Pan Weicheng, Cheng Jinlian, Lin Shanshan, Li Qianxi, Liang Yuanyuan, Li Huiying, Nong Xianxian, Nong Huizhen
Department of Obstetrics and Gynecology, Wuming Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Front Immunol. 2025 May 20;16:1564407. doi: 10.3389/fimmu.2025.1564407. eCollection 2025.
Programmed cell death (PCD) refers to a regulated and active process of cellular demise, initiated by specific biological signals. PCD plays a crucial role in the development, progression, and drug resistance of uterine corpus endometrial carcinoma (UCEC), making the exploration of its relationship with UCEC prognosis highly clinically relevant.
Data from UCEC patients and control cohorts were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified and subsequently intersected with a PCD gene set to discern PCD-related differentially expressed genes (PCD-DEGs). To isolate core prognostic PCD-DEGs, methods including consistency clustering analysis, weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and five machine learning techniques for dimensionality reduction were utilized. Validation of three core prognostic PCD-DEGs was conducted using RT-qPCR, and these genes were used to develop a prognostic model. Additionally, an analysis of drug sensitivity was performed.
Consistency clustering analysis revealed significant differences in prognosis and tumor microenvironment among subtypes, strongly associated with various immune subtypes. The three core prognostic PCD-DEGs identified-SRPX, NT5E, and ATP6V1C2-were instrumental in constructing the lasso prognostic model and nomogram. Receiver Operating Characteristic (ROC) curve analysis confirmed the model's strong prognostic performance and clinical applicability. The high-risk group exhibited lower tumor mutation frequencies, a higher propensity for immune escape, reduced response to immune therapy, and potential benefits from potent chemotherapy drugs.
This study developed a prognostic model related to PCD for UCEC using comprehensive bioinformatics analyses. The model demonstrates robust predictive performance and holds significant potential for clinical application, thereby facilitating precise stratification and personalized treatment of UCEC patients.
程序性细胞死亡（PCD）是指由特定生物信号引发的一种受调控的细胞主动死亡过程。PCD在子宫内膜癌（UCEC）的发生、发展及耐药性中起着关键作用，因此探究其与UCEC预后的关系具有高度的临床相关性。
从癌症基因组图谱（TCGA）数据库获取UCEC患者和对照队列的数据。鉴定差异表达基因（DEG），随后将其与PCD基因集进行交集分析，以识别与PCD相关的差异表达基因（PCD-DEG）。为了分离核心预后PCD-DEG，采用了一致性聚类分析、加权基因共表达网络分析（WGCNA）、单变量Cox回归分析以及五种机器学习降维技术。使用RT-qPCR对三个核心预后PCD-DEG进行验证，并利用这些基因构建预后模型。此外，还进行了药物敏感性分析。
一致性聚类分析显示各亚型在预后和肿瘤微环境方面存在显著差异，与多种免疫亚型密切相关。鉴定出的三个核心预后PCD-DEG——SRPX、NT5E和ATP6V1C2——有助于构建套索预后模型和列线图。受试者工作特征（ROC）曲线分析证实了该模型具有强大的预后性能和临床适用性。高危组肿瘤突变频率较低，免疫逃逸倾向较高，对免疫治疗的反应降低，且可能从强效化疗药物中获益。
本研究通过综合生物信息学分析，为UCEC建立了一个与PCD相关的预后模型。该模型具有强大的预测性能，具有显著的临床应用潜力，从而有助于UCEC患者的精准分层和个性化治疗。
