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

Comprehensive analysis identifies ubiquitin ligase FBXO42 as a tumor-promoting factor in neuroblastoma

Neuroblastoma, the deadliest solid tumor in children, exhibits alarming mortality rates, particularly among high-risk cases. To enhance survival rates, a more precise risk stratification for patients is imperative. Utilizing proteomic data from 34 cases with or without N-Myc amplification, we identified 28 differentially expressed ubiquitination-related proteins (URGs). From these, a prognostic signature comprising 6 URGs was constructed. A nomogram incorporating clinical-pathological parameters yielded impressive AUC values of 0.88, 0.93, and 0.95 at 1, 3, and 5 years, respectively. Functional experiments targeting the E3 ubiquitin ligase FBXO42, a component of the prognostic signature, revealed its TP53-dependent promotion of neuroblastoma cell proliferation. In conclusion, our ubiquitination-related prognostic model robustly predicts patient outcomes, guiding clinical decisions. Additionally, the newfound pro-proliferative role of FBXO42 offers a novel foundation for understanding the molecular mechanisms of neuroblastoma.

Identification of immune-related signature with prognosis in children with stage 4 and 4S neuroblastoma

OBJECTIVE

Spontaneous regression of tumors is an attractive phenomenon that most commonly occurs in stage 4S neuroblastoma (NB). However, the mechanism underlying this phenomenon remains unclear.

METHODS

Datasets correlated with NB were downloaded from online public databases, the differentially expressed genes (DEGs) between stage 4 and 4S associated with immunity were identified, and functional enrichment analysis was utilized to explore the potential functions and signaling pathways of these DEGs. In addition, based on these DEGs, a prognostic signature was constructed and validated, and differences in immune cell infiltration were analyzed.

RESULTS

A total of 13 DEGs were finally identified, and functional enrichment analysis revealed that these DEGs were primarily enriched in the positive regulation of neuron differentiation and TGF-β signaling pathway. The signature successfully stratifies patients into two risk score groups and performs well in judging prognosis and predicting overall survival time. In addition, the prognostic value of the risk score calculated by the signature was independent of clinical factors. The results of immune cell infiltration showed that patients with a high infiltration of resting CD4 + memory T cells had a better prognosis, while plasma cells had a worse prognosis.

CONCLUSION

The results of the functional enrichment analysis of these identified DEGs suggested that these DEGs may be related to spontaneous regression of NB. In addition, the prognostic signature has the potential to create new risk stratification in patients with NB.

Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma

INTRODUCTION

Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblastoma (NB) remains elusive.

METHODS

We utilized the transcriptome and methylation data of stage 4S and 4 NB patients to perform Enhancer Linking by Methylation/Expression Relationships (ELMER) analysis, discovering a differently expressed motif within 67 enhancers between stage 4S and 4 NB. Harnessing the 67 motif genes, we established the INSS stage related signature (ISRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms across 113 and 101 ML combinations to precisely diagnose stage 4 NB among all NB patients and to predict the prognosis of NB patients. Based on risk scores calculated by prognostic ISRS, patients were categorized into high and low-risk groups according to median risk score. We conducted comprehensive comparisons between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single-cell analysis. Ultimately, we empirically validated the differential expressions of two ISRS model genes, CAMTA2 and FOXD1, through immunochemistry staining.

RESULTS

Through leave-one-out cross-validation, in both feature selection and model construction, we selected the random forest algorithm to diagnose stage 4 NB, and Enet algorithm to develop prognostic ISRS, due to their highest average C-index across five NB cohorts. After validations, the ISRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and several clinic variables. We stratified NB patients into high and low-risk group based on median risk score, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a decreased mutation landscape, and an enhanced sensitivity to immunotherapy. Single-cell analysis between two risk groups reveals biologically cellular variations underlying ISRS. Finally, we verified the significantly higher protein levels of CAMTA2 and FOXD1 in stage 4S NB, as well as their protective prognosis value in NB.

CONCLUSION

Based on multi-omics data and ML algorithms, we successfully developed the ISRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular mechanisms of spontaneous regression and clinical utilization of ISRS.

