Development of a prognostic model for overall survival in neuroblastoma based on Schwann cell-specific genes, clinical predictors, and MYCN amplification

Introduction

Neuroblastoma (NBL) is one of the most prevalent extracranial solid tumors in children, representing about 7–10% of all pediatric cancers and primarily affecting those under 5 years old (1,2). The disease arises from neuroblasts, precursor cells of the sympathetic nervous system, and is typically located in the adrenal glands or along the sympathetic chain (3). Clinical presentations of NBL vary and, according to the International Neuroblastoma Staging System (INSS), are classified into three patterns: widespread disease that can spontaneously regress without treatment (Stage 4S); localized tumors that may recur but do not metastasize to bone or bone marrow (Stages 1, 2, 3); and metastatic disease that initially responds to cytotoxic therapy but frequently relapses, often leading to fatal outcomes (Stage 4) (4-7). Over half of NBL patients belong to this category, and if diagnosed after one year of age, the overall survival (OS) rate is typically less than 20%. In contrast, most patients with non-stage 4 NBL have a favorable prognosis even without treatment (8). Therefore, early intervention and accurate risk assessment are essential for improving survival outcomes in NBL patients.

Several models reported in previous studies have been developed to predict outcomes in NBL, primarily based on genetic markers such as MYCN amplification, ALK mutations, and P53 pathway abnormalities (9-13). These models have been instrumental in identifying high-risk patients who may benefit from more aggressive treatment regimens. However, these existing models have notable limitations. While genetic markers like MYCN amplification are critical for assessing prognosis, most models focus primarily on genetic signatures, often overlooking the crucial roles of specific cell types within the tumor microenvironment (TME) (14,15). Moreover, despite their informative nature, the predictive accuracy of current models remains limited. For example, some models based solely on genetic markers report moderate performance, with areas under the curve (AUCs) ranging from 0.718 to 0.815 (14,16,17), highlighting the need for further refinement. Therefore, there is a clinical need for a more comprehensive prognostic model that incorporates both the contribution of the TME and specific cellular markers. By integrating genetic markers, clinical factors, and specific cell types, a new model could offer a more accurate and individualized prediction of patient outcomes.

Therefore, our study aims to fill this research gap by focusing on the cell types that play crucial roles in NBL tumor biology. By developing an advanced prognostic model incorporating specific cellular characteristic genes and other key prognostic factors, this study aims to offer more precise treatment decision support for NBL patients, ultimately enhancing treatment outcomes and survival rates. The research process is illustrated in Figure 1. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2048/rc).

Figure 1 Research process for constructing comprehensive prognostic model for NBL. INSS, International Neuroblastoma Staging System; KM, Kaplan-Meier; NBL, neuroblastoma; RNA-seq, RNA sequencing; ROC, receiver operating characteristic; scRNA-seq, single-cell RNA sequencing; TARGET, Therapeutically Applicable Research to Generate Effective Treatments; UMAP, uniform manifold approximation and projection; WGCNA, weighted gene co-expression network analysis.

Methods

Data acquisition and processing

RNA sequencing and clinical data were from two primary sources: the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, which includes data from 106 NBL patients used as a test set, and the Gene Expression Omnibus (GEO; GSE85047), which contains data from 238 NBL patients used as a train set. Additionally, single-cell RNA sequencing (scRNA-seq) data from 16 NBL patients, encompassing 160,910 cells, were included (GEO, GSE137804). The “Seurat” R package was used for data preprocessing. Cells with gene expression levels below 300 or above 6,500, and those with mitochondrial gene expression exceeding 10%, were excluded.

For the test set, MYCN amplification was present in 79.2% (n=84) patients, and 95.3% (n=101) were classified as Stage 4 according to INSS staging. The median age at diagnosis was 1,069 (range, 3–5,734) days, and the median follow-up for OS was 920.5 (range, 7–3,776) days. For the train set, MYCN amplification was present in 79.4% (n=189) of patients, and 57.6% (n=137) were classified as Stage 4. The median age at diagnosis was 399.5 (range, 0–7,100) days, and the median follow-up for OS was 1,873 (range, 1–6,355) days.

