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投稿时间:2023-03-29 网络发布日期:2023-07-19
投稿时间:2023-03-29 网络发布日期:2023-07-19
中文摘要: 目的 探讨免疫相关基因预后指数(IRGPI)模型的构建,以提高子宫内膜癌(UCEC)预后预测的准确性,并为其诊治提供潜在的生物标志物和分子治疗靶点。方法 从癌症基因组图谱(TCGA)数据库获得UCEC数据集用于分析。使用Cox回归分析和加权基因共表达网络分析(WGCNA)鉴定免疫相关枢纽基因并构建IRGPI模型,通过Kaplan-Meier(K-M)生存曲线分析验证IRGPI模型的准确性。利用R软件将IRGPI组分为高、低IRGPI亚组,分析两亚组之间免疫细胞浸润和免疫功能的差异。结果 从TCGA 数据库获得UCEC数据集(23例正常组织和554例肿瘤组织)。共筛选出31个免疫相关基因用于构建IRGPI模型。在554例肿瘤组织中剔除无效组织11例,K-M生存曲线分析显示,高IRGPI 亚组(n=271)的总生存期显著低于低IRGPI 亚组(n=272, P<0.01)。ROC曲线表明,IRGPI模型预测UCEC预后的AUC(0.885)显著大于肿瘤免疫功能障碍及免疫排斥(TIDE)模型(0.490)和肿瘤炎症特征(TIS)模型(0.427)。IRGPI亚组之间的免疫细胞浸润和免疫功能差异有统计学意义(P<0.05)。结论 IRGPI模型可以准确预测UCEC患者的预后,可为UCEC的治疗提供潜在的生物标志物和分子治疗靶标。
Abstract:Objective To explore the construction of immune-related gene prognostic index (IRGPI) model in order to improve the accuracy of predicting the prognosis of uterine corpus endometrial carcinoma (UCEC) and provide potential biomarkers and molecular therapeutic targets for its diagnosis and treatment.
Methods From the Cancer Genome Atlas (TCGA) database, the UCEC dataset was obtained for an analysis. Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were used for the identification of immune-related hub genes and the establishment of IRGPI models. Kaplan Meier (K-M) survival curve was used to verify the accuracy of IRGPI model. High IRGPI subgroup and low IRGPI subgroup were designed by R software to analyze the differences in immune cell infiltration and immune function between two subgroups.
Results Out of 23 cases of normal tissues and 554 cases of tumor tissues obtained in UCEC dataset from TCGA database, 31 immune-related genes were selected for constructing IRGPI model. Excluding 11 invalid tissues from 554 tumor tissues, K-M survival curve analysis showed that the overall survival of the high IRGPI subgroup (n=271) was significantly lower than that of the low IRGPI subgroup (n=272, P<0.01). ROC curve revealed that the AUC of IRGPI model in predicting the prognosis of UCEC (0.885)was obviously greater than that of the tumor immune dysfunction and exclusion (TIDE) model (0.490) and the tumor inflammation signature (TIS) model (0.427). There were statistical differences in immune cell infiltration and immunological function between high IRGPI subgroup and low IRGPI subgroup (P<0.05).
Conclusion IRGPI model can accurately predict the prognosis of patients with UCEC, and provide potential biomarkers and molecular therapeutic targets for the treatment of UCEC.
keywords: Uterine corpus endometrial carcinoma Immune-related gene Prognostic model The Cancer Genome Atlas Weighted gene co-expression network analysis Tumor immune dysfunction and immune exclusion Tumor inflammation signature
文章编号: 中图分类号:R737.33 文献标志码:A
基金项目:
作者 | 单位 |
赵丽娟1 | 1. 南京医科大学第四附属医院(南京市浦口医院)妇产科,江苏 南京 210031; 2. 东南大学医学院附属南京同仁医院妇产科,江苏 南京 210000 |
徐彩虹2 | 1. 长沙市第三医院病理科,湖南 长沙 410000 |
Author Name | Affiliation |
ZHAO Lijuan*, XU Caihong | * Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210031, China |
引用文本:
赵丽娟,徐彩虹.子宫内膜癌免疫相关基因预后指数模型的构建[J].中国临床研究,2023,36(7):1005-1011.
赵丽娟,徐彩虹.子宫内膜癌免疫相关基因预后指数模型的构建[J].中国临床研究,2023,36(7):1005-1011.