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J Pain Res. 2018 Nov 21;11:2981-2990. doi: 10.2147/JPR.S169499. eCollection 2018.

A predictive algorithm to identify genes that discriminate individuals with fibromyalgia syndrome diagnosis from healthy controls.

Author information

1
Acute and Chronic Care, Johns Hopkins School of Nursing, Baltimore, MD, USA, nada.lukkahatai@jhu.edu.
2
Symptoms Biology Unit, National Institute of Nursing Research (NINR), Bethesda, MD, USA.
3
Group of Inverse Problems, Optimization and Machine Learning, Applied Mathematics, University of Oviedo, Oviedo, Spain.

Abstract

Objectives:

Fibromyalgia syndrome (FMS) is a chronic and often debilitating condition that is characterized by persistent fatigue, pain, bowel abnormalities, and sleep disturbances. Currently, there are no definitive prognostic or diagnostic biomarkers for FMS. This study attempted to utilize a novel predictive algorithm to identify a group of genes whose differential expression discriminated individuals with FMS diagnosis from healthy controls.

Methods:

Secondary analysis of gene expression data from 28 women with FMS and 19 age-and race-matched healthy women. Expression of discriminatory genes were identified using fold-change differential and Fisher's ratio (FR). Discriminatory accuracy of the differential expression of these genes was determined using leave-one-out-cross-validation. Functional networks of the discriminating genes were described from the Ingenuity's Knowledge Base.

Results:

The small-scale signature contained 57 genes whose expressions were highly discriminatory of the FMS diagnosis. The combination of these high discriminatory genes with FR higher than 1.45 provided a leave-one-out-cross-validation accuracy for the FMS diagnosis of 85.11%. The discriminatory genes were associated with 3 canonical pathways: hepatic stellate cell activation, oxidative phosphorylation, and airway pathology related to COPD.

Conclusion:

The discriminating genes, especially the 2 with the highest accuracy, are associated with mitochondrial function or oxidative phosphorylation and glutamate signaling. Further validation of the clinical utility of this finding is warranted.

KEYWORDS:

chronic pain; machine learning; medically unexplained symptoms; microarray

Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

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