Machine learning models integrating iron biomarkers and SLC17A4 expression achieved high discriminative performance (AUC = 0.828-0.849) and demonstrated good calibration and net clinical benefit according to calibration and decision curve analyses, supporting their potential clinical applicability.<h4>Conclusion</h4>TSAT confers protective effects in CRS, and SLC17A4 represents a promising biomarker and therapeutic target. The gene discussed is SLC17A4; the disease is congenital rubella syndrome.