The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma
Abstract T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Te...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-40662-z |
| _version_ | 1797452769435582464 |
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| author | Yahua Wu Bin Du Mingqiang Lin Xiaohui Ji Chengliu Lv Jinhuo Lai |
| author_facet | Yahua Wu Bin Du Mingqiang Lin Xiaohui Ji Chengliu Lv Jinhuo Lai |
| author_sort | Yahua Wu |
| collection | DOAJ |
| description | Abstract T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making. |
| first_indexed | 2024-03-09T15:13:28Z |
| format | Article |
| id | doaj.art-3397de82087f4bf4bbe3ae9e60ab5f53 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:13:28Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj.art-3397de82087f4bf4bbe3ae9e60ab5f532023-11-26T13:12:41ZengNature PortfolioScientific Reports2045-23222023-08-0113111710.1038/s41598-023-40662-zThe identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinomaYahua Wu0Bin Du1Mingqiang Lin2Xiaohui Ji3Chengliu Lv4Jinhuo Lai5Department of Medical Oncology, Fujian Medical University Union HospitalDepartment of Medical Oncology, Fujian Medical University Union HospitalDepartment of Radiation Oncology, Fujian Medical University Cancer HospitalDepartment of Medical Oncology, Chongqing University Cancer HospitalDepartment of Medical Oncology, Fujian Medical University Union HospitalDepartment of Medical Oncology, Fujian Medical University Union HospitalAbstract T-cell exhaustion (Tex) is considered to be a reason for immunotherapy resistance and poor prognosis in lung adenocarcinoma. Therefore, we used weighted correlation network analysis to identify Tex-related genes in the cancer genome atlas (TCGA). Unsupervised clustering approach based on Tex-related genes divided patients into cluster 1 and cluster 2. Then, we utilized random forest and the least absolute shrinkage and selection operator to identify nine key genes to construct a riskscore. Patients were classified as low or high-risk groups. The multivariate cox analysis showed the riskscore was an independent prognostic factor in TCGA and GSE72094 cohorts. Moreover, patients in cluster 2 with high riskscore had the worst prognosis. The immune response prediction analysis showed the low-risk group had higher immune, stromal, estimate scores, higher immunophenscore (IPS), and lower tumor immune dysfunction and exclusion score which suggested a better response to immune checkpoint inhibitors (ICIs) therapy in the low-risk group. In the meantime, we included two independent immunotherapy cohorts that also confirmed a better response to ICIs treatment in the low-risk group. Besides, we discovered differences in chemotherapy and targeted drug sensitivity between two groups. Finally, a nomogram was built to facilitate clinical decision making.https://doi.org/10.1038/s41598-023-40662-z |
| spellingShingle | Yahua Wu Bin Du Mingqiang Lin Xiaohui Ji Chengliu Lv Jinhuo Lai The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma Scientific Reports |
| title | The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma |
| title_full | The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma |
| title_fullStr | The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma |
| title_full_unstemmed | The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma |
| title_short | The identification of genes associated T-cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma |
| title_sort | identification of genes associated t cell exhaustion and construction of prognostic signature to predict immunotherapy response in lung adenocarcinoma |
| url | https://doi.org/10.1038/s41598-023-40662-z |
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