Genome wide DNA methylation analysis identifies novel molecular subgroups and predicts survival in neuroblastoma

Methods in DNA methylation array dataset analysis: A review

Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.

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.

Overview of modern genomic tools for diagnosis and precision therapy of childhood solid cancers

PURPOSE OF REVIEW

The application of technology and computational analyses to generate new data types from pediatric solid cancers is transforming diagnostic accuracy. This review provides an overview of such new capabilities in the pursuit of improved treatment for essentially rare and underserved diseases that are the highest cause of mortality in children over one year of age. Sophisticated ways of identifying therapeutic vulnerabilities for highly personalized treatment are presented alongside cutting-edge disease response monitoring by liquid biopsy.

RECENT FINDINGS

Precision molecular profiling data are now being combined with conventional pathology-based evaluation of pediatric cancer tissues. The resulting diagnostic information can be used to guide therapeutic decision-making, including the use of small molecule inhibitors and of immunotherapies. Integrating somatic and germline variant profiles constitutes a critical component of this emerging paradigm, as does tissue-of-origin derivation from methylation profiling, and rapid screening of potential therapies. These new approaches are poised for use in disease response and therapy resistance monitoring.

SUMMARY

The integration of clinical molecular profiling data with pathology can provide a highly precise diagnosis, identify therapeutic vulnerabilities, and monitor patient responses, providing next steps toward precision oncology for improved outcomes, including reducing lifelong treatment-related sequelae.

Epigenetic Dysregulation in MYCN-Amplified Neuroblastoma

Neuroblastoma (NB), a childhood cancer arising from the neural crest, poses significant clinical challenges, particularly in cases featuring amplification of the MYCN oncogene. Epigenetic factors play a pivotal role in normal neural crest and NB development, influencing gene expression patterns critical for tumorigenesis. This review delves into the multifaceted interplay between MYCN and known epigenetic modifications during NB genesis, shedding light on the intricate regulatory networks underlying the disease. We provide an extensive survey of known epigenetic mechanisms, encompassing DNA methylation, histone modifications, non-coding RNAs, super-enhancers (SEs), bromodomains (BET), and chromatin modifiers in MYCN-amplified (MNA) NB. These epigenetic changes collectively contribute to the dysregulated gene expression landscape observed in MNA NB. Furthermore, we review emerging therapeutic strategies targeting epigenetic regulators, including histone deacetylase inhibitors (HDACi), histone methyltransferase inhibitors (HMTi), and DNA methyltransferase inhibitors (DNMTi). We also discuss and summarize current drugs in preclinical and clinical trials, offering insights into their potential for improving outcomes for MNA NB patients.

Identification of TIAM1 as a Potential Synthetic-Lethal-like Gene in a Defined Subset of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the most common type of liver cancer, has very poor outcomes. Current therapies often have low efficacy and significant toxicities. Thus, there is a critical need for the development of novel therapeutic approaches for HCC. We have developed a novel bioinformatics pipeline, which integrates genome-wide DNA methylation and gene expression data, to identify genes required for the survival of specific molecular cancer subgroups but not normal cells. Targeting these genes may induce cancer-specific "synthetic lethality". Initially, five potential HCC molecular subgroups were identified based on global DNA methylation patterns. Subgroup-2 exhibited the most unique methylation profile and two candidate subtype-specific vulnerability or SL-like genes were identified for this subgroup, including TIAM1, a guanine nucleotide exchange factor encoding gene known to activate Rac1 signalling. siRNA targeting TIAM1 inhibited cell proliferation in TIAM1-positive (subgroup-2) HCC cell lines but had no effect on the normal hepatocyte HHL5 cell line. Furthermore, TIAM1-positive/subgroup-2 cell lines were significantly more sensitive to the TIAM1/RAC1 inhibitor NSC23766 compared with TIAM1-negative HCC lines or the normal HHL5 cell line. The results are consistent with a synthetic lethal role for TIAM1 in a methylation-defined HCC subgroup and suggest it may be a viable therapeutic target in this subset of HCC patients.

DNA Methylation and Histone Modification in Low-Grade Gliomas: Current Understanding and Potential Clinical Targets

Gliomas, the most common type of malignant primary brain tumor, were conventionally classified through WHO Grades I-IV (now 1-4), with low-grade gliomas being entities belonging to Grades 1 or 2. While the focus of the WHO Classification for Central Nervous System (CNS) tumors had historically been on histopathological attributes, the recently released fifth edition of the classification (WHO CNS5) characterizes brain tumors, including gliomas, using an integration of histological and molecular features, including their epigenetic changes such as histone methylation, DNA methylation, and histone acetylation, which are increasingly being used for the classification of low-grade gliomas. This review describes the current understanding of the role of DNA methylation, demethylation, and histone modification in pathogenesis, clinical behavior, and outcomes of brain tumors, in particular of low-grade gliomas. The review also highlights potential diagnostic and/or therapeutic targets in associated cellular biomolecules, structures, and processes. Targeting of MGMT promoter methylation, TET-hTDG-BER pathway, association of G-CIMP with key gene mutations, PARP inhibition, IDH and 2-HG-associated processes, TERT mutation and ARL9-associated pathways, DNA Methyltransferase (DNMT) inhibition, Histone Deacetylase (HDAC) inhibition, BET inhibition, CpG site DNA methylation signatures, along with others, present exciting avenues for translational research. This review also summarizes the current clinical trial landscape associated with the therapeutic utility of epigenetics in low-grade gliomas. Much of the evidence currently remains restricted to preclinical studies, warranting further investigation to demonstrate true clinical utility.

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.