Integrating Machine Learning Algorithms to Construct a Triaptosis-Related Prognostic Model in Melanoma
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View abstract on PubMed
Summary
This summary is machine-generated.This study reveals triaptosis, a programmed cell death (PCD) mechanism, is linked to melanoma prognosis and immune cell infiltration. Triaptosis-associated signatures may serve as biomarkers and therapeutic targets for melanoma treatment.
Area Of Science
- Oncology
- Molecular Biology
- Immunology
Background
- Melanoma is an aggressive skin cancer with high mortality due to metastasis and therapy resistance.
- Programmed cell death (PCD), including apoptosis and ferroptosis, influences tumor progression and treatment response.
- Triaptosis, a recently identified PCD pathway, has unexplored implications in melanoma.
Purpose Of The Study
- To investigate the role of triaptosis in melanoma.
- To identify key triaptosis-related genes and pathways in melanoma.
- To develop a prognostic signature for melanoma based on triaptosis.
Main Methods
- Integrated single-cell and bulk RNA sequencing data.
- Constructed a prognostic signature using machine learning (SurvivalSVM) with TCGA-SKCM and GEO datasets.
- Performed survival analysis, ROC curve analysis, PCA, and immune infiltration analysis.
Main Results
- Developed a robust triaptosis-associated signature (TAS) with high predictive performance.
- High-risk patients identified by TAS showed significantly worse overall survival.
- TAS was significantly associated with immune cell populations and tumor microenvironment.
Conclusions
- Triaptosis-related gene expression patterns correlate with melanoma prognosis and immune infiltration.
- Triaptosis presents a potential biomarker and therapeutic target for melanoma.
- Findings offer strategies to enhance melanoma treatment efficacy and overcome resistance.
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