Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a 6-gene programmed cell death index (PPCDI) to predict outcomes in acute myeloid leukemia (AML). The PPCDI signature aids in forecasting prognosis and guiding personalized drug selection for AML patients.
Area Of Science
- Oncology
- Molecular Biology
- Bioinformatics
Background
- Acute myeloid leukemia (AML) is characterized by heterogeneity and resistance to chemotherapy.
- Aberrant programmed cell death (PCD) pathways are implicated in AML pathogenesis.
- PCD-related signatures may serve as biomarkers for clinical outcomes and drug response.
Purpose Of The Study
- To develop and validate a predictive model for acute myeloid leukemia (AML) prognosis and drug response using programmed cell death (PCD) pathways.
- To identify a novel signature for predicting clinical outcomes and guiding personalized therapy in AML.
Main Methods
- Utilized 13 PCD pathways and 73 machine learning combinations from 10 algorithms.
- Analyzed bulk and single-cell RNA-sequencing data from TCGA-AML, Tyner, and GSE37642-GPL96 cohorts.
- Constructed and validated a 6-gene pan-programmed cell death-related genes index (PPCDI) signature.
Main Results
- The PPCDI signature was developed and validated in external cohorts, showing association with worse prognosis in AML.
- Prognostic nomograms incorporating PPCDI accurately predicted AML patient outcomes.
- High PPCDI correlated with chemotherapy resistance but sensitivity to specific drugs like dasatinib and methotrexate.
- Multi-omics analysis revealed PPCDI links to immunological features and the AML immune microenvironment.
Conclusions
- The novel PPCDI model effectively predicts clinical prognosis and drug sensitivity in AML.
- PPCDI holds potential for guiding personalized therapy selection in AML patients.
- This signature offers a new tool for understanding AML heterogeneity and treatment resistance.
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