Allergy Asthma Immunol Res.  2011 Oct;3(4):265-272. 10.4168/aair.2011.3.4.265.

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

• Shin SW
• Oh TJ
• Park SM
• Park JS
• Jang AS
• Park SW
• Uh ST
• An S
• Park CS

• KMID: 2167078
• DOI: http://doi.org/10.4168/aair.2011.3.4.265

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< or =0.001 and > or =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< or =0.001 and > or =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< or =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.

Keyword

Asthma; gene expression profiling; PBMC; ROC

MeSH Terms

Area Under Curve
Asthma
Biomarkers
Gene Expression
Gene Expression Profiling
Genetic Markers
Logistic Models
Microarray Analysis
RNA
ROC Curve
Genetic Markers
RNA

Figure

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  Fig. 1 Gene expression profiling strategy and general workflow.

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  Fig. 2 (A) Volcano graph. The x-axis represents the logarithm of the fold-change value, while the y-axis represents the negative logarithm of the P value. A and B denote areas satisfying the following criteria: ≥2-fold change and P≤0.001. (B) Hierarchical clustering graph and heat map.

• 

  Fig. 3 Volcano graph. The x-axis represents the logarithm of the fold-change value, while the y-axis represents the negative logarithm of the P value. A and B denote areas satisfying the following criteria: ≥5-fold change and P≤0.001.

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  Fig. 4 (A) Distribution of the average AUCs for the top five in group n. (B) Distribution of the average log of the P values for the top five in group n. The dashed line indicates the cut-off value (P<0.05).

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  Fig. 5 (A) Values of the AUCs for each of the eight genes, two-gene combinations, and three-gene combinations. (B) ROC curve of the best model, consisting of MEPE, MLSTD1, and TRIM37 (P value: 0.000001; asymptotic 95% confidence interval lower bound: 0.977, upper bound: 1; AUC: 0.9928).

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