Machine learning-based identification of small RNA signatures in aqueous humor as a step toward precision diagnosis...

Glaucoma is a progressive neurodegenerative disease of the optic nerve and one of the leading causes of irreversible blindness worldwide. Small RNAs (including miRNAs) play an important role in the pathogenesis of the disease. Despite extensive research, the exact mechanisms underlying glaucoma remain incompletely understood and current diagnostic tools primarily detect the disease at later stages, when substantial neuronal loss has already occurred. This study aimed to comprehensively analyze small RNA expression in the aqueous humor of patients with glaucomatous disease and identify specific RNA biomarkers associated with disease progression.

This prospective, single-center, cross-sectional study involved 51 individuals: 31 adult patients with primary open-angle glaucoma (POAG) and 20 patients with cataracts qualified for elective surgery. A gradient-boosting decision tree classifier was implemented to classify POAG from cataracts. The model performance was rigorously assessed during cross-validation using a suite of metrics, including receiver operating characteristic (ROC) curves, area under the ROC curve, accuracy, recall, sensitivity, F1 score, specificity, precision, and negative predictive value.

Expression of hsa-miR-21-5p was significantly higher in POAG than that in cataracts, indicating its potential as both a biomarker and therapeutic target. Moreover, miR-210-3p expression was associated with apoptosis occurrence in trabecular meshwork cells treated with TGF-β1.

Our study highlights the diagnostic potential of small-RNAs for distinguishing POAG from cataracts by integrating molecular profiling with advanced bioinformatics approaches. These findings provide new avenues for early diagnosis, personalized medicine, and improved clinical management.