目的:卵巢癌(OVC)是一种常见的,侵略性,和异质性恶性肿瘤,预后变化很大。随着现代免疫学的发展,肥大细胞(MCs)已被证明在某些恶性肿瘤的预后中起着重要作用。然而,肥大细胞在OVC预后中的作用尚不清楚.
方法:在本研究中,使用MC相关预后基因(MRGs)对来自癌症基因组图谱(TCGA)-OVC队列的OVC进行分类。使用单变量cox回归分析评估基因。使用LASSO-COX分析鉴定了29个预后基因特征。使用COX回归模型和主成分分析(PCA)算法构建MRG评分和个体MRG模式。在TCGA-乳腺癌(BRCA)和IMsporyp210队列中进行外部验证。使用CIBERSORT进行基于MRGs的免疫分析,和GSVA方法,使用TIDE网站评估免疫治疗反应。
结果:使用TCGA-OVC数据,我们使用PCA算法,基于29个确定的预后基因特征,建立了构建MRG评分的模型.发现MRG评分与免疫细胞浸润密切相关,并且是OVC患者预后的良好预测因子。低MRG评分与更好的预后和更好的免疫治疗和化疗反应相关。
结论:MC相关的预后特征表征了OVC的免疫景观并预测了OVC的预后。了解MC相关基因特征与免疫治疗和化疗之间的相关性可能会改善个性化临床治疗策略的开发。
OBJECTIVE: Ovarian cancer (OVC) is a common, aggressive, and heterogeneous malignancy, with a widely variable prognosis. With the advances of modern immunology, mast cells (MCs) have been shown to play a significant role in the prognosis of some malignant tumors. However, the role of mast cells in the prognosis of OVC is unknown.
METHODS: In this study, MC-associated prognostic genes (MRGs) were used to classify OVC from The Cancer Genome Atlas (TCGA)-OVC cohort. Genes were evaluated using univariate cox regression analysis. Twenty-nine prognostic gene signatures were identified using LASSO-COX analysis. COX regression models and principal component analysis (PCA) algorithms were used to construct MRG scores and individual MRGs patterns. External validation was performed in the TCGA-breast cancer (BRCA) and IMvigor210 cohorts. Immunity analysis based on MRGs was performed using CIBERSORT, and GSVA methods, and immunotherapy response was evaluated using the TIDE website.
RESULTS: Using TCGA-OVC data, we established a model for constructing MRG scores based on the twenty-nine identified prognostic gene signatures using the PCA algorithm. MRG scores were found to be strongly correlated with immune cell infiltration and were excellent predictors of prognosis in patients with OVC. Low MRG scores were associated with better prognosis and better response to immunotherapy and chemotherapy.
CONCLUSIONS: MC-related prognosis signature characterizes the immune landscape and predicts the prognosis of OVC. Understanding the correlation between MC-related gene signatures and immunotherapy and chemotherapy may improve the development of personalized clinical treatment strategies.