原发性干燥综合征患者疲劳的转录特征

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

作者信息

James Katherine, Al-Ali Shereen, Tarn Jessica, Cockell Simon J, Gillespie Colin S, Hindmarsh Victoria, Locke James, Mitchell Sheryl, Lendrem Dennis, Bowman Simon, Price Elizabeth, Pease Colin T, Emery Paul, Lanyon Peter, Hunter John A, 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

机构信息

Interdisciplinary Computing and Complex BioSystems Research Group, Newcastle University, Newcastle upon Tyne, United Kingdom.

Musculoskeletal Research Group, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, United Kingdom.

出版信息

PLoS One. 2015 Dec 22;10(12):e0143970. doi: 10.1371/journal.pone.0143970. eCollection 2015.

DOI:10.1371/journal.pone.0143970

PMID:26694930

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4687914/

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.

摘要

背景

疲劳是一种使人衰弱的状况，对患者的生活质量有重大影响。原发性干燥综合征（pSS）患者经常报告有疲劳症状，pSS是一种以眼干和口干为特征的慢性自身免疫性疾病。然而，尽管疲劳在pSS中很常见，但并非所有患者都会出现，这为探索潜在的生物学机制提供了一个绝佳的模型。

方法

英国原发性干燥综合征登记处收集了133例根据疲劳情况分层的完全表型化pSS患者的全血样本，用于全基因组微阵列分析。所得数据在逐个基因的基础上进行分析，并使用预先定义的基因组进行分析。最后，基因集富集分析（GSEA）被用作一种特征选择技术，输入到支持向量机（SVM）分类器中。使用受试者工作特征曲线下面积（AUC）和威尔科克森统计量的标准误差SE(W)评估分类情况。

结果

虽然未发现单个基因与疲劳相关，但使用GSEA在高疲劳患者组中富集了19条代谢途径。分析表明，这些富集来自55个基因的一个子集的存在。以这个基因子集作为输入的径向核SVM分类器，与使用所有途径基因作为输入的分类器相比，表现出显著改善。这些分类器的AUC分别为0.866（SE(W) 0.002）和0.525（SE(W) 0.006）。