Diagnosis of Endometriosis: Dual-Amplification Strategy Driven by Copper Nanoclusters

Endometriosis is a prevalent gynecologic disorder associated with infertility and increased cancer risk, necessitating the development of sensitive and reliable diagnostic methods. Circulating microRNAs (miRNAs) have emerged as promising noninvasive biomarkers for early disease detection. However, low abundance and sequence similarity among miRNA family members hinder accurate detection. Herein, we first conducted differential expression analysis of publicly available miRNA-sequencing data sets to identify potential diagnostic biomarkers for endometriosis, from which miR-199a-5p was selected as a representative target. Building on this selection, we subsequently developed a highly sensitive and specific fluorescent biosensing platform for miR-199a-5p detection by integrating poly(thymine) (polyT) DNA-templated copper nanoclusters (CuNCs) with a dual isothermal amplification strategy. The biosensing system utilizes a 3'-phosphorylated, biotinylated hairpin DNA probe immobilized on streptavidin-coated magnetic beads. Upon hybridization with the target miR-199a-5p, duplex-specific nuclease (DSN) mediates selective cleavage, enabling target recycling and simultaneously generating a 3'-hydroxyl terminus. This newly exposed terminus subsequently serves as a primer for terminal deoxynucleotidyl transferase (TdT)-catalyzed polyT elongation. The resulting polyT sequence functions as an effective scaffold for the in situ formation of copper nanoclusters (CuNCs), thus producing a label-free fluorescence signal within 2 h. In this design, magnetic beads not only facilitate efficient separation from serum matrices but also enhance reaction efficiency through surface-initiated enzymatic polymerization. As a result, the sensing platform exhibits excellent specificity, including reliable discrimination of single-base mismatches, and maintains robust performance in complex biological samples. This integrated platform, combining bioinformatic prescreening with a CuNCs-based sensing strategy, offers rapid, cost-effective, and label-free detection of miRNA, showing promise for early diagnosis and clinical monitoring of endometriosis.