Study Identifies Potential Gene Markers

Chronic fatigue syndrome (CFS) poses a diagnostic challenge because of the unknown mechanisms underlying the illness and the difficulty in making an objective assessment of pathological fatigue. Not surprisingly, researchers are searching for reliable biomarkers for diagnosing CFS.

In recent years research has uncovered altered gene expression profiles in CFS patients. This has led investigators to try to hone in on specific gene expression patterns that might be useful for diagnosis. In one of the latest efforts of this nature, a Japanese team led by Takuya Saiki, MD, used a custom DNA microarray to measure 1,467 genes in the blood of CFS patients compared to healthy control subjects, as well as CFS patients compared to people with lasting fatigue not caused by CFS. Their results were published in the journal Molecular Medicine (July 2008 epub ahead of print).

The investigators first compared gene expression profiles in peripheral blood between 11 drug-free patients with CFS and an equal number of age- and sex-matched healthy subjects. Using a custom microarray with DNA probes for 1,467 stress-responsive genes, investigators identified 12 genes whose mRNA (messenger RNA containing a chemical "blueprint") levels were significantly changed in CFS patients. Of those 12 genes, quantitative real-time PCR—a technique used to validate results of microarray analysis—confirmed the changes in 9 genes. The identified genes are involved in T cell or natural killer cell activity (GZMA), energy regulation (ATP5J2, COX5B, and DBI), protein processing through proteasome subunits (PSMA3 and PSMA4) and putative protein kinase c inhibitor (HINT), GTPase (ARHC), and cytokine-related transcription (STAT5A).

When the same analysis was performed on 3 additional CFS patients and 20 other patients whose chief complaint is long-lasting fatigue related to other disorders, investigators found that the relative mRNA expressions of the 9 genes were again able to identify distinctions, correctly classifying 79 percent (11/14) of the CFS patients and 85 percent of the non-CFS patients. This suggests that this gene cluster may also be effective for differential diagnosis.

To further validate the study’s results, real-time PCR measurements of the levels of the 9 genes were done in another group of 18 CFS and 12 non-CFS patients. This time the expression pattern correctly classified 94 percent of the CFS patients and 92 percent of the non-CFS patients.

Though more testing is required, the results of this study hold promise that this defined gene cluster may ultimately be useful for detecting pathological responses in CFS patients and for differential diagnosis of the illness.

Saiki T, Kawai T, Morita K, Ohta M, Saito T, Rokutan K, Ban N. Identification of Marker Genes for Differential Diagnosis of Chronic Fatigue Syndrome. Molecular Medicine. 2008 July 3.

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