Genetic testing may allow physicians to better distinguish scleroderma from its overlap syndromes and help to predict the disease’s likely course, a study reports.
The study, “Whole blood gene expression profiling distinguishes systemic sclerosis-overlap syndromes from other subsets,” was published in the Journal of the European Academy of Dermatology and Venereology.
Scleroderma, characterized by the buildup of collagen that leads to thickening and scarring of the skin and internal organs, has two major disease subsets: limited and the more severe diffuse scleroderma.
A considerable number of scleroderma patients also exhibit features of other connective tissue diseases, collectively known as scleroderma-overlap syndromes. Examples include mixed connective tissue disease and polymyositis-scleroderma syndrome.
Such clinical diversity supports the need for an approach that distinguishes scleroderma from its overlap syndromes.
To identify genes associated with such syndromes, investigators at the University of Cologne, in Germany, and colleagues did a genetic analysis of blood samples from 150 scleroderma patients and 40 healthy individuals serving as controls.
Within the patient group, 86 people had limited or diffuse disease, and 64 had scleroderma-overlap syndromes.
A total of 41 genes were differently expressed — meaning the production of RNA from DNA, a first step in protein generation — between patients and controls.
Most of these genes were associated with antimicrobial and inflammatory responses, with cellular processes (including cell-to-cell communication), and with cell death and survival. Seven of the 41 genes are linked in interferon-mediated signaling, or signaling by proteins secreted by immune cells to fight off viruses, bacteria, and other foreign substances.
Almost 60% of these 41 genes have been previously linked to connective tissue diseases and other inflammatory skin disorders, including psoriasis.
Importantly, the researchers identified 31 genes that differentiated overlap syndromes from scleroderma. Besides immune response and cellular functions, such genes were related to inducible nitric oxide synthase and toll-like receptor signaling, two pathways linked to inflammation.
Machine learning algorithms were able to distinguish scleroderma from overlap syndromes with a 72% accuracy. Using this computer approach, 89 genes were identified, 57 of which were related to interferon signaling, indicating this immune protein’s crucial role in scleroderma.
“[O]ur approach allows to define a set of predictive genes allowing the classification of patients between SSc [scleroderma] and SSc-overlap patients based on a blood sample. This may enable us to identify patients at an early stage and to predict better the course of the disease in individual patients,” the researchers wrote.
This findings may also guide treatment choices, they added.