Chen Bingnan, Wang Di, Li Jiapo, Hou Yue, Qiao Chong
Department of Obstetrics and Gynaecology, Shengjing Hospital of China Medical University, Shenyang, China.
Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China.
Front Oncol. 2020 Dec 1;10:554214. doi: 10.3389/fonc.2020.554214. eCollection 2020.
Endometrioid endometrial adenocarcinoma (EEA) is one of the most common tumors in the female reproductive system. With the further understanding of immune regulation mechanism in tumor microenvironment, immunotherapy is emerging in tumor treatment. However, there are few systematic studies on EEA immune infiltration.
In this study, prognostic tumor-infiltrating immune cells (TIICs) and related genes of EEA were comprehensively analyzed for the first time through the bioinformatics method with CIBERSORT algorithm as the core. Gene expression profile data were downloaded from the TCGA database, and the abundance ratio of TIICs was obtained. Kaplan-Meier analysis and Cox regression analysis were used to identify prognostic TIICs. EEA samples were grouped according to the risk score in Cox regression model. Differential analysis and functional enrichment analyses were performed on high- and low-risk groups to find survival-related hub genes, which were verified by Tumor Immune Estimation Resource (TIMER).
Four TIICs including memory CD4+ T cells, regulatory T cells, natural killer cells and dendritic cells were identified. And two hub gene modules were found, in which six hub genes including , and were significantly related to overall survival and were closely correlated with some certain TIICs in the validation of TIMER.
In this study, four prognostic TIICs and six hub genes were found to be closely related to EEA. These findings provided new potential targets for EEA immunotherapy.
子宫内膜样腺癌（EEA）是女性生殖系统中最常见的肿瘤之一。随着对肿瘤微环境中免疫调节机制的进一步了解，免疫疗法正在肿瘤治疗中兴起。然而，关于EEA免疫浸润的系统研究较少。
在本研究中，首次以CIBERSORT算法为核心，通过生物信息学方法全面分析了EEA的预后肿瘤浸润免疫细胞（TIICs）及相关基因。从TCGA数据库下载基因表达谱数据，获得TIICs的丰度比例。采用Kaplan-Meier分析和Cox回归分析来识别预后TIICs。根据Cox回归模型中的风险评分对EEA样本进行分组。对高风险组和低风险组进行差异分析和功能富集分析，以寻找与生存相关的枢纽基因，并通过肿瘤免疫评估资源（TIMER）进行验证。
鉴定出包括记忆性CD4 + T细胞、调节性T细胞、自然杀伤细胞和树突状细胞在内的四种TIICs。发现了两个枢纽基因模块，其中包括[具体基因名称缺失]等六个枢纽基因与总生存期显著相关，并且在TIMER验证中与某些特定的TIICs密切相关。
在本研究中，发现四种预后TIICs和六个枢纽基因与EEA密切相关。这些发现为EEA免疫治疗提供了新的潜在靶点。
