| 摘要: |
| 目的 探讨肺鳞状细胞癌组织免疫细胞浸润全景，构建预后风险评估模型，分析并评估患者的预后。方法 利用癌症基因组图谱（TCGA）数据库肺鳞状细胞癌全转录组数据，通过R 4.0.3软件利用CIBERSORT反卷积算法计算不同免疫细胞浸润相对含量，用单因素和多因素Cox回归分析建立预后风险评估模型，并用ROC曲线评估模型效能，结合临床变量绘制预测患者3、5、10年生存率列线图。结果 共涉及22种免疫细胞类型。正常组织中的8种免疫细胞亚群（初始B细胞、浆细胞、激活记忆CD4 T细胞、滤泡辅助性T细胞、调节性T细胞、M0型巨噬细胞、M1型巨噬细胞、休眠树突状细胞）浸润相对含量均低于肺鳞状细胞癌组织（P<0.05或P<0.01），正常组织中的另外8种免疫细胞亚群[休眠记忆CD4 T细胞、休眠自然杀伤（NK）细胞、单核细胞、M2型巨噬细胞、激活树突状细胞、休眠肥大细胞、嗜酸性粒细胞、中性粒细胞]浸润相对含量均高于肺鳞状细胞癌组织（P<0.01或P<0.05），其余免疫细胞亚群（记忆性B细胞、CD8 T细胞、初始CD4 T细胞、γ/δ T细胞，激活NK细胞、激活肥大细胞）在两组间差异均无统计学意义（P均>0.05）。初始CD4 T细胞、休眠记忆CD4 T细胞对肺鳞状细胞癌患者是危险因素（HR>1），而激活记忆CD4 T细胞、滤泡辅助性T细胞、休眠树突状细胞则是保护因素（HR<1）。激活记忆CD4 T细胞、休眠树突状细胞浸润相对含量高的肺鳞状细胞癌患者预后较相对含量低的患者好（P<0.05）。用激活记忆CD4 T细胞、滤泡辅助性T细胞、休眠树突状细胞构建的肺鳞状细胞癌患者预后风险评估模型的效能良好，ROC曲线的AUC为0.678。结论 休眠树突状细胞、激活记忆CD4 T细胞与肺鳞状细胞癌的发生、预后有关，并且可以作为独立的预后因子。预后相关免疫细胞亚群构建的预后风险评估模型效能良好。 |
| 关键词: 肺肿瘤 鳞状细胞癌 免疫细胞 预后 癌症基因组图谱 |
| DOI：10.16781/j.0258-879x.2021.04.0391 |
| 投稿时间：2020-12-19修订日期：2021-02-27 |
| 基金项目: |
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| The infiltration of immune cells in lung squamous cell carcinoma and its prognostic analysis based on The Cancer Genome Atlas |
| ZHOU Chao1,SHI Xiao-qian2,HONG Han-han1,YIN Ji-zhong1,WANG Yu-sheng1,LI Bing1* |
(1. Department of Respiratory and Critical Care Medicine, Changzheng Hospital, Naval Medical University(Second Military Medical University), Shanghai 200003, China;
2. Department of Respiratory and Critical Care Medicine, Shanghai Fourth People's Hospital, Tongji University, Shanghai 200439, China
*Corresponding author) |
| Abstract: |
| Objective To explore the proportion of immune cell infiltration in lung squamous cell carcinoma and to establish a prognostic risk assessment model for the patients. Methods Based on the whole transcriptome data of lung squamous cell carcinoma from The Cancer Genome Atlas (TCGA) database, R 4.0.3 software and CIBERSORT deconvolution algorithm were used to calculate the proportion of different immune cell infiltrations. The prognostic risk assessment model was established by univariate and multivariate Cox regression analysis, and the effectiveness of the model was evaluated by receiver operating characteristic (ROC) curve. The nomogram for predicting 3-, 5-, and 10-year survival rates of patients was plotted with clinical variables. Results A total of 22 types of immune cells were involved. The relative infiltration rates of 8 types of immune cell subsets (naive B cells, plasma cells, CD4 memory activated T cells, follicular helper T cells, regulatory T cells, M0 macrophage, M1 macrophage, and resting dendritic cells) were significantly lower than those in lung squamous carcinoma tissue (P<0.05 or P<0.01), while the infiltration rates of other 8 types (CD4 memory resting T cells, resting natural killer (NK) cells, monocytes, M2 macrophages, activated dendritic cells, resting mast cells, eosinophils, and neutrophils) were significantly higher (P<0.01 or P<0.05); and there were no significant differences in the rest of immune cell subsets (memory B cells, CD8 T cells, CD4 naive T cells, γ/δ T cells, activated NK cells, or activated mast cells) (all P>0.05). CD4 naive T cells and CD4 memory resting T cells were risk factors in patients with lung squamous cell carcinoma (HR>1), while CD4 memory activated T cells, follicular helper T cells, and resting dendritic cells were protective factors (HR<1). The prognosis of patients with high infiltration rates of CD4 memory activated T cells and resting dendritic cells was better than those of patients with low rates (P<0.05). The prognostic risk assessment model of patients with lung squamous cell carcinoma constructed by CD4 memory activated T cells, follicular helper T cells and resting dendritic cells was effective, with the area under curve value of (ROC) curve being 0.678. Conclusion Resting dendritic cells and CD4 memory activated T cells are associated with the occurrence and prognosis of lung squamous cell carcinoma, and can be used as independent prognostic factors. The prognostic risk assessment model constructed by prognosis related immune cell subsets is effective. |
| Key words: lung neoplasms squamous cell carcinoma immune cells prognosis The Cancer Genome Atlas |
