背景:卵巢癌中最普遍的突变是TP53突变,影响疾病的发展和预后。我们研究了TP53突变如何将卵巢癌的免疫表型与疾病的预后相关联。
方法:我们调查了不同文化群体和数据集中TP53突变的状态和表达谱,并开发了一种免疫浸润预测模型,该模型依赖于TP53WT和TP53MUT卵巢癌病例之间不同表达的免疫相关基因。我们旨在构建免疫浸润预测模型(IPM)以提高卵巢癌的预后,并研究IPM对免疫微环境的影响。
结果:TP53诱变影响了77个免疫应答相关基因的表达。对卵巢癌患者实施并评估IPM,以区分低IPM和高IPM亚组生存率差的个体。用于诊断和治疗用途,因此创建了一个列线图。根据途径富集分析,人类免疫应答和免疫功能异常的途径是与IPM基因最相关的功能和途径。此外,高风险组的患者显示低比例的巨噬细胞M1,活化的NK细胞,CD8+T细胞,CTLA-4、PD-1、PD-L1和TIM-3高于低危组患者。
结论:IPM模型可以识别高危患者,并整合其他临床参数来预测他们的总体生存率。提示它是一种优化卵巢癌预后的潜在方法.
The most prevalent mutation in ovarian cancer is the TP53 mutation, which impacts the development and prognosis of the disease. We looked at how the TP53 mutation associates the immunophenotype of ovarian cancer and the prognosis of the disease.
We investigated the state of TP53 mutations and expression profiles in culturally diverse groups and datasets and developed an immune infiltration predictive model relying on immune-associated genes differently expressed between TP53 WT and TP53 MUT ovarian cancer cases. We aimed to construct an immune infiltration predictive model (
IPM) to enhance the prognosis of ovarian cancer and investigate the impact of the
IPM on the immunological microenvironment.
TP53 mutagenesis affected the expression of seventy-seven immune response-associated genes. An
IPM was implemented and evaluated on ovarian cancer patients to distinguish individuals with low- and high-
IPM subgroups of poor survival. For diagnostic and therapeutic use, a nomogram is thus created. According to pathway enrichment analysis, the pathways of the human immune response and immune function abnormalities were the most associated functions and pathways with the IPM genes. Furthermore, patients in the high-risk group showed low proportions of macrophages M1, activated NK cells, CD8+ T cells, and higher CTLA-4, PD-1, PD-L1, and TIM-3 than patients in the low-risk group.
The
IPM model may identify high-risk patients and integrate other clinical parameters to predict their overall survival, suggesting it is a potential methodology for optimizing ovarian cancer prognosis.