Identification of the ultrahigh-risk subgroup in neuroblastoma cases through DNA methylation analysis and its treatment exploiting cancer metabolism

Emerging Trends in Neuroblastoma Diagnosis, Therapeutics, and Research

This review explores the current understanding and recent advancements in neuroblastoma, one of the most common extracranial solid pediatric cancers, accounting for ~ 15% of childhood cancer-related mortality. The hallmarks of NBL, including angiogenesis, metastasis, apoptosis resistance, cell cycle dysregulation, drug resistance, and responses to hypoxia and ROS, underscore its complex biology. The tumor microenvironment's significance in disease progression is acknowledged in this study, along with the pivotal role of cancer stem cells in sustaining tumor growth and heterogeneity. A number of molecular signatures are being studied in order to better understand the disease, with many of them serving as targets for the development of new therapeutics. This includes inhibitor therapies for NBL patients, which notably concentrate on ALK signaling, MDM2, PI3K/Akt/mTOR, Wnt, and RAS-MAPK pathways, along with regulators of epigenetic mechanisms. Additionally, this study offers an extensive understanding of the molecular therapies used, such as monoclonal antibodies and CAR-T therapy, focused on both preclinical and clinical studies. Radiation therapy's evolving role and the promise of stem cell transplantation-mediated interventions underscore the dynamic landscape of NBL treatment. This study has also emphasized the recent progress in the field of diagnosis, encompassing the adoption of artificial intelligence and liquid biopsy as a non-intrusive approach for early detection and ongoing monitoring of NBL. Furthermore, the integration of innovative treatment approaches such as CRISPR-Cas9, and cancer stem cell therapy has also been emphasized in this review.

Exploring FAM13A-N-Myc interactions to uncover potential targets in MYCN-amplified neuroblastoma: a study of protein interactions and molecular dynamics simulations

Neuroblastoma (NB), a common infantile neuroendocrine tumor, presents a substantial therapeutic challenge when MYCN is amplified. Given that the protein structure of N-Myc is disordered, we utilized Alphafold for prediction and GROMACS for optimization of the N-Myc structure, thereby improving the reliability of the predicted structure. The publicly available datasets GSE49710 and GSE73517 were adopted, which contain the transcriptome data of clinical samples from 598 NB patients. Through various machine learning algorithms, FAM13A was identified as a characteristic gene of MYCN. Cell functional experiments, including those on cell proliferation, apoptosis, and cell cycle, also indicate that FAM13A is a potential risk factor. Additionally, Alphafold and GROMACS were employed to predict and optimize the structure of FAM13A. Protein-protein docking and molecular dynamic modeling techniques were then used to validate the enhanced protein stability resulting from the interaction between N-Myc and FAM13A. Consequently, targeting FAM13A holds the potential to reduce the stability of N-Myc, hinder the proliferation of NB cells, and increase the infiltration of immune cells. This multi-faceted approach effectively combats tumor cells, making FAM13A a prospective therapeutic target for MYCN-amplified NB.

Prediction of Composite Clinical Outcomes for Childhood Neuroblastoma Using Multi-Omics Data and Machine Learning

Neuroblastoma is a common malignant tumor in childhood that seriously endangers the health and lives of children, making it essential to find effective prognostic markers to accurately predict their clinical outcomes. The development of high-throughput technology in the biomedical field has made it possible to obtain multi-omics data, whose integration can compensate for missing or unreliable information in a single data source. In this study, we integrated clinical data and two omics data, i.e., gene expression and DNA methylation data, to study the prognosis of neuroblastoma. Since the features in omics data are redundant, it is crucial to conduct feature selection on them. We proposed a two-step feature selection (TSFS) method to quickly and accurately select the optimal features, where the first step aims at selecting candidate features and the second step is to remove redundant features among them using our proposed maximal association coefficient (MAC). Our goal is to predict composite clinical outcomes for neuroblastoma patients, i.e., their survival time and vital status at the last follow-up, which was validated to be two inter-correlated tasks. We conducted a series of experiments and evaluated the experimental results using accuracy and AUC (area under the ROC curve) evaluation metrics, which indicated that by the combination of the integration of the three types of data, our proposed TSFS method and a multi-task learning method can synergistically improve the reliability and accuracy of the prediction models.