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of NBL prognostic cell types and feature gene selection

The “Seurat” R package performed uniform manifold approximation and projection (UMAP) analysis on filtered single-cell data to identify and classify distinct cell clusters. The “SingleR” R package was used for cell classification, and bubble plots were used to validate marker gene expression in different cell types. The “WGCNA” R package was used for co-expression network analysis on the scRNA-seq dataset, constructing networks and determining connectivity strength with the pickSoftThreshold function. This approach facilitated hierarchical clustering and module detection, linking modules to cell type characteristics. Subsequently, NBL prognosis-related cell types and characteristic genes were identified. The “survival” R package was used for survival analysis to validate the prognostic significance of these genes.

Construction of the genetic feature prognostic model

The genetic feature prognostic model was developed using the identified NBL prognostic feature genes. The “ggplot2” R package generated scatter plots and heatmaps to visualize gene expression across various patient risk levels, categorized by a defined cutoff value. The “survivalROC” R package was used to generate receiver operating characteristic (ROC) curves and evaluate the predictive model’s performance. The “survival” R package generated Kaplan-Meier survival curves to assess the model’s prognostic performance.

Model optimization

Univariate Cox regression analysis was conducted to identify other factors associated with NBL patient survival, which were then incorporated into the prognostic model to improve its predictive accuracy and clinical relevance. The “rms” R package was used to generate nomograms, visually representing the model. The model’s predictive performance was assessed using ROC curves. Calibration curves were applied to compare the model’s predictions with observed survival rates, demonstrating the model’s accuracy.

Assessment of immune cell infiltration and drug sensitivity analysis

The “CIBERSORT” R package was used to estimate the infiltration levels of 22 immune cell types in two risk groups of NBL patients according to their risk scores. Box plots depict the differences in immune cell infiltration between high-risk and low-risk patients. The “pRRophetic” R package was used to evaluate the drug responsiveness of NBL patients in these risk groups, with half maximal inhibitory concentration (IC50) data obtained from the Cancer Drug Sensitivity Genomics (GDSC) (https://www.cancerrxgene.org/).

Statistical analysis

All statistical analyses, model fitting, and graphical visualizations were performed using R software. Kaplan-Meier survival analysis and log-rank tests were used to evaluate OS rates. Univariate and multivariate Cox regression analyses were applied to assess prognostic significance. ROC curve analysis and the AUC were used to evaluate the reliability and sensitivity of prognostic features and models. An AUC value of ≥0.8 was considered indicative of good predictive accuracy. In all analyses, a P value of less than 0.05 with two-sided testing, was considered statistically significant to control for Type I errors.

Data availability statement

The genomic and clinical datasets used in this study were obtained from the TARGET database (https://ocg.cancer.gov/programs/target) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/). scRNA-seq data were acquired from the publication cited as (18). These datasets are publicly available and can be accessed according to the guidelines and conditions stipulated by the respective repositories and publications. For further inquiries regarding data availability, please contact the corresponding author.

Results

Schwann cells identified as relevant to NBL prognosis

UMAP analysis identified and classified five distinct cell clusters: endothelial cells, tumor cells, Schwann cells, mesenchymal cells, and immune cells (Figure 2A). Bubble plots illustrated gene expression variability across the primary cell groups, with tumor cells exhibiting high expression levels of multiple marker genes (Figure 2B). Weighted gene co-expression network analysis (WGCNA) identified associations between distinct cell types and specific gene modules (Figure 2C), clearly depicted in the module-trait association heatmap (Figure 2D). Characteristic genes of each cell type were mapped to an RNA-seq dataset containing prognostic information. Univariate Cox regression analysis showed that all selected cell types were significantly associated with the prognosis of NBL patients (P all <0.001, Figure 2E). Multivariate Cox regression analysis further confirmed that Schwann cell marker genes significantly influenced prognosis after adjusting for other variables (P smallest =0.01, Figure 2F).

