A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren’s Syndrome

James, Katherine, Al-Ali, Shereen, Tarn, Jessica, Cockell, Simon, Gillespie, Colin, Hindmarsh, Victoria, Locke, James, Mitchell, Sheryl, Lendrem, Dennis, Bowman, Simon, Price, Elizabeth, Pease, Colin, Emery, Paul, Lanyon, Peter, Hunter, John, Gupta, Monica, Bombardieri, Michele, Sutcliffe, Nurhan, Pitzalis, Costantino, McLaren, John, Cooper, Annie, Regan, Marian, Giles, Ian, Isenberg, David, Saravanan, Vadivelu, Coady, David, Dasgupta, Bhaskar, McHugh, Neil, Young-Min, Steven, Moots, Robert, Gendi, Nagui, Akil, Mohammed, Griffiths, Bridget, Wipat, Anil, Newton, Julia, Jones, David E., Isaacs, John, Hallinan, Jennifer, Ng, Wan-Fai and Hackett, Kate (2015) A Transcriptional Signature of Fatigue Derived from Patients with Primary Sjögren’s Syndrome. PLoS ONE, 10 (12). e0143970. ISSN 1932-6203

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Abstract

Background: Fatigue is a debilitating condition with a significant impact on patients' quality of life. Fatigue is frequently reported by patients suffering from primary Sjögren's Syndrome (pSS), a chronic autoimmune condition characterised by dryness of the eyes and the mouth. However, although fatigue is common in pSS, it does not manifest in all sufferers, providing an excellent model with which to explore the potential underpinning biological mechanisms. Methods: Whole blood samples from 133 fully-phenotyped pSS patients stratified for the presence of fatigue, collected by the UK primary Sjögren's Syndrome Registry, were used for whole genome microarray. The resulting data were analysed both on a gene by gene basis and using pre-defined groups of genes. Finally, gene set enrichment analysis (GSEA) was used as a feature selection technique for input into a support vector machine (SVM) classifier. Classification was assessed using area under curve (AUC) of receiver operator characteristic and standard error of Wilcoxon statistic, SE(W). Results: Although no genes were individually found to be associated with fatigue, 19 metabolic pathways were enriched in the high fatigue patient group using GSEA. Analysis revealed that these enrichments arose from the presence of a subset of 55 genes. A radial kernel SVM classifier with this subset of genes as input displayed significantly improved performance over classifiers using all pathway genes as input. The classifiers had AUCs of 0.866 (SE(W) 0.002) and 0.525 (SE(W) 0.006), respectively. Conclusions: Systematic analysis of gene expression data from pSS patients discordant for fatigue identified 55 genes which are predictive of fatigue level using SVM classification. This list represents the first step in understanding the underlying pathophysiological mechanisms of fatigue in patients with pSS.

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