Shuangshuang Mao1, Yuan Li1, Zhiliang Lu1, Yun Che1, Jianbing Huang1, Yuanyuan Lei1, Yalong Wang1, Xinfeng Wang1, Chengming Liu1, Sufei Zheng1, Ning Li1, Jiagen Li1, Nan Sun2, Jie He3. 1. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. 2. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. sunnan@vip.126.com. 3. Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China. prof.jiehe@gmail.com.
Abstract
PURPOSE: Lung adenocarcinoma (LUAD) is the predominant subtype of lung cancer, with increasing evidence showing clinical benefits of immunotherapy. However, a lack of integrated profiles of complex LUAD immune microenvironments hampers the application of immunotherapy, resulting in limited eligible patient populations as well as drug resistance problems. Here, we aimed to systematically profile the immune signatures of LUADs and to assess the role of the immune microenvironment in patient outcome. METHODS: We systematically profiled the immune signatures of LUADs deposited in the TCGA and GEO databases using a total of 730 immune-related genes. Differential expression analysis was used to identify dysregulated genes. Univariate Cox analysis followed by robust likelihood-based survival analysis and multivariate Cox analysis were applied to construct an immune-related prognostic model. RESULTS: We found that differentially expressed immune genes were mainly enriched in immune cell proliferation, migration, activation and the NF-κB and TNF signaling pathways. The 10-immune gene predictive model that we constructed could differentiate LUAD patients with different overall survival times in several datasets, with areas under the curve (AUCs) of 0.67, 0.69, 0.72 and 0.74. LUAD patients with high- or low-risk scores exhibited distinct immune cell compositions, which may explain the prognostic significance of our model. CONCLUSIONS: Our results add to the current knowledge of immune processes in LUADs and underscore the critical role of the immune microenvironment in LUAD patient outcome.
PURPOSE:Lung adenocarcinoma (LUAD) is the predominant subtype of lung cancer, with increasing evidence showing clinical benefits of immunotherapy. However, a lack of integrated profiles of complex LUAD immune microenvironments hampers the application of immunotherapy, resulting in limited eligible patient populations as well as drug resistance problems. Here, we aimed to systematically profile the immune signatures of LUADs and to assess the role of the immune microenvironment in patient outcome. METHODS: We systematically profiled the immune signatures of LUADs deposited in the TCGA and GEO databases using a total of 730 immune-related genes. Differential expression analysis was used to identify dysregulated genes. Univariate Cox analysis followed by robust likelihood-based survival analysis and multivariate Cox analysis were applied to construct an immune-related prognostic model. RESULTS: We found that differentially expressed immune genes were mainly enriched in immune cell proliferation, migration, activation and the NF-κB and TNF signaling pathways. The 10-immune gene predictive model that we constructed could differentiate LUAD patients with different overall survival times in several datasets, with areas under the curve (AUCs) of 0.67, 0.69, 0.72 and 0.74. LUAD patients with high- or low-risk scores exhibited distinct immune cell compositions, which may explain the prognostic significance of our model. CONCLUSIONS: Our results add to the current knowledge of immune processes in LUADs and underscore the critical role of the immune microenvironment in LUAD patient outcome.
Entities:
Keywords:
Immune signatures; Lung adenocarcinoma; Overall survival; Prognostic model