Integration of clinical characteristics and molecular signatures of the tumor microenvironment to predict the prognosis of neuroblastoma

This study aimed to analyze the clinical characteristics, cell types, and molecular characteristics of the tumor microenvironment to better predict the prognosis of neuroblastoma (NB). The gene expression data and corresponding clinical information of 498 NB patients were obtained from the Gene Expression Omnibus (GEO: GSE62564) and ArrayExpress (accession: E-MTAB-8248). The relative cell abundances were estimated using single-sample gene set enrichment analysis (ssGSEA) with the R gene set variation analysis (GSVA) package. We performed Cox regression analyses to identify marker genes indicating cell subsets and combined these with prognostically relevant clinical factors to develop a new prognostic model. Data from the E-MTAB-8248 cohort verified the predictive accuracy of the prognostic model. Single-cell RNA-seq data were analyzed by using the R Seurat package. Multivariate survival analysis for each gene, using clinical characteristics as cofactors, identified 34 prognostic genes that showed a significant correlation with both event-free survival (EFS) and overall survival (OS) (log-rank test, P value < 0.05). The pathway enrichment analysis revealed that these prognostic genes were highly enriched in the marker genes of NB cells with mesenchymal features and protein translation. Ultimately, USP39, RPL8, IL1RAPL1, MAST4, CSRP2, ATP5E, International Neuroblastoma Staging System (INSS) stage, age, and MYCN status were selected to build an optimized Cox model for NB risk stratification. These samples were divided into two groups using the median of the risk score as a cutoff. The prognosis of samples in the poor prognosis group (PP) was significantly worse than that of samples in the good prognosis group (GP) (log-rank test, P value < 0.0001, median EFS: 640.5 vs. 2247 days, median OS: 1279.5 vs. 2519 days). The risk model was also regarded as a prognostic indicator independent of MYCN status, age, and stage. Finally, through scRNA-seq data, we found that as an important prognostic marker, USP39 might participate in the regulation of RNA splicing in NB. Our study established a multivariate Cox model based on gene signatures and clinical characteristics to better predict the prognosis of NB and revealed that mesenchymal signature genes of NB cells, especially USP39, were more abundant in patients with a poor prognosis than in those with a good prognosis. KEY MESSAGES: Our study established a multivariate Cox model based on gene signatures and clinical characteristics to better predict the prognosis of NB and revealed that mesenchymal signature genes of NB cells, especially USP39, were more abundant in patients with a poor prognosis than in those with a good prognosis. USP39, RPL8, IL1RAPL1, MAST4, CSRP2, ATP5E, International Neuroblastoma Staging System (INSS) stage, age, and MYCN status were selected to build an optimized Cox model for NB risk stratification. These samples were divided into two groups using the median of the risk score as a cutoff. The prognosis of samples in the poor prognosis group (PP) was significantly worse than that of samples in the good prognosis group (GP). Finally, through scRNA-seq data, we found that as an important prognostic marker, USP39 might participate in the regulation of RNA splicing in NB.

Integrative analysis of multi-omics data for discovery of ferroptosis-related gene signature predicting immune activity in neuroblastoma

Immunotherapy for neuroblastoma remains unsatisfactory due to heterogeneity and weak immunogenicity. Exploring powerful signatures for the evaluation of immunotherapy outcomes remain the primary purpose. We constructed a ferroptosis-related gene (FRG) signature by least absolute shrinkage and selection operator and Cox regression, identified 10 independent prognostic FRGs in a training cohort (GSE62564), and then verified them in an external validation cohort (TCGA). Associated with clinical factors, the signature accurately predicts overall survival of 3, 5, and 10 years. An independent prognostic nomogram, which included FRG risk, age, stage of the International Neuroblastoma Staging System, and an MYCN status, was constructed. The area under the curves showed satisfactory prognostic predicting performance. Through bulk RNA-seq and proteomics data, we revealed the relationship between hub genes and the key onco-promoter MYCN gene and then validated the results in MYCN-amplified and MYCN-non-amplified cell lines with qRT-PCR. The FRG signature significantly divided patients into high- and low-risk groups, and the differentially expressed genes between the two groups were enriched in immune actions, autophagy, and carcinogenesis behaviors. The low-risk group embodied higher positive immune component infiltration and a higher expression of immune checkpoints with a more favorable immune cytolytic activity (CYT). We verified the predictive power of this signature with data from melanoma patients undergoing immunotherapy, and the predictive power was satisfactory. Gene mutations were closely related to the signature and prognosis. AURKA and PRKAA2 were revealed to be nodal hub FRGs in the signature, and both were shown to have significantly different expressions between the INSS stage IV and other stages after immunohistochemical validation. With single-cell RNA-seq analysis, we found that genes related to T cells were enriched in TNFA signaling and interferon-γ hallmark. In conclusion, we constructed a ferroptosis-related gene signature that can predict the outcomes and work in evaluating the effects of immunotherapy.

