Comparison of Stage 4 and Stage 4s Neuroblastoma Identifies Autophagy-Related Gene and LncRNA Signatures Associated With Prognosis

Meng, Xinyao; Li, Honglin; Fang, Erhu; Feng, Jiexiong; Zhao, Xiaoli

doi:10.3389/fonc.2020.01411

Cited by 16 publications

(12 citation statements)

References 56 publications

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“…4), including FAM13A-AS, which is an autophagy-related lnc-RNA [41]. Using their DEG analysis, we compared stages IV and IVs NBs and showed that the enhancer region was hypermethylated in group A and hypomethylated in group C, which is consistent with the previous report [41]. Accordingly, the FAM13A-AS lower expression was associated with worse prognosis for NB patients (Group A, Fig.…”

Section: Discussionsupporting

confidence: 85%

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Comparative epigenomics by machine learning approach for neuroblastoma

Sugino

Ohira

Mansai

2022

Preprint

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Background Neuroblastoma (NB) is the second most common pediatric solid tumor. Because the number of genetic mutations found in tumors are small, even in some patients with unfavorable NB, epigenetic variation is expected to play an important role in NB progression. DNA methylation is a major epigenetic mechanism, and its relationship with NB prognosis has been a concern. One limitation with the analysis of variation in DNA methylation is the lack of a suitable analytical model. Therefore, in this study, we performed a random forest (RF) analysis of the DNA methylome data of NB from multiple databases. Results RF is a popular machine learning model owing to its simplicity, intuitiveness, and computational cost. RF analysis identified novel intermediate-risk patient groups with characteristic DNA methylation patterns within the low-risk group. Feature selection analysis based on probe annotation revealed that enhancer-annotated regions had strong predictive power, particularly for MYCN-amplified NBs. We developed a gene-based analytical model to identify candidate genes related to disease progression, such as PRDM8 and FAM13A-AS1. RF analysis revealed sufficient predictive power compared to other machine learning models. Conclusions RF is a useful tool for DNA methylome analysis in cancer epigenetic studies, and has potential to identify a novel cancer-related genes.

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Section: Discussionsupporting

confidence: 85%

“…We identi ed some candidate genes (Fig. 4), including FAM13A-AS, which is an autophagy-related lnc-RNA [41]. Using their DEG analysis, we compared stages IV and IVs NBs and showed that the enhancer region was hypermethylated in group A and hypomethylated in group C, which is consistent with the previous report [41].…”

Section: Discussionsupporting

confidence: 85%

Comparative epigenomics by machine learning approach for neuroblastoma

Sugino

Ohira

Mansai

2022

Preprint

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“…Autophagy has been considered a double-edged sword in molecular mechanisms of NB. The autophagy-related biological processes are highly enriched and supposed to act on the spontaneous regression of low-risk NB ( Xinyao et al, 2020 ). Meanwhile, it mainly promotes cell proliferation and acts on the process of tumor resistance to chemotherapy ( Wang et al, 2014 ; Yu et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…For example, high expression of beclin 1 is associated with a poor prognosis for NB ( Belounis et al, 2016 ). Meanwhile, ATGs and long noncoding RNAs are differentially expressed between INSS stage 4 and INSS stage 4s NB, which highly indicates that autophagy plays an important role in spontaneous regression ( Xinyao et al, 2020 ). Therefore, the net outcome between autophagy and immune in NB needs to be evaluated, as well as identifying the key prognostic ATGs that regulate the microenvironment.…”

Section: Introductionmentioning

confidence: 99%

Identification of an Autophagy-Related Risk Signature Correlates With Immunophenotype and Predicts Immune Checkpoint Blockade Efficacy of Neuroblastoma

Kang

Zhao

et al. 2021

Front. Cell Dev. Biol.

