Gu Xinyu, Pan Jie, Li Yanle, Feng Liushun
College of Clinical Medicine, The First Affiliated Hospital, Henan University of Science and Technology, Luoyang, 471000, China.
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
Discov Oncol. 2024 Mar 11;15(1):71. doi: 10.1007/s12672-024-00924-2.
Programmed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis and therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients and develop a prognostic model for predicting prognosis and therapeutic responses.
Consensus clustering analysis was performed to subtype HCC patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) among the subtypes were filtered and subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis to filter prognostic genes. A PCD-related prognostic gene signature in TCGA was constructed and validated in ICGC-LIRI-JP and GSE14520 datasets. TME was analyzed using CIBERSORT, MCP-counter, TIMER and EPIC algorithms. Drug sensitivity was predicted by oncoPredict package. Spearman analysis was used to detect correlation.
Four molecular subtypes were categorized based on PCD-related genes. Subtype C1 showed the poorest prognosis, the most infiltration of Fibroblasts, dentritic cell (DC) and cancer-associated fibroblasts (CAFs), and the highest TIDE score. C4 had a better prognosis survival outcome, and lowest immune cell infiltration. The survival outcomes of C2 and C3 were intermediate. Next, a total of 69 co-DEGs were screened among the four subtypes and subsequently we identified five prognostic genes (MCM2, SPP1, S100A9, MSC and EPO) for developing the prognostic model. High-risk patients not only had unfavorable prognosis, higher clinical stage and grade, and more inflammatory pathway enrichment, but also possessed higher possibility of immune escape and were more sensitive to Cisplatin and 5. Fluorouracil. The robustness of the prognostic model was validated in external datasets.
This study provides new insights into clinical subtyping and the PCD-related prognostic signature may serve as a useful tool to predict prognosis and guide treatments for patients with HCC.
程序性细胞死亡(PCD)在癌症中发挥着关键作用,与PCD相关的基因与肿瘤微环境(TME)、癌症患者的预后及治疗反应相关。本研究对肝细胞癌(HCC)患者进行分层,并建立一个预测预后和治疗反应的预后模型。
在癌症基因组图谱(TCGA)数据库中对HCC患者进行一致性聚类分析以进行亚型分类。筛选各亚型间的差异表达基因(DEG),并进行最小绝对收缩和选择算子(LASSO)回归分析及单变量Cox回归分析以筛选预后基因。在TCGA中构建一个与PCD相关的预后基因特征,并在ICGC-LIRI-JP和GSE14520数据集中进行验证。使用CIBERSORT、MCP-counter、TIMER和EPIC算法分析TME。通过oncoPredict软件包预测药物敏感性。采用Spearman分析检测相关性。
基于与PCD相关的基因将患者分为四种分子亚型。C1亚型预后最差,成纤维细胞、树突状细胞(DC)和癌症相关成纤维细胞(CAF)浸润最多,TIDE评分最高。C4亚型预后生存结局较好,免疫细胞浸润最少。C2和C3亚型的生存结局处于中间水平。接下来,在四种亚型中总共筛选出69个共同差异表达基因,随后我们鉴定出五个预后基因(MCM2、SPP1、S100A9、MSC和EPO)用于构建预后模型。高危患者不仅预后不良、临床分期和分级较高、炎症通路富集更多,而且免疫逃逸可能性更高,对顺铂和5-氟尿嘧啶更敏感。该预后模型的稳健性在外部数据集中得到验证。
本研究为临床亚型分类提供了新的见解,与PCD相关的预后特征可能是预测HCC患者预后和指导治疗的有用工具。