Genetic markers and canonical pathways associated with medulloblastoma: a systematic review and meta-analysis

Introduction

Medulloblastoma is the most common malignant brain tumor of childhood, accounting for 25% of pediatric central nervous system (CNS) neoplasms (1). There are approximately 500 new diagnoses in the United States every year (2). The World Health Organization (WHO) classification system outlines 4 main subgroups of medulloblastoma: wingless/integrated (WNT)-activated, sonic hedgehog (SHH)-activated tumors with and without tumor protein p53 (TP53) mutations, and non-WNT/non-SHH (group 3 and 4) tumors.

With standard of care treatment, including a combination of surgical resection, craniospinal radiation therapy, and chemotherapy, the 5-year overall survival is as high as 75.5% for children (3). However, tumor recurrence carries a poor prognosis, with 5-year overall survival as low as 12.4% (4). The subgroup associated with medulloblastoma has a considerable impact on survival. The WNT-activated subtype is associated with the best prognosis, while group 3 tumors are associated with the poorest outcomes. SHH-activated and group 4 tumors have intermediate outcomes that vary highly based on genetic and clinical characteristics (5).

Although a definitive cause remains largely unknown, recent advancements have provided insight into the pathogenesis of the disease. These findings have revealed the importance of genetics and its influence on extent of disease, necessity of tumor resection, and age of onset. For example, MYC gene amplification is a known poor prognostic marker, particularly in Group 3 tumors (6,7). Several other genetic/epigenetic factors have been discovered relating to medulloblastoma beyond the tumor subgroups. For instance, chromatin regulator mutations are frequently found in patients with medulloblastoma (8). CpG island hypermethylation of tumor suppressor genes and histone modification, including enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2)-mediated H3K27me3 accumulation, are additional epigenetic changes found to influence the progression and immune response to these tumors (9).

Investigations into the genetics of medulloblastoma, especially regarding differential expression of genes, are also important because they can reveal genes that can serve as therapeutic targets for treatment. For example, centromere protein E (CENPE), a gene linked with microencephaly, has been found to be a highly likely biomarker and potential target for treating non-WNT/non-SHH medulloblastoma (10). Similarly, dysregulation of the Shh pathway has been shown to lead to hyperactivation of paired box 6 (Pax6), while the NK2 homeobox 2 (Nkx2.2) transcription factor acts as a suppressor of Pax6. This interplay reveals another set of targets for new therapeutic strategies for the treatment of medulloblastoma (11).

Long-term sequelae of traditional treatment modalities include neurocognitive impairment, endocrinopathies, hearing loss, secondary malignancies, and psychosocial dysfunction (12-16). The significant long-term sequelae and poor survival associated with some subgroups highlight the importance of identifying biomarkers and key genes involved in the pathogenesis and propagation of this disease. Herein, we present an innovative meta-analysis aiming to highlight novel genetic markers, a necessary first step toward the development of screening modalities and novel targeted therapeutics for medulloblastoma. We present this article in accordance with the PRISMA reporting checklist (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-420/rc).

Methods

NCBI’s Gene Expression Omnibus (GEO) is a publicly available database containing data from millions of samples collected in prior studies. The Search Tag Analyze Resource for GEO (STARGEO) enables scientists to search for a free-text attribute or series number, tag samples with attributes, and complete a meta-analysis comparing gene expression in different experimental groups. This program was utilized to conduct a comparison between genetic signatures of medulloblastoma and healthy cerebellum tissue. Series containing samples of non-medulloblastoma tissue (e.g., glioblastoma, astrocytoma), samples that have undergone treatment or genetic alterations that may alter the validity of results, series with no control samples, and series containing cell lines or xenograft tissue were excluded from the study. Series containing untreated, human, primary medulloblastoma samples of all four WHO subgroups (WNT-activated, SHH-activated +/− TP53 mutation, and non-WNT/non-SHH group 3 and 4 tumors) were included for analysis. Control samples consisted of fetal, pediatric, infant, and adult cerebellum and whole brain samples with no tumoral evidence. 12 series met the inclusion criteria and were included for analysis (GSE109401, GSE202043, GSE223606, GSE28192, GSE39182, GSE42656, GSE62600, GSE68928, GSE68956, GSE74195, GSE95684). Table S1 contains further series and sample details. Within the 12 series, there were 480 medulloblastoma samples and 62 control samples that were tagged as “MB” and “MB_control”, respectively.

