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

Area of Science:

• Biomarker discovery
• Omics data analysis
• Neuroscience

Background:

• Chronic pain involves neuroplasticity and neuroinflammation, potentially reflected in microRNA profiles.
• Acupuncture analgesia involves neurotransmitter and synaptic plasticity modulation.
• High-dimensional small-sample (HDSS) data presents biomarker discovery challenges due to feature-to-sample size ratios.

Purpose of the Study:

• To develop and validate a robust framework for HDSS biomarker discovery.
• To establish a simulation-validated pipeline addressing preprocessing instability and model selection in extreme HDSS scenarios.
• To identify microRNA biomarkers for chronic neck pain and acupuncture response.

Main Methods:

• Analysis of plasma microRNA profiles from 6 chronic neck pain patients undergoing acupuncture.
• Integration of robust preprocessing (Winsorization, arcsinh, scaling) and nested cross-validation (LASSO/Elastic Net).
• Application of permutation testing (10,000 iterations) and extensive HDSS factorial simulations (19,440 runs).

Main Results:

• A statistically significant predictive model (permutation p<0.001) with modest performance (Spearman ρ=-0.217).
• Simulation benchmarking placed the correlation coefficient at the 91st percentile.
• Identification of a three-microRNA panel (miR-3681-3p, miR-4743-5p, miR-6822-5p) associated with pain and acupuncture response.
• Pathway analysis linked microRNAs to PI3K-Akt/mTOR, TGF-β signaling, synaptic plasticity, and neuroinflammation.

Conclusions:

• A rigorously validated framework for HDSS biomarker discovery was developed.
• The framework demonstrates statistically significant predictive capability in challenging HDSS data.
• The methodology is broadly applicable to other HDSS omics studies where traditional validation fails.