免疫细胞浸润在非小细胞肺癌诊断与预后中的应用  

Application of immune cell infiltration in the diagnosis and prognosis of non-small cell lung cancer

作　　者：万辉辉 刘振浩 澹小秀 王广志 徐勇 谢鹭[2] 林勇[1] Huihui Wan;Zhenhao Liu;Xiaoxiu Tan;Guangzhi Wang;Yong Xu;Lu Xie;Yong Lin(School of Medical Instrument and Food Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Center for Bioinformation Technology,Shanghai 201203,China;Xiangya Hospital,Central South University,Changsha 410008,Hunan,China;College of Food Science and Technology,Shanghai Ocean University,Shanghai 201306,China)

机构地区：[1]上海理工大学医疗器械与食品学院,上海200093 [2]上海生物信息技术研究中心,上海201203 [3]中南大学湘雅医院,湖南长沙410008 [4]上海海洋大学食品科学与技术学院,上海201306

出　　处：《生物工程学报》2020年第4期740-749,共10页Chinese Journal of Biotechnology

摘　　要：免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer(NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.

关 键 词：非小细胞肺癌 免疫细胞浸润 机器学习 随机森林 分类模型 LASSO回归 预后模型 

分 类 号：R734.2[医药卫生—肿瘤]