Artificial intelligence-based miRNA analysis for precision oncology: diagnostic and prognostic insights

Area of Science:

• Oncology
• Bioinformatics
• Artificial Intelligence

Background:

• MicroRNAs (miRNAs) are small molecules regulating gene activity, frequently altered in cancer.
• Stable miRNA profiles in body fluids offer potential for non-invasive cancer detection and monitoring.
• Integrating AI with miRNA analysis presents opportunities and challenges for clinical application.

Purpose of the Study:

• To review the application of AI in analyzing miRNA signatures for cancer.
• To assess AI's role in early cancer detection, outcome prediction, and personalized therapy.
• To identify challenges and future directions for AI-miRNA integration in oncology.

Main Methods:

• A comprehensive literature search was conducted using miRNA and AI/ML terms until July 2025.
• Included studies focused on early detection, outcome prediction, and personalized treatment guidance.
• Analyzed AI methods like SVM, RF, ANN, LR, PCA, and HC for miRNA data interpretation.

Main Results:

• AI models analyzing miRNA signatures achieve high accuracy (AUC > 0.90) in diagnosing cancers like gastric, breast, and lung cancer.
• Specific miRNA combinations identified by AI can predict cancer stage and chemotherapy resistance.
• AI aids in identifying prognostic and predictive miRNA biomarkers.

Conclusions:

• The synergy of AI and miRNA analysis is transforming oncology, enabling earlier detection and personalized treatments.
• Clinical integration necessitates trustworthy AI models, diverse datasets for fairness, and interdisciplinary collaboration.
• This convergence paves the way for proactive, precise, and accessible global cancer management.