Medulloblastoma (MB) is the most common malignant brain tumor in children, typically arising during infancy and childhood. Despite multimodal therapies achieving a response rate of 70% in children older than 3 years, treatment remains challenging. Ferroptosis, a form of regulated cell death, can be induced in medulloblastoma cells in vitro using erastin or RSL3. Using two independent medulloblastoma RNA-sequencing cohorts (MB-PBTA and MTAB-10767), we investigated the expression of ferroptosis-related molecules through multiple approaches, including Weighted Gene Co-Expression Network Analysis (WGCNA), molecular subtype stratification, protein–protein interaction (PPI) networks, and univariable and multivariable overall survival analyses. A prognostic expression score was computed based on a cross-validated ferroptosis signature. In training and validation cohorts, the regulation of the ferroptosis transcriptional program distinguished the four molecular subtypes of medulloblastoma. WGCNA identified nine gene modules in the MB tumor transcriptome; five correlated with molecular subtypes, implicating pathways related to oxidative stress, hypoxia, and trans-synaptic signaling. One module, associated with disease recurrence, included epigenetic regulators and nucleosome organizers. Univariable survival analyses identified a 45-gene ferroptosis prognostic signature associated with nutrient sensing, cysteine and methionine metabolism, and trans-sulfuration within a one-carbon metabolism. The top ten unfavorable ferroptosis genes included CCT3, SNX5, SQOR, G3BP1, CARS1, SLC39A14, FAM98A, FXR1, TFAP2C, and ATF4. Patients with a high ferroptosis score showed a worse prognosis, particularly in the G3 and SHH subtypes. The PPI network highlighted IL6 and CBS as unfavorable hub genes. In a multivariable overall survival model, which included gender, age, and the molecular subtype classification, the ferroptosis expression score was validated as an independent adverse prognostic marker (hazard ratio: 5.8; p-value = 1.04 × 10⁻⁹). This study demonstrates that the regulation of the ferroptosis transcriptional program is linked to medulloblastoma molecular subtypes and patient prognosis. A cross-validated ferroptosis signature was identified in two independent RNA-sequencing cohorts, and the ferroptosis score was confirmed as an independent and adverse prognostic factor in medulloblastoma.

Despite decades of concern over the carcinogenic potential of agricultural pesticides, toxicological studies relying on single endpoints have yet to establish a definitive link between environmental pesticide exposure and cancer in real-world contexts. Here we use an integrative spatial Bayesian framework that merges high-resolution environmental pesticide risk modelling with comprehensive cancer registry data to map pesticide-linked cancer clusters in Peru with unprecedented precision. Our process-based model, encompassing 31 key pesticide active ingredients, together with an innovative stratification of cancer cases by developmental lineage, reveals a robust spatial association between environmental pesticide exposure risk and cancer incidence. In pesticide-associated cancer hotspots, exposomic profiling of liver tissue—a primary target of chemical carcinogens—uncovers a distinct transcriptomic signature of pesticide exposure, implicating a non-genotoxic mode of action that disrupts core regulatory circuitries sustaining cell identity. Collectively, these findings strongly support a mechanistic link between pesticide exposure and cancer, challenging assumptions of human non-carcinogenicity derived from reductionist experimental models. This study redefines the exposome as a lineage-conditioned, mechanistically tractable framework and shows how complex pesticide mixtures can contribute to carcinogenic trajectories, with profound and far-reaching implications for global health policy and socio-ecological equity.

(1) Background: Hepatoblastoma and medulloblastoma are two types of pediatric tumors with embryonic origins. Both tumor types can exhibit genetic alterations that affect the β-catenin and Wnt pathways; (2) Materials and Methods: This study used bioinformatics and integrative analysis of multi-omics data at both the tumor and single-cell levels to investigate two distinct pediatric tumors: medulloblastoma and hepatoblastoma; (3) Results: The cross-transcriptome analysis revealed a commonly regulated expression signature between hepatoblastoma and medulloblastoma tumors. Among the commonly upregulated genes, the transcription factor LEF1 was significantly expressed in both tumor types. In medulloblastoma, LEF1 upregulation is associated with the WNT-subtype. The analysis of LEF1 genome binding occupancy in H1 embryonic stem cells identified 141 LEF1 proximal targets activated in WNT medulloblastoma, 13 of which are involved in Wnt pathway regulation: RNF43, LEF1, NKD1, AXIN2, DKK4, DKK1, LGR6, FGFR2, NXN, TCF7L1, STK3, YAP1, and NFATC4. The ROC curve analysis of the combined expression of these 13 WNT-related LEF1 targets yielded an area under the curve (AUC) of 1.00, indicating 100% specificity and sensitivity for predicting the WNT subtype in the PBTA medulloblastoma cohort. An expression score based on these 13 WNT-LEF1 targets accurately predicted the WNT subtype in two independent medulloblastoma transcriptome cohorts. At the single-cell level, the WNT-LEF1 expression score was exclusively positive in WNT-medulloblastoma tumor cells. This WNT-LEF1-dependent signature was also confirmed as activated in the hepatoblastoma tumor transcriptome. At the single-cell level, the WNT-LEF1 expression score was higher in tumor cells from both human hepatoblastoma samples and a hepatoblastoma patient-derived xenotransplant model; (4) Discussion: This study uncovered a shared transcriptional activation of a LEF1-dependent embryonic program, which orchestrates the regulation of the Wnt signaling pathway in tumor cells from both hepatoblastoma and medulloblastoma.