Fine construction of gene coexpression network analysis using GTOM and RECODE detected a critical module of neuroblastoma stages 4 and 4S

BACKGROUND

Stage 4 neuroblastoma (NBL), a solid tumor of childhood, has a poor prognosis. Despite intensive molecular genetic studies, no targetable gene abnormalities have been identified. Stage 4S NBL has a characteristic of spontaneous regression, and elucidation of the mechanistic differences between stages 4 and 4S may improve treatment. Conventional NBL studies have mainly focused on the detection of abnormalities in individual genes and have rarely examined abnormalities in gene networks. While the gene coexpression network is expected to contribute to the detection of network abnormalities, the fragility of the network due to data noise and the extraction of arbitrary topological structures for the high-dimensional network are issues.

RESULTS

The present paper concerns the classification method of stages 4 and 4S NBL patients using highly accurate gene coexpression network analysis based on RNA-sequencing data of transcription factors (TFs). In particular, after applying a noise reduction method RECODE, generalized topological overlapping measure (GTOM), which weighs the connections of nodes in the network structure, succeeded in extracting a cluster of TFs that showed high classification performance for stages 4 and 4S. In addition, we investigated how these clusters correspond to clinical information and to TFs which control the normal adrenal tissue and NBL characters.

CONCLUSIONS

A clustering method is presented for finding intermediate-scale clusters of TFs that give considerable separation performance for distinguishing between stages 4 and 4S. It is suggested that this method is useful as a way to extract factors that contribute to the separation of groups from multiple pieces of information such as gene expression levels.

Targeting DNA Methylation Machinery in Pediatric Solid Tumors

DNA methylation is a key epigenetic regulatory mechanism that plays a critical role in a variety of cellular processes, including the regulation of cell fate during development, maintenance of cell identity, and genome stability. DNA methylation is tightly regulated by enzymatic reactions and its deregulation plays an important role in the development of cancer. Specific DNA methylation alterations have been found in pediatric solid tumors, providing new insights into the development of these tumors. In addition, DNA methylation profiles have greatly contributed to tune the diagnosis of pediatric solid tumors and to define subgroups of patients with different risks of progression, leading to the reduction in unwanted toxicity and the improvement of treatment efficacy. This review highlights the dysregulated DNA methylome in pediatric solid tumors and how this information provides promising targets for epigenetic therapies, particularly inhibitors of DNMT enzymes (DNMTis). Opportunities and limitations are considered, including the ability of DNMTis to induce viral mimicry and immune signaling by tumors. Besides intrinsic action against cancer cells, DNMTis have the potential to sensitize immune-cold tumors to immunotherapies and may represent a remarkable option to improve the treatment of challenging pediatric solid tumors.

TET3 gene rs828867 G>A polymorphism reduces neuroblastoma risk in Chinese children

Objective

Neuroblastoma (NB) is a prevalent pediatric tumor originating from primordial neural crest cells. As one of the latest epigenetics investigations focuses, RNA 5-methylcytosine (m5C) is closely related to cancer risk. TET methylcytosine dioxygenase 3 (TET3) is a demethylase for m5C modification. Whether there is an association between TET3 gene polymorphisms and neuroblastoma risk remains unclear.

Methods

We conducted an epidemiological study in 402 patients and 473 controls to evaluate the relationship between TET3 gene SNPs (rs7560668 T > C, rs828867 G > A, and rs6546891 A > G) and NB susceptibility.

