Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm.

OBJECTIVES

Fibromyalgia (FM) is a common pain disorder characterized by nociceptive dysregulation. The basic biology of FM is poorly understood. Herein we have used agnostic gene expression as a potential probe for informing its underlying biology and the development of a proof-of-concept diagnostic gene expression signature.

METHODS

We analyzed RNA expression in 70 FM patients and 70 healthy controls. The isolated RNA was amplified and hybridized to Affymetrix® Human Gene 1.1 ST Peg arrays. The data was analyzed using Partek Genomics Suite version 6.6.

RESULTS

Fibromyalgia patients exhibited a differential expression of 421 genes (p<0.001), several relevant to pathways for pain processing, such as glutamine/glutamate signaling and axonal development. There was also an upregulation of several inflammatory pathways and downregulation of pathways related to hypersensitivity and allergy. Using rigorous diagnostic modeling strategies, we show "locked" gene signatures discovered on Training and Test cohorts, that have a mean Area Under the Curve (AUC) of 0.81 on randomized, independent external data cohorts. Lastly, we identified a subset of 10 probesets that provided a diagnostic sensitivity for FM of 95% and a specificity of 96%. We also show that the signatures for FM were very specific to FM rather than common FM comorbidities.

CONCLUSIONS

These findings provide new insights relevant to the pathogenesis of FM, and provide several testable hypotheses that warrant further exploration and also establish the foundation for a first blood-based molecular signature in FM that needs to be validated in larger cohorts of patients.