Multiple cell-death patterns predict the prognosis and drug sensitivity of melanoma patients
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View abstract on PubMed
Summary
This summary is machine-generated.A new Cell Death Index (CDI) model predicts melanoma prognosis and drug sensitivity. This model, based on programmed cell death (PCD) genes, identifies patients with worse outcomes and treatment resistance.
Area Of Science
- Oncology
- Molecular Biology
- Bioinformatics
Background
- Melanoma treatment faces challenges with current modalities.
- There's a need for better models to predict melanoma prognosis and drug sensitivity.
- Programmed cell death (PCD) modes are critical in tumor progression and could be key indicators.
Purpose Of The Study
- To develop and validate a novel model for assessing melanoma prognosis and drug sensitivity.
- To investigate the role of 13 programmed cell death (PCD) modes in melanoma.
- To establish a Cell Death Index (CDI) for clinical application.
Main Methods
- Analyzed 13 PCD modes and associated genes.
- Constructed a Cell Death Index (CDI) using machine learning algorithms.
- Validated the CDI model using transcriptomic, genomic, and clinical data from TCGA-SKCM, GSE19234, and GSE65904 cohorts.
Main Results
- A ten-gene signature CDI was established, dividing patients into high and low CDI groups.
- The high CDI group showed fewer immune-infiltrating cells and resistance to docetaxel and axitinib.
- Higher CDI values correlated with worse postoperative prognoses in melanoma patients (p < 0.01).
Conclusions
- The CDI model accurately predicts clinical prognosis in melanoma.
- The CDI model demonstrates efficacy in predicting drug sensitivity for melanoma patients.
- This multi-PCD mode model offers a promising tool for personalized melanoma treatment.
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