Kang X, Xu Y, Wu X, Liang Y, Wang C, Guo J et al.. Proteomic fingerprints for potential application to early diagnosis of severe acute respiratory syndrome. Clin Chem 51: 56-64

Academy of Military Medical Sciences, T’ien-ching-shih, Tianjin Shi, China
Clinical Chemistry (Impact Factor: 7.91). 01/2005; 51(1):56-64. DOI: 10.1373/clinchem.2004.032458
Source: PubMed


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

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    • "Proteomic analysis provides a unique tool for the identification of diagnostic biomarkers, evaluation of disease progression and development of drugs [41]. SELDI-TOF-MS has been used to resolve proteins in biological specimens through binding to biochemically distinct ProteinChips [34], [42], and has many other advantages compared with traditional approaches: 1) it is much faster to perform; 2) it has high-throughput capability; 3) it requires only small amount of protein sample; 4) it has relatively high sensitivity to detect proteins at picomole to attamole range; 5) it can effectively resolve low mass proteins (2–20 KDa) and 6) it is directly applicable for development of clinical assays [26]. "
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    • "There are many variants of decision trees; in the simplest form, 'yes'/'no' paths are followed throughout the classification process; in others, probability distributions over the classes are used in order to estimate the conditional probability that an item reaching a leaf belongs to the class if defines [39]. In biology, it has been used in Parkinson's disease management [40], disease severity profiling [41,42], toxicity analysis [43], large-scale proteomic studies [44,45], microarray data classification [46] and phylogenetic analysis, among other applications. Depending on the number of factors that will be considered to classify the samples, decision trees may be made by hand or constructed automatically using a learning or an optimization algorithm [38,47]. "
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    • "A new method for diagnosing the early stage of PBC is still an unmet need in clinical practice. Proteomics has been shown to be a promising method for the early detection of cancer, neuropathic disease, infectious disease, and rheumatic diseases (Shiwa et al., 2003; Dotzlaw et al., 2004; De Seny et al., 2005; Kang et al., 2005; Agranoff et al., 2006). In this study, we use proteomic approaches to identify relevant biomarkers that could replace invasive and nonspecific tests for the early diagnosis of PBC. "
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