Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures
BackgroundImmunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and progn...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2022.993118/full |
| _version_ | 1798028997550931968 |
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| author | Nana Wang Abiyasi Nanding Xiaocan Jia Yuping Wang Chaojun Yang Jingwen Fan Ani Dong Guowei Zheng Jiaxin Ma Xuezhong Shi Yongli Yang |
| author_facet | Nana Wang Abiyasi Nanding Xiaocan Jia Yuping Wang Chaojun Yang Jingwen Fan Ani Dong Guowei Zheng Jiaxin Ma Xuezhong Shi Yongli Yang |
| author_sort | Nana Wang |
| collection | DOAJ |
| description | BackgroundImmunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.MethodsThe Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score.ResultsA total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group.ConclusionThis study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient’s prognosis and accurate immunotherapy. |
| first_indexed | 2024-04-11T19:17:16Z |
| format | Article |
| id | doaj.art-f39f6ca9149547ccbf2e6edb9ed948fe |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-11T19:17:16Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-f39f6ca9149547ccbf2e6edb9ed948fe2022-12-22T04:07:24ZengFrontiers Media S.A.Frontiers in Immunology1664-32242022-10-011310.3389/fimmu.2022.993118993118Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signaturesNana Wang0Abiyasi Nanding1Xiaocan Jia2Yuping Wang3Chaojun Yang4Jingwen Fan5Ani Dong6Guowei Zheng7Jiaxin Ma8Xuezhong Shi9Yongli Yang10Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Pathology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Gynecology, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaDepartment of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, ChinaBackgroundImmunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.MethodsThe Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score.ResultsA total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group.ConclusionThis study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient’s prognosis and accurate immunotherapy.https://www.frontiersin.org/articles/10.3389/fimmu.2022.993118/fullcervical cancerimmunotherapybiomarkerstumor immune microenvironmentimmunohistochemistry |
| spellingShingle | Nana Wang Abiyasi Nanding Xiaocan Jia Yuping Wang Chaojun Yang Jingwen Fan Ani Dong Guowei Zheng Jiaxin Ma Xuezhong Shi Yongli Yang Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures Frontiers in Immunology cervical cancer immunotherapy biomarkers tumor immune microenvironment immunohistochemistry |
| title | Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures |
| title_full | Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures |
| title_fullStr | Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures |
| title_full_unstemmed | Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures |
| title_short | Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures |
| title_sort | mining of immunological and prognostic related biomarker for cervical cancer based on immune cell signatures |
| topic | cervical cancer immunotherapy biomarkers tumor immune microenvironment immunohistochemistry |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2022.993118/full |
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