AUTHOR=Jiang Yale , Jiang Dingyuan , Costabel Ulrich , Dai Huaping , Wang Chen 

TITLE=A transcriptomics-based meta-analysis identifies a cross-tissue signature for sarcoidosis

JOURNAL=Frontiers in Medicine

VOLUME=9

YEAR=2022

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.960266

DOI=10.3389/fmed.2022.960266

ISSN=2296-858X

ABSTRACT=<p>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 <italic>LINC01278</italic> (<italic>P</italic> = 3.11 × 10<sup>–13</sup>), <italic>GBP5</italic> (<italic>P</italic> = 5.56 × 10<sup>–07</sup>), and <italic>PSMB9</italic> (<italic>P</italic> = 1.11 × 10<sup>–06</sup>). 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.</p>