Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma
BackgroundLung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-assoc...
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1217590/full |
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| author | Weibiao Zeng Weibiao Zeng Jin Wang Jian Yang Jian Yang Zhike Chen Zhike Chen Yuan Cui Yuan Cui Qifan Li Qifan Li Gaomeng Luo Gaomeng Luo Hao Ding Hao Ding Sheng Ju Sheng Ju Baisong Li Jun Chen Jun Chen Yufeng Xie Yufeng Xie Xin Tong Xin Tong Mi Liu Jun Zhao Jun Zhao |
| author_facet | Weibiao Zeng Weibiao Zeng Jin Wang Jian Yang Jian Yang Zhike Chen Zhike Chen Yuan Cui Yuan Cui Qifan Li Qifan Li Gaomeng Luo Gaomeng Luo Hao Ding Hao Ding Sheng Ju Sheng Ju Baisong Li Jun Chen Jun Chen Yufeng Xie Yufeng Xie Xin Tong Xin Tong Mi Liu Jun Zhao Jun Zhao |
| author_sort | Weibiao Zeng |
| collection | DOAJ |
| description | BackgroundLung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-associated gene (IAG) signature for prognosis prediction and immunotherapy response assessment in LUAD has not been established. Therefore, it is critical to explore such gene signatures.MethodsRNA sequencing profiles and corresponding clinical parameters of LUAD were extracted from the TCGA and GEO databases. Unsupervised consistency clustering analysis based on immune activation-related genes was performed on the enrolled samples. Subsequently, prognostic models based on genes associated with prognosis were built using the last absolute shrinkage and selection operator (LASSO) method and univariate Cox regression. The expression levels of four immune activation related gene index (IARGI) related genes were validated in 12 pairs of LUAD tumor and normal tissue samples using qPCR. Using the ESTIMATE, TIMER, and ssGSEA algorithms, immune cell infiltration analysis was carried out for different groups, and the tumor immune dysfunction and rejection (TIDE) score was used to evaluate the effectiveness of immunotherapy.ResultsBased on the expression patterns of IAGs, the TCGA LUAD cohort was classified into two clusters, with those in the IAG-high pattern demonstrating significantly better survival outcomes and immune cell infiltration compared to those in the IAG-low pattern. Then, we developed an IARGI model that effectively stratified patients into different risk groups, revealing differences in prognosis, mutation profiles, and immune cell infiltration within the tumor microenvironment between the high and low-risk groups. Notably, significant disparities in TIDE score between the two groups suggest that the low-risk group may exhibit better responses to ICIs therapy. The IARGI risk model was validated across multiple datasets and demonstrated exceptional performance in predicting overall survival in LUAD, and an IARGI-integrated nomogram was established as a quantitative tool for clinical practice.ConclusionThe IARGI can serve as valuable biomarkers for evaluating the tumor microenvironment and predicting the prognosis of LUAD patients. Furthermore, these genes probably provide valuable guidance for establishing effective immunotherapy regimens for LUAD patients. |
| first_indexed | 2024-03-13T00:30:46Z |
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| language | English |
| last_indexed | 2024-03-13T00:30:46Z |
| publishDate | 2023-07-01 |
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| series | Frontiers in Immunology |
| spelling | doaj.art-3e75869f9c174915bc8404e05dc327b82023-07-10T13:32:44ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-07-011410.3389/fimmu.2023.12175901217590Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinomaWeibiao Zeng0Weibiao Zeng1Jin Wang2Jian Yang3Jian Yang4Zhike Chen5Zhike Chen6Yuan Cui7Yuan Cui8Qifan Li9Qifan Li10Gaomeng Luo11Gaomeng Luo12Hao Ding13Hao Ding14Sheng Ju15Sheng Ju16Baisong Li17Jun Chen18Jun Chen19Yufeng Xie20Yufeng Xie21Xin Tong22Xin Tong23Mi Liu24Jun Zhao25Jun Zhao26Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Pharmaceutics, College of Pharmaceutical Sciences, Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Pharmaceutics, College of Pharmaceutical Sciences, Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Pharmaceutics, College of Pharmaceutical Sciences, Soochow University, Suzhou, ChinaInstitute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, ChinaBackgroundLung adenocarcinoma (LUAD) is a major subtype of non-small cell lung cancer (NSCLC) with a highly heterogeneous tumor microenvironment. Immune checkpoint inhibitors (ICIs) are more effective in tumors with a pre-activated immune status. However, the potential of the immune activation-associated gene (IAG) signature for prognosis prediction and immunotherapy response assessment in LUAD has not been established. Therefore, it is critical to explore such gene signatures.MethodsRNA sequencing profiles and corresponding clinical parameters of LUAD were extracted from the TCGA and GEO databases. Unsupervised consistency clustering analysis based on immune activation-related genes was performed on the enrolled samples. Subsequently, prognostic models based on genes associated with prognosis were built using the last absolute shrinkage and selection operator (LASSO) method and univariate Cox regression. The expression levels of four immune activation related gene index (IARGI) related genes were validated in 12 pairs of LUAD tumor and normal tissue samples using qPCR. Using the ESTIMATE, TIMER, and ssGSEA algorithms, immune cell infiltration analysis was carried out for different groups, and the tumor immune dysfunction and rejection (TIDE) score was used to evaluate the effectiveness of immunotherapy.ResultsBased on the expression patterns of IAGs, the TCGA LUAD cohort was classified into two clusters, with those in the IAG-high pattern demonstrating significantly better survival outcomes and immune cell infiltration compared to those in the IAG-low pattern. Then, we developed an IARGI model that effectively stratified patients into different risk groups, revealing differences in prognosis, mutation profiles, and immune cell infiltration within the tumor microenvironment between the high and low-risk groups. Notably, significant disparities in TIDE score between the two groups suggest that the low-risk group may exhibit better responses to ICIs therapy. The IARGI risk model was validated across multiple datasets and demonstrated exceptional performance in predicting overall survival in LUAD, and an IARGI-integrated nomogram was established as a quantitative tool for clinical practice.ConclusionThe IARGI can serve as valuable biomarkers for evaluating the tumor microenvironment and predicting the prognosis of LUAD patients. Furthermore, these genes probably provide valuable guidance for establishing effective immunotherapy regimens for LUAD patients.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1217590/fulllung adenocarcinomaimmune activationimmune infiltrationimmunotherapy efficacyprognosis |
| spellingShingle | Weibiao Zeng Weibiao Zeng Jin Wang Jian Yang Jian Yang Zhike Chen Zhike Chen Yuan Cui Yuan Cui Qifan Li Qifan Li Gaomeng Luo Gaomeng Luo Hao Ding Hao Ding Sheng Ju Sheng Ju Baisong Li Jun Chen Jun Chen Yufeng Xie Yufeng Xie Xin Tong Xin Tong Mi Liu Jun Zhao Jun Zhao Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma Frontiers in Immunology lung adenocarcinoma immune activation immune infiltration immunotherapy efficacy prognosis |
| title | Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma |
| title_full | Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma |
| title_fullStr | Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma |
| title_full_unstemmed | Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma |
| title_short | Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma |
| title_sort | identification of immune activation related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma |
| topic | lung adenocarcinoma immune activation immune infiltration immunotherapy efficacy prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1217590/full |
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