Comprehensive evaluation of reference miRNA for extracellular miRNA quantification in CKD.

MicroRNAs (miRNAs) are promising biomarkers and therapeutic targets; however, reliable extracellular miRNA quantification by RT-qPCR depends on appropriate normalization, and no universal endogenous reference has been established. This study aimed to identify optimal endogenous control for extracellular miRNA analyses. Candidate reference miRNAs were first identified through next-generation sequencing of plasma (n = 289) and urine (n = 109) using samples from the Chronic Renal Insufficiency Cohort (CRIC). Candidates were prioritized using NormFinder algorithm combined with complementary stability metrics (including biological coefficient of variation, expression difference, mean expression, proportion of detectable samples) and evidence from literature. The top candidate miRNAs identified in discovery phase were subsequently validated by RT-qPCR in 250 plasma and 205 urine samples randomly selected from the remaining CRIC participants. Both descriptive statistical analysis and NormFinder identified hsa-miR-126-3p, hsa-miR-30e-5p, and hsa-miR-24-3p as most stably expressed miRNAs in plasma, whereas members of let-7 family, hsa-miR-423-5p, and hsa-miR-16-5p showed the highest stability in urine. RT-qPCR validation showed that hsa-miR-24-3p was detectable in 97% of the validation cohort and showed significant superior expression stability (p < 0.0001), identifying it as the most robust endogenous reference miRNA for plasma normalization. The addition of hsa-miR-30e-5p moderately improves expression stability if dual-reference normalization is considered. In urine, candidate reference miRNAs showed low detectability; although hsa-miR-16-5p was more stable than hsa-miR-423-5p, neither is recommended for routine normalization without substantial technical optimization. Overall, hsa-miR-24-3p represents a reliable and robust endogenous control for plasma miRNA normalization within this CKD cohort, whereas urinary miRNAs require rigorous, context-specific validation to ensure accurate and reproducible quantification.