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Neuroblastoma is one of the malignant solid tumors with the highest mortality in childhood. Targeted immunotherapy still cannot achieve satisfactory results due to heterogeneity and tolerance. Exploring markers related to prognosis and evaluating the immune microenvironment remain the major obstacles. Herein, we constructed an autophagy-related gene (ATG) risk model by multivariate Cox regression and least absolute shrinkage and selection operator regression, and identified four prognostic ATGs (BIRC5, GRID2, HK2, and RNASEL) in the training cohort, then verified the signature in the internal and external validation cohorts. BIRC5 and HK2 showed higher expression in MYCN amplified cell lines and tumor tissues consistently, whereas GRID2 and RNASEL showed the opposite trends. The correlation between the signature and clinicopathological parameters was further analyzed and showing consistency. A prognostic nomogram using risk score, International Neuroblastoma Staging System stage, age, and MYCN status was built subsequently, and the area under curves, net reclassification improvement, and integrated discrimination improvement showed more satisfactory prognostic predicting performance. The ATG prognostic signature itself can significantly divide patients with neuroblastoma into high- and low-risk groups; differentially expressed genes between the two groups were enriched in autophagy-related behaviors and immune cell reactions in gene set enrichment analysis (false discovery rate q -value < 0.05). Furthermore, we evaluated the relationship of the signature risk score with immune cell infiltration and the cancer-immunity cycle. The low-risk group was characterized by more abundant expression of chemokines and higher immune checkpoints (PDL1, PD1, CTLA-4, and IDO1). The risk score was significantly correlated with the proportions of CD8+ T cells, CD4+ memory resting T cells, follicular helper T cells, memory B cells, plasma cells, and M2 macrophages in tumor tissues. In conclusion, we developed and validated an autophagy-related signature that can accurately predict the prognosis, which might be meaningful to understand the immune microenvironment and guide immune checkpoint blockade.

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“…In addition, Meng et al . showed that EIF4EBP1 is part of a gene signature that predicts poor overall survival [ 35 ]. However, it was not investigated whether EIF4EBP1 expression alone can predict NB patient prognosis.…”

Section: Introductionmentioning

confidence: 99%

EIF4EBP1 is transcriptionally upregulated by MYCN and associates with poor prognosis in neuroblastoma

et al. 2022

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Neuroblastoma (NB) accounts for 15% of cancer-related deaths in childhood despite considerable therapeutic improvements. While several risk factors, including MYCN amplification and alterations in RAS and p53 pathway genes, have been defined in NB, the clinical outcome is very variable and difficult to predict. Since genes of the mechanistic target of rapamycin (mTOR) pathway are upregulated in MYCN-amplified NB, we aimed to define the predictive value of the mTOR substrate-encoding gene eukaryotic translation initiation factor 4E-binding protein 1 (EIF4EBP1) expression in NB patients. Using publicly available data sets, we found that EIF4EBP1 mRNA expression is positively correlated with MYCN expression and elevated in stage 4 and high-risk NB patients. In addition, high EIF4EBP1 mRNA expression is associated with reduced overall and event-free survival in the entire group of NB patients in three cohorts, as well as in stage 4 and high-risk patients. This was confirmed by monitoring the clinical value of 4EBP1 protein expression, which revealed that high levels of 4EBP1 are significantly associated with prognostically unfavorable NB histology. Finally, functional analyses revealed that EIF4EBP1 expression is transcriptionally controlled by MYCN binding to the EIF4EBP1 promoter in NB cells. Our data highlight that EIF4EBP1 is a direct transcriptional target of MYCN whose high expression is associated with poor prognosis in NB patients. Therefore, EIF4EBP1 may serve to better stratify patients with NB.

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Comparison of Stage 4 and Stage 4s Neuroblastoma Identifies Autophagy-Related Gene and LncRNA Signatures Associated With Prognosis

Cited by 16 publications

References 56 publications

Comparative epigenomics by machine learning approach for neuroblastoma

Comparative epigenomics by machine learning approach for neuroblastoma

Identification of an Autophagy-Related Risk Signature Correlates With Immunophenotype and Predicts Immune Checkpoint Blockade Efficacy of Neuroblastoma

EIF4EBP1 is transcriptionally upregulated by MYCN and associates with poor prognosis in neuroblastoma

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