Identification of a novel eighteen-gene signature of recurrent metastasis neuroblastoma

Neuroblastoma is the most common malignant tumor in childhood, and metastases occur in more than 30% patients. Recurrent metastasis is the main cause of poor prognosis and high mortality in neuroblastoma. In this regard, there is still a lack of sufficient biomarkers and effective therapies. Therefore, we performed a multi-omics analysis of neuroblastoma patients from Therapeutically Applicable Research To Generate Effective Treatments (TARGET). With clinical relapse site information, tumor samples derived from the primary site were divided into recurrent metastasis and primary tumor groups. The initial gene signature was obtained by comparing RNA-Seq and copy number variation differences. Survival data was used to further filter prognosis-related genes. This 18-gene signature consists of three clusters: tumor suppression, cell proliferation, and immunity. A super enhancer is involved in the enhanced expression of NCAPG in cluster2 together with IRF3. Based on the gene signature expression in primary neuroblastoma, it is possible to predict tumor metastasis before it occurs. According to the anticancer drug dataset of Genomics of Drug Sensitivity in Cancer (GDSC), vinorelbine and docetaxel were predicted to have high sensitivity against recurrent metastatic neuroblastoma. In conclusion, our study offers a novel metastasis biomarker and helps understand the mechanisms of tumor recurrent metastasis. KEY MESSAGES: We identified a novel eighteen-gene signature of recurrent metastasis neuroblastoma and build risk and classification models. We dissected the regulatory role of NCAPG in signatures. We found immune exhaustion and immunosuppression in recurrent metastasis neuroblastoma. Vinorelbine and docetaxel were predicted to have high sensitivity against recurrent metastatic neuroblastoma.

Study on differentially expressed genes between stage M and stage MS neuroblastoma

Objective

To search for the DEGs between stage MS NB and stage M NB and speculate the possible mechanism of spontaneous regression of stage MS NB.

Materials and methods

The NB datasets GSE49710 and GSE45547 in the GEO database were selected to screen the DEGs between children with NB stage MS vs. stage M, < 18 months. GO enrichment and KEGG pathway analysis of DEGs was performed using DAVID. The intersecting genes among DEGs and RCD-related genes were selected, and their survival roles and functions were assessed. We then used the collected clinical samples to validate the expression of these genes at the protein level using IHC methods and further analysis to explore their role.

Results

BIRC5, SLCO4A1, POPDC3, and HK2 were found to be downregulated in stage MS NB and related to apoptosis. BIRC5 and HK2 also participate in autophagy. The TF gene is upregulated in stage MS NB and related to ferroptosis. The above five genes are closely related to the survival of children with NB. And the expression levels of all five genes at the protein level were verified by IHC to be consistent with the results of the preliminary screening described above.

Conclusion

BIRC5, SLCO4A1, POPDC3, HK2 and TF are expected to become new important indicators to predict the prognosis of NB and can be used as the basis for further explored the benign prognosis and spontaneous regression mechanism of stage MS NB.

Comparative epigenomics by machine learning approach for neuroblastoma

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.

Ferroptosis-related gene signatures in neuroblastoma associated with prognosis

Background: Ferroptosis, a form of regulatory cell death, has been linked to the development of various tumors. Peripheral neuroblastoma (NB) is one of the most common extracranial solid tumors in children, and it has been proposed that regulating tumor cell ferroptosis may be a future treatment for NB. However, it is unclear how ferroptosis contributes to NB development.

Methods: Expression data were collected from two independent cohorts (GEO and Arrayexpress databases). Univariate Cox analysis, multivariate Cox analysis, and the least absolute shrinkage and selection operator (Lasso) algorithm were applied to create a prognostic signature, whose performance was quantified using the area under the receiver operating characteristic curve (AUC) and Kaplan–Meier curves. A prognostic meta-analysis was used to test the suitability and stability of the FRG signature. Drug sensitivity analyses were performed using the data collected from Cell Miner™.

Results:PROM2, AURKA, STEAP3, CD44, ULK2, MAP1LC3A, ATP6V1G2, and STAT3 are among the eight genes in the FRG prognostic signature, all of which were highly expressed in stage 1 NB, except AURKA. Furthermore, the high-risk group, which was stratified by signature, had a lower overall survival rate than the low-risk group. GSEA revealed that high-risk groups have more biological processes related to ferroptosis.

Conclusion: Ferroptosis-related genes are expressed differently between stages 1 and 4 NB. The FRG signature successfully stratified NB patients into two risk groups and can accurately predict the overall survival in NB. In addition, we found that the gene AURKA might have the potential to be a prognostic marker in NB.

