A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients
The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely l...
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| Format: | Article |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2023.1192777/full |
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| author | Kemiao Yuan Songyun Zhao Bicheng Ye Qi Wang Yuan Liu Pengpeng Zhang Jiaheng Xie Hao Chi Yu Chen Chao Cheng Jinhui Liu |
| author_facet | Kemiao Yuan Songyun Zhao Bicheng Ye Qi Wang Yuan Liu Pengpeng Zhang Jiaheng Xie Hao Chi Yu Chen Chao Cheng Jinhui Liu |
| author_sort | Kemiao Yuan |
| collection | DOAJ |
| description | The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC. |
| first_indexed | 2024-03-13T10:10:07Z |
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| id | doaj.art-03800633e58e4259a7000a346c3cd5cf |
| institution | Directory Open Access Journal |
| issn | 1663-9812 |
| language | English |
| last_indexed | 2024-03-13T10:10:07Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Pharmacology |
| spelling | doaj.art-03800633e58e4259a7000a346c3cd5cf2023-05-22T04:29:48ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122023-05-011410.3389/fphar.2023.11927771192777A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patientsKemiao Yuan0Songyun Zhao1Bicheng Ye2Qi Wang3Yuan Liu4Pengpeng Zhang5Jiaheng Xie6Hao Chi7Yu Chen8Chao Cheng9Jinhui Liu10Department of Oncology, Traditional Chinese Medicine Hospital of Wuxi, Wuxi, ChinaDepartment of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaSchool of Clinical Medicine, Yangzhou Polytechnic College, Yangzhou, ChinaDepartment of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, ChinaDepartment of General Surgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaThe First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaSouthwest Medical University, Luzhou, ChinaWuxi Maternal and Child Health Care Hospital, Wuxi, ChinaDepartment of Neurosurgery, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi, ChinaDepartment of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, ChinaThe phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.https://www.frontiersin.org/articles/10.3389/fphar.2023.1192777/fullt cell exhaustionOCScRNA-seqmachine learningimmunotherapy |
| spellingShingle | Kemiao Yuan Songyun Zhao Bicheng Ye Qi Wang Yuan Liu Pengpeng Zhang Jiaheng Xie Hao Chi Yu Chen Chao Cheng Jinhui Liu A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients Frontiers in Pharmacology t cell exhaustion OC ScRNA-seq machine learning immunotherapy |
| title | A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients |
| title_full | A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients |
| title_fullStr | A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients |
| title_full_unstemmed | A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients |
| title_short | A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients |
| title_sort | novel t cell exhaustion related feature can accurately predict the prognosis of oc patients |
| topic | t cell exhaustion OC ScRNA-seq machine learning immunotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2023.1192777/full |
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