Statistical analysis

Differential expression analysis was performed in STARGEO using DerSimonian-Laird inverse-variance weighted fixed and random effects models. This program generated a meta-p-value describing the differential genetic expression between medulloblastoma and control samples. Over 23,000 genes were included in STARGEO’s analysis. The genes identified in STARGEO’s analysis were restricted to P<0.05 and an experimental log₂ fold change ratio less than −0.2 or greater than 0.2 for inclusion in the pathway analysis in Ingenuity Pathway Analysis (IPA). A total of 4,142 analysis-ready molecules were identified, including 2,231 downregulated and 1,911 upregulated. Significant genes were then imported into QIAGEN’s IPA for functional annotation, canonical pathway enrichment, and upstream regulator analysis. QIAGEN’s IPA is an innovative tool utilized for pathway analysis, causal analysis, and analysis of associated disease processes (17). IPA utilizes a vast collection of publicly available data and literature to overlay biological connections and functional genomics data.

Results

Upregulated and downregulated genes

All 4,142 molecules included in IPA’s analysis meet statistical significance at the level of P<0.05. The top upregulated and downregulated genes identified are those with the greatest difference in magnitude of change between expression in medulloblastoma and control, as indicated by the experimental (expr) log ratio. Table 1 details the top 10 upregulated and 10 downregulated genes in medulloblastoma.

Canonical pathways

Canonical pathways are pathways that have been well characterized by previous experimental studies and literature. Known connections between molecules within pathways enable the prediction of overall expression based on differential gene expression of molecules involved in the pathway. The canonical pathways are ranked according to the P value of overlap, thus the most prominent canonical pathways that IPA has predicted are those that have a high probability of being differentially expressed on the basis of the observed differential gene expression in the medulloblastoma dataset. Further, IPA predicts these pathways to be activated or inhibited according to the degree and direction of differential expression of those genes within the pathway. Figure 1 contains all canonical pathways predicted as differentially expressed in medulloblastoma.

Figure 1 Bubble diagram displaying all canonical pathways in medulloblastoma versus control. Orange indicates predicted upregulation and blue indicates predicted downregulation. The size of the bubble corresponds to the number of genes included in the analysis that overlap with the canonical pathway.

The most prominent canonical pathways in medulloblastoma versus healthy brain tissue are cell cycle checkpoints (z-score 8.2), synaptogenesis signaling and pathway (z-score −6.034), mitotic metaphase and anaphase (z-score 6.736), glutaminergic receptor signaling pathway (z-score −6.293), and mitotic prometaphase (z-score 6.405). Cell cycle checkpoints, mitotic metaphase and anaphase, and mitotic prometaphase are all predicted as activated, whereas synaptogenesis signaling and pathway, as well as glutaminergic receptor signaling pathway, are predicted as inhibited. The upregulated gene Ubiquitin-conjugating enzyme E2C (UBE2C; expr log ratio 1.583) is included in the cell cycle checkpoints pathway and the mitotic metaphase and anaphase pathway. The downregulated gene solute carrier family 1 member 6 (SLC1A6; expr log ratio −2.336) is a component of the glutaminergic receptor signaling pathway, which is depicted in Figure 2.

Figure 2 One of the most downregulated canonical pathways in medulloblastoma – glutaminergic signaling pathway. Z-score =−6.293. The glutaminergic signaling pathway includes the top downregulated gene, SLC1A6.

