Evidence map›Paper›PMID 41060497›Full record

ArticleDiscover oncology2025

Development of a novel 4-protein signature for prognostic prediction in hepatitis B virus-induced hepatocellular carcinoma.

HaiFeng Xie, Yuhong Zhao, Xianzhong Luo, Heran Zhou, Pengcheng Tong

Abstract read
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Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

HaiFeng XieLaboratory Medicine Center, Hangzhou Traditional Chinese Medicine (TCM) Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China. haydensure@163.com.
Yuhong ZhaoDepartment of Blood Transfusion, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Xianzhong LuoLaboratory Medicine Center, Hangzhou Traditional Chinese Medicine (TCM) Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
Heran ZhouDepartment of Oncology, Hangzhou Traditional Chinese Medicine (TCM) Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
Pengcheng TongLaboratory Medicine Center, Hangzhou Traditional Chinese Medicine (TCM) Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.

Funding

the Key Discipline Fund of Clinical Laboratory Medicine at Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University YJZDXK202208
6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) remains a major global health challenge, with the pathogenesis of HBV-induced HCC incompletely understood. Due to HCC's significant heterogeneity, more accurate prognostic models and reliable biomarkers are urgently needed. This study aimed to develop such tools for HBV-related HCC.

methodsProtein expression profiles and clinicopathological data were obtained from the CPTAC (Clinical Proteomic Tumor Analysis Consortium) database. Differential protein expression analysis between HBV-induced HCC and normal tissues were performed, followed by univariate and multivariate Cox regression to construct a prognostic protein signature. The four-protein signature's prognostic power was assessed using time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, multivariate Cox regression, and a nomogram. Protein expression levels of the four proteins were validated in clinical specimens of HBV-related HCC. The signature was further validated in an independent cohort.

resultsA novel four-protein signature (SMC2, SMC4, UBE2C, UHRF1) was established for prognostic prediction in HBV-related HCC. ROC curves demonstrated good survival prediction performance in both the CPTAC HCC cohort and the validation cohort. Transcriptomic and immunohistochemical (IHC) analyses revealed significant overexpression of these four proteins at both mRNA and protein levels in HCC tissues. The signature stratified patients into high-risk and low-risk groups with significantly different survival outcomes. Multivariate Cox regression analysis confirmed that the 4-protein signature independently predicted overall survival (OS). Furthermore, the signature showed strong discriminatory power between HBV-related HCC and normal tissue. Time-dependent ROC analysis indicated that the protein signature exhibited superior specificity and sensitivity compared to alpha-fetoprotein (AFP) for diagnosing HBV-associated HCC.

conclusionsOur study establishes a novel 4-protein signature and nomogram for predicting OS in HBV-related HCC, potentially aiding individualized clinical treatment decisions.

Indexed as

HBVHepatocellular carcinomaPrognostic markerProtein signature

Identifiers

PMID41060497
PMCPMC12508369

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.