The peripheral blood transcriptome identifies the presence and extent of disease in idiopathic pulmonary fibrosis.

• Main Authors: Ivana V Yang, Leah G Luna, Jennifer Cotter, Janet Talbert, Sonia M Leach, Raven Kidd, Julia Turner, Nathan Kummer, Dolly Kervitsky, Kevin K Brown, Kathy Boon, Marvin I Schwarz, David A Schwartz, Mark P Steele
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2012-01-01
• Series: PLoS ONE
• Online Access: http://europepmc.org/articles/PMC3382229?pdf=render

Peripheral blood biomarkers are needed to identify and determine the extent of idiopathic pulmonary fibrosis (IPF). Current physiologic and radiographic prognostic indicators diagnose IPF too late in the course of disease. We hypothesize that peripheral blood biomarkers will identify disease in its early stages, and facilitate monitoring for disease progression.Gene expression profiles of peripheral blood RNA from 130 IPF patients were collected on Agilent microarrays. Significance analysis of microarrays (SAM) with a false discovery rate (FDR) of 1% was utilized to identify genes that were differentially-expressed in samples categorized based on percent predicted D(L)CO and FVC.At 1% FDR, 1428 genes were differentially-expressed in mild IPF (D(L)CO >65%) compared to controls and 2790 transcripts were differentially- expressed in severe IPF (D(L)CO >35%) compared to controls. When categorized by percent predicted D(L)CO, SAM demonstrated 13 differentially-expressed transcripts between mild and severe IPF (< 5% FDR). These include CAMP, CEACAM6, CTSG, DEFA3 and A4, OLFM4, HLTF, PACSIN1, GABBR1, IGHM, and 3 unknown genes. Principal component analysis (PCA) was performed to determine outliers based on severity of disease, and demonstrated 1 mild case to be clinically misclassified as a severe case of IPF. No differentially-expressed transcripts were identified between mild and severe IPF when categorized by percent predicted FVC.These results demonstrate that the peripheral blood transcriptome has the potential to distinguish normal individuals from patients with IPF, as well as extent of disease when samples were classified by percent predicted D(L)CO, but not FVC.