Weng Jialei, Zhou Chenhao, Zhou Qiang, Chen Wanyong, Yin Yirui, Atyah Manar, Dong Qiongzhu, Shi Yi, Ren Ning
Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.
Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
J Hepatocell Carcinoma. 2021 Mar 29;8:193-209. doi: 10.2147/JHC.S300633. eCollection 2021.
Hepatocellular carcinoma (HCC) is a malignant tumor with great variation in prognosis among individuals. Changes in metabolism influence disease progression and clinical outcomes. The objective of this study was to determine the overall survival (OS) risk of HCC patients from a metabolic perspective.
The model was constructed using the least absolute shrinkage and selection operator (LASSO) COX regression based on The Cancer Genome Atlas (TCGA, n=342) dataset. The International Cancer Genome Consortium (ICGC, n=232), GSE14520 (n=242) datasets, and a clinical cohort (n=64) were then used to assess the prognostic value of the signature.
A 10 metabolic gene-based signature was constructed and verified as a robust and independent prognostic classifier in public and real-world validation cohorts. Meanwhile, the signature enabled the identification of HCC molecular subtypes, yielding an AUC value of 0.678 [95% CI: 0.592-0.763]. Besides, the signature was associated with metabolic processes like glycolysis, supported by a clear correlation between the risk score and expression of rate-limiting enzymes. Furthermore, high-risk tumor was likely to have a high tumor infiltration status of immunosuppressive cells, as well as elevated expression of some immune checkpoint molecules. For final clinical translation, a nomogram integrating the signature and tumor stage was established, and showed improved predictive accuracy of 3- and 5-year OS and brought more net benefit to patients.
We developed a prognostic signature based on 10 metabolic genes, which has proven to be an independent and reliable prognostic predictor for HCC and reflects the metabolic and immune characteristics of tumors.
肝细胞癌（HCC）是一种预后个体差异很大的恶性肿瘤。代谢变化会影响疾病进展和临床结局。本研究的目的是从代谢角度确定HCC患者的总生存（OS）风险。
基于癌症基因组图谱（TCGA，n = 342）数据集，使用最小绝对收缩和选择算子（LASSO）COX回归构建模型。然后使用国际癌症基因组联盟（ICGC，n = 232）、GSE14520（n = 242）数据集以及一个临床队列（n = 64）来评估该特征的预后价值。
构建了一个基于10个代谢基因的特征，并在公共和真实世界验证队列中被验证为一个稳健且独立的预后分类器。同时，该特征能够识别HCC分子亚型，曲线下面积（AUC）值为0.678 [95%可信区间：0.592 - 0.763]。此外，该特征与糖酵解等代谢过程相关，风险评分与限速酶表达之间存在明显相关性，为其提供了支持。此外，高危肿瘤可能具有较高的免疫抑制细胞肿瘤浸润状态，以及一些免疫检查点分子的表达升高。为了最终实现临床转化，建立了一个整合该特征和肿瘤分期的列线图，显示出3年和5年OS的预测准确性提高，并为患者带来了更多净效益。
我们开发了一种基于10个代谢基因的预后特征，已证明它是HCC独立且可靠的预后预测指标，并反映了肿瘤的代谢和免疫特征。
