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Serum Protein Profiling of Smear-Positive and Smear-Negative Pulmonary Tuberculosis Using SELDI-TOF Mass Spectrometry

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Abstract

The focus of this study was to detect novel sera biomarkers for smear-positive and smear-negative pulmonary tuberculosis and to establish respective diagnostic models using the surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF MS) technique. A total of 155 sera samples from smear-positive pulmonary tuberculosis (SPPTB) and smear-negative pulmonary tuberculosis (SNPTB) patients and non-tuberculosis (non-TB) controls were analyzed with SELDI-TOF MS. The study was divided into a preliminary training set and a blinded testing set. A classification tree of spectra derived from 31 SPPTB patients, 22 SNPTB patients, and 42 non-TB controls were used to develop an optimal classification tree that discriminated them respectively in the training set. Then, the validity of the classification tree was challenged with another independent blinded testing set, which included 20 SPPTB patients, 14 SNPTB patients, and 26 non-TB controls. SNPTB patients and non-TB controls also were analyzed alone using the same method. The optimal decision tree model with a panel of nine biomarkers with mass:charge ratios (m/z) of 4821.45, 3443.22, 9284.93, 4473.86, 4702.84, 3443.22, 5343.26, 3398.27, and 3193.61 determined in the training set could detect 93.55%, 95.46%, and 88.09% accuracy for classifying SPPTB patients, SNPTB patients, and non-TB controls specimens, respectively. Validation of an independent, blinded testing set gave an accuracy of 80.77% for controls, 75.00% for SPPTB, and 71.43% for SNPTB samples using the same classification tree. With the peaks displaying differences between SNPTB patients and non-TB controls, a simplified dendrogram (m/z 4821.45, 4792.74) demonstrated classification efficacy of 85.94% (sensitivity 86.36% and specificity 85.71%) for distinguishing SNPTB patients from non-TB controls. The independent blinded testing set containing 14 SNPTB patients and 26 non-TB controls gained an accuracy of 81.59% (sensitivity 78.57% and specificity 84.62%) for diagnosing SNPTB. Special proteins/peptides may change in SPPTB and SNPTB patients and those changes may be used to distinguish them with the proper discriminant analytical method and to pursue and identify some involved proteins underlying the biological process of tuberculosis.

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Acknowledgments

This study was supported by Grants #30425007, 30370627, 30670921 from National Natural Science Foundation of China and 00-722 and 06-834 from China Medical Board of New York to Dr. Fuqiang Wen.

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Authors and Affiliations

  1. Division of Pulmonary Disease, State Key Laboratory of Biotherapy, Department of Respiratory Medicine, West China Hospital, West China School of Medicine, Sichuan University, Chengdu, Sichuan, 610041, People’s Republic of China

    Qi Liu, Xuerong Chen & Fuqiang Wen

  2. Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, People’s Republic of China

    Chaojun Hu

  3. Chengdu Infectious Disease Hospital, Chengdu, Sichuan, 610061, People’s Republic of China

    Renqing Zhang & Guihui Wu

  4. Chengdu Tuberculosis Hospital, Chengdu, Sichuan, 610016, People’s Republic of China

    Ji Yue

  5. Chengdu Hospital Affiliated to Tibet Autonomous Region Government, Chengdu, Sichuan, 610041, People’s Republic of China

    Xiaoping Li & Yunhong Wu

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  1. Qi Liu
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  2. Xuerong Chen
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  7. Xiaoping Li
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  9. Fuqiang Wen
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Correspondence to Fuqiang Wen.

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Liu, Q., Chen, X., Hu, C. et al. Serum Protein Profiling of Smear-Positive and Smear-Negative Pulmonary Tuberculosis Using SELDI-TOF Mass Spectrometry. Lung 188, 15–23 (2010). https://doi.org/10.1007/s00408-009-9199-6

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  • DOI: https://doi.org/10.1007/s00408-009-9199-6

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