Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma
Jiaheng Xie,1,* Min Zhang,1,* Min Qi2 1Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China; 2Department of Burns and Plastic Surgery, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, Peo...
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Dove Medical Press
2025-06-01
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| author | Xie J Zhang M Qi M |
| author_facet | Xie J Zhang M Qi M |
| author_sort | Xie J |
| collection | DOAJ |
| description | Jiaheng Xie,1,* Min Zhang,1,* Min Qi2 1Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China; 2Department of Burns and Plastic Surgery, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Min Qi, Department of Burns and Plastic Surgery, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People’s Republic of China, Email qimin05@csu.edu.cnIntroduction: Melanoma is a highly aggressive skin cancer that accounts for a disproportionate number of skin cancer-related deaths due to early metastasis and therapy resistance. Programmed cell death (PCD), including ferroptosis and apoptosis, plays a crucial role in tumor progression and therapy response. Among these, triaptosis is a newly described form of PCD. It represents a novel mechanism of cell death with potential implications for cancer treatment. However, its role in melanoma remains largely unexplored.Methods: We explored the role of triaptosis in melanoma by integrating single-cell and bulk RNA sequencing data. Key triaptosis-related genes and pathways were identified and incorporated into machine learning models to construct a prognostic signature. The TCGA-SKCM cohort served as the training dataset, and GEO datasets were used for validation.Results: A robust prognostic model based on triaptosis-associated signature (TAS) was established using the SurvivalSVM algorithm. This model showed superior predictive performance, with consistently high concordance index (C-index) values across independent validation datasets. Kaplan–Meier survival analysis indicated that high-risk patients had significantly worse overall survival than low-risk patients. The model’s predictive accuracy was confirmed through receiver operating characteristic (ROC) curve analysis and principal component analysis (PCA). Moreover, immune infiltration and tumor microenvironment (TME) analyses revealed significant associations between TAS and immune cell populations.Conclusion: Triaptosis-related gene expression patterns are closely linked with melanoma prognosis and immune infiltration. Our findings provide novel insights into triaptosis as a potential biomarker and therapeutic target, offering strategies to overcome treatment resistance in melanoma.Keywords: melanoma, cancer, tumor microenvironment, cell death, target |
| format | Article |
| id | doaj-art-649badeaa44d401797418ede6bbab9e3 |
| institution | OA Journals |
| issn | 1179-1322 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Cancer Management and Research |
| spelling | doaj-art-649badeaa44d401797418ede6bbab9e32025-08-20T02:24:13ZengDove Medical PressCancer Management and Research1179-13222025-06-01Volume 17Issue 111271141103875Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in MelanomaXie J0Zhang M1Qi M2Plastic SurgeryXiangya Hospital, Central South UniversityDepartment of Plastic SurgeryJiaheng Xie,1,* Min Zhang,1,* Min Qi2 1Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, People’s Republic of China; 2Department of Burns and Plastic Surgery, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Min Qi, Department of Burns and Plastic Surgery, Shenzhen Hospital of Southern Medical University, Shenzhen, Guangdong, People’s Republic of China, Email qimin05@csu.edu.cnIntroduction: Melanoma is a highly aggressive skin cancer that accounts for a disproportionate number of skin cancer-related deaths due to early metastasis and therapy resistance. Programmed cell death (PCD), including ferroptosis and apoptosis, plays a crucial role in tumor progression and therapy response. Among these, triaptosis is a newly described form of PCD. It represents a novel mechanism of cell death with potential implications for cancer treatment. However, its role in melanoma remains largely unexplored.Methods: We explored the role of triaptosis in melanoma by integrating single-cell and bulk RNA sequencing data. Key triaptosis-related genes and pathways were identified and incorporated into machine learning models to construct a prognostic signature. The TCGA-SKCM cohort served as the training dataset, and GEO datasets were used for validation.Results: A robust prognostic model based on triaptosis-associated signature (TAS) was established using the SurvivalSVM algorithm. This model showed superior predictive performance, with consistently high concordance index (C-index) values across independent validation datasets. Kaplan–Meier survival analysis indicated that high-risk patients had significantly worse overall survival than low-risk patients. The model’s predictive accuracy was confirmed through receiver operating characteristic (ROC) curve analysis and principal component analysis (PCA). Moreover, immune infiltration and tumor microenvironment (TME) analyses revealed significant associations between TAS and immune cell populations.Conclusion: Triaptosis-related gene expression patterns are closely linked with melanoma prognosis and immune infiltration. Our findings provide novel insights into triaptosis as a potential biomarker and therapeutic target, offering strategies to overcome treatment resistance in melanoma.Keywords: melanoma, cancer, tumor microenvironment, cell death, targethttps://www.dovepress.com/integrating-machine-learning-algorithms-to-construct-a-triaptosis-rela-peer-reviewed-fulltext-article-CMARMelanomaCancerTumor microenvironmentCell DeathTarget |
| spellingShingle | Xie J Zhang M Qi M Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma Cancer Management and Research Melanoma Cancer Tumor microenvironment Cell Death Target |
| title | Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma |
| title_full | Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma |
| title_fullStr | Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma |
| title_full_unstemmed | Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma |
| title_short | Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma |
| title_sort | integrating machine learning algorithms to construct a triaptosis related prognostic model in melanoma |
| topic | Melanoma Cancer Tumor microenvironment Cell Death Target |
| url | https://www.dovepress.com/integrating-machine-learning-algorithms-to-construct-a-triaptosis-rela-peer-reviewed-fulltext-article-CMAR |
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