Yin Yupeng, Luo Min
Department of Obstetrics and Gynecology, General Hospital of Southern Theatre Command, Guangzhou, 510010, China.
The First Clinical Medical College, Southern Medical University, Guangzhou, China.
Discov Oncol. 2025 May 6;16(1):677. doi: 10.1007/s12672-025-02524-0.
Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecological cancer characterized by varied clinical outcomes and responses to treatment. Developing effective prognostic models is essential for guiding clinical decision-making. Recent research indicates that lactylation-a process impacting gene expression and immune responses-can affect tumor growth, metastasis, and immune evasion through histone modification. This study introduces a lactylation-related risk model aimed at predicting UCEC prognosis and providing insights into treatment efficacy.
We analyzed transcriptomic data from The Cancer Genome Atlas (TCGA) for UCEC patients and identified two distinct lactylation-related patterns using consensus clustering. A risk model developed using Cox and Lasso regression has been studied for its ability to predict prognosis, immune cell infiltration, and treatment response. Additionally, we investigated the relationship between IGSF1 gene expression and clinical features. Gene Set Enrichment Analysis (GSEA) was performed to explore the function of the IGSF1 gene.
Two distinct lactylation-related clusters were identified, along with 156 differentially expressed genes between these clusters that are associated with the prognosis of UCEC. A risk model was developed based on three genes: IGSF1, ZFHX4, and SCGB2A1. This model effectively predicts clinical characteristics of UCEC patients, including immune cell infiltration, genetic variations, drug sensitivity, and response to immunotherapy. Notably, IGSF1 is linked to poor prognosis and is associated with immune activity, tumorigenesis, and cancer metabolism.
This study demonstrates that the lactylation-related risk model plays a crucial role in predicting prognosis and the efficacy of immunotherapy in UCEC, offering valuable insights for personalized treatment approaches.
子宫体子宫内膜癌（UCEC）是一种常见的妇科癌症，其临床结局和对治疗的反应各不相同。开发有效的预后模型对于指导临床决策至关重要。最近的研究表明，乳酰化——一种影响基因表达和免疫反应的过程——可以通过组蛋白修饰影响肿瘤生长、转移和免疫逃逸。本研究引入了一种与乳酰化相关的风险模型，旨在预测UCEC的预后并深入了解治疗效果。
我们分析了来自癌症基因组图谱（TCGA）的UCEC患者的转录组数据，并使用一致性聚类确定了两种不同的与乳酰化相关的模式。对使用Cox和Lasso回归开发的风险模型进行了研究，以评估其预测预后、免疫细胞浸润和治疗反应的能力。此外，我们研究了IGSF1基因表达与临床特征之间的关系。进行基因集富集分析（GSEA）以探索IGSF1基因的功能。
确定了两个不同的与乳酰化相关的簇，以及这些簇之间与UCEC预后相关的156个差异表达基因。基于三个基因（IGSF1、ZFHX4和SCGB2A1）开发了一个风险模型。该模型有效地预测了UCEC患者的临床特征，包括免疫细胞浸润、基因变异、药物敏感性和对免疫治疗的反应。值得注意的是，IGSF1与预后不良相关，并与免疫活性、肿瘤发生和癌症代谢有关。
本研究表明，与乳酰化相关的风险模型在预测UCEC的预后和免疫治疗疗效方面起着关键作用，为个性化治疗方法提供了有价值的见解。
