A novel risk signature that combines 10 long noncoding RNAs to predict neuroblastoma prognosis.

Neuroblastoma (NBL) is the most frequently encountered extracranial solid neoplasm and impacts significantly on the survival of patients, especially in cases of advanced tumor stage or relapse. A long noncoding RNA (lncRNA) signature to predict the survival of patients with NBL is proposed in this paper. Differentially expressed lncRNA (DElncRNA) was selected using the Limma plus Voom package in R based on the RNA-sequencing data downloaded from the Therapeutically Applicable Research To Generate Effective Treatments database and Genotype-Tissue Expression database. Univariate cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate cox regression analysis were conducted to identify candidate DElncRNAs for the risk signature. Consequently, 10 DElncRNAs were designated as candidate DElncRNAs for the risk signature. Time-dependent receiver operating characteristic curves and Kapan-Meier survival curves confirmed the efficacy of the risk signature in predicting the survival of patients with NBL (area under the curve = 0.941; p ≤ .001). One of the DElncRNA constituent subparts (LINC01010) was significantly associated with the survival outcome of patients with NBL in GSE62564 (p = .004). Thus, a risk signature comprising 10 DElncRNAs was identified as effective for individual risk stratification and the survival prediction outcomes of patients with NBL.

RNA-Sequencing, Connectivity Mapping, and Molecular Docking to Investigate Ligand-Protein Binding for Potential Drug Candidates for the Treatment of Wilms Tumor.

BACKGROUND Wilms tumor, or nephroblastoma, is a malignant pediatric embryonal renal tumor that has a poor prognosis. This study aimed to use bioinformatics data, RNA-sequencing, connectivity mapping, molecular docking, and ligand-protein binding to identify potential targets for drug therapy in Wilms tumor. MATERIAL AND METHODS Wilms tumor and non-tumor samples were obtained from high throughput gene expression databases, and differentially expressed genes (DEGs) were analyzed using the voom method in the limma package. The overlapping DEGs were obtained from the intersecting drug target genes using the Connectivity Map (CMap) database, and systemsDock was used for molecular docking. Gene databases were searched for gene expression profiles for complementary analysis, analysis of clinical significance, and prognosis analysis to refine the study. RESULTS From 177 cases of Wilms tumor, there were 648 upregulated genes and 342 down-regulated genes. Gene Ontology (GO) enrichment analysis showed that the identified DEGs that affected the cell cycle. After obtaining 21 candidate drugs, there were seven overlapping genes with 75 drug target genes and DEGs. Molecular docking results showed that relatively high scores were obtained when retinoic acid and the cyclin-dependent kinase inhibitor, alsterpaullone, were docked to the overlapping genes. There were significant standardized mean differences for three overlapping genes, CDK2, MAP4K4, and CRABP2. However, four upregulated overlapping genes, CDK2, MAP4K4, CRABP2, and SIRT1 had no prognostic significance. CONCLUSIONS RNA-sequencing, connectivity mapping, and molecular docking to investigate ligand-protein binding identified retinoic acid and alsterpaullone as potential drug candidates for the treatment of Wilms tumor.

The Expression and Potential Role of Tubulin Alpha 1b in Wilms' Tumor.

We explored the difference in expression of tubulin alpha 1b (TUBA1B) between Wilms' tumor (WT) and normal tissues (NT) from in-house patients and databases, to determine TUBA1B expression in WT and the predictive pathways of coexpressed genes. In-house RNA-sequencing data were performed with WT and NT from three patients from our institute. Other four RNA-sequencing and microarray data were also downloaded from multiple public databases. The TUBA1B expression between WT and NT was analyzed by Student's t-test and meta-analysis. The correlation between the expression of TUBA1B and other genes in each study was analyzed. Genes with p < 0.05 and r > 0.5 were considered as the coexpressing genes of TUBA1B. Overlapping the coexpressed genes of the five studies, including three in-house patients (3 WT vs. 3 NT), GTEx-TARGET (126 WT vs. 51 NT), GSE2172 (18 WT vs. 3 NT), GSE11024 (27 WT vs. 12 NT), and GSE73209 (32 WT vs. 6 NT), were performed with limma and VennDiagram packages in R software. The website of WEB-based GEne SeT AnaLysis toolkit were used to analyze the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotations for the overlapped genes. The results showed that the relative expression of TUBA1B in WT tissues from in-house three patients was 280.0086, 141.7589, and 303.8292 and that in NT was 16.5836, 104.8141, and 12.79 (3 WT vs. 3 NT, p = 0.0285, ROC = 100%, SMD = 2.74). Student's t-test and meta-analysis in all studies revealed that the expression of TUBA1B was upregulated in WT tissues compared to that in NT (p < 0.05, SMD = 2.89, sROC = 0.98). Finally, the research identified the expression of TUBA1B in WT tissues was significantly upregulated than that in NT. The coexpressed genes of TUBA1B were enriched in the pathway of DNA replication, mismatch repair, cell cycle, pathogenic Escherichia coli infection, and spliceosome.

Nomogram for predicting overall survival in children with neuroblastoma based on SEER database.

PURPOSE: This study was performed to establish and validate a nomogram for predicting the overall survival in children with neuroblastoma. METHODS: The latest clinical data of neuroblastoma in Surveillance, Epidemiology, and End Results (SEER) database was extracted from 2000 to 2016. The cases included were randomly divided into training and validation cohorts. The survival curves were drawn with a Kaplan-Meier estimator to investigate the influences of certain single factors on overall survival. Also, least absolute shrinkage and selection operator regression was applied to further select the prognostic variables for neuroblastoma. Additionally, receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the accuracy of the nomogram. RESULTS: In total, 1,262 patients were collected and 8 independent prognostic factors were achieved, including patients' age, sex, race, tumor grade, radiotherapy, chemotherapy, tumor site, and tumor size. Then we constructed a nomogram by using the data of the training cohort with 886 cases. Subsequently, the nomogram was validated internally and externally with 886 and 376 cases, respectively. The internal validation revealed that the area under the curves (AUC) of ROC curves of 1-, 3-, and 5-year overall survival were 0.69, 0.78, and 0.81, respectively. Accordingly, the external validation also showed that the AUC of 1-, 3-, and 5-year overall survival were all ≥0.69. Both methods of validation demonstrated that the predictive calibration curves were consistent with standard curves. CONCLUSION: The nomogram possess the potential to be a new tool in predicting the survival rate of neuroblastoma patients.