Glycometabolism-related gene signature of hepatocellular carcinoma predicts prognosis and guides immunotherapy.
Glycometabolism-related gene signature of hepatocellular carcinoma predicts prognosis and guides immunotherapy.
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肝细胞癌糖代谢相关基因特征预测预后并指导免疫治疗
DOI：
10.3389/fcell.2022.940551
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发表时间：
2022
影响因子：
5.5
通讯作者：
中科院分区：
文献类型：
--
作者：
肝细胞癌（Hepatocellular carcinoma，HCC）是严重危害人类健康的恶性肿瘤。我们构建了一个新的糖代谢相关风险评分，以预测肝癌患者的预后和免疫治疗策略。HCC数据集从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）获得，糖代谢相关基因集从分子特征数据库获得。最小绝对收缩和选择算子（LASSO）回归模型用于构建基于糖代谢相关基因的风险评分。构建了一个简单的可视化诺模图模型，并评价了其校准效果、准确性和临床价值。我们还探讨了糖代谢相关风险评分与分子途径、免疫细胞和功能之间的相关性。低风险组的患者对抗CTLA-4免疫检查点治疗的反应更好，并受益于免疫检查点抑制剂（ICI）治疗。研究发现，糖代谢相关风险评分可以有效区分HCC患者的预后、分子和免疫相关特征，可能为个体化治疗提供新的策略。
Hepatocellular carcinoma (HCC) is a severe cancer endangering human health. We constructed a novel glycometabolism-related risk score to predict prognosis and immunotherapy strategies in HCC patients. The HCC data sets were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, and the glycometabolism-related gene sets were obtained from the Molecular Signature Database. The least absolute contraction and selection operator (LASSO) regression model was used to construct a risk score based on glycometabolism-related genes. A simple visual nomogram model with clinical indicators was constructed and its effectiveness in calibration, accuracy, and clinical value was evaluated. We also explored the correlation between glycometabolism-related risk scores and molecular pathways, immune cells, and functions. Patients in the low-risk group responded better to anti-CTLA-4 immune checkpoint treatment and benefited from immune checkpoint inhibitor (ICI) therapy. The study found that glycometabolism-related risk score can effectively distinguish the prognosis, molecular and immune-related characteristics of HCC patients, and may provide a new strategy for individualized treatment.
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影响因子：
7.3
作者：
Kang S;Tang H
通讯作者：
Tang H
影响因子：
30.5
作者：
Guo Y;Xie YQ;Gao M;Zhao Y;Franco F;Wenes M;Siddiqui I;Bevilacqua A;Wang H;Yang H;Feng B;Xie X;Sabatel CM;Tschumi B;Chaiboonchoe A;Wang Y;Li W;Xiao W;Held W;Romero P;Ho PC;Tang L
通讯作者：
Tang L
影响因子：
3.7
作者：
Liu L;Cai L;Liu C;Yu S;Li B;Pan L;Zhao J;Zhao Y;Li W;Yan X
通讯作者：
Yan X
影响因子：
16.2
作者：
Feng, Rui-Mei;Zong, Yi-Nan;Xu, Rui-Hua
通讯作者：
Xu, Rui-Hua
影响因子：
7.4
作者：
Li, Wenhui;Xu, Ming;Yi, Pengfei
通讯作者：
Yi, Pengfei