# Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning

## TL;DR

This study identifies urinary microRNAs, including a novel one, that could help diagnose endometriosis non-invasively using machine learning.

## Contribution

The discovery of hsa-miR-10400-5p as a novel, significantly downregulated biomarker for endometriosis.

## Key findings

- Urinary miRNA profiling combined with machine learning can diagnose endometriosis non-invasively.
- Random forest model achieved an AUC of 0.91 using a 20-miRNA signature.
- hsa-miR-10400-5p is a novel, significantly downregulated biomarker in endometriosis.

## Abstract

Endometriosis is a chronic gynecological disease associated with pain, infertility, and delayed diagnosis. Non-invasive biomarkers are urgently needed to facilitate earlier detection and reduce the reliance on diagnostic laparoscopy. MicroRNAs (miRNAs) are stable in body fluids and hold promise as diagnostic tools.

In this pilot study, urine samples from 34 patients with histologically confirmed endometriosis and 18 control patients (laparoscopically confirmed absence of disease) were analyzed using next-generation miRNA sequencing. Differential expression analysis was performed with DESeq2. Feature selection applied variance filtering, univariate analysis, mutual information, and recursive feature elimination (RFE). The top 20 miRNAs were used to train four classification models: logistic regression, decision tree, random forest, and support vector machine (SVM). Model performance was evaluated by accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).

Among all detected miRNAs, hsa-miR-10400-5p was significantly downregulated in endometriosis compared to controls (log₂ fold change − 2.70; adjusted p = 0.015). RFE identified 20 miRNAs, including hsa-mir-183, hsa-mir-500a, hsa-miR-3184-5p, hsa-miR-151b, and hsa-mir-196a-1, as the most informative for classification. The random forest model achieved the best performance (AUC = 0.91; accuracy and F1-score = 0.81), outperforming logistic regression (AUC = 0.83) and SVM (AUC = 0.81). Several identified miRNAs have been previously implicated in endometriosis pathogenesis, and we additionally identified hsa-miR-10400-5p as a significantly downregulated and previously unreported biomarker candidate, representing a novel finding with potential diagnostic relevance.

Urinary miRNA profiling, combined with machine learning, shows promise as a completely non-invasive approach for the diagnosis of endometriosis. The identified miRNA signature, particularly the novel hsa-miR-10400-5p, warrants validation in larger, independent cohorts to confirm its clinical utility and potential to reduce diagnostic delays.

The online version contains supplementary material available at 10.1186/s12958-025-01517-6.