背景：肝脏是人体主要的代谢器官，代谢异常是影响肝细胞癌（HCC）的主要因素。本研究旨在确定谷氨酰胺代谢对 HCC 异质性的影响，并基于 HCC 肿瘤内和组织间谷氨酰胺代谢的异质性研究开发预后评估器。方法：从 GSE149614 数据集中提取单细胞转录组数据，并使用 R 中的 Seurat 包进行处理，以对这些数据进行质量控制。癌症基因组图谱和 GSE14520 数据集中的 HCC 亚型是通过基于谷氨酰胺家族氨基酸代谢 (GFAAM) 过程基因的共识聚类来识别的。利用机器学习算法梯度提升机、支持向量机、随机森林、极限梯度提升、决策树、最小绝对收缩和选择算子来建立分子基因亚型之间差异表达基因的预后模型。结果：GSE149614数据集中的样本包括10种细胞类型，GFAAM通路没有显着差异。根据GFAAM过程基因将HCC分为三种分子亚型，在预后、临床病理特征和免疫细胞浸润方面表现出分子异质性。 C1的存活率最差，免疫评分和免疫细胞浸润最高。在亚型之间构建了预后和免疫治疗反应的六基因模型，计算出的高风险评分与 HCC 预后不良、免疫丰度高和免疫治疗反应率低显着相关。结论：我们发现 GFAAM 相关标记基因可能有助于进一步解读其在 HCC 发生和进展中的作用。特别是，这种六基因预后模型可以作为 HCC 患者治疗和预后的预测因子。版权所有 © 2023 Bao 和 Yu。

Background: The liver is the major metabolic organ of the human body, and abnormal metabolism is the main factor influencing hepatocellular carcinoma (HCC). This study was designed to determine the effect of glutamine metabolism on HCC heterogeneity and to develop a prognostic evaluator based on the heterogeneity study of glutamine metabolism within HCC tumors and between tissues. Methods: Single-cell transcriptome data were extracted from the GSE149614 dataset and processed using the Seurat package in R for quality control of these data. HCC subtypes in the Cancer Genome Atlas and the GSE14520 dataset were identified via consensus clustering based on glutamine family amino acid metabolism (GFAAM) process genes. The machine learning algorithms gradient boosting machine, support vector machine, random forest, eXtreme gradient boosting, decision trees, and least absolute shrinkage and selection operator were utilized to develop the prognosis model of differentially expressed genes among the molecular gene subtypes. Results: The samples in the GSE149614 dataset included 10 cell types, and there was no significant difference in the GFAAM pathway. HCC was classified into three molecular subtypes according to GFAAM process genes, showing molecular heterogeneity in prognosis, clinicopathological features, and immune cell infiltration. C1 showed the worst survival rate and the highest immune score and immune cell infiltration. A six-gene model for prognostic and immunotherapy responses was constructed among subtypes, and the calculated high-risk score was significantly correlated with poor prognosis, high immune abundance, and a low response rate of immunotherapy in HCC. Conclusion: Our discovery of GFAAM-associated marker genes may help to further decipher the role in HCC occurrence and progression. In particular, this six-gene prognostic model may serve as a predictor of treatment and prognosis in HCC patients.Copyright © 2023 Bao and Yu.