Dataset Information

Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma.

ABSTRACT: Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods. Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events. Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

SUBMITTER: Zhou JG

PROVIDER: S-EPMC6904333 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma.

Zhou Jian-Guo JG Liang Bo B Jin Su-Han SH Liao Hui-Ling HL Du Guo-Bo GB Cheng Long L Ma Hu H Gaipl Udo S US

Frontiers in oncology 20191204

<b>Objective:</b> The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare. <b>Methods:</b> RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival pre ...[more]

PMID: 31867276

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Project description:Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature. Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided. In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures. Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

Project description:Neuroblastoma (NBL) originating from the sympathetic nervous system is the most prevalent solid tumor in infancy. Although there is sufficient variability in prognosis among different age pyramids, age-related gene expression profiles and biomarkers remain poorly explored. The present study aimed to construct a signature based on differentially expressed genes (DEGs) between two age groups in NBL. Univariate Cox regression, multivariate Cox regression, and LASSO analyses were used to identify the optimal prognostic factors. The prediction ability of the model was assessed using the receiver operating characteristic (ROC) curve and C-index. Functional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes and gene ontology databases. A total of 1,160 DEGs were identified between the two groups, and 204 DEGs impacted the survival of NBL. Functional enrichment analysis revealed that the DEGs were involved in retinol metabolism, cholesterol metabolism, and glycolysis/gluconeogenesis pathways. Five RNAs, namely F8A3, PDF, ANKRD24, FAXDC2, and TMEM160 were recruited into the signature. They were correlated with COG risk classification, INSS stage, and histology. MYCN amplification was linked to FAXDC2, TMEM160, PDF, and F8A3. The expression levels of ANKRD24, PDF, and TMEM160 were lower in the hyperdiploid groups. Only FAXDC2 levels were different in the different MKI grades. The ROC curve showed that the five-RNA-based signatures effectively predicted the OS of NBL (3-years AUC = 0.791, 5-years AUC = 0.816) in the TARGET cohort. The predictive capability was also validated by the GSE49711 cohort (3-years AUC = 0.851, 5-years AUC = 0.848). The C-index in the TARGET and GSE49711 cohorts was 0.749 and 0.809, respectively. The potential mechanisms of the five RNAs were also explored via gene set enrichment analysis, and candidate drugs targeting the five genes, including dabrafenib, vemurafenib, and bafetinib, were screened. In conclusion, we constructed a five-RNA-based signature to predict the survival of NBL and screened candidate agents against NBL.

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Project description:BackgroundNeuroblastoma (NBL) is the most common extracranial solid tumor in childhood, and patients with high-risk neuroblastoma had a relatively poor prognosis despite multimodal treatment. To improve immunotherapy efficacy in neuroblastoma, systematic profiling of the immune landscape in neuroblastoma is an urgent need.MethodsRNA-seq and according clinical information of neuroblastoma were downloaded from the TARGET database and GEO database (GSE62564). With an immune-related-gene set obtained from the ImmPort database, Immune-related Prognostic Gene Pairs for Neuroblastoma (IPGPN) for overall survival (OS) were established with the TARGET-NBL cohort and then verified with the GEO-NBL cohort. Immune cell infiltration analysis was subsequently performed. The integrated model was established with IPGPN and clinicopathological parameters. Immune cell infiltration was analyzed with the XCELL algorithm. Functional enrichment analysis was performed with clusterProfiler package in R.ResultsImmune-related Prognostic Gene Pairs for Neuroblastoma was successfully established with seven immune-related gene pairs (IGPs) involving 13 unique genes in the training cohort. In the training cohort, IPGPN successfully stratified neuroblastoma patients into a high and low immune-risk groups with different OS (HR=3.92, P = 2 × 10-8) and event-free survival (HR=3.66, P=2 × 10-8). ROC curve analysis confirmed its predictive power. Consistently, high IPGPN also predicted worse OS (HR=1.84, P = .002) and EFS in validation cohort (HR=1.38, P = .06) Moreover, higher activated dendritic cells, M1 macrophage, Th1 CD4+, and Th2 CD4+ T cell enrichment were evident in low immune-risk group. Further integrating IPGPN with age and stage demonstrated improved predictive performance than IPGPN alone.ConclusionHerein, we presented an immune landscape with IPGPN for prognosis prediction in neuroblastoma, which complements the present understanding of the immune signature in neuroblastoma.

Dataset Information

Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma.

Publications

Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma.

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