Upstream regulators and causal networks

The upstream regulators predicted to contribute to the observed downstream effects in medulloblastoma compared to the control group are beta-estradiol (z-score 1.851), huntingtin (HTT; z-score 1.795), transforming growth factor beta 1 (TGFB1; z-score −0.670), TP53 (z-score −2.803), and T-box transcription factor 3 (TBX3; z-score 5.695). TP53 is predicted as inhibited, indicating that predicted decreased expression of TP53 leads to the observed downstream effects, whereas TBX3 is predicted to be activated. Beta-estradiol, HTT, and TGFB1 lack a predicted activation state due to having absolute value z-scores less than 2. The upstream regulators are determined by a z-score prediction algorithm (17). The downstream effects of these two upstream regulators (TP53 and TBX3) are displayed in Figure 3A,3B, respectively. Additionally, the impact on genes in the top 10 upregulated and downregulated list are overlaid on the mechanistic network. Through numerous intermediates, downregulation of TP53 may lead to the observed increased expression of LIM homeobox 2 (LHX2), prolactin (PRL), UBE2C, PCNA clamp associated factor (PCLAF), SRY-box transcription factor 11 (SOX11), and T-box brain transcription factor 1 (TBR1) and to the observed decreased expression of solute carrier family 32 member 1 (SLC32A1), visinin like 1 (VSNL1), aldolase fructose-bisphosphate C (ALDOC), and SLC1A6. TP53 does show significant differential expression (P=0.003), however it has observed increased expression (expr log ratio 0.541). The upstream regulator TBX3 is predicted to lead to the observed increased expression of LHX2, PCLAF, UBE2C, PRL, and potassium voltage-gated channel subfamily A member 5 (KCNA5) as well as the decreased expression of ALDOC, SLC32A1, and SPHK1 interactor AKAP domain containing (SPHKAP). TBX3 does not show significant differential expression, with only slightly increased expression (expr log ratio of 0.058, P=0.14).

Figure 3 Top upstream regulators (A) TP53 and (B) TBX3 and associated downstream effects. Orange, predicted upregulation; blue, predicted downregulation; red, observed decreased expression; green, observed increased expression.

The causal networks identified are also upstream molecules or regulators predicted to contribute to the observed downstream expression of genes in the dataset based on known relationships and links between molecules identified in the literature. Causal networks differ from upstream regulators in that they include indirect relationships between the regulator and the targets to trace the root cause further back. The top 5 causal networks are SRY-box transcription factor 2 (SOX2; z-score −3.928), vir like m6A methyltransferase associated (VIRMA; z-score 3.219), DNA methyltransferase 3 alpha (DNMT3A; z-score 4.116), anterior gradient 2 protein disulphide isomerase family member (AGR2; z-score 3.148), and cullin 7 (CUL7; z-score 3.133). SOX2 is predicted as inhibited while the remaining causal networks are predicted as activated.

Diseases and functions

IPA predicts diseases and functions that may be associated with the disease of interest, medulloblastoma, based on the differential expression of genes in the dataset and how they coincide with genes known to contribute to a particular disease or function. The diseases & functions predicted as most activated in medulloblastoma are cell proliferation of tumor cell lines, motor function, movement disorders, and seizure disorders. Cell proliferation of tumor cell lines has an activation z-score of 7.082 and motor function or movement disorders have an activation z-score of 5.976. The most inhibited diseases and/or functions include abnormal morphology of nucleus, misalignment of chromosomes, abnormal morphology of the chromosomes, and sensitivity of cells with z-scores −5.504, −5.42, −5.33, and −5.253, respectively.

Discussion

In our meta-analysis, we have attempted to describe novel complex relationships between a variety of genetic participants within medulloblastoma. These results build upon the current framework by providing further insight into relevant molecular processes, including the role of cell cycle regulation and glutamatergic signaling in the development of medulloblastoma, as well as other unique mechanisms of medulloblastoma pathogenesis.

Dysregulation of cell cycle control

Uncontrolled proliferation of cells is a hallmark for the development of many cancers, and our analysis revealed significant enrichment of mitotic processes, including prometaphase, metaphase and anaphase. UBE2C regulates the degradation of abnormal proteins, such as those that affect the spindle checkpoint, during the transition of metaphase and anaphase (18). It is also responsible for the inactivation and progression through the mitotic cell cycle spindle assembly checkpoint, which leads to increased cell proliferation. UBE2C has been found to be associated with chromosome instability/aneuploidy, increased cell proliferation, invasion, and migration, which forms the basis on which a cancer like medulloblastoma may develop (19). Although UBE2C expression has been linked to poor prognosis and high histological grade in glioblastoma (20), its role in medulloblastoma has not been previously described. Our findings suggest that UBE2C may contribute to medulloblastoma pathogenesis by promoting unchecked mitotic progression. Future research should evaluate UBE2C across medulloblastoma subgroups and assess its potential as a prognostic marker.

PCLAF promotes tumor development by interacting with proliferating cell nuclear antigen (PCNA) to facilitate DNA replication and cell cycle progression, thereby enhancing uncontrolled cell proliferation (21). In neuroblastoma, PCLAF has been shown to activate the PCLAF/E2F1/PTTG1 axis, promoting the G1/S cell cycle transition and correlating with poor prognosis (22). While this axis has not yet been characterized in medulloblastoma, our findings suggest a similar mechanism may be at play. This notion is further supported by the finding of G1/S checkpoint regulation among our top toxicity functions in our analysis. Together, UBE2C and PCLAF highlight the central role of disrupted cell cycle control in medulloblastoma tumorigenesis.

