Circulating MicroRNA Biomarkers for Chronic Pain and Acupuncture Response: An Exploratory High-Dimensional Small-Sample Study.

Background: Chronic pain involves complex neuroplastic changes and neuroinflammatory processes that may be reflected in circulating microRNA profiles. Acupuncture analgesia operates through multiple neurobiological mechanisms including modulation of neurotransmitter release and synaptic plasticity. High-dimensional small-sample (HDSS) datasets pose significant challenges for biomarker discovery in clinical omics studies, where the number of features vastly exceeds sample size. Existing approaches often lack robust validation frameworks, leading to overfitted models with poor generalizability. We developed and validated a comprehensive framework for HDSS biomarker discovery, demonstrated on circulating microRNA data from a chronic neck pain acupuncture trial. Our objective was to establish a simulation-validated pipeline that addresses preprocessing instability, model selection uncertainty, and statistical significance assessment in extreme HDSS scenarios. Methods: In this exploratory high-dimensional small-sample study, we analyzed plasma microRNA profiles from 6 participants in a chronic neck pain acupuncture trial, generating 9 paired measurements of microRNA fold-changes and pain reduction (ΔVAS) across baseline, 4-week, and 8-week time points. Our framework integrated: (1) robust preprocessing with Winsorization, arcsinh transformation, and median/MAD scaling; (2) nested leave-one-out cross-validation with LASSO/Elastic Net regularization; (3) permutation testing (10,000 iterations); and (4) comprehensive HDSS factorial simulation (19,440 runs across varying sample sizes, sparsity, signal-to-noise ratios, and preprocessing strategies). Results: The observed model achieved MAE=10.58, RMSE=13.70, and Spearman Conclusion: We present a rigorously validated framework for HDSS biomarker discovery that demonstrates modest but statistically significant predictive signal in extremely challenging conditions. This methodology is broadly applicable to other HDSS omics problems where traditional validation approaches fail.