Peripheral Blood Gene Expression in Fibromyalgia Patients Reveals Potential Biological Markers and Physiological Pathways

Fibromyalgia (FM) is a common pain disorder characterized by dysregulation in the processing of pain. Although FM has similarities with other rheumatologic pain disorders, the search for objective markers has not been successful. In the current study we analyzed gene expression in the whole blood of 70 fibromyalgia patients and 70 healthy matched controls. Global molecular profiling revealed an upregulation of several inflammatory molecules in FM patients and downregulation of specific pathways related to hypersensitivity and allergy. There was a differential expression of genes in known pathways for pain processing, such as glutamine/glutamate signaling and axonal development. We also identified a panel of candidate gene expression-based classifiers that could establish an objective blood-based molecular diagnostic to objectively identify FM patients and guide design and testing of new therapies. Ten classifier probesets (CPA3, C11orf83, LOC100131943, RGS17, PARD3B, ANKRD20A9P, TTLL7, C8orf12, KAT2B and RIOK3) provided a diagnostic sensitivity of 95% and a specificity of 96%. Molecular scores developed from these classifiers were able to clearly distinguish FM patients from healthy controls. An understanding of molecular dysregulation in fibromyalgia is in its infancy; however the results described herein indicate blood global gene expression profiling provides many testable hypotheses that deserve further exploration. Blood samples were collected in PAXgene tubes and collected samples were stored at -80oC. The RNA was isolated using the PAXgene RNA isolation kit according to standard protocols. Total RNA was quantified on a Nanodrop spectrophotometer and visualized for quality on an Agilent Bioanalyzer. Samples with an average RIN (RNA Integrity Number) >8, indicating good quality RNA, were processed. 200ng of total RNA was amplified and then hybridized to Affymetrix® Human Gene 1.1 ST Peg arrays using standard manufacturer’s protocols on a Gene Titan MC instrument. Data was analyzed using Partek Genomics Suite (version 6.6) using RMA normalization. All genes with Log2 signal intensity less than 4.8 were excluded from analysis based on low expression. Differential expression analysis was carried out using a one way ANOVA by using Method of Moments and Fisher's Least Significant Difference (LSD) tests with a threshold p-value <0.005 for the biological and molecular function analyses, and a more conservative threshold p-value <0.001 (FDR q-value range 0.002% to 13%) for candidate diagnostic signatures.