Sun Zhao, Liu Hao, Zhao Qian, Li Jie-Han, Peng San-Fei, Zhang Zhen, Yang Jing-Hua, Fu Yang
Department of Gastrointestinal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Clinical Systems Biology Key Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
NPJ Precis Oncol. 2024 Sep 8;8(1):194. doi: 10.1038/s41698-024-00693-9.
Regulated cell death (RCD) plays a crucial role in the immune microenvironment, development, and progression of hepatocellular carcinoma (HCC). However, reliable immune-related cell death signatures have not been explored. In this study, we collected 12 RCD modes (e.g., apoptosis, ferroptosis, and cuproptosis), including 1078 regulators, to identify immune-related cell death genes based on HCC immune subgroups. Using a developed competitive machine learning framework, nine genes were screened to construct the immune-related cell death index (IRCDI), which is available for online application. Multi-omics data, along with clinical features, were analyzed to explore the HCC malignant heterogeneity. To validate the efficacy of this model, more than 18 independent cohorts, including survival and diverse treatment cohorts and datasets, were utilized. These findings were further validated using in-house samples and molecular biological experiments. Overall, the IRCDI may have a wide application in individual therapeutic decision-making and improving outcomes for HCC patients.
程序性细胞死亡（RCD）在肝细胞癌（HCC）的免疫微环境、发生发展过程中起着至关重要的作用。然而，尚未探索出可靠的免疫相关细胞死亡特征。在本研究中，我们收集了12种程序性细胞死亡模式（如凋亡、铁死亡和铜死亡），包括1078个调节因子，以基于肝癌免疫亚组鉴定免疫相关细胞死亡基因。使用开发的竞争性机器学习框架，筛选出9个基因构建免疫相关细胞死亡指数（IRCDI），该指数可供在线应用。分析多组学数据以及临床特征，以探索肝癌的恶性异质性。为验证该模型的有效性，使用了18多个独立队列，包括生存队列和不同治疗队列及数据集。使用内部样本和分子生物学实验进一步验证了这些发现。总体而言，IRCDI可能在个体治疗决策和改善肝癌患者预后方面有广泛应用。
