ArticlePediatric surgery international2026
Can urinary exosomal lncRNAs HOTAIR and MALAT1 predict surgical need and outcomes in unilateral antenatal hydronephrosis?
Article in Pediatric surgery international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeAntenatal hydronephrosis is the most common prenatal urinary tract abnormality, and some cases, mainly due to ureteropelvic junction obstruction, require surgery. Noninvasive biomarkers are needed to improve diagnosis and guide management. To evaluate urinary exosomal long non-coding RNAs, specifically MALAT1 and HOTAIR, as biomarkers for predicting surgical necessity in children with unilateral antenatal hydronephrosis, and to develop an AI-based predictive model.
methodsThis retrospective case-control study included 88 children (38 requiring pyeloplasty, 26 non-obstructive dilatation, 24 controls). Urinary exosomes were isolated, and lncRNA expression was quantified by RT-qPCR. Clinical variables included anteroposterior pelvic diameter and split renal function. Diagnostic performance was assessed by ROC analysis and logistic regression. A SVM model was developed integrating biomarker and clinical data, with 5-fold cross-validation and an interactive Shiny web application for clinical translation.
resultsPreoperative MALAT1 and HOTAIR levels were significantly elevated compared to non-obstructive dilatation and controls and decreased after surgery (p < 0.001). MALAT1 remained an independent predictor of surgical intervention (OR = 1.272, p < 0.001) in multivariate analysis, alongside APD and SRF. MALAT1 showed an AUC of 0.745 (cut-off: 9.06), and HOTAIR an AUC of 0.685 (cut-off: 11.76). The SVM model achieved 91.9% accuracy, 89.5% sensitivity, 95.8% specificity, and an AUC of 0.951.
conclusionsUrinary exosomal MALAT1 and HOTAIR are promising noninvasive biomarker for predicting surgical need in pediatric ureteropelvic junction obstruction. Integration with clinical parameters in an SVM-based model enhances diagnostic precision, and the development of a freely accessible web application supports real-time individualized risk prediction.
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