Results

Our results showed that rs828867 G > A significantly reduced NB risk in Chinese children [GA vs. GG, adjusted odds ratio (OR) = 0.72, 95% confidence interval (CI) = 0.52-0.98, P=0.040; GA/AA vs. GG, adjusted OR = 0.74, 95% CI = 0.55-0.998, P=0.048]. Individuals with 2-3 risk genotypes had a significantly higher NB risk than those with 0-1 risk genotypes (adjusted OR = 1.40, 95% CI = 1.04-1.88, P=0.027). The stratified analysis showed that the rs828867 G > A associated with decreased NB risk is remarkable among children aged >18 months (adjusted OR = 0.67, 95% CI = 0.46-0.96, P=0.029) and patients at clinical III + IV stages (adjusted OR = 0.67, 95% CI = 0.45-0.98, P=0.040). Compared with the 0-1 risk genotype, the concurrence of 2-3 risk genotypes significantly increased NB risk in the following subgroups: children aged >18 months and patients at clinical III + IV stages. GTEx analysis suggested that rs828867 G > A was significantly associated with RP11-287D1.4 and POLE4 mRNA expression.

Conclusions

Overall, our results revealed that rs828867 G > A in the TET3 gene is significantly associated with predisposition to 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.

Construction of methylation driver gene-related prognostic signature and development of a new prognostic stratification strategy in neuroblastoma

BACKGROUND

Aberrant DNA methylation is one of the major epigenetic alterations in neuroblastoma.

OBJECTIVE

Exploring the prognostic significance of methylation driver genes in neuroblastoma could help to comprehensively assess patient prognosis.

METHODS

After identifying methylation driver genes (MDGs), we used the LASSO algorithm and stepwise Cox regression to construct methylation driver gene-related risk score (MDGRS), and evaluated its predictive performance by multiple methods. By combining risk grouping and MDGRS grouping, we developed a new prognostic stratification strategy and explored the intrinsic differences between the different groupings.

RESULTS

We identified 44 stably expressed MDGs in neuroblastoma. MDGRS showed superior predictive performance in both internal and external cohorts and was strongly correlated with immune-related scores. MDGRS can be an independent prognostic factor for neuroblastoma, and we constructed the nomogram to facilitate clinical application. Based on the new prognostic stratification strategy, we divided the patients into three groups and found significant differences in overall prognosis, clinical characteristics, and immune infiltration between the different subgroups.

CONCLUSION

MDGRS was an accurate and promising tool to facilitate comprehensive pre-treatment assessment. And the new prognostic stratification strategy could be helpful for clinical decision making.

Dauricine attenuates Oct4/sonic hedgehog co-activated stemness and induces reactive oxygen species-mediated mitochondrial apoptosis via AKT/β-catenin signaling in human neuroblastoma and glioblastoma stem-like cells

Neuroblastoma and glioblastoma are primary malignant tumors of the nervous system, with frequent relapse and limited clinical therapeutic drugs. The failure of their treatment is due to the tumor cells exhibiting cancer stem-like cells (CSLCs) properties. Octamer binding transcription factor 4 (Oct4) is involved in mediating CSLCs, our previous work found that Oct4-driven reprogramming of astrocytes into induced neural stem cells was potentiated with continuous sonic hedgehog (Shh) stimulation. In this study, we aimed to study the importance of Oct4 and Shh combination in the stemness properties induction of neuroblastoma and glioblastoma cells, and evaluate the anti-stemness effect of dauricine (DAU), a natural product of bis-benzylisoquinoline alkaloid. The effect of Oct4 and Shh co-activation on cancer stemness was evaluated by tumor spheres formation model and flow cytometry analysis. Then the effects of DAU on SH-SY5Y and T98-G cells were assessed by the MTT, colony formation, and tumor spheres formation model. DAU acts on Oct4 were verified using the Western blotting, MTT, and so on. Mechanistic studies were explored by siRNA transfection assay, Western blotting, and flow cytometry analysis. We identified that Shh effectively improved Oct4-mediated generation of stemness in SH-SY5Y and T98-G cells, and Oct4 and Shh co-activation promoted cell growth, the resistance of apoptosis. In addition, DAU, a natural product, was found to be able to attenuate Oct4/Shh co-activated stemness and induce cell cycle arrest and apoptosis via blocking AKT/β-catenin signaling in neuroblastoma and glioblastoma, which contributed to the neuroblastoma and glioblastoma cells growth inhibition by DAU. In summary, our results indicated that the treatment of DAU may be served as a potential therapeutic method in neuroblastoma and glioblastoma.

Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma.