MicroRNAs signatures in small extracellular vesicles for psychological resilience in young adults using machine learning.

Aims: Psychological resilience refers to an individual's capacity to adapt to adverse events. MicroRNAs (miRNAs) play a crucial role in regulating post-transcriptional processes, while small extracellular vesicles (sEVs) act as transport vehicles. This study aimed to employ genome-wide profiling to identify and validate differences in the expression of resilience-associated sEV-miRNAs between low resilience (LR) and high resilience (HR) in young adults.

Methods: Eighty participants were divided into LR or HR based on the Connor - Davidson Resilience Scale (CD-RISC). The expression levels of the target sEV-miRNAs in LR and HR were compared and analyzed.

Results: Expression analyses demonstrated significant differences in let-7b, miR-151b, miR-335, and miR-193a between LR and HR (p < 0.01), with let-7b showing the highest discriminative ability. The AUC values for each sEV-miRNA ranged from 0.74 to 0.94, based on logistic regression and three machine learning models: random forest, support vector machine, and eXtreme gradient boosting. Based on leave-one-out cross-validation in different models, the combined four sEV-miRNAs demonstrated strong performance for detecting LR (AUC = 0.87-0.90). Sex-specific differences were also observed, with female participants showing more pronounced resilience signatures in targeted sEV-miRNAs.

Conclusions: These findings suggest that sEV-miRNAs hold potential as biomarkers for psychological resilience in young adults.