Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning
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.
Authors
- Tomáš Kupec (ORCID: https://orcid.org/0000-0003-0629-8576)
- Laila Najjari (ORCID: https://orcid.org/0000-0002-2102-623X)
- Philipp Meyer-Wilmes (ORCID: https://orcid.org/0000-0002-9853-0004)
- Julia Wittenborn (ORCID: https://orcid.org/0000-0002-1841-4207)
- Rebecca Senger
- Elmar Stickeler
- Jochen Maurer
- Chao-Chung Kuo
Institutions
- RWTH Aachen University (DE)
Publication Details
- Journal
- Reproductive Biology and Endocrinology
- Published
- 2026-01-07
- DOI
- https://doi.org/10.1186/s12958-025-01517-6
- Primary Topic
- Endometriosis Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00