Received on February 9, 2004
Accepted on October 21, 2004

Proteomics and Protein Markers

Proteomic Fingerprints for Potential Application to Early Diagnosis of Severe Acute Respiratory Syndrome

¹ Center for Laboratory Diagnosis, Beijing Tiantan Hospital and Capital University of Medical Sciences, Beijing, China
² Ciphergen Biosystems, Inc., Beijing, China
³ Deyi Diagnosis Institute, Beijing, China
⁴ Taizhou Municipal Hospital, Taizhou, Zhejiang Province, China
⁵ Institute of Respiratory Medicine
⁶ Department of Cell Biology, National Institute for the Control of Pharmaceutical and Biological Products (NICPBP), Beijing, China
⁷ Institute of Virology, Chinese Academy of Preventive Medicine, China
⁸ Department of Quality Control, Beijing Red Cross Blood Center, Beijing, China
⁹ Basic Medical Research Center, Chaoyang Hospital and Capital University of Medical Science, Beijing, China

^* To whom correspondence should be addressed. E-mail: hongtang{at}sun.im.ac.cn.

Background: Definitive early-stage diagnosis of severe acute respiratory syndrome (SARS) is important despite the number of laboratory tests that have been developed to complement clinical features and epidemiologic data in case definition. Pathologic changes in response to viral infection might be reflected in proteomic patterns in sera of SARS patients.

Methods: We developed a mass spectrometric decision tree classification algorithm using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Serum samples were grouped into acute SARS (n = 74; <7 days after onset of fever) and non-SARS [n = 1067; fever and influenza A (n = 203), pneumonia (n = 176), lung cancer (n = 29), and healthy controls (n = 659)] cohorts. Diluted samples were applied to WCX-2 ProteinChip arrays (Ciphergen), and the bound proteins were assessed on a ProteinChip Reader (Model PBS II). Bioinformatic calculations were performed with Biomarker Wizard software 3.1.1 (Ciphergen).

Results: The discriminatory classifier with a panel of four biomarkers determined in the training set could precisely detect 36 of 37 (sensitivity, 97.3%) acute SARS and 987 of 993 (specificity, 99.4%) non-SARS samples. More importantly, this classifier accurately distinguished acute SARS from fever and influenza with 100% specificity (187 of 187).

Conclusions: This method is suitable for preliminary assessment of SARS and could potentially serve as a useful tool for early diagnosis.