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.
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 dat...
| Publicado en: | BioMed Research International pp. 1 - 28 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/27/2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145340382&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145340382 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/27/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 145340382 145340382 145340382 10.1155/2020/5843874 145340382 ppf: 1 ppct: 27 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: 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. aug: au: Wang, Xiaoqing Wu, Xiangyu Li, Tianyou Cui, Mingyu Zhu, Lichao Wang, Gang Guo, Feng affil: Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China sug: subj: Biological Markers Kidney Neoplasms Prognosis Neoplasms, Complex and Mixed Prognosis RNA, Messenger Blood MicroRNA Blood Risk Assessment Human Survival Gene Expression Univariate Statistics Cox Proportional Hazards Model Log-Rank Test Kaplan-Meier Estimator ROC Curve Models, Statistical ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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