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.

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.

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.

Long noncoding RNAs (lncRNAs) participate in cancer immunity. We characterized the clinical significance of an immune-related lncRNA model and evaluated its association with immune infiltrations and chemosensitivity in bladder cancer. Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed, and receiver operator characteristic curves of one-, three- and five-year survival were established. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups. Totally, 90 immune-related lncRNA pairs were identified, 15 of which were screened for constructing the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered patients into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. The risk model was related to survival status, age, stage and TNM. Compared with conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and served as an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate. Collectively, the model conducted by paring immune-related lncRNAs regardless of expressions exhibits a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.