Identification of a prognostic evaluator from glutamine metabolic heterogeneity studies within and between tissues in hepatocellular carcinoma.
Identification of a prognostic evaluator from glutamine metabolic heterogeneity studies within and between tissues in hepatocellular carcinoma.
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DOI：
10.3389/fphar.2023.1241677
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发表时间：
2023
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
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作者：
背景资料：肝脏是人体的主要代谢器官，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。 研究方法：从GSE 149614数据集中提取单细胞转录组数据，并使用R中的Seurat软件包进行处理，以进行这些数据的质量控制。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鉴定癌症基因组图谱和GSE 14520数据集中的HCC亚型。利用梯度提升机、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等机器学习算法建立分子基因亚型间差异表达基因的预测模型。 结果：GSE149614数据集中的样本包括10种细胞类型，GFAAM途径无显著差异。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组存活率最低，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，计算的高风险评分与HCC的预后不良、高免疫丰度和免疫治疗反应率低显著相关。 结论：GFAAM相关标志基因的发现有助于进一步阐明其在肝癌发生发展中的作用。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。
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.
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影响因子：
9
作者：
Li M;Zhao X;Yong H;Xu J;Qu P;Qiao S;Hou P;Li Z;Chu S;Zheng J;Bai J
通讯作者：
Bai J
DOI：
10.1186/s12964-022-00909-0
发表时间：
2022-07-27
期刊：
Cell communication and signaling : CCS
影响因子：
--
作者：
通讯作者：
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DOI：
10.1016/j.apsb.2021.09.019
发表时间：
2022-03
期刊：
Acta pharmaceutica Sinica. B
影响因子：
--
作者：
Du D;Liu C;Qin M;Zhang X;Xi T;Yuan S;Hao H;Xiong J
通讯作者：
Xiong J
影响因子：
3.7
作者：
Egeland, Nina Gran;Austdal, Marie;Jonsdottir, Kristin
通讯作者：
Jonsdottir, Kristin
肝细胞癌（HCC）的新兴疗法。
影响因子：
5.2
作者：
通讯作者：
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