Identification of marker genes for differential diagnosis of chronic fatigue syndrome

Abstract

INTRODUCTION Chronic fatigue syndrome (CFS) is a clinically defined condition character- ized by long-lasting disabling fatigue, resulting in severe impairment in daily functioning and associated symptoms such as memory and concentration diffi- culties, muscle aches, sleep distur- bances, and headache (1). CFS poses a diagnostic challenge because of the un- known mechanism underlying this syn- drome and the difficulty in making an objective assessment of pathological fatigue. Several groups have been searching for reliable biomarkers for diagnosing CFS and have shown altered gene ex- pression profiles in peripheral blood leukocyte populations, which can distin- guish the majority of CFS cases (2–5). Recently, a data-intensive analysis has been conducted successfully by the Wichita CFS project (6). In the 2-day in- hospital study, gene expression levels of 20,000 genes in isolated peripheral blood mononuclear cells were analyzed to identify biologically and clinically mean- ingful signatures of gene expression rele- vant to classification, diagnosis, and treatment of CFS (6). Peripheral leukocytes express recep- tors for stress mediators, such as hor- mones, neurotransmitters, growth fac- tors, and cytokines. Also, leukocytes produce a number of mediators, includ- ing cytokines, some of which can activate the hypothalamus-pituitary-adrenal (HPA) axis (7). Leukocytes may be poten- tial targets for mediators eliciting patho- logical responses associated with stress- related disorders. CFS has been hypothesized to involve an abnormal re- sponse to various stressful experiences such as infection, overwork, or psycho- logical stresses resulting in immunologic dysfunction, dysregulation of the HPA axis, and dysautonomia (8–11). At the same time, psychological and sociocultu- ral factors, when present in patients with CFS, also influence the severity of the ill- ness and treatment outcome (8–10). In M O L M E D 1 4 ( 9 - 1 0 ) 5 9 9 - 6 0 7 , S E P T E M B E R - O C T O B E

Key Biomarkers

Symptom Clusters