Figure 2 Analysis of the tumor microenvironment and gene expression in NBL. (A) UMAP visualization depicting distinct cell type clustering. (B) Dot plot illustrates the expression levels of selected marker genes across identified cell clusters. (C) Module-trait relationships identified by WGCNA. (D) Heatmap of gene expression profiles. (E) Univariate Cox regression analysis of cell types. (F) Multivariate Cox regression analysis of cell types. NBL, neuroblastoma; UMAP, uniform manifold approximation and projection; WGCNA, weighted gene co-expression network analysis.

Prognostic genes in Schwann cells

Univariate Cox regression analysis showed that Schwann cell characteristic genes CALR [hazard ratio (HR) =3.825; P<0.001] and MAPRE1 (HR =2.556; P=0.04) were significantly associated with poor prognosis, whereas KLF10 (HR =0.528; P=0.007), UBL3 (HR =0.520; P=0.008), and NXF1 (HR =0.422; P=0.03) were linked to a favorable prognosis (Figure 3A). Kaplan-Meier survival curves further confirmed the prognostic impact of these genes, revealing that high CALR expression was associated with lower survival rates (P<0.001, Figure 3B), whereas elevated expression of KLF10 (P<0.001, Figure 3C) and UBL3 (P=0.04, Figure 3D) correlated with higher survival rates. In contrast, the expression levels of MAPRE1 (P=0.30, Figure 3E) and NXF1 (P=0.15, Figure 3F) were not significantly correlated with patient survival rates.

Figure 3 Impact of gene expression on NBL survival. (A) Univariate Cox regression showing HRs for key genes associated with NBL prognosis. (B-F) KM survival curves illustrating the impact of high vs. low expression of CALR, KLF10, UBL3, MAPRE1, and NXF1. 95H, upper limit of the 95% confidence interval for the hazard ratio; 95L, lower limit of the 95% confidence interval for the hazard ratio; HR, hazard ratio; KM, Kaplan-Meier; NBL, neuroblastoma.

Evaluation of the genetic feature prognostic model

The genetic feature prognostic model showed that high-risk patients had significantly shorter survival times compared to low-risk patients (Figure 4A,4B). ROC curve analysis demonstrated good predictive accuracy in the train set (AUC =0.832, Figure 4C) and acceptable accuracy in the test set (AUC =0.777, Figure 4D). Kaplan-Meier survival analysis revealed significant differences in survival probabilities between high-risk and low-risk patients in both the train and test sets (P both <0.001, Figure 4E,4F).

Figure 4 Evaluation of the genetic feature prognostic model. (A,B) Top: risk scores for each patient, stratified into high- and low-risk groups. Middle: survival status of patients over time. Bottom: expression heatmap for key genes across patients, illustrating gene expression differences between risk groups in the train and test sets. (C,D) ROC curves for the train and test sets. (E,F) KM curves for the train and test sets. AUC, area under the curve; KM, Kaplan-Meier; ROC, receiver operating characteristic; TARGET, Therapeutically Applicable Research to Generate Effective Treatments.

Establishment and evaluation of the advanced NBL prognostic model

Multivariate Cox regression analysis showed that age, INSS stage, and MYCN amplification significantly affected the survival rates of NBL patients (P both <0.001, Figure 5A). A nomogram was constructed, integrating expression scores of three genes (KLF10, CALR, UBL3), two clinical features (age and INSS stage), and the molecular marker MYCN amplification (Figure 5B). Kaplan-Meier survival curves indicated a significant difference in survival between high-risk and low-risk groups (P<0.001, Figure 5C). The model’s predictive performance was validated by ROC curve (AUC =0.857, Figure 5D). Calibration curves demonstrated consistent predictions for 1-, 2-, and 3-year survival, confirming the model’s reliability at these intervals (Figure 5E-5G).

