Monge, C.; Francés, R.; Marchio, A.; Pineau, P.; Desterke, C.; Mata-Garrido, J. Activated Metabolic Transcriptional Program in Tumor Cells from Hepatoblastoma. Preprints2024, 2024090699. https://doi.org/10.20944/preprints202409.0699.v1
APA Style
Monge, C., Francés, R., Marchio, A., Pineau, P., Desterke, C., & Mata-Garrido, J. (2024). Activated Metabolic Transcriptional Program in Tumor Cells from Hepatoblastoma. Preprints. https://doi.org/10.20944/preprints202409.0699.v1
Chicago/Turabian Style
Monge, C., Christophe Desterke and Jorge Mata-Garrido. 2024 "Activated Metabolic Transcriptional Program in Tumor Cells from Hepatoblastoma" Preprints. https://doi.org/10.20944/preprints202409.0699.v1
Abstract
(1) Background: Hepatoblastoma is the most common primary liver malignancy in children. During its development, metabolic reprogramming becomes particularly relevant due to the liver's intrinsic metabolic functions. Enhanced glycolysis, glutaminolysis, and fatty acid synthesis have been implicated in the proliferation and survival of hepatoblastoma cells. In this study we screened altered over expression of metabolic enzymes in hepatoblastoma tumors at tissue and single cell level. A hepatoblastoma tumor expression metabolic score was established and validated by machine learning; (2) Methods: Starting from Mammalian Metabolic Enzyme Database, bulk RNA-sequencing from GSE104766 dataset was investigated by supervised analyses (tumors versus adjacent liver tissue). Overexpressed enzymes in hepatoblastoma tumors were functionally enriched on KEGG metabolic database to draw a metabolic network with Cytoscape. Activated metabolic markers were used to compute a single-cell metabolic score in human and PDX hepatoblastoma samples from GSE180665 dataset (sc-RNAseq). ROC and area under curve (AUC) were computed on metabolic score. Elasticnet model tuning for metabolic marker expression was performed with r-caret on single cell transcriptome and revealed importance of individual marker to predict tumor cell status; (3) Results: Differential expression analysis on bulk transcriptome identified 287 significantly regulated enzymes between tumor and adjacent liver tissues: 59 of them were found overexpressed in tumors. 45 of the 59 over expressed enzymes were recognized in KEGG database which highlighted a main metabolic network enriched in amino acid metabolism but also carbohydrate, steroid, one carbon, purine, and glucoaminoglycan metabolisms. Based on expression of the 59 over expressed enzymes, the single cell metabolism score computed on GSE180665 dataset allowed to predict tumor cell status with and AUC of 0.98 (sensibility 0.93, specificity: 0.94). Elasticnet model tuned at 0.97 of AUC on individual marker single cell expression ranked top tumor predictive markers by decreasing importance: FKBP10, ATP1A2, NT5DC2, UGT3A2, PYCR1, CKB, GPX7, DNMT3B, GSTP1, and OXCT1; (4) Conclusion: An activated metabolic transcriptional program potentially affecting epigenetic functions was observed by bulk RNAseq in tumors of hepatoblastoma and confirmed at single cell levels in tumor cells.
Keywords
hepatoblastoma; epigenetics; one-carbon; metabolism; cancer; DNA methylation
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
