基于机器学习的蛋白激酶D3（PRKD3）作为肝细胞癌协同诊断生物标志物的评估研究

李婧; 赵一帆; 马毅成; 谢蓓; 黄莉; 杨海棠; 马兴媛; 邓浩华; 王帅阳; 孙婵娟; 曹鹏飞; 李林静

doi:10.1631/jzus.B2500607

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基于机器学习的蛋白激酶D3（PRKD3）作为肝细胞癌协同诊断生物标志物的评估研究

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Research Article | Updated：2026-07-29

基于机器学习的蛋白激酶D3（PRKD3）作为肝细胞癌协同诊断生物标志物的评估研究
Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2026年27卷第7期页码：788-805
Affiliations：

1.Department of Clinical Laboratory Center, Lanzhou University Second Hospital, Lanzhou 730030, China
2.School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
3.Department of Immunology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China
4.Department of Pediatric Nephrology, Lanzhou University Second Hospital, Lanzhou 730030, China
5.School of Second Clinical Medical, Lanzhou University, Lanzhou 730030, China
6.School of Environment and Architecture, University of Shanghai for Science and Technology, Shanghai 200093, China
Funds：

the National Natural Science Foundation of China(82573290;82272405;82060531;81602622);the General Projects of Gansu Provincial Joint Research Fund(23JRRA1503);the Key Research and Development Program of Gansu Province(25YFFA052);the Natural Science Foundation of Gansu Province(23JRRA0984;24JRRA431);the Lanzhou Talent Innovation and Entrepreneurship Project(2023RC28);the Lanzhou Science and Technology Plan Project(2025-2-88);the Chengguan District Talent Innovation and Entrepreneurship Project in Lanzhou City(2024-rc-2);the Cuiying Scientific and Technological Innovation Program of Lanzhou University Second Hospital(CY2024-ZD-01);the Key Incubation Project Funds of the Second Hospital and Clinical Medical School, Lanzhou University(2025-22-zdfy-001;2025-25-zdfy-002)
DOI：10.1631/jzus.B2500607
中图分类号：
CSTR：
收稿：2025-09-21，

修回：2026-01-18，

网络首发：2026-07-29，

纸质出版：2026-07-15
Accepted：

李婧,赵一帆,马毅成等.基于机器学习的蛋白激酶D3（PRKD3）作为肝细胞癌协同诊断生物标志物的评估研究[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(07):788-805.

Jing LI, Yifan ZHAO, Yicheng MA, et al. Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(7): 788-805.
李婧,赵一帆,马毅成等.基于机器学习的蛋白激酶D3（PRKD3）作为肝细胞癌协同诊断生物标志物的评估研究[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2026,27(07):788-805. DOI： 10.1631/jzus.B2500607.

Jing LI, Yifan ZHAO, Yicheng MA, et al. Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma[J]. Journal of Zhejiang University-SCIENCE B, 2026, 27(7): 788-805. DOI： 10.1631/jzus.B2500607.

摘要

为阐明蛋白激酶D3（PRKD3）在肝细胞癌（HCC）中的诊断价值及其临床意义，本研究结合生物信息学分析、血浆PRKD3表达水平检测及机器学习方法，对PRKD3作为协同诊断生物标志物的潜力进行了系统评估。基于癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库分析结果，本研究发现

PRKD3

在HCC组织中显著高表达，且其高表达与患者不良预后密切相关。进一步收集392例临床血浆样本（包括健康人群、肝硬化及失代偿期肝硬化患者和HCC患者），对血浆PRKD3表达水平进行检测。结果显示，与健康对照相比，肝硬化、失代偿期肝硬化及HCC患者血浆中PRKD3表达均显著升高（

0.01），其受试者工作曲线下面积（AUC）分别为0.8107、0.7899和0.7177。为进一步提升HCC的诊断效能，本研究采用极端随机树（ET）、梯度提升（GB）、随机森林（RF）及支持向量机（SVM）等多种机器学习算法，构建融合PRKD3、甲胎蛋白（AFP）、维生素K缺乏诱导蛋白Ⅱ（PIVKA-II）及多项临床生化指标的联合诊断模型。结果表明，该多参数联合模型在测试集中表现出优异的诊断性能：准确率为0.861，灵敏度为0.863，特异度为0.925，精确度为0.862。综上所述，PRKD3作为传统生物标志物的有效补充，通过与机器学习方法相结合，有望为HCC的早期筛查和精准诊断提供新的技术策略。

Abstract

To elucidate the diagnostic value and clinical relevance of protein kinase D3 (PRKD3) in hepatocellular carcinoma (HCC)

we analyzed data retrieved from The Cancer Genome Atlas (TCGA) database

which revealed high expression of

PRKD3

in HCC tissues. Subsequently

we collected a total of 392 clinical plasma samples from healthy individuals

patients with cirrhosis or decompensated cirrhosis

and patients with HCC. Plasma PRKD3 levels were then determined across HCC patients and individuals at high risk of developing the disease. The results revealed significan

tly elevated PRKD3 concentrations in patients with cirrhosis

decompensated cirrhosis

and HCC compared to healthy controls (

0.01). The areas under the receiver operating characteristic (ROC) curve for these three groups were 0.8107

0.7899

and 0.7177

respectively. To further evaluate the efficacy of PRKD3 as an adjunctive diagnostic biomarker for HCC

we employed a panel of machine learning algorithms as primary classifiers

including extra trees (ET)

gradient boosting (GB)

random forest (RF)

and support vector machine (SVM). A multi-parameter joint diagnostic model was constructed by combining PRKD3 expression data with a set of clinical parameters

including gender

age

total bilirubin (TBIL)

alanine aminotransferase (ALT)

aspartate aminotransferase (AST)

alkaline phosphatase (ALP)

albumin (ALB)

alpha-fetoprotein (AFP)

and prothrombin induced by vitamin K absence-II (PIVKA-II). This integrated approach exhibited substantially improved diagnostic performance

achieving an accuracy of 0.861

sensitivity of 0.863

specificity of 0.925

and precision of 0.862. Collectively

these findings highlight the potential of PRKD3 as an integral component of a comprehensive diagnostic tool for the early identification of HCC.

关键词

Keywords

references

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