRNAs. lncRNAs, long non-coding RNAs; LASSO, least absolute shrinkage, and selection operator.

Figure 2

Risk score analyses of 124 WT patients in low- and high-risk groups. (A) Risk score distribution against the rank of the risk score. (B) OS status of patients. (C) Heatmap of the expression profiles. of the 10 lncRNAs. WT, Wilms tumor; OS, overall survival

Figure 3

Kaplan-Meier OS and EFS analyses between the high- and low-risk groups represented respectively by the red and blue curves. (A) All patients’ OS. (B) All patients’ EFS. (C) Male patients’ OS. (D) Male patients’ EFS. (E) Female patients’ OS. (F) Female patients’ EFS. (G) NWTS-5 patients’OS. (H) NWTS-5 patients’ EFS. (I) Stage I-II patients’ OS. (J) Stage I-II patients’ EFS. (K) Stage III-IV patients’ OS. (L) Stage III-IV patients’ EFS. (M) FHWT patients’ OS. (N) FHWT patients’ EFS. (O) DAWT patients’ OS. (P) DAWT patients’ EFS. OS, overall survival; EFS, event-free survival; NWTS-5, the fifth National Wilms’ Tumor Study; FHWT, favorable histology Wilms tumor; DAWT, diffuse anaplastic Wilms tumor.

Figure 4

Construction and validation of the nomogram in WT patients. (A) The nomogram integrating the risk score of lncRNAs and 3 clinicopathological factors (gender, cooperative group protocol, and stage) to predict 1‐, 3‐, and 5‐year OS. The time-dependent ROC curves for predicting probabilities of patients with 1-year (B), 3-year (C), and 5-year (D) OS. The calibration curve (E) and the DCA curve (F) of the nomogram for predicting probabilities of patients with 500-day OS. WT, Wilms tumor; lncRNAs, long non-coding RNAs; OS, overall survival; ROC, receiver operating characteristic; DCA, decision curve analysis.

Figure 5

Identification of differentially expressed genes and construction of PPI network. Volcano plots of differentially expressed protein-coding genes (A) and lncRNAs (B) between the low-risk group and high-risk group. (C) PPI network of differentially expressed protein-coding genes in the high-risk group. (D,E) Two densely connected regions recognized by MCODE. The upregulated and downregulated genes in the high-risk group were represented by red and blue, respectively. PPI, protein-protein interaction; lncRNAs, long non-coding RNAs; MCODE, Molecular Complex Detection.

Figure 6

The results of GSEA between the low- risk group and high-risk group. (A) Energy-dependent regulation of mTOR by LKB1-AMPK from the Reactome subset, which was significantly enriched in the high-risk group. (B,C,D) Three pathways were significantly enriched in the low-risk group. (E) Histogram including nominal P-values of GO enrichment analysis. GSEA, Gene Set Enrichment Analysis; GO, Gene Ontology.

Figure 7

Analysis of immune cell infiltration levels. Comparisons of the percentage of 22 immune cells (A), the stromal scores (B), and the immune scores (C) between the low- and high-risk groups of WT patients. WT, Wilms tumor.