Identification of Biomarkers to Construct a Competing Endogenous RNA Network and Establishment of a Genomic-Clinicopathologic Nomogram to Predict Survival for Children with Rhabdoid Tumors of the Kidney

Abstract

Rhabdoid tumor of the kidney (RTK) is a rare and severely malignant tumor occurring in infancy and early childhood, with the overall outcomes remain poor. Neither gene regulatory networks nor biomarkers to predict the prognostic outcomes have been elucidated in RTK. In this study, RNA sequencing data were obtained to identify differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) between RTK samples and normal samples. A total of 4217 mRNAs, 284 lncRNAs, and 286 miRNAs were screened out. Of those, 103 mRNAs, 80 lncRNAs, and 45 miRNAs were identified for a competing endogenous RNA (ceRNA) regulatory network, in which three significant modules were identified. A protein-protein interaction (PPI) network was constructed, and the hub-gene cluster consisted of four core genes (EXOSC2, PAK1IP1, WDR43, and POLR1D) was selected. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed to analyze the functional characteristics of differentially expressed mRNAs. Subsequently, among 211 mRNAs, 8 lncRNAs, and 12 miRNAs associated with overall survival (OS) obtained by univariate Cox analysis, 5 mRNAs, 7 lncRNAs, and 7 miRNAs were identified and the risk score formulas were constructed correspondingly using the least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. The log-rank tests and Kaplan-Meier analyses were performed to confirm the predictive value of the risk scores for OS in RTK patients. A genomic-clinicopathologic nomogram integrating the stage and risk scores based on RNAs was established and demonstrated high predictive accuracy and clinical value, which was validated through calibration curves, time-dependent receiver operating characteristic (ROC) curve analyses, and decision curve analysis (DCA). In conclusion, this study not only provided potential insights into the mechanisms underlying RTK, but also presented a practicable tool for predicting the prognosis in children with RTK.

Figure 1

The flow chart of the analysis procedure. GDC: Genomic Data Commons Data Portal; TARGET: Therapeutically Applicable Research to Generate Effective Treatments; ceRNA: competing endogenous RNA; PPI: protein-protein interaction; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; DEMs: differentially expressed mRNAs; LASSO: the least absolute shrinkage and selection operator; RTK: rhabdoid tumor of the kidney; ROC: receiver operating characteristic; DCA: decision curve analysis.

Figure 2

Volcano plots of differentially expressed mRNAs (a), lncRNAs (b), and miRNAs (c). The red dot represents upregulated and the blue dot represents downregulated. FDR: false discovery rate; FC: fold change.

Figure 3

ceRNA regulation network (a) and three significant modules identified by MCODE in Cytoscape (b–d). Ellipse indicates mRNAs, triangle indicates miRNAs, and rectangle indicates lncRNAs. Red represents upregulated and blue represents downregulated.

Figure 4

PPI network of target genes in the ceRNA network (a) and densely connected regions of the PPI network identified by MCODE (b). Red represents upregulated and blue represents downregulated.

Figure 5

GO and KEGG pathways of up- and downregulated genes. (a–c) The bubble plots showing GO functional enrichment analysis for genes that were upregulated. (d–f) The bubble plots showing GO functional enrichment analysis for downregulated genes. (g–i) The bubble and bar plots showing KEGG pathways analysis of up- and downregulated genes. GO: Gene Ontology; BP: biological process; CC: cellular component; MF: molecular function; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Figure 6

10-fold cross-validation and lambda min (a–c) and coefficient profile (d–f) of mRNAs, lncRNAs, and miRNAs in the LASSO model.

Figure 7

Risk score distribution (a–c), survival status (d–f), and the differentially expressed levels of identified mRNAs, lncRNAs, and miRNAs via heat map plot (g–i) for patients in low- and high-risk groups.

Figure 8

Kaplan-Meier survival analyses (a) and its subgroup analyses based on stages I-II (b) and III-IV (c) for patients in low- and high-risk groups represented by the blue and red curves, respectively.

Figure 9

Establishment and validation of the predictive nomogram. The nomogram (a), the calibration curves (b–d), the time-dependent ROC curves (e–g), and the DCA curves (h–j) of the nomogram for predicting probabilities of patients with 1-, 3-, and 5-year OS. ROC: receiver operating characteristic; DCA: decision curve analysis.