Diagnosis of hepatocellular carcinoma based on salivary protein glycopatterns and machine learning algorithms
Author:
Tang Zhen1, Zhang Fan1, Wang Yuan2, Zhang Chen1, Li Xia1, Yin Mengqi1, Shu Jian1, Yu Hanjie1, Liu Xiawei1, Guo Yonghong3, Li Zheng1ORCID
Affiliation:
1. Laboratory for Functional Glycomics , College of Life Sciences, Northwest University , Xi’an , P.R. China 2. Department of Infectious Diseases , Second Affiliated Hospital of Xi’an Jiaotong University , Xi’an , P.R. China 3. The infectious disease department , Gongli Hospital , Pudong New Area, Shanghai , P.R. China
Abstract
Abstract
Objectives
Hepatocellular carcinoma (HCC) is difficult to diagnose early and progresses rapidly, making it one of the most deadly malignancies worldwide. This study aimed to evaluate whether salivary glycopattern changes combined with machine learning algorithms could help in the accurate diagnosis of HCC.
Methods
Firstly, we detected the alteration of salivary glycopatterns by lectin microarrays in 118 saliva samples. Subsequently, we constructed diagnostic models for hepatic cirrhosis (HC) and HCC using three machine learning algorithms: Least Absolute Shrinkage and Selector Operation, Support Vector Machine (SVM), and Random Forest (RF). Finally, the performance of the diagnostic models was assessed in an independent validation cohort of 85 saliva samples by a series of evaluation metrics, including area under the receiver operator curve (AUC), accuracy, specificity, and sensitivity.
Results
We identified alterations in the expression levels of salivary glycopatterns in patients with HC and HCC. The results revealed that the glycopatterns recognized by 22 lectins showed significant differences in the saliva of HC and HCC patients and healthy volunteers. In addition, after Boruta feature selection, the best predictive performance was obtained with the RF algorithm for the construction of models for HC and HCC. The AUCs of the RF-HC model and RF-HCC model in the validation cohort were 0.857 (95% confidence interval [CI]: 0.780–0.935) and 0.886 (95% CI: 0.814–0.957), respectively.
Conclusions
Detecting alterations in salivary protein glycopatterns with lectin microarrays combined with machine learning algorithms could be an effective strategy for diagnosing HCC in the future.
Funder
Pudong New Area special Fund for Livelihood Research Project of Science and Technology Development Fund
Publisher
Walter de Gruyter GmbH
Subject
Biochemistry (medical),Clinical Biochemistry,General Medicine
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