JZUS - Journal of Zhejiang University SCIENCE

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 LI^1, Yifan ZHAO^2, 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

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Jing LI^1*, Yifan ZHAO^2*, 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

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author="Jing LI^1*, Yifan ZHAO^2*, 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"
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%0 Journal Article
%T Machine learning-driven evaluation of PRKD3 as a co-diagnostic biomarker in hepatocellular carcinoma
%A Jing LI^1*
%A Yifan ZHAO^2*
%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
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%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/jzus.B2500607"

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A1 - Yifan ZHAO^2*
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
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doi="https://doi.org/10.1631/jzus.B2500607"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

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.

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