Intrahepatic cholangiocarcinoma (ICA) is a highly aggressive primary liver cancer, which originates from the epithelial cells of the bile ducts. The transcriptional profile of metabolic enzymes was investigated at both bulk and single-cell levels in tumor samples from distinct ICA cohorts. In a training cohort (TCGA consortium), 16 genes encoding for metabolic enzymes were found overexpressed in cases with poor survival. A computed metabolic gene expression score was significantly associated with worse ICA prognosis at the univariate level (overall survival [OS] log-rank p = 8.2e-4). After adjusting for Ishak fibrosis score and tumor staging, the metabolic expression remained an independent predictor of poor prognosis (multivariate OS log-rank p = 0.01). Seven genes encoding key enzymes (FH, MAT2B, PLOD2, PLOD1, PDE6D, ALDOC, and NT5DC3) were validated as markers of the proliferative subclass of ICA in the GSE32225 dataset, related to poor prognosis. The metabolic score was significantly different between the inflammatory and proliferative subclasses in the validation cohort (p < 2.2e-16). At the single-cell level, in the tumor microenvironment of 10 ICA patients, these seven enzymes were predominantly expressed by malignant cells. The single-cell metabolic score was thus higher in malignant cells. This study identifies a metabolic transcriptional program linked to poor prognosis in ICA, independent of fibrosis and tumor staging.

Background: Hepatoblastoma, the most common pediatric liver malignancy, is characterized by significant molecular heterogeneity and poor prognosis in advanced stages. Recent studies highlight the importance of metabolic reprogramming and epigenetic dysregulation in hepatoblastoma pathogenesis. This review aims to explore the metabolic alterations and epigenetic mechanisms involved in hepatoblastoma and how these processes contribute to tumor progression and survival. Methods: Relevant literature on metabolic reprogramming, including enhanced glycolysis, mitochondrial dysfunction, and shifts in lipid and amino acid metabolism, as well as epigenetic mechanisms like DNA methylation, histone modifications, and non-coding RNAs, was reviewed. The interplay between these pathways and their potential as therapeutic targets were examined. Results: Hepatoblastoma exhibits metabolic shifts that support tumor growth and survival, alongside epigenetic changes that regulate gene expression and promote tumor progression. These pathways are interconnected, with metabolic changes influencing the epigenetic landscape and vice versa. Conclusions: The dynamic interplay between metabolism and epigenetics in hepatoblastoma offers promising avenues for therapeutic intervention. Future research should focus on integrating metabolic and epigenetic therapies to improve patient outcomes, addressing current gaps in knowledge to develop more effective treatments.

Hepatoblastoma is the most common primary liver cancer in children. Poor outcomes are primarily associated with patients who have distant metastases. Using the Mammalian Metabolic Enzyme Database, we investigated the overexpression of metabolic enzymes in hepatoblastoma tumors compared to noncancerous liver tissue in the GSE131329 transcriptome dataset. For the overexpressed enzymes, we applied ElasticNet machine learning to assess their predictive value for metastasis. A metabolic expression score was then computed from the significant enzymes and integrated into a clinical-biological logistic regression model. Forty-one overexpressed enzymes distinguished hepatoblastoma tumors from noncancerous liver tissues. Eighteen of these enzymes predicted metastasis status with an AUC of 0.90, demonstrating 85.7% sensitivity and 92.3% specificity. ElasticNet machine learning identified DNMT3B and PFKFB4 as key predictors of metastasis. Univariate analyses confirmed the significance of these enzymes, with respective p-values of 0.0058 and 0.0091. A metabolic score based on DNMT3B and PFKFB4 expression discriminated metastasis status and high-risk CHIC scores (p-value = 0.005). The metabolic score was more sensitive than the C1/C2 classifier in predicting metastasis (accuracy: 0.72 vs. 0.55). In a regression model integrating the metabolic score with epidemiological parameters (gender, age at diagnosis, histological type, and clinical PRETEXT stage), the metabolic score was confirmed as an independent adverse predictor of metastasis (p-value = 0.003, odds ratio: 2.12). This study identified the dual overexpression of PFKFB4 and DNMT3B in hepatoblastoma patients at risk of metastasis (high-risk CHIC classification). The combined tumor expression of DNMT3B and PFKFB4 was used to compute a metabolic score, which was validated as an independent predictor of metastatic status in hepatoblastoma.