Machine learning-based plasma-derived extracellular vesicle signatures for digestive system cancers prediction
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View abstract on PubMed
Summary
This summary is machine-generated.Plasma-derived extracellular vesicle RNA shows promise for diagnosing digestive system cancers (DSCs). A machine learning model identified nine exosomal RNA biomarkers, offering a potential non-invasive diagnostic tool for these challenging malignancies.
Area Of Science
- Oncology
- Molecular Diagnostics
- Bioinformatics
Background
- Digestive system cancers (DSCs) are difficult to diagnose early, with limited accuracy from traditional biomarkers.
- Plasma-derived extracellular vesicle (PDEV) RNA is a promising diagnostic tool, but its use in DSCs requires further investigation.
Purpose Of The Study
- To develop and validate a machine learning-based diagnostic model for DSCs using PDEV RNA.
- To identify novel exosomal RNA biomarkers for non-invasive DSCs detection.
Main Methods
- Integrated PDEV sequencing data from exoRBase 2.0 for 444 participants (326 DSCs patients, 118 healthy).
- Constructed and validated a PDEV-diagnostic model using machine learning algorithms (XGBoost) with 5-fold cross-validation.
- Assessed exosomal RNA features using bulk and single RNA-seq data.
Main Results
- The XGBoost model achieved high diagnostic accuracy, with AUCs of 0.83 (training) and 0.94 (test sets).
- Identified nine exosomal RNA predictors (BANK1, MALAT1, FGA, UBR4, ILR-7, FGB, PLPP5, PCAT19, CIITA) for DSCs.
- Demonstrated the model's performance across training and test datasets.
Conclusions
- Machine learning models utilizing PDEV RNA show high accuracy for DSCs diagnosis.
- The identified nine exosomal mRNAs/lncRNAs are promising non-invasive biomarkers for early DSCs detection.
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