Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index
IntroductionMelanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.MethodsIn this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based o...
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Frontiers Media S.A.
2024-03-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1343425/full |
| _version_ | 1797256558907752448 |
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| author | Shaoluan Zheng Anqi He Chenxi Chen Jianying Gu Jianying Gu Chuanyuan Wei Zhiwei Chen Jiaqi Liu Jiaqi Liu |
| author_facet | Shaoluan Zheng Anqi He Chenxi Chen Jianying Gu Jianying Gu Chuanyuan Wei Zhiwei Chen Jiaqi Liu Jiaqi Liu |
| author_sort | Shaoluan Zheng |
| collection | DOAJ |
| description | IntroductionMelanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.MethodsIn this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment.ResultsNotably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature.DiscussionOur findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types. |
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| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-04-24T22:23:40Z |
| publishDate | 2024-03-01 |
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| series | Frontiers in Immunology |
| spelling | doaj.art-7d79cce8b02a4b3dabe343064af094212024-03-20T04:35:18ZengFrontiers Media S.A.Frontiers in Immunology1664-32242024-03-011510.3389/fimmu.2024.13434251343425Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene indexShaoluan Zheng0Anqi He1Chenxi Chen2Jianying Gu3Jianying Gu4Chuanyuan Wei5Zhiwei Chen6Jiaqi Liu7Jiaqi Liu8Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, ChinaDepartment of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, ChinaDepartment of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, ChinaDepartment of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, ChinaArtificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, ChinaBig Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University, Shanghai, ChinaDepartment of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, ChinaArtificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, ChinaIntroductionMelanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.MethodsIn this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment.ResultsNotably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature.DiscussionOur findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1343425/fullmelanomabioinformaticsprognosisimmunotherapy responsetumor microenvironmentmolecular docking |
| spellingShingle | Shaoluan Zheng Anqi He Chenxi Chen Jianying Gu Jianying Gu Chuanyuan Wei Zhiwei Chen Jiaqi Liu Jiaqi Liu Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index Frontiers in Immunology melanoma bioinformatics prognosis immunotherapy response tumor microenvironment molecular docking |
| title | Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index |
| title_full | Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index |
| title_fullStr | Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index |
| title_full_unstemmed | Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index |
| title_short | Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index |
| title_sort | predicting immunotherapy response in melanoma using a novel tumor immunological phenotype related gene index |
| topic | melanoma bioinformatics prognosis immunotherapy response tumor microenvironment molecular docking |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1343425/full |
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