Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification
BackgroundFerroptosis, a regulated form of cell death, has emerged as a critical modulator of melanoma's tumor progression and immune evasion. However, its integration with the tumor immune microenvironment (TME) and clinical prognostication remains underexplored. This study aims to construct a...
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Frontiers Media S.A.
2025-08-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1624691/full |
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| author | Lei Wang Lei Wang Xueying Jin Yuchen Wu Runing Qiu Jianfang Wang |
| author_facet | Lei Wang Lei Wang Xueying Jin Yuchen Wu Runing Qiu Jianfang Wang |
| author_sort | Lei Wang |
| collection | DOAJ |
| description | BackgroundFerroptosis, a regulated form of cell death, has emerged as a critical modulator of melanoma's tumor progression and immune evasion. However, its integration with the tumor immune microenvironment (TME) and clinical prognostication remains underexplored. This study aims to construct a multi-omics framework combining ferroptosis-related signatures, immune infiltration patterns, and machine-learning approaches to stratify melanoma patients and guide therapeutic decision-making.MethodsWe developed a multi-omics framework integrating bulk transcriptomics (TCGA/GEO), single-cell RNA sequencing, and machine learning to decode melanoma's ferroptosis-immune axis. Ferroptosis-immune subtypes were identified through consensus clustering and immune profiling, while prognostic models were constructed via LASSO/stepwise Cox regression and machine learning optimization.ResultsThree ferroptosis-immune subtypes exhibiting distinct survival outcomes and immune phenotypes were identified. A 40-gene prognostic signature (externally validated) effectively stratified patient survival risk and predicted chemotherapy sensitivity. Single-cell analysis revealed elevated ferroptosis activity within an immunosuppressive microenvironment, specifically implicating POSTN–ITGB5 signaling in fibroblast-immune cell crosstalk. A clinically applicable nomogram integrating risk scores and clinical factors demonstrated robust predictive accuracy (AUC 0.829–0.845). Machine learning refined a 4-gene prognostic signature (CLN6, GMPR, AP1S2, ITGA6), with functional validation confirming the role of CLN6 in proliferation and migration.ConclusionThis study establishes a prognostic framework and therapeutic roadmap for precision immuno-oncology in melanoma, bridging multi-omics discovery with clinical translation. |
| format | Article |
| id | doaj-art-69b40ddc69624b0bb3210a7dcc15574e |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-69b40ddc69624b0bb3210a7dcc15574e2025-08-20T02:51:57ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-08-011610.3389/fimmu.2025.16246911624691Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratificationLei Wang0Lei Wang1Xueying Jin2Yuchen Wu3Runing Qiu4Jianfang Wang5Department of Oncology, Shaoxing People’s Hospital, The First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, ChinaLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Oncology, Shaoxing People’s Hospital, The First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, ChinaLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaLaboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang Province, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, ChinaDepartment of Oncology, Shaoxing People’s Hospital, The First Affiliated Hospital of Shaoxing University, Shaoxing, Zhejiang, ChinaBackgroundFerroptosis, a regulated form of cell death, has emerged as a critical modulator of melanoma's tumor progression and immune evasion. However, its integration with the tumor immune microenvironment (TME) and clinical prognostication remains underexplored. This study aims to construct a multi-omics framework combining ferroptosis-related signatures, immune infiltration patterns, and machine-learning approaches to stratify melanoma patients and guide therapeutic decision-making.MethodsWe developed a multi-omics framework integrating bulk transcriptomics (TCGA/GEO), single-cell RNA sequencing, and machine learning to decode melanoma's ferroptosis-immune axis. Ferroptosis-immune subtypes were identified through consensus clustering and immune profiling, while prognostic models were constructed via LASSO/stepwise Cox regression and machine learning optimization.ResultsThree ferroptosis-immune subtypes exhibiting distinct survival outcomes and immune phenotypes were identified. A 40-gene prognostic signature (externally validated) effectively stratified patient survival risk and predicted chemotherapy sensitivity. Single-cell analysis revealed elevated ferroptosis activity within an immunosuppressive microenvironment, specifically implicating POSTN–ITGB5 signaling in fibroblast-immune cell crosstalk. A clinically applicable nomogram integrating risk scores and clinical factors demonstrated robust predictive accuracy (AUC 0.829–0.845). Machine learning refined a 4-gene prognostic signature (CLN6, GMPR, AP1S2, ITGA6), with functional validation confirming the role of CLN6 in proliferation and migration.ConclusionThis study establishes a prognostic framework and therapeutic roadmap for precision immuno-oncology in melanoma, bridging multi-omics discovery with clinical translation.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1624691/fullferroptosismelanomaimmune microenvironmentsingle-cell RNA sequencingmachine learning |
| spellingShingle | Lei Wang Lei Wang Xueying Jin Yuchen Wu Runing Qiu Jianfang Wang Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification Frontiers in Immunology ferroptosis melanoma immune microenvironment single-cell RNA sequencing machine learning |
| title | Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification |
| title_full | Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification |
| title_fullStr | Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification |
| title_full_unstemmed | Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification |
| title_short | Single-cell and machine learning integration reveals ferroptosis-driven immune landscapes for melanoma stratification |
| title_sort | single cell and machine learning integration reveals ferroptosis driven immune landscapes for melanoma stratification |
| topic | ferroptosis melanoma immune microenvironment single-cell RNA sequencing machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1624691/full |
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