Journal of Zhejiang University SCIENCE  B

Accepted manuscript available online (unedited version)


Machine learning-driven evaluation of PRKD3 as a co-diagnostic biomarker in hepatocellular carcinoma


Author(s):  Jing LI1*, Yifan ZHAO2*, Yicheng MA1, Bei XIE3, Li HUANG4, Haitang YANG1, Xingyuan MA5, Haohua DENG5, Shuaiyang WANG5, Chanjuan SUN6, Pengfei CAO2, Linjing LI1

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

Corresponding email(s):  Linjing Li, lilinj@lzu.edu.cn Pengfei Cao, caopf@lzu.edu.cn

Key Words:  Hepatocellular carcinoma; Machine learning; Combined diagnosis


Share this article to： More <<< Previous Paper|Next Paper >>>

Jing LI1*, Yifan ZHAO2*, Yicheng MA1, Bei XIE3, Li HUANG4, Haitang YANG1, Xingyuan MA5, Haohua DENG5, Shuaiyang WANG5, Chanjuan SUN6, Pengfei CAO2, Linjing LI1. Machine learning-driven evaluation of PRKD3 as a co-diagnostic biomarker in hepatocellular carcinoma[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2500607

@article{title="Machine learning-driven evaluation of PRKD3 as a co-diagnostic biomarker in hepatocellular carcinoma",
author="Jing LI1*, Yifan ZHAO2*, Yicheng MA1, Bei XIE3, Li HUANG4, Haitang YANG1, Xingyuan MA5, Haohua DENG5, Shuaiyang WANG5, Chanjuan SUN6, Pengfei CAO2, Linjing LI1",
journal="Journal of Zhejiang University Science B",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/jzus.B2500607"
}

%0 Journal Article
%T Machine learning-driven evaluation of PRKD3 as a co-diagnostic biomarker in hepatocellular carcinoma
%A Jing LI1*
%A Yifan ZHAO2*
%A Yicheng MA1
%A Bei XIE3
%A Li HUANG4
%A Haitang YANG1
%A Xingyuan MA5
%A Haohua DENG5
%A Shuaiyang WANG5
%A Chanjuan SUN6
%A Pengfei CAO2
%A Linjing LI1
%J Journal of Zhejiang University SCIENCE B
%P
%@ 1673-1581
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/jzus.B2500607"

TY - JOUR
T1 - Machine learning-driven evaluation of PRKD3 as a co-diagnostic biomarker in hepatocellular carcinoma
A1 - Jing LI1*
A1 - Yifan ZHAO2*
A1 - Yicheng MA1
A1 - Bei XIE3
A1 - Li HUANG4
A1 - Haitang YANG1
A1 - Xingyuan MA5
A1 - Haohua DENG5
A1 - Shuaiyang WANG5
A1 - Chanjuan SUN6
A1 - Pengfei CAO2
A1 - Linjing LI1
J0 - Journal of Zhejiang University Science B
SP -
EP -
%@ 1673-1581
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/jzus.B2500607"


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 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 significantly elevated PRKD3 concentrations in patients with cirrhosis, decompensated cirrhosis, and HCC compared to healthy controls (P<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, Gradient Boosting, Random Forest, and Support Vector Machine. A multi-parameter joint diagnostic model was constructed by combining PRKD3 expression data with a set of clinical parameters, including gender, age, total bilirubin, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, albumin, alpha-fetoprotein, and prothrombin induced by vitamin K absence-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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Full Text:  <149>

CLC number: 

On-line Access: 2026-03-09

Received: 2025-09-21

Revision Accepted: 2026-01-18

Crosschecked: 0000-00-00

Cited: 0

Clicked: 442

Citations:  Bibtex RefMan EndNote GB/T7714

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2026 Journal of Zhejiang University-SCIENCE