Li Hongxuan, Zhang Lei, Shu Bin, Wang Xiaojuan, Yang Shizhong
Hepatopancereatobiliary Center.
Department of Ultrasound, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University.
Eur J Gastroenterol Hepatol. 2025 Apr 1;37(4):454-465. doi: 10.1097/MEG.0000000000002894. Epub 2024 Nov 25.
Hepatocellular carcinoma (HCC) has limited therapeutic options and a poor prognosis. The endoplasmic reticulum (ER) plays a crucial role in tumor progression and response to stress, making it a promising target for HCC stratification. This study aimed to develop a risk stratification model using ER stress-related signatures.
We utilized transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus, which encompass whole-genome expression profiles and clinical annotations. Machine learning algorithms, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination, were applied to the key genes associated with HCC prognosis. A prognostic system was developed using univariate Cox hazard analysis and least absolute shrinkage and selection operator Cox regression, followed by validation using Kaplan-Meier analysis and receiver operating characteristic curves. Tumor immune dysfunction and exclusion tools were used to predict immunotherapy responsiveness.
Two distinct clusters associated with ER stress were identified in HCC, each exhibiting unique clinical and biological features. Using a computational approach, a prognostic risk model, namely the ER stress-related signature, was formulated, demonstrating enhanced predictive accuracy compared with that of existing prognostic models. An effective clinical nomogram was established by integrating the risk model with clinicopathological factors. Patients with lower risk scores exhibited improved responsiveness to various chemotherapeutic, targeted, and immunotherapeutic agents.
The critical role of ER stress in HCC is highlighted. The ER stress-related signature developed in this study is a powerful tool to assess the risk and clinical treatment of HCC.
肝细胞癌（HCC）的治疗选择有限且预后较差。内质网（ER）在肿瘤进展和应激反应中起关键作用，使其成为HCC分层的一个有前景的靶点。本研究旨在利用内质网应激相关特征建立一个风险分层模型。
我们利用了来自癌症基因组图谱（The Cancer Genome Atlas）和基因表达综合数据库（Gene Expression Omnibus）的转录组数据，这些数据包含全基因组表达谱和临床注释。将包括最小绝对收缩和选择算子（least absolute shrinkage and selection operator）、随机森林以及支持向量机递归特征消除等机器学习算法应用于与HCC预后相关的关键基因。使用单变量Cox风险分析和最小绝对收缩和选择算子Cox回归开发了一个预后系统，随后使用Kaplan-Meier分析和受试者工作特征曲线进行验证。使用肿瘤免疫功能障碍和排除工具来预测免疫治疗反应性。
在HCC中鉴定出两个与内质网应激相关的不同簇，每个簇都表现出独特的临床和生物学特征。通过计算方法，制定了一个预后风险模型，即内质网应激相关特征，与现有预后模型相比，其预测准确性有所提高。通过将风险模型与临床病理因素相结合，建立了一个有效的临床列线图。风险评分较低的患者对各种化疗、靶向和免疫治疗药物的反应性有所改善。
强调了内质网应激在HCC中的关键作用。本研究中开发的内质网应激相关特征是评估HCC风险和临床治疗的有力工具。
