Chen Chao, Pei Lipeng, Ren Wei, Sun Jingli
Department of Obstetrics and Gynecology, General Hospital of Northern Theater Command, Shenyang, Liaoning, China.
Medicine (Baltimore). 2024 Dec 6;103(49):e40820. doi: 10.1097/MD.0000000000040820.
Endometrial cancer (EC) is the most common gynecologic malignancy with increasing incidence and mortality. The tumor immune microenvironment significantly impacts cancer prognosis. Weighted Gene Co-Expression Network Analysis (WGCNA) is a systems biology approach that analyzes gene expression data to uncover gene co-expression networks and functional modules. This study aimed to use WGCNA to develop a prognostic prediction model for EC based on immune cell infiltration, and to identify new potential therapeutic targets. WGCNA was performed using the Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma dataset to identify hub modules associated with T-lymphocyte cell infiltration. Prognostic models were developed using LASSO regression based on genes in these hub modules. The Search Tool for the Retrieval of Interacting Genes/Proteins was used for protein-protein interaction network analysis of the hub module. Gene Set Variation Analysis identified differential gene enrichment analysis between high- and low-risk groups. The relationship between the model and microsatellite instability, tumor mutational burden, and immune cell infiltration was analyzed using The Cancer Genome Atlas data. The model's correlation with chemotherapy and immunotherapy resistance was examined using the Genomics of Drug Sensitivity in Cancer and Cancer Immunome Atlas databases. Immunohistochemical staining of EC tissue microarrays was performed to analyze the relationship between the expression of key genes and immune infiltration. The green-yellow module was identified as a hub module, with 4 genes (ARPC1B, BATF, CCL2, and COTL1) linked to CD8+ T cell infiltration. The prognostic model constructed from these genes showed satisfactory predictive efficacy. Differentially expressed genes in high- and low-risk groups were enriched in tumor immunity-related pathways. The model correlated with EC-related phenotypes, indicating its potential to predict immunotherapeutic response. Basic leucine zipper activating transcription factor-like transcription factor(BATF) expression in EC tissues positively correlated with CD8+ T cell infiltration, suggesting BATF's crucial role in EC development and antitumor immunity. The prognostic model comprising ARPC1B, BATF, CCL2, and COTL1 can effectively identify high-risk EC patients and predict their response to immunotherapy, demonstrating significant clinical potential. These genes are implicated in EC development and immune infiltration, with BATF emerging as a potential therapeutic target for EC.
子宫内膜癌（EC）是最常见的妇科恶性肿瘤，其发病率和死亡率呈上升趋势。肿瘤免疫微环境对癌症预后有显著影响。加权基因共表达网络分析（WGCNA）是一种系统生物学方法，通过分析基因表达数据来揭示基因共表达网络和功能模块。本研究旨在利用WGCNA基于免疫细胞浸润建立EC的预后预测模型，并确定新的潜在治疗靶点。使用癌症基因组图谱子宫体子宫内膜癌数据集进行WGCNA，以识别与T淋巴细胞细胞浸润相关的枢纽模块。基于这些枢纽模块中的基因，使用LASSO回归建立预后模型。利用检索相互作用基因/蛋白质的搜索工具对枢纽模块进行蛋白质-蛋白质相互作用网络分析。基因集变异分析确定了高风险组和低风险组之间的差异基因富集分析。利用癌症基因组图谱数据分析模型与微卫星不稳定性、肿瘤突变负荷和免疫细胞浸润之间的关系。使用癌症药物敏感性基因组学和癌症免疫组图谱数据库检查模型与化疗和免疫治疗耐药性的相关性。对EC组织微阵列进行免疫组织化学染色，以分析关键基因表达与免疫浸润之间的关系。绿黄色模块被确定为枢纽模块，有4个基因（ARPC1B、BATF、CCL2和COTL1）与CD8 + T细胞浸润相关。由这些基因构建的预后模型显示出令人满意的预测效果。高风险组和低风险组中差异表达的基因在肿瘤免疫相关途径中富集。该模型与EC相关表型相关，表明其具有预测免疫治疗反应的潜力。EC组织中碱性亮氨酸拉链激活转录因子样转录因子（BATF）的表达与CD8 + T细胞浸润呈正相关，提示BATF在EC发生发展和抗肿瘤免疫中起关键作用。由ARPC1B、BATF、CCL2和COTL1组成的预后模型可以有效识别高风险EC患者并预测其对免疫治疗的反应，具有显著的临床潜力。这些基因与EC的发生发展和免疫浸润有关，BATF成为EC的潜在治疗靶点。
