Identification of prognostic biomarkers associated with T and melanoma cell subpopulations in melanoma through integrating machine learning and multiomics
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies MITF+ T-cells and M2-cells as key prognostic factors in melanoma. A novel prognostic risk score (PRS) model was developed for accurate melanoma patient outcome prediction.
Area Of Science
- Oncology
- Immunology
- Bioinformatics
Background
- Melanoma is a lethal cancer with unclear prognostic factors.
- The roles of T-cells and melanoma cells in the tumor microenvironment and their prognostic impact are not well understood.
Purpose Of The Study
- To identify specific T-cell and melanoma cell subpopulations associated with melanoma prognosis.
- To develop a novel prognostic risk score (PRS) model for melanoma patients.
Main Methods
- Analysis of single-cell RNA sequencing (scRNA-seq) and gene expression data from public databases (GEO, TCGA-SKCM).
- Utilized Scissor to correlate cell subpopulations with survival outcomes.
- Stratified patients based on 108 prognostic genes and constructed a PRS model using differentially expressed genes (DEGs).
Main Results
- Identified MITF+ T-cells and M2-cells as novel subpopulations linked to melanoma prognosis.
- Stratified TCGA-SKCM patients into two groups with distinct clinical outcomes and immune profiles.
- Developed and validated a 11-gene PRS model with accurate prognostic predictive ability.
Conclusions
- MITF+ T-cells and M2-cells are critical prognostic factors in melanoma.
- The novel PRS model offers accurate prediction of melanoma patient outcomes.
- Findings may aid clinical decision-making for melanoma treatment.
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