Identification of predictive cytokine biomarkers of scleroderma via local causal neighborhood methods

Background: : Scleroderma is an autoimmune disease with established relationship between immune cytokines and prognosis. Therefore, it is necessary to identify and investigate the causal relationship between cytokines and scleroderma diagnosis and to use this information to identify predictive biomarkers of scleroderma status. Methods: : Forty scleroderma positive patients and twenty-four healthy controls have been included in this study. Twenty-nine cytokines implicated in scleroderma have been measured in the bronchoalveolar lavage fluid of these patients, and eight have been found to be univariately associated with scleroderma status. Results: : Using local causal neighborhood learning methods, we have found two cytokines, Osteoprotegerin (OPG), also known as osteoclast genesis inhibitory factor, or tumor necrosis factor receptor superfamily member 11B and macrophage inflammatory protein-1 delta, to be causally related to the scleroderma status. Logistic regression predictor based on these cytokines achieves 73% AUC for the task of identifying the scleroderma status. Conclusions: : Our results demonstrate the feasibility of developing predictive local causal neighborhood biomarkers of scleroderma status based on bronchoalveolar lavage fluid.