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Sarcoidosis is a granulomatous disease of unknown etiology, immunologically characterized by a Th1 immune response. Transcriptome-wide expression studies in various types of sarcoid tissues contributed to better understanding of disease mechanisms. We performed a systematic database search on Gene Expression Omnibus (GEO) and utilized transcriptomic data from blood and sarcoidosis-affected tissues in a meta-analysis to identify a cross-tissue, cross-platform signature. Datasets were further separated into training and testing sets for development of a diagnostic classifier for sarcoidosis. A total of 690 differentially expressed genes were identified in the analysis among various tissues. 29 of the genes were robustly associated with sarcoidosis in the meta-analysis both in blood and in lung-associated tissues. Top genes included LINC01278 (P = 3.11 × 10–13), GBP5 (P = 5.56 × 10–07), and PSMB9 (P = 1.11 × 10–06). Pathway enrichment analysis revealed activated IFN-γ, IL-1, and IL-18, autophagy, and viral infection response. IL-17 was observed to be enriched in peripheral blood specific signature genes. A 16-gene classifier achieved excellent performance in the independent validation data (AUC 0.711–0.964). This study provides a cross-tissue meta-analysis for expression profiles of sarcoidosis and identifies a diagnostic classifier that potentially can complement more invasive procedures.

Supplemental data for "IL-17A promotes lung fibrosis through impairing mitochondrial homeostasis in type II alveolar epithelial cells"

Additional file 1: Table S1. Clinical characteristic differences between patients with SAD and without SAD. Table S2. Univariate logistic regression analysis of the risk factors as categorical variables for SAD. Figure S1. Forest plot of univariate analysis of the risk factor as continuous variables for SAD. Figure S2. ROC curves of spirometry parameters in predicting SAD. A, FEV1; B, FVC; C, FEV1/FVC; D, FEV3/FVC; E, PEF.