Literature DB >> 20012079

Serum protein profiling of smear-positive and smear-negative pulmonary tuberculosis using SELDI-TOF mass spectrometry.

Qi Liu1, Xuerong Chen, Chaojun Hu, Renqing Zhang, Ji Yue, Guihui Wu, Xiaoping Li, Yunhong Wu, Fuqiang Wen.   

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.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 20012079     DOI: 10.1007/s00408-009-9199-6

Source DB:  PubMed          Journal:  Lung        ISSN: 0341-2040            Impact factor:   2.584


  32 in total

1.  Point: Proteomic patterns in biological fluids: do they represent the future of cancer diagnostics?

Authors:  Eleftherios P Diamandis
Journal:  Clin Chem       Date:  2003-08       Impact factor: 8.327

2.  Clinical proteomics: written in blood.

Authors:  Lance A Liotta; Mauro Ferrari; Emanuel Petricoin
Journal:  Nature       Date:  2003-10-30       Impact factor: 49.962

3.  Lessons from controversy: ovarian cancer screening and serum proteomics.

Authors:  David F Ransohoff
Journal:  J Natl Cancer Inst       Date:  2005-02-16       Impact factor: 13.506

4.  Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach.

Authors:  Dominique de Seny; Marianne Fillet; Marie-Alice Meuwis; Pierre Geurts; Laurence Lutteri; Clio Ribbens; Vincent Bours; Louis Wehenkel; Jacques Piette; Michel Malaise; Marie-Paule Merville
Journal:  Arthritis Rheum       Date:  2005-12

5.  Early diagnostic potential for hepatocellular carcinoma using the SELDI ProteinChip system.

Authors:  Shuji Kanmura; Hirofumi Uto; Kazunori Kusumoto; Yoichi Ishida; Satoru Hasuike; Kenji Nagata; Katsuhiro Hayashi; Akio Ido; Sherri Oliver Stuver; Hirohito Tsubouchi
Journal:  Hepatology       Date:  2007-04       Impact factor: 17.425

Review 6.  Performance of purified antigens for serodiagnosis of pulmonary tuberculosis: a meta-analysis.

Authors:  Karen R Steingart; Nandini Dendukuri; Megan Henry; Ian Schiller; Payam Nahid; Philip C Hopewell; Andrew Ramsay; Madhukar Pai; Suman Laal
Journal:  Clin Vaccine Immunol       Date:  2008-12-03

7.  Tuberculosis contacts but not patients have higher gamma interferon responses to ESAT-6 than do community controls in The Gambia.

Authors:  J Vekemans; C Lienhardt; J S Sillah; J G Wheeler; G P Lahai; M T Doherty; T Corrah; P Andersen; K P McAdam; A Marchant
Journal:  Infect Immun       Date:  2001-10       Impact factor: 3.441

8.  Identifying pulmonary tuberculosis in patients with negative sputum smear results.

Authors:  A M Kanaya; D V Glidden; H F Chambers
Journal:  Chest       Date:  2001-08       Impact factor: 9.410

9.  Putative protein markers in the sera of men with prostatic neoplasms.

Authors:  S Lehrer; J Roboz; H Ding; S Zhao; E J Diamond; J F Holland; N N Stone; M J Droller; R G Stock
Journal:  BJU Int       Date:  2003-08       Impact factor: 5.588

10.  Comparison of normalisation methods for surface-enhanced laser desorption and ionisation (SELDI) time-of-flight (TOF) mass spectrometry data.

Authors:  Wouter Meuleman; Judith Ymn Engwegen; Marie-Christine W Gast; Jos H Beijnen; Marcel Jt Reinders; Lodewyk Fa Wessels
Journal:  BMC Bioinformatics       Date:  2008-02-07       Impact factor: 3.169

View more
  10 in total

1.  Comparative proteomic analysis of serum diagnosis patterns of sputum smear-positive pulmonary tuberculosis based on magnetic bead separation and mass spectrometry analysis.

Authors:  Jiyan Liu; Tingting Jiang; Feng Jiang; Dandan Xu; Liliang Wei; Chong Wang; Zhongliang Chen; Xing Zhang; Jicheng Li
Journal:  Int J Clin Exp Med       Date:  2015-02-15

Review 2.  Clinical immunology and multiplex biomarkers of human tuberculosis.

Authors:  Gerhard Walzl; Mariëlle C Haks; Simone A Joosten; Léanie Kleynhans; Katharina Ronacher; Tom H M Ottenhoff
Journal:  Cold Spring Harb Perspect Med       Date:  2014-12-04       Impact factor: 6.915

Review 3.  Immunological biomarkers of tuberculosis.

Authors:  Gerhard Walzl; Katharina Ronacher; Willem Hanekom; Thomas J Scriba; Alimuddin Zumla
Journal:  Nat Rev Immunol       Date:  2011-04-08       Impact factor: 53.106

Review 4.  Tuberculosis Biomarkers: From Diagnosis to Protection.

Authors:  Delia Goletti; Elisa Petruccioli; Simone A Joosten; Tom H M Ottenhoff
Journal:  Infect Dis Rep       Date:  2016-06-24

5.  Proteome analysis of the macroscopically affected colonic mucosa of Crohn's disease and intestinal tuberculosis.

Authors:  Lokesh A Rukmangadachar; Govind K Makharia; Asha Mishra; Prasenjit Das; Gururao Hariprasad; Alagiri Srinivasan; Siddhartha Datta Gupta; Vineet Ahuja; Subrat K Acharya
Journal:  Sci Rep       Date:  2016-03-18       Impact factor: 4.379

6.  Evaluation of Host Serum Protein Biomarkers of Tuberculosis in sub-Saharan Africa.

Authors:  Thomas C Morris; Clive J Hoggart; Novel N Chegou; Martin Kidd; Tolu Oni; Rene Goliath; Katalin A Wilkinson; Hazel M Dockrell; Lifted Sichali; Louis Banda; Amelia C Crampin; Neil French; Gerhard Walzl; Michael Levin; Robert J Wilkinson; Melissa S Hamilton
Journal:  Front Immunol       Date:  2021-02-25       Impact factor: 7.561

7.  Host Protein Biomarkers Identify Active Tuberculosis in HIV Uninfected and Co-infected Individuals.

Authors:  Jacqueline M Achkar; Laetitia Cortes; Pascal Croteau; Corey Yanofsky; Marija Mentinova; Isabelle Rajotte; Michael Schirm; Yiyong Zhou; Ana Paula Junqueira-Kipnis; Victoria O Kasprowicz; Michelle Larsen; René Allard; Joanna Hunter; Eustache Paramithiotis
Journal:  EBioMedicine       Date:  2015-07-30       Impact factor: 8.143

8.  The discovery and identification of a candidate proteomic biomarker of active tuberculosis.

Authors:  Jiyan Liu; Tingting Jiang; Liliang Wei; Xiuyun Yang; Chong Wang; Xing Zhang; Dandan Xu; Zhongliang Chen; Fuquan Yang; Ji-Cheng Li
Journal:  BMC Infect Dis       Date:  2013-10-29       Impact factor: 3.090

Review 9.  Diagnostic 'omics' for active tuberculosis.

Authors:  Carolin T Haas; Jennifer K Roe; Gabriele Pollara; Meera Mehta; Mahdad Noursadeghi
Journal:  BMC Med       Date:  2016-03-23       Impact factor: 8.775

Review 10.  A review of clinical models for the evaluation of human TB vaccines.

Authors:  Matthew K O'Shea; Helen McShane
Journal:  Hum Vaccin Immunother       Date:  2016-01-25       Impact factor: 3.452

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.