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An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis
Srinivasulu Yerukala Sathipati1, Ming-Ju Tsai2,3, Nikhila Aimalla4
1Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI 54449, USA.
NAR Genomics and Bioinformatics
|February 26, 2024
Summary
A new method, BSig, identifies a 12-microRNA signature for accurate breast cancer (BC) detection. This tool aids in early diagnosis and potential targeted therapies for BC patients.
Area of Science:
- Oncology
- Biomarkers
- Bioinformatics
Background:
- Breast cancer (BC) is a leading global cancer diagnosis.
- MicroRNAs (miRNAs) are crucial regulatory molecules and potential cancer biomarkers.
- Identifying circulating miRNA signatures can improve BC survival and therapeutic strategies.
Purpose of the Study:
- To develop a method for identifying a circulating miRNA signature for BC diagnostic prediction.
- To establish a compact set of miRNA markers for early BC detection.
Main Methods:
- Developed an evolutionary learning-based method named BSig.
- Analyzed serum miRNA expression profiles from 1280 BC patients and 2686 healthy controls.
- Utilized bioinformatics and gene-set enrichment analysis for target gene identification and mechanism understanding.
Main Results:
- BSig achieved outstanding prediction performance with 99.90% accuracy and 0.99 AUC on an independent test set.
- Identified a signature of 12 specific miRNAs (e.g., hsa-miR-3185, hsa-miR-3648) crucial for BC diagnostic prediction.
- Discovered 65 BC-specific miRNA-target genes and elucidated their underlying mechanisms.
Conclusions:
- BSig is a powerful tool for accurate breast cancer detection using a circulating miRNA signature.
- The identified miRNAs and target genes offer potential for novel diagnostic and therapeutic strategies in BC.
- BSig is publicly available to facilitate BC detection and therapeutic selection.
