Assessment of a 44 Gene Classifier for the Evaluation of Chronic Fatigue Syndrome from Peripheral Blood Mononuclear Cell Gene Expression

Abstract

Chronic fatigue syndrome (CFS) is a clinically defined illness estimated to affect millions of people worldwide causing significant morbidity and an annual cost of billions of dollars. Currently there are no laboratory-based diagnostic methods for CFS. However, differences in gene expression profiles between CFS patients and healthy persons have been reported in the literature. Using mRNA relative quantities for 44 previously identified reporter genes taken from a large dataset comprising both CFS patients and healthy volunteers, we derived a gene profile scoring metric to accurately classify CFS and healthy samples. This metric out-performed any of the reporter genes used individually as a classifier of CFS.To determine whether the reporter genes were robust across populations, we applied this metric to classify a separate blind dataset of mRNA relative quantities from a new population of CFS patients and healthy persons with limited success. Although the metric was able to successfully classify roughly two-thirds of both CFS and healthy samples correctly, the level of misclassification was high. We conclude many of the previously identified reporter genes are study-specific and thus cannot be used as a broad CFS diagnostic.

Biomarkers for chronic fatigue

“…However, when assessed on a new, blinded 128-sample test set only 58% of samples were predicted correctly. Using a variety of methods, it was demonstrated that the 44-gene classifier set did not robustly identify patients with CFS/ME disease (Frampton, 2011). Byrnes et al (2009) were unable to identify a candidate biomarker for CFS/ME when comparing identical twin sets discordant for illness.…”

Chronic Immune Response Hypothesis for Chronic Fatigue Syndrome: Experimental Results and Literature Overview

“…The results published differ significantly between research groups [Kaushik N et al, 2005;Fang H et al, 2006;Carmel L et al, 2006;Frampton D et al, 2011]. For example, upregulated expression of ABCD4, PRKCL1, MRPL23, CD2BP2, GSN, NTE, POLR2G, PEX16, EIF2B4, EIF4G1, ANAPC11, PDCD2, KHSRP, BRMS1, and GABARAPL1 was found by Kaushik N et al [2005].…”

“…A completely different gene set (PTPRR, DEFB1, FLJ, HSFY1, EST, HPRT1, GUCA1B, CACNG2, ESR2, MOG, DFFA, ACBD6 and 12 others) was identified by www.intechopen.com Fang H et al [2006]. The most recent publication attempted and failed to predict the diagnosis basing on genomic data [Frampton D et al, 2011]. Thus, genes involved in CFS still to be found more accurately.…”

Methylation Profile of CD4+ T Cells in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis

“…CFS/ME involves disruption to immunological processes including reduced cytotoxic activity and elevated levels of regulatory T cells [4][5][6]. Furthermore, CFS/ME patients may exhibit differential expression in genes that regulate various physiological processes known to be abnormal in CFS/ME [4,[7][8][9][10][11][12]. To date a succinct pathomechanism for CFS/ME and concrete diagnostic biomarkers have not been identified.…”