Machine learning model for early diagnosis of breast cancer based on PiRNA expression with CA153
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View abstract on PubMed
Summary
This summary is machine-generated.Circulating PIWI-interacting RNAs (piRNAs) show promise for early breast cancer detection. A machine learning model combining a tri-piRNA signature with CA153 achieved superior diagnostic accuracy.
Area Of Science
- Molecular Biology
- Oncology
- Bioinformatics
Background
- PIWI-interacting RNAs (piRNAs) play roles in cancer development.
- Circulating piRNAs are potential biomarkers for cancer detection.
Purpose Of The Study
- To investigate the diagnostic utility of circulating piRNAs for breast cancer (BC).
- To develop and validate machine learning (ML) models for early BC diagnosis using piRNA signatures and clinical factors.
Main Methods
- Serum piRNA sequencing identified a tri-piRNA signature (piR-139966, piR-2572505, piR-2570061).
- Quantitative PCR (qPCR) validated piRNA expression levels.
- Machine learning algorithms were employed to build predictive models integrating piRNA data with CA153 levels.
Main Results
- The identified tri-piRNA signature was significantly upregulated in early-stage BC patients.
- The piRNA panel enhanced diagnostic precision for BC detection, complementing CA153.
- An XGBoost-based ML model integrating piRNA expression and CA153 demonstrated optimal performance for early BC identification.
Conclusions
- Circulating piRNAs represent a promising biomarker for non-invasive early breast cancer diagnosis.
- Machine learning frameworks can effectively integrate multi-modal data (piRNAs and CA153) for improved BC diagnostic accuracy.
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