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Combined analysis of RNA-sequence and microarray data reveals effective metabolism-based prognostic signature for neuroblastoma.

ABSTRACT: The relationship between metabolism reprogramming and neuroblastoma (NB) is largely unknown. In this study, one RNA-sequence data set (n = 153) was used as discovery cohort and two microarray data sets (n = 498 and n = 223) were used as validation cohorts. Differentially expressed metabolic genes were identified by comparing stage 4s and stage 4 NBs. Twelve metabolic genes were selected by LASSO regression analysis and integrated into the prognostic signature. The metabolic gene signature successfully stratifies NB patients into two risk groups and performs well in predicting survival of NB patients. The prognostic value of the metabolic gene signature is also independent with other clinical risk factors. Nine metabolism-related long non-coding RNAs (lncRNAs) were also identified and integrated into the metabolism-related lncRNA signature. The lncRNA signature also performs well in predicting survival of NB patients. These results suggest that the metabolic signatures have the potential to be used for risk stratification of NB. Gene set enrichment analysis (GSEA) reveals that multiple metabolic processes (including oxidative phosphorylation and tricarboxylic acid cycle, both of which are emerging targets for cancer therapy) are enriched in the high-risk NB group, and no metabolic process is enriched in the low-risk NB group. This result indicates that metabolism reprogramming is associated with the progression of NB and targeting certain metabolic pathways might be a promising therapy for NB.

SUBMITTER: Meng X

PROVIDER: S-EPMC7521294 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Combined analysis of RNA-sequence and microarray data reveals effective metabolism-based prognostic signature for neuroblastoma.

Meng Xinyao X Feng Chenzhao C Fang Erhu E Feng Jiexiong J Zhao Xiang X

Journal of cellular and molecular medicine 20200719 18

The relationship between metabolism reprogramming and neuroblastoma (NB) is largely unknown. In this study, one RNA-sequence data set (n = 153) was used as discovery cohort and two microarray data sets (n = 498 and n = 223) were used as validation cohorts. Differentially expressed metabolic genes were identified by comparing stage 4s and stage 4 NBs. Twelve metabolic genes were selected by LASSO regression analysis and integrated into the prognostic signature. The metabolic gene signature succes ...[more]

PMID: 32683778

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Project description:BackgroundCurrent clinical risk factors stratify patients with neuroblastoma (NB) for appropriate treatments, yet patients with similar clinical behaviors evoke variable responses. MYCN amplification is one of the established drivers of NB and, when combined with high-risk displays, worsens outcomes. Growing high-throughput transcriptomics studies suggest long noncoding RNA (lncRNA) dysregulation in cancers, including NB. However, expression-based lncRNA signatures are altered by MYCN amplification, which is associated with high-risk, and patient prognosis remains limited.MethodsWe investigated RNA-seq-based expression profiles of lncRNAs in MYCN status and risk status in a discovery cohort (n = 493) and validated them in three independent cohorts. In the discovery cohort, a prognostic association of lncRNAs was determined by univariate Cox regression and integrated into a signature using the risk score method. A novel risk score threshold selection criterion was developed to stratify patients into risk groups. Outcomes by risk group and clinical subgroup were assessed using Kaplan-Meier survival curves and multivariable Cox regression. The performance of lncRNA signatures was evaluated by receiver operating characteristic curve. All statistical tests were two-sided.ResultsIn the discovery cohort, 16 lncRNAs that were differentially expressed (fold change ≥ 2 and adjusted P ≤ 0.01) integrated into a prognostic signature. A high risk score group of lncRNA signature had poor event-free survival (EFS; P < 1E-16). Notably, lncRNA signature was independent of other clinical risk factors when predicting EFS (hazard ratio = 3.21, P = 5.95E-07). The findings were confirmed in independent cohorts (P = 2.86E-02, P = 6.18E-03, P = 9.39E-03, respectively). Finally, the lncRNA signature had higher accuracy for EFS prediction (area under the curve = 0.788, 95% confidence interval = 0.746 to 0.831).ConclusionsHere, we report the first (to our knowledge) RNA-seq 16-lncRNA prognostic signature for NB that may contribute to precise clinical stratification and EFS prediction.

Project description: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.

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.

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Combined analysis of RNA-sequence and microarray data reveals effective metabolism-based prognostic signature for neuroblastoma.

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Combined analysis of RNA-sequence and microarray data reveals effective metabolism-based prognostic signature for neuroblastoma.

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