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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1624691/full |
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| Summary: | 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. |
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| ISSN: | 1664-3224 |