Several upstream regulatory genes have been found to have associations with medulloblastoma, the relations of which can provide insight into the mechanisms behind the disease. TP53, a master regulator of apoptosis, cell cycle arrest, and differentiation, was predicted to be activated and is known to be frequently mutated in SHH medulloblastoma, where it is associated with poor prognosis and treatment failure (23,24). TP53 dysfunction may contribute to the upregulation of cell cycle drivers such as UBE2C and neurodevelopmental regulators like LHX2, consistent with our findings. TBX3, another predicted upstream regulator, is overexpressed in various epithelial and mesenchymal tumors and promotes proliferation by repressing cell cycle inhibitors such as p14^ARF/p19^ARF, p21^WAF1/CIP1, p57^KIP2 or phosphatase and tensin homolog (PTEN) (25). Its activation in our dataset may contribute to unchecked proliferation and could be linked to β-catenin pathway signaling, particularly in the WNT subtype of medulloblastoma, as previously reported in hepatocellular carcinoma with CTNNB1 mutations (26).

Neural differentiation and development

Several genes involved in neural development and differentiation were significantly upregulated in our dataset, notably SOX11 and LHX2. SOX11, a transcription factor that is typically downregulated after brain maturation, has been shown to be highly overexpressed in medulloblastoma, up to 84-fold increase compared with normal cerebellum tissue (27). Its overexpression may reflect a reactivation of developmental programs, contributing to dedifferentiation and enhanced tumorigenic potential (28).

Similarly, LHX2 has been described as a regulator of the neuron-astrocyte cell fate switch during hippocampal development, specifically as a “brake” on the Notch-Nfia pathway, preventing premature gliogenesis until neurogenesis is complete (29). The upregulation of LHX2 found in this study represents a suppression of astrocyte development in favor of persistent neurogenesis, which is a phenotype consistent with immature cell populations characteristic of medulloblastoma. Together, SOX11 and LHX2 highlight the disruption of tightly regulated developmental transitions that normally balance differentiation and proliferation in the cerebellum.

Glutamatergic and synaptic signaling

Synaptogenesis signaling pathways, specifically glutaminergic neuron interactions, are well represented in the genes that are differentially regulated within the medulloblastoma samples. TBR1 encodes a T-box transcription factor involved in the development of glutamatergic neurons within the CNS and was found to be upregulated in our analysis. Histologically, medulloblastoma cells resemble granule cell precursors (GCPs), which originate in the external granule layer of the developing cerebellum and differentiate into glutamatergic neurons (30). Therefore, TBR1 is implicated in the development of medulloblastoma by influencing the degree of glutamatergic neuronal differentiation. TBR1 and TBR2, along with PAX6, regulate this transition from radial glia to mature glutamatergic neurons (31). Its upregulation in our analysis suggests a developmental arrest or aberrant differentiation process that may contribute to tumor formation.

Prior studies have shown that TBR1 and TBR2 had significant differential expression within medulloblastoma subgroups, with higher levels specifically within non-WNT/non-SHH patients, along with associated DNA hypomethylation (32). Our pooled dataset did not allow for subgroup-specific analysis, the observed upregulation is consistent with these findings and supports the use of TBR1 as a broadly applicable biomarker for medulloblastoma.

On the other hand, genes like SLC32A1 and SLC1A6 identified in our study are involved directly with the excitatory and inhibitory synapse signaling within the CNS, and the disruption of both may cause dysfunctional communication between neurons. SLC32A1 codes for vesicular inhibitory amino acid transporter (VIAAT), the only known protein in human and other mammals capable of transporting the inhibitory neurotransmitters GABA and glycine into synaptic vesicles, thus is vital in the functioning of the human nervous system (33). The downregulation of SLC32A1 may impair the generation of inhibitory signals and potentially shift the neuronal microenvironment into excitatory dominance. Similarly, SLC1A6 encodes a glutamate transporter critical for maintaining extracellular glutamate homeostasis. Dysfunction of glutamate transporters is associated with excitotoxicity and neuronal damage in other CNS pathologies. Until now, these well-studied interactions with glutamate imbalance have not been discussed in the context of the development of medulloblastoma.

