ArticleJournal of cancer research and clinical oncology2007
Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree classification.
Article in Journal of cancer research and clinical oncology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Clinical significance of novel biomarkers to predict the natural course of hepatitis B infection.Frontiers in public health · 2022Article
- Glycoproteomic markers of hepatocellular carcinoma-mass spectrometry based approaches.Mass spectrometry reviews · 2019Review
- Potential Serum Markers for Monitoring the Progression of Hepatitis B Virus-Associated Chronic Hepatic Lesions to Liver Cirrhosis.Gut and liver · 2015Article
- Serum biomarkers identification by mass spectrometry in high-mortality tumors.International journal of proteomics · 2013Article
- Two classifiers based on serum peptide pattern for prediction of HBV-induced liver cirrhosis using MALDI-TOF MS.BioMed research international · 2013Article
- Clinical proteomics for liver disease: a promising approach for discovery of novel biomarkers.Proteome science · 2010Article
- Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker.Annals of surgical oncology · 2010Article
- Serum protein biomarkers screening in HCC patients with liver cirrhosis by ICAT-LC-MS/MS.Journal of cancer research and clinical oncology · 2010Article
- Plasma gelsolin protein: a candidate biomarker for hepatitis B-associated liver cirrhosis identified by proteomic approach.Blood transfusion = Trasfusione del sangue · 2010Article
- The current state of proteomics in GI oncology.Digestive diseases and sciences · 2009Review
- Screening serum hepatocellular carcinoma-associated proteins by SELDI-based protein spectrum analysis.World journal of gastroenterology · 2008Article
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14 authors.
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Abstract
purposeTo screen potential serological biomarkers and develop decision tree classifications of chronic hepatitis B, liver cirrhosis (LC) and hepatocellular carcinoma (HCC), respectively, with high prediction score for improving diagnosis of liver diseases.
methodsThe total serum samples were randomly divided into three training sets (41 HBV and 35 health; 36 LC and 35 health; 39 HCC and 35 health) and three testing groups (34 HBV and 38 health; 18 LC and 52 health; 42 HCC and 47 health). Selected WCX2 protein chip capture followed by SELDI-TOF-MS analysis was applied to generate the serum protein profiles. Subsequently serum protein spectra were normalized and aligned by Ciphergen SELDI Software 3.1.1 with Biomarker Wizard including baseline subtraction, mass accuracy calibration, automatic peak detection. Once the intensities of selected significant peaks from the training data set were transferred to further BPS analysis, an optimized classification tree with sequence-decision was established to divide training data set into disease group and control group successfully. A double blind test was employed to determine the clinical sensitivity and clinical specificity of three models.
resultsAfter comparative analysis of SELDI based serum protein profile between the cases of disease and healthy, a HCC decision tree classification with sensitivity of 94.872% and specificity of 94.286%; a LC decision tree classification with sensitivity of 91.667% and specificity of 94.286% and a HBV decision tree classification with sensitivity of 95.122% and specificity of 94.286% were produced by BPS respectively. When three decision tree models were challenged by the double-blind test samples, clinical sensitivity and clinical specificity of these models were predicted in diagnosis of three liver diseases (HCC: 90.48 and 89.36%; cirrhosis: 100 and 86.5%; HBV: 85.29 and 84.21%).
conclusionSELDI-based decision tree classifications showed great advantages over conventional serological biomarkers in the diagnosis of chronic hepatitis B, LC as well as HCC.
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