Asthma-Predictive Genetic Markers in Gene Expression Profiling of Peripheral Blood Mononuclear Cells

Abstract

Purpose: We sought to identify asthma-related genes and to examine the potential of these genes to predict asthma, based on expression levels. Methods: The subjects were 42 asthmatics and 10 normal healthy controls. PBMC RNA was subjected to microarray analysis using a 35K array; t-tests were used to identify genes that were expressed differentially between the two groups. A multiple logistic regression analysis was applied to the differentially expressed genes, and area under the curve (AUC) values from receiver operating characteristic (ROC) curves were obtained. Results: In total, 170 genes were selected using the following criteria: P <= 0.001 and >= 2-fold change. Among these genes, 57 were up-regulated and 113 were down-regulated in asthmatics versus normal controls. A multiple logistic regression analysis was done using more stringent criteria (P <= 0.001 and >= 5-fold change), and eight genes were selected as candidate asthma biomarkers. Using these genes, 255 models (2(8)-1) were generated. Among them, only 85 showed P <= 0.05 by multiple logistic regression analysis. Based on the AUCs from ROC curves for the 85 models, we found that the best model consisted of the genes MEPE, MLSTD1, and TRIM37 The model showed 0.9928 of the AUC with 98% sensitivity and 80% specificity. Conclusions: MEPE, MLSTD1, and TRIM37 may be useful biomarkers for asthma.