Downregulated gene targets

HepaCAM, a cell adhesion molecule involved in glial differentiation, was among the most significantly downregulated genes in our analysis. Prior studies have shown that hepaCAM expression increases during astrocyte maturation and promotes glial fibrillary acidic protein (GFAP) expression, reducing proliferation and promoting differentiation (34). Its marked downregulation in medulloblastoma suggests a shift toward a less differentiated, more proliferative state, the hallmark of malignancy. Moreover, cells with high expression of hepaCAM display distinct morphological changes typical of differentiated astrocytes. Given the magnitude of downregulation observed, hepaCAM may play a key role in medulloblastoma pathogenesis and warrants further investigation as a potential tumor suppressor or prognostic marker.

ALDOC, a glycolytic enzyme, was significantly downregulated in our dataset. In other cancers, ALDOC overexpression has been linked to activation of the Wnt/β-catenin pathway through upregulation of c-MYC, WNT3A, and β-catenin (35). The downregulation of ALDOC identified in our study may suggest an abnormal regulation of the β-catenin pathway, which is in contrast with the expected overactivation of the Wnt/β-catenin pathway that leads to the development of medulloblastoma. This discrepancy may suggest dysregulation or expression-altering mutations in the intermediates of this pathway and warrants further investigation into possible alternative mechanisms affecting the Wnt pathway.

Subtype associated biomarkers

Our analysis also identified differential expression of genes previously associated with medulloblastoma subtypes. VSNL1, a neuronal calcium sensor and proposed biomarker for Alzheimer’s disease (36), has been reported to be highly upregulated in medulloblastoma with extensive nodularity (MBEN), particularly within the SHH subtype (37,38). However, in our dataset, VSNL1 was downregulated, which may reflect the lack of subgroup stratification or limited MBEN representation. Similarly, PVALB a Ca2þ-binding protein, was also found to be downregulated. Prior studies reported a decreased expression of PVALB in Group 3, Group 4, and SHH medulloblastoma, suggesting its broader relevance as a biomarker across subtypes (39). These findings support the utility of VSNL1 and PVALB in distinguishing medulloblastoma variants, though further subgroup analysis is needed.

Lastly, HTT, the gene mutated in Huntington’s disease, was also predicted to be an upstream regulator. HTT may influence Wnt/β-catenin signaling through β-catenin stabilization, though its role in medulloblastoma remains unclear (40). While intriguing, this association is speculative and requires further investigation to determine its biological significance in this context.

Limitations

In this study, we describe several novel biomarkers involved in the development of medulloblastoma. Notably, the use of the STARGEO database offers limited patient-level information for included samples. Thus, complete demographic information, including age, sex, and treatment status, was not available for analysis. Due to a limited number of healthy brain tissue samples, the control group consisted of samples from across the age spectrum. Furthermore, while all medulloblastoma subgroups were represented within the series, potential differences in genetic expression between subgroups could not be assessed due to inadequate sample-specific histologic identification, precluding stratification. Further research investigating the differential gene expression between the medulloblastoma subgroups is indicated to validate our findings and explore subgroup-specific implications.

Conclusions

Our study leveraged metadata to highlight novel and previously implicated genes and pathways involved in medulloblastoma pathogenesis. Using meta-analysis of gene expression data, we found consistent upregulation of cell cycle regulators (UBE2C, PCLAF) and neural development genes (TBR1, SOX11, LHX2) that may drive unchecked proliferation and impaired differentiation. Dysregulation of glutamatergic signaling and synaptic balance was also evident through altered expression of SLC21A1 and SLC1A6. Strong downregulation of hepaCAM and ALDOC suggests a shift away from glial differentiation and metabolic homeostasis. Upstream regulators, including TP53 and TBX3, further support the involvement of core oncogenic pathways. While our dataset combined samples from all medulloblastoma subgroups and lacked patient-level metadata, these findings offer insight into common molecular drivers and potential therapeutic targets. These findings contribute to a growing body of literature on potential medulloblastoma gene targets and provide a framework for future validation studies that aim to evaluate their prognostic and therapeutic utility in medulloblastoma.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-420/rc

Peer Review File: Available at https://tp.amegroups.com/article/view/10.21037/tp-2025-420/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tp.amegroups.com/article/view/10.21037/tp-2025-420/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.

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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