Figure 5 Evaluation of the comprehensive NBL prognostic model. (A) Multivariate Cox proportional hazards model. (B) Nomogram for predicting 1-, 2-, and 3-year survival. (C) Kaplan-Meier survival curves based on risk scores from the nomogram. (D) ROC curve for the nomogram. (E-G) Calibration plots for 1-, 2-, and 3-year survival predictions. AUC, area under the curve; CI, confidence interval; INSS, International Neuroblastoma Staging System; NBL, neuroblastoma; OS, overall survival; ROC, receiver operating characteristic.

Immune cell infiltration and drug sensitivity

Analysis of immune cell infiltration showed that the high-risk group had significantly elevated levels of Macrophages M0 (P<0.001), whereas the low-risk group displayed higher levels of Macrophages M2 (P=0.04), Naive B cells (P=0.005), resting CD4 memory T cells (P=0.004), and Macrophages M1 (P=0.007) (Figure 6A). Drug sensitivity analysis indicated that high-risk patients had significantly lower IC50 values than low-risk patients for specific chemotherapeutic agents (P for dactinomycin =0.005, P for 5-fluorouracil =0.007, P for cisplatin =0.03, P for oxaliplatin =0.02, P for docetaxel =0.006, Figure 6B), targeted therapies (P for buparlisib =0.006, P for sorafenib =0.01, P for palbociclib =0.006, P for alisertib =0.02, P for dabrafenib =0.04, Figure 6C), and immunotherapeutic and experimental drugs (P for tamoxifen =0.04, P for AZD7762 =0.007, P for bortezomib =0.04, P for vorinostat =0.03, P for MK-1775 =0.03, Figure 6D). Additional results are presented in Table S1.

Figure 6 Analysis of immune cell infiltration and drug sensitivity. (A) Box plots showing differences in infiltration levels of various immune cells. (B) Sensitivity to chemotherapy drugs in high- vs. low-risk groups. (C) Sensitivity to targeted therapy drugs in high- vs. low-risk groups. (D) Sensitivity to immunotherapy and novel drugs in clinical trials in high- vs. low-risk groups.

Discussion

Our study developed an advanced prognostic model for NBL that integrates multi-level biological information, including expression patterns of Schwann cell-specific genes, key clinical variables, and the known prognostic molecular marker MYCN amplification, demonstrating good predictive accuracy and significant clinical applicability. The primary innovation of this study is its emphasis on specific cell types within the TME, a focus that has been underexplored in previous studies. Integrating Schwann cell molecular characteristics enhanced the model’s predictive performance, elucidated the complexity of the NBL microenvironment, and provided new insights for improving precision medicine strategies and patient survival outcomes.

Our study highlights the critical role of Schwann cells in NBL prognosis. Schwann cells have traditionally been considered supportive cells involved in nerve repair and regeneration, mainly functioning in the peripheral nervous system (19). However, recent studies have revealed their potential roles in tumor biology. For example, Schwann cells have been shown to influence tumor cell survival and proliferation by secreting biologically active substances such as nerve growth factor (NGF) and transforming growth factor-beta (TGF-β), a function confirmed in colorectal and pancreatic cancers (20,21). Additionally, in the TME, Schwann cells modulate immune responses and facilitate tumor cell migration and invasion by producing prostaglandin E and secreting extracellular matrix components such as collagen, thereby promoting tumor spread (22). Schwann cells also remodel the extracellular matrix, enhancing tumor metastasis. For instance, a melanoma study (23) showed that Schwann cell signaling reprogramming directly promotes tumor growth and spread. Cell communication analysis (Figure S1) revealed a complex network of signaling interactions between Schwann cells and tumors, immune, mesenchymal, and endothelial cells. These interactions regulate tumor progression directly and indirectly promote tumor development by modulating the microenvironmental balance. These provide a basis for incorporating Schwann cell-specific genes into the NBL prognostic model.

