Identification of differential miRNAs in serum exosomes and development of an integrated diagnostic model for early gastric cancer.

Early detection of gastric cancer (GC) improves patient outcomes. However, conventional serum biomarkers show limited sensitivity in early disease stages. Circulating microRNAs (miRNAs) are remarkably stable in body fluids. These molecules have emerged as promising noninvasive biomarkers. In this study, we isolated serum-derived exosomes from healthy controls and GC patients. Transmission electron microscopy, nanoparticle tracking analysis, and Western blotting confirmed exosomal characteristics. Small RNA sequencing then established miRNA expression profiles to identify progression-associated molecules. We conducted bioinformatics analyses to explore their biological roles and validated candidate miRNAs using quantitative real-time PCR. Notably, miR-17-3p levels declined progressively during disease development. This expression level correlated significantly with tumor differentiation (P = 0.0324), TNM stage (P = 0.0100), and neural invasion (P = 0.0100). Combining miR-17-3p with conventional markers increased the area under the curve (AUC) from 0.7847 to 0.8472 for staging. These findings demonstrate that serum exosomal miR-17-3p is a promising biomarker for GC diagnosis and evaluation.