Research progress on biomarkers associated with neonatal respiratory distress syndrome

Neonatal respiratory distress syndrome (NRDS), resulting from pulmonary surfactant deficiency, continues to be a significant cause of morbidity and mortality in preterm infants. Conventional diagnostic and therapeutic approaches have notable limitations in early warning and prognostic stratification. Consequently, biomarker research has emerged as a promising new direction in this field. We systematically searched PubMed, Web of Science, and Embase for studies published from January 2010 to December 2025, focusing on those that evaluated the application of inflammatory markers, microRNAs, protein and peptide biomarkers, and lung ultrasound score (LUS) in NRDS. The main findings were as follows: ① inflammatory biomarkers: IL-6, IL-17, and related markers were positively associated with disease severity, although their specificity remained limited; IL-37 showed potential as an anti-inflammatory therapeutic target. ② microRNAs: miR-375, miR-363, and others show better performance when combined with lung ultrasound and blood gas parameters, but this is based on preliminary single-center data. ③ Protein biomarkers: ANGPTL4 has diagnostic and prognostic value; combining it with lung ultrasound increases AUC, but evidence is still early and lacks external validation. ④ Clinical tools: Lung ultrasound, as a non-invasive real-time bedside tool, has good diagnostic performance and is convenient for repeated exams, but its combined use with emerging molecular markers requires further study. ⑤ Machine learning: although it has shown advantages in multimodal data integration, most models still lack external validation, and concerns regarding overfitting and limited interpretability continue to restrict clinical implementation. Overall, combined biomarker strategies and tools like lung ultrasound have shown promise in some studies, but current evidence is insufficient for broader clinical use. Most emerging biomarkers, including miRNAs and peptide markers, face challenges with assay standardization, independent validation, and prospective evaluation. Future studies should aim to translate biomarkers from laboratory discoveries to bedside applications while enhancing the robustness and interpretability of machine learning models.