Construction of a Prognostic Nomogram Based on Autophagy-Related Genes for Children With Neuroblastoma

Neuroblastoma (NB) is the most common solid malignancy in children. MYCN gene amplification is the most relevant genetic alteration in patients with NB and is associated with poor prognosis. Autophagy plays specific roles in the occurrence, development, and progression of NB. Here, we aimed to identify and assess the prognostic effects of autophagy-related genes (ARGs) in patients with NB and MYCN gene amplification. Differentially expressed ARGs were identified in patients with NB with and without MYCN gene amplification, and the ARG expression patterns and related clinical data from the Therapeutically Applicable Research to Generate Effective Treatments database were used as the training cohort. Least absolute shrinkage and selection operator analyses were used to identify prognostic ARGs associated with event-free survival (EFS), and a prognostic risk score model was developed. Model performance was assessed using the Kaplan–Meier method and receiver operating characteristic (ROC) curves. The prognostic ARG mode l was verified using the validation cohort dataset, GSE49710. Finally, a nomogram was constructed by combining the ARGbased risk score with clinicopathological factors. Three ARGs (GABARAPL1, NBR1, and PINK1) were selected to build a prognostic risk score model. The EFS in the low-risk group was significantly better than that in the high-risk group in both the training and validation cohorts. A nomogram incorporating the prognostic risk score, age, and International Neuroblastoma Staging System stage showed a favorable predictive ability for EFS rates according to the area under the ROC curve at 3 years (AUC = 0.787) and 5 years (AUC = 0.787). The nomogram demonstrated good discrimination and calibration. Our risk score model for the 3 ARGs can be used as an independent prognostic factor in patients with NB and MYCN gene amplification. The model can accurately predict the 3- and 5-year survival rates.

Linking neural crest development to neuroblastoma pathology

Although rare, childhood (paediatric) cancers are a major cause of death in young children. Unlike many adult cancers, paediatric cancers, such as neuroblastoma (NB), are developmental diseases that rarely show genetic predispositions. NB is the most common extracranial solid tumour in children, accounting for ∼15% of paediatric cancer deaths. This heterogeneous cancer arises from undifferentiated neural crest-derived progenitor cells. As neural crest cells are multipotent and migratory, they are often considered the embryonic paradigm of cancer stem cells. However, very little is known about the events that trigger tumour initiation and progression. Here, we discuss recent insights into sympathoadrenal lineage specification, as well as genetic factors associated with NB. With this in mind, we consider the molecular underpinnings of NB in the context of developmental trajectories of the neural crest lineage. This allows us to compare distinct subtypes of the disease and gene-function interactions during sensitive phases of neural crest development.

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

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.

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

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.

Prognostic Signature of Immune Genes and Immune-Related LncRNAs in Neuroblastoma: A Study Based on GEO and TARGET Datasets

Background

The prognostic value of immune-related genes and lncRNAs in neuroblastoma has not been elucidated, especially in subgroups with different outcomes. This study aimed to explore immune-related prognostic signatures.

Materials and Methods

Immune-related prognostic genes and lncRNAs were identified by univariate Cox regression analysis in the training set. The top 20 C-index genes and 17 immune-related lncRNAs were included in prognostic model construction, and random forest and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms were employed to select features. The risk score model was constructed and assessed using the Kaplan-Meier plot and the receiver operating characteristic curve. Functional enrichment analysis of the immune-related lncRNAs was conducted using the STRING database.

Results

In GSE49710, five immune genes (CDK4, PIK3R1, THRA, MAP2K2, and ULBP2) were included in the risk score five genes (RS5_G) signature, and eleven immune-related lncRNAs (LINC00260, FAM13A1OS, AGPAT4-IT1, DUBR, MIAT, TSC22D1-AS1, DANCR, MIR137HG, ERC2-IT1, LINC01184, LINC00667) were brought into risk score LncRNAs (RS_Lnc) signature. Patients were divided into high/low-risk score groups by the median. Overall survival and event/progression-free survival time were shortened in patients with high scores, both in training and validation cohorts. The same results were found in subgroups. In grouping ability assessment, the area under the curves (AUCs) in distinguishing different groups ranged from 0.737 to 0.94, better in discriminating MYCN status and high risk in training cohort (higher than 0.9). Multivariate Cox analysis demonstrated that RS5_G and RS_Lnc were the independent risk factors for overall and event/progression-free survival (all p-values <0.001). Correlation analysis showed that RS5_G and RS_Lnc were negatively associated with aDC, CD8+ T cells, but positively correlated with Th2 cells. Functional enrichment analyzes demonstrated that immune-related lncRNAs are mainly enriched in cancer-related pathways and immune-related pathways.

Conclusion

We identified the immune-related prognostic signature RS5_G and RS_Lnc. The predicting and grouping ability is close to being even better than those reported in other studies, especially in subgroups. This study provided prognostic signatures that may help clinicians to choose optimal treatment strategies and showed a new insight for NB treatment. These results need further biological experiments and clinical validation.