Guan HaoTong, Xiong QiuShuang, Xiong JiaQiang, Liu Yanyan, Zhang Wei
Department of Gynecologic, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Front Immunol. 2025 Apr 28;16:1542669. doi: 10.3389/fimmu.2025.1542669. eCollection 2025.
As an important component in preventing the progression of endometrial cancer, CD8 T cells play a crucial role in this process and are important targets for immunotherapy. However, the status of CD8+ T cells in endometrial cancer and the key genes influencing their activation still remain to be elucidated.
Genes associated with the activation of CD8+ T cells were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). A risk score model was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. The clinical characteristics and differences between the high-risk group and the low-risk group were explored, and the applicability of the model to chemotherapy, poly (ADP-ribose) polymerase (PARP) inhibitors, and immune checkpoint inhibitors was evaluated. The characteristics of the model at the single-cell level were studied, and the tumor-suppressive effect of ASB2 was verified through experiments on endometrial cancer cells.
A risk model based on genes related to the activation of CD8+ T cells was constructed, and the prognostic differences were verified using the Kaplan-Meier curve. A nomogram was designed to predict the survival probability. Pathway analysis showed that it was related to metabolism and DNA repair. There were significant differences between the high-risk and low-risk groups in terms of tumor mutational burden (TMB), checkpoint molecules, and major histocompatibility complex (MHC) class I molecules, and they had different sensitivities to different therapies. The tumor-suppressive effect of ASB2 was confirmed in experiments on cell proliferation, invasion, and migration.
This study provides a predictive tool for endometrial cancer. The classification based on the status of CD8+ T cells can distinguish the prognosis and treatment response, highlighting the potential of this model in personalized treatment.
作为预防子宫内膜癌进展的重要组成部分，CD8 T细胞在此过程中发挥关键作用，是免疫治疗的重要靶点。然而，子宫内膜癌中CD8 + T细胞的状态以及影响其激活的关键基因仍有待阐明。
通过差异分析和加权基因共表达网络分析（WGCNA）鉴定与CD8 + T细胞激活相关的基因。使用最小绝对收缩和选择算子（LASSO）和多变量Cox回归构建风险评分模型。探索高危组和低危组之间的临床特征和差异，并评估该模型对化疗、聚（ADP - 核糖）聚合酶（PARP）抑制剂和免疫检查点抑制剂的适用性。研究该模型在单细胞水平的特征，并通过对子宫内膜癌细胞的实验验证ASB2的肿瘤抑制作用。
构建了基于与CD8 + T细胞激活相关基因的风险模型，并使用Kaplan - Meier曲线验证了预后差异。设计了列线图以预测生存概率。通路分析表明它与代谢和DNA修复有关。高危组和低危组在肿瘤突变负担（TMB）、检查点分子和主要组织相容性复合体（MHC）I类分子方面存在显著差异，并且它们对不同疗法具有不同的敏感性。在细胞增殖、侵袭和迁移实验中证实了ASB2的肿瘤抑制作用。
本研究为子宫内膜癌提供了一种预测工具。基于CD8 + T细胞状态的分类可以区分预后和治疗反应，突出了该模型在个性化治疗中的潜力。
