An association between gene expression and disease severity in scleroderma patients has been found, Dartmouth Medical School researchers reported online in the journal PLoS One on July 16.The chronic autoimmune disease, which affects about four times as many women as men, causes skin hardening and internal organ dysfunction, as well as other related conditions. The most severe cases may be systemic and life-threatening. Unfortunately, the basic processes underlying the disease remain elusive.

Since relatively little is known about the disease, it has previously been categorized by clinical findings alone, such as by measuring the extent of skin thickening. While two main classifications exist for the disease, limited and diffuse, the latter of which is more severe, this new genetic data shows that the disease is much more diverse than can be observed simply by clinical observations.

The research team, headed by genetics professor Michael Whitfield, analyzed the gene activity of 28 patients and six control subjects using data from DNA microarrays. Two skin biopsies were taken from each patient, from the forearm and another from the back. Interestingly, although samples taken from the back appear clinically unaffected, they showed the same harmful genes as the affected skin, demonstrating the systemic nature of the disease.

Four distinct groups characterized by specific gene expression profiles were found, and a 177-gene signature was discovered shown to correlate with the severity of this skin disease. Different gene expression was found in patients with limited and diffuse scleroderma, both of which were distinct from the control group. Considered to be the most extensive gene analysis study of scleroderma to date, this research is the first to show that scleroderma can be classified by patterns of gene expression alone.

Ultimately, the aim is to use these genetic signatures to predict the disease trajectory, such as by identifying aggressive cases, and then treat patients accordingly. The ability to now combine clinical phenotypic data and gene analysis opens the possibility for targeted treatment and more accurate diagnoses.