Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications
Background: T cells are crucial for immunosurveillance and tumor eradication, with their dysregulation or absence in the tumor microenvironment linked to immunotherapy resistance. In lung adenocarcinoma (LUAD), this resistance is a significant barrier to effective treatment, highlighting the need fo...
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Elsevier
2025-05-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523325000634 |
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| author | Shiquan Liu Hao Sun Tianye Song Ce Liang Lele Deng Haiyong Zhu Fangchao Zhao Shujun Li |
| author_facet | Shiquan Liu Hao Sun Tianye Song Ce Liang Lele Deng Haiyong Zhu Fangchao Zhao Shujun Li |
| author_sort | Shiquan Liu |
| collection | DOAJ |
| description | Background: T cells are crucial for immunosurveillance and tumor eradication, with their dysregulation or absence in the tumor microenvironment linked to immunotherapy resistance. In lung adenocarcinoma (LUAD), this resistance is a significant barrier to effective treatment, highlighting the need for robust biomarkers and therapeutic targets to improve clinical outcomes. Methods: T cell-related markers were identified through single-cell RNA sequencing analysis. The TCGA dataset was used for consensus clustering to define molecular subtypes associated with distinct survival outcomes and immune profiles. A T cell-related prognostic signature was developed by integrating LUAD datasets from TCGA, GSE31210, GSE50081, and GSE68465 using 10 machine learning algorithms. Further analysis linked risk scores to immune infiltration and drug sensitivity. The role of a hub gene in CD4+ T cell function and its involvement in tumor immunity was explored through in vitro experiments and molecular biology techniques. Results: Cluster analysis identified three LUAD subtypes, with cluster1 showing the best prognosis and immune characteristics. A Lasso + PLSRcox-based signature was a significant risk factor for predicting LUAD patient outcomes, outperforming traditional clinicopathological factors. The risk score correlated with immune microenvironment features, immune cell infiltration, and sensitivity to immunotherapy and chemotherapy. CPA3 expression was elevated in activated CD4+ T cells, particularly in Th1 cells, promoting differentiation and IFN-γ secretion. Overexpression of CPA3 enhanced tumor cell apoptosis and increased Granzyme B and IFN-γ levels, highlighting its role in immune responses. Conclusion: We developed a powerful prognostic signature in LUAD that accurately predicts clinical outcomes and can guide immunotherapy and chemotherapy responses. |
| format | Article |
| id | doaj-art-d18f9a54aec44656a33f6cbaf2c244fa |
| institution | DOAJ |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-d18f9a54aec44656a33f6cbaf2c244fa2025-08-20T03:03:27ZengElsevierTranslational Oncology1936-52332025-05-015510233210.1016/j.tranon.2025.102332Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implicationsShiquan Liu0Hao Sun1Tianye Song2Ce Liang3Lele Deng4Haiyong Zhu5Fangchao Zhao6Shujun Li7Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Department of Thoracic Surgery, Affiliated Hospital of Chengde Medical University, Chengde, Hebei, ChinaXinqiao Hospital, Army Military Medical University, Chongqing, China; Faculty of Science, Autonomous University of Madrid, Spainish National Research Council (UAM-CSIC), Madrid, SpainDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, ChinaDepartment of Experimental Surgery, Tangdu Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, ChinaDepartment of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Corresponding authors at: Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, China.Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Corresponding authors at: Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, China.Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China; Corresponding authors at: Department of Thoracic Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, China.Background: T cells are crucial for immunosurveillance and tumor eradication, with their dysregulation or absence in the tumor microenvironment linked to immunotherapy resistance. In lung adenocarcinoma (LUAD), this resistance is a significant barrier to effective treatment, highlighting the need for robust biomarkers and therapeutic targets to improve clinical outcomes. Methods: T cell-related markers were identified through single-cell RNA sequencing analysis. The TCGA dataset was used for consensus clustering to define molecular subtypes associated with distinct survival outcomes and immune profiles. A T cell-related prognostic signature was developed by integrating LUAD datasets from TCGA, GSE31210, GSE50081, and GSE68465 using 10 machine learning algorithms. Further analysis linked risk scores to immune infiltration and drug sensitivity. The role of a hub gene in CD4+ T cell function and its involvement in tumor immunity was explored through in vitro experiments and molecular biology techniques. Results: Cluster analysis identified three LUAD subtypes, with cluster1 showing the best prognosis and immune characteristics. A Lasso + PLSRcox-based signature was a significant risk factor for predicting LUAD patient outcomes, outperforming traditional clinicopathological factors. The risk score correlated with immune microenvironment features, immune cell infiltration, and sensitivity to immunotherapy and chemotherapy. CPA3 expression was elevated in activated CD4+ T cells, particularly in Th1 cells, promoting differentiation and IFN-γ secretion. Overexpression of CPA3 enhanced tumor cell apoptosis and increased Granzyme B and IFN-γ levels, highlighting its role in immune responses. Conclusion: We developed a powerful prognostic signature in LUAD that accurately predicts clinical outcomes and can guide immunotherapy and chemotherapy responses.http://www.sciencedirect.com/science/article/pii/S1936523325000634Single-cell RNA sequencingT cellLung adenocarcinomaTumor immune environmentPrognostic risk model |
| spellingShingle | Shiquan Liu Hao Sun Tianye Song Ce Liang Lele Deng Haiyong Zhu Fangchao Zhao Shujun Li Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications Translational Oncology Single-cell RNA sequencing T cell Lung adenocarcinoma Tumor immune environment Prognostic risk model |
| title | Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications |
| title_full | Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications |
| title_fullStr | Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications |
| title_full_unstemmed | Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications |
| title_short | Comprehensive characterization of T cell subtypes in lung adenocarcinoma: Prognostic, predictive, and therapeutic implications |
| title_sort | comprehensive characterization of t cell subtypes in lung adenocarcinoma prognostic predictive and therapeutic implications |
| topic | Single-cell RNA sequencing T cell Lung adenocarcinoma Tumor immune environment Prognostic risk model |
| url | http://www.sciencedirect.com/science/article/pii/S1936523325000634 |
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