Familial and sporadic idiopathic pulmonary fibrosis: making the diagnosis from peripheral blood

BMC Genomics

Mark P Steele⁴  David A Schwartz¹  Paul W Noble³  Allison Ashley⁵  Hamish Patel⁷  Marvin I Schwarz¹  Kevin K Brown²  Ivana V Yang¹  William T Barry⁶  Eric B Meltzer⁵ 

[1] Department of Medicine, Anshutz Medical Campus, University of Colorado, Aurora, CO, USA;National Jewish Health, Denver, CO, USA;Department of Medicine, Cedars-Sinai, Los Angeles, CA, USA;Division of Allergy, Pulmonary, and Critical Care, Vanderbilt University Medical Center, 1313 21st Avenue South, 1105 Oxford House, Nashville, TN, USA;Current address: Department of Medicine, Duke University Medical Center, Durham, NC, USA;Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA;Edward Via College of Osteopathic Medicine, Spartanburg, SC, USA

关键词: Bayesian probit regression;    gene signature;    FIP;    IPF;   

Others  :  1128454
DOI  :  10.1186/1471-2164-15-902

 received in 2014-01-27, accepted in 2014-07-10,  发布年份 2014

【 摘 要 】

Background

Peripheral blood biomarkers might improve diagnostic accuracy for idiopathic pulmonary fibrosis (IPF).

Results

Gene expression profiles were obtained from 89 patients with IPF and 26 normal controls. Samples were stratified according to severity of disease based on pulmonary function. The stratified dataset was split into subsets; two-thirds of the samples were selected to comprise the training set, while one-third was reserved for the validation set. Bayesian probit regression was used on the training set to develop a gene expression model for IPF versus normal. The gene expression model was tested by using it on the validation set to perform class prediction. Unsupervised clustering failed to discriminate between samples of different severity. Therefore, samples of all severities were included in the training and validation sets, in equal proportions. A gene signature model was developed from the training set. The model was built in an iterative fashion with the number of gene features selected to minimize the misclassification error in cross validation. The final model was based on the top 108 discriminating genes in the training set. The signature was successfully applied to the validation set, ROC area under the curve = 0.893, p < 0.0001. Using the optimal threshold (0.74) accurate class predictions were made for 77% of the test cases with sensitivity = 0.70, specificity = 1.00.

Conclusions

By using Bayesian probit regression to develop a model, we show that it is entirely possible to make a diagnosis of IPF from the peripheral blood with gene signatures.

【 授权许可】

   
2014 Meltzer et al.; licensee BioMed Central Ltd.

【 图 表 】

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