Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree
Jiefeng Cui1, Xiaonan Kang, Zhi Dai
1Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai, 200032, China.
Insights
This study developed novel SELDI-based decision tree models for diagnosing liver diseases. These models demonstrated high accuracy in identifying chronic hepatitis B, liver cirrhosis (LC), and hepatocellular carcinoma (HCC), outperforming traditional biomarkers.
Area of Science:
- Biomarker Discovery
- Proteomics
- Medical Diagnostics
Background:
- Chronic hepatitis B, liver cirrhosis (LC), and hepatocellular carcinoma (HCC) pose significant global health challenges.
- Accurate and early diagnosis is crucial for effective management and improved patient outcomes.
- Current diagnostic methods for liver diseases have limitations, necessitating the search for novel biomarkers.
Purpose of the Study:
- To screen for serological biomarkers for chronic hepatitis B, LC, and HCC.
- To develop decision tree classifications with high prediction scores for these liver diseases.
- To enhance the diagnostic accuracy of liver diseases using novel serological biomarkers.
Main Methods:
- Serum samples from patients with chronic hepatitis B, LC, HCC, and healthy controls were analyzed.
- Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF-MS) was used to generate serum protein profiles.
- Decision tree algorithms were developed and validated using training and testing datasets.
Main Results:
- High sensitivity and specificity were achieved in classifying HCC (94.872% sensitivity, 94.286% specificity) and LC (91.667% sensitivity, 94.286% specificity) using decision tree models.
- The model for chronic hepatitis B also showed high accuracy (95.122% sensitivity, 94.286% specificity).
- In double-blind testing, the models maintained strong diagnostic performance for all three liver diseases.
Conclusions:
- SELDI-based decision tree classifications offer a promising approach for diagnosing chronic hepatitis B, LC, and HCC.
- These novel models demonstrate superior diagnostic capabilities compared to conventional serological biomarkers.
- The findings support the clinical utility of SELDI-TOF-MS in developing advanced diagnostic tools for liver diseases.
Purpose:
To 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.
Methods:
The 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.
Results:
After 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%).
Conclusion:
SELDI-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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