Translation of miRNA blood-based discovery to molecular testing for clinical diagnosis of endometriosis

Yanqin Yu, Winghing Wong, Yao Hu, Yirong Shen, Xiaochun Xu, Lu Shen, Libo Zhu, Xinmei Zhang, Farideh Z. Bischoff
In: npj Women's Health · 2025 · vol. 3(1) · doi:10.1038/s44294-025-00116-5 · W4416848785
article OA: diamond CC0
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study developed a blood-based miRNA test using NGS and machine learning that achieved over 90% accuracy for endometriosis diagnosis, though qPCR validation highlighted challenges in clinical translation.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 ⓘ

This proof-of-concept study aimed to translate blood-based microRNA (miRNA) biomarkers into a clinically feasible molecular diagnostic test for endometriosis by integrating unbiased miRNA sequencing in serum with subsequent qPCR validation. Serum from 20 women with endometriosis and 20 controls collected during the secretory phase was used for miRNA-seq to identify differentially expressed miRNAs, and a machine-learning model using all NGS-derived differentially expressed biomarkers reported ≥90% accuracy, while qPCR validation confirmed some but not all findings, highlighting limitations in adapting NGS discoveries to routine PCR testing. A key caveat is the small sample size (and the translation difficulty reflected by incomplete overlap between NGS and qPCR results), though the authors also attempted to improve assay robustness by identifying and validating endometriosis-specific endogenous reference controls for normalization for qPCR and ddPCR. This paper is centrally about endometriosis — it develops and tests a serum miRNA-based panel and associated qPCR/normalization strategy for clinical diagnosis of endometriosis.

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Abstract

Endometriosis is a common yet often underdiagnosed condition, partly due to the lack of reliable diagnostics. This study examines the clinical feasibility of a blood-based, miRNA-driven test to diagnose endometriosis and address the challenges of translating next-generation sequencing (NGS) findings into clinical use. Serum from 20 patients and 20 controls underwent miRNA sequencing to identify diagnostic biomarkers. A machine learning model built on all NGS-based differentially expressed miRNA biomarkers achieved ≥90% accuracy. Validation by qPCR confirmed some but not all findings, underscoring the difficulty of adapting NGS discoveries for routine diagnostics. Nonetheless, serum miRNA biomarkers show strong promise for non-invasive endometriosis detection, with further optimization needed for clinical translation.

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endometriosis

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Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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last seen: 2026-06-10T17:14:06.276822+00:00
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