We identified three key NBL prognostic genes expressed in Schwann cells: CALR, KLF10, and UBL3. CALR, located in the endoplasmic reticulum, affects calcium signaling and protein folding, and it regulates immune evasion in cancer. This function may influence immune surveillance in Schwann cells and the TME (24,25). KLF10, a crucial transcription factor, regulates the cell cycle and differentiation. It suppresses tumor cell proliferation and induces apoptosis through specific signaling pathways (26-28). UBL3, a ubiquitin-like protein involved in protein modification, is closely linked to tumor growth and prognosis. A recent study (29) indicates that UBL3 interacts with sEV proteins, influencing cancer characteristics and cell proliferation, making it a potential therapeutic target. These findings are consistent with our results, highlighting the significance of Schwann cell-specific genes in the NBL prognostic model.

The NBL prognostic model based on these genes showed strong predictive performance (AUC =0.832). Compared to previous models (14,16,17) based mainly on genetic information, with AUCs ranging from 0.718 to 0.815, our model, incorporating key cell type-specific genes, demonstrates a clear advantage. Furthermore, the National Comprehensive Cancer Network (NCCN) guidelines (30), recommend considering factors like patient age, pathological type, tumor stage, and MYCN amplification status in treatment strategies. Our results are consistent with these recommendations. We optimized the genetic feature model by including key prognostic factors like age, INSS stage, and MYCN amplification, which improved its predictive accuracy (AUC =0.857). This suggests that our model not only follows current treatment guidelines but also underscores the effectiveness of incorporating molecular characteristics of specific cell types into the prognostic model.

Our study used nomograms to visually represent the NBL prognostic model, effectively converting disease risk into quantifiable risk levels. Survival rates significantly decreased in the high-risk group, while low-risk patients maintained higher survival rates (P<0.001). This difference confirms the model’s ability to distinguish between prognostic risk groups and suggests that precise treatment strategies for high-risk patients could significantly improve overall NBL outcomes. Drug sensitivity analysis showed that high-risk patients are more responsive to specific treatments compared to low-risk patients, needing lower doses to achieve equivalent therapeutic effects. For instance, high-risk patients using chemotherapeutic agents like dactinomycin and cisplatin can reduce common side effects, such as myelosuppression and neurotoxicity, by lowering the dosage. Targeted drugs like buparlisib and dabrafenib more effectively inhibit tumor cell survival and proliferation pathways in high-risk patients by targeting specific molecular markers (31). Although tamoxifen is typically used to treat breast cancer (32), our results indicate that it may also have therapeutic potential for high-risk NBL patients.

Despite the significant predictive power of the prognostic model developed in this study, some limitations remain. First, the study mainly relies on public database data, which, despite providing a solid foundation, have a relatively limited sample size. Second, although the model includes some key prognostic indicators, it does not account for other complex clinical factors that could impact NBL prognosis, potentially limiting the predictions’ comprehensiveness and accuracy. Additionally, while drug sensitivity analysis indicates that high-risk patients may have suboptimal responses to current treatments, these predictions require further validation in clinical settings. Future study should incorporate more clinical trial data and multi-omics analyses to refine the model and verify its applicability in various patient groups.

In conclusion, the advanced NBL prognostic model developed in this study shows superior predictive accuracy and clinical applicability. Compared to previous studies, our study emphasizes the crucial role of Schwann cell characteristics in the microenvironment, particularly in developing personalized treatment strategies for high-risk patients.

Conclusions

In conclusion, this study presents an advanced prognostic model for NBL that integrates Schwann cell-specific genes, clinical factors, and MYCN amplification to provide a more accurate prediction of OS. The model’s strong predictive performance demonstrates its potential for guiding clinical decision-making, especially for high-risk patients. By emphasizing the TME and incorporating cell-specific characteristics, the model offers new insights into the molecular basis of NBL prognosis. This approach could be used to better personalize treatment strategies, potentially reducing treatment-related side effects and improving survival outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2048/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2048/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-2048/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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