Monge Claudia, Francés Raquel, Marchio Agnès, Pineau Pascal, Desterke Christophe, Mata-Garrido Jorge
Unité Organisation Nucléaire et Oncogenèse, INSERM U993, Institut Pasteur, Université Paris Cité, 75015 Paris, France.
Energy & Memory, Brain Plasticity Unit, CNRS, ESPCI Paris, PSL Research University, 75006 Paris, France.
Int J Mol Sci. 2024 Dec 4;25(23):13044. doi: 10.3390/ijms252313044.
Hepatoblastoma is the most common primary liver malignancy in children, with metabolic reprogramming playing a critical role in its progression due to the liver's intrinsic metabolic functions. Enhanced glycolysis, glutaminolysis, and fatty acid synthesis have been implicated in hepatoblastoma cell proliferation and survival. In this study, we screened for altered overexpression of metabolic enzymes in hepatoblastoma tumors at tissue and single-cell levels, establishing and validating a hepatoblastoma tumor expression metabolic score using machine learning. Starting from the Mammalian Metabolic Enzyme Database, bulk RNA sequencing data from GSE104766 and GSE131329 datasets were analyzed using supervised methods to compare tumors versus adjacent liver tissue. Differential expression analysis identified 287 significantly regulated enzymes, 59 of which were overexpressed in tumors. Functional enrichment in the KEGG metabolic database highlighted a network enriched in amino acid metabolism, as well as carbohydrate, steroid, one-carbon, purine, and glycosaminoglycan metabolism pathways. A metabolic score based on these enzymes was validated in an independent cohort (GSE131329) and applied to single-cell transcriptomic data (GSE180665), predicting tumor cell status with an AUC of 0.98 (sensitivity 0.93, specificity 0.94). Elasticnet model tuning on individual marker expression revealed top tumor predictive markers, including FKBP10, ATP1A2, NT5DC2, UGT3A2, PYCR1, CKB, GPX7, DNMT3B, GSTP1, and OXCT1. These findings indicate that an activated metabolic transcriptional program, potentially influencing epigenetic functions, is observed in hepatoblastoma tumors and confirmed at the single-cell level.
肝母细胞瘤是儿童最常见的原发性肝脏恶性肿瘤，由于肝脏固有的代谢功能，代谢重编程在其进展中起着关键作用。糖酵解、谷氨酰胺分解和脂肪酸合成增强与肝母细胞瘤细胞的增殖和存活有关。在本研究中，我们在组织和单细胞水平上筛选了肝母细胞瘤肿瘤中代谢酶的异常过表达情况，利用机器学习建立并验证了肝母细胞瘤肿瘤表达代谢评分。从哺乳动物代谢酶数据库开始，使用监督方法分析来自GSE104766和GSE131329数据集的批量RNA测序数据，以比较肿瘤与相邻肝组织。差异表达分析确定了287种显著调控的酶，其中59种在肿瘤中过表达。KEGG代谢数据库中的功能富集突出显示了一个富含氨基酸代谢以及碳水化合物、类固醇、一碳、嘌呤和糖胺聚糖代谢途径的网络。基于这些酶的代谢评分在一个独立队列（GSE131329）中得到验证，并应用于单细胞转录组数据（GSE180665），预测肿瘤细胞状态的曲线下面积（AUC）为0.98（敏感性0.93，特异性0.94）。对个体标志物表达进行弹性网络模型调整，揭示了顶级肿瘤预测标志物，包括FKBP10、ATP1A2、NT5DC2、UGT3A2、PYCR1、CKB、GPX7、DNMT3B、GSTP1和OXCT1。这些发现表明，在肝母细胞瘤肿瘤中观察到了一个激活的代谢转录程序，可能影响表观遗传功能，并在单细胞水平上得到了证实。
