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

ABSTRACT:

Background

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.

Methods

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 fo

SUBMITTER: Kupec T 

PROVIDER: S-EPMC12781734 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature