A novel autoantibody panel as potential diagnostic markers for hepatocellular carcinoma.

IF 2.1 4区医学 Q3 MEDICINE, RESEARCH & EXPERIMENTAL

Biomarkers in medicine Pub Date : 2026-04-01 Epub Date: 2026-04-21 DOI:10.1080/17520363.2026.2659658

Xiaodan Zhang, Yin Lu, Qian Yang, Jicun Zhu, Jiaxin Zhang, Donglin Jiang, Ling Liu, Yuqi Liu, Yue Wang, Jianxiang Shi, Keyan Wang, Peng Wang, Chunhua Song, Kaijuan Wang, Hua Ye

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引用次数: 0

Abstract

Aims: Hepatocellular carcinoma (HCC) represents a major global health burden. Tumor-associated autoantibodies (TAAs) represent promising biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody panels in HCC.

Patients and methods: Candidate antigens were identified via multi-omics screening (Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA)) and validated by enzyme-linked immunosorbent assay (ELISA) in 280 HCC patients and 280 controls. Diagnostic models were constructed using eight machines learning algorithms.

Results: A total of 10 TAAs were identified, with AUCs ranging from 0.610 to 0.729. Logistic regression (LR) was identified as the optimal model. The LR model predicted that the positive rate of early HCC (62.39%) was significantly higher than that of AFP (47.71%). Notably, this model demonstrated superior predictive capability for AFP-negative HCC (AUC = 0.751). Combining the LR model with AFP for diagnosis achieved a positive rate of 96.36%, significantly higher than the 64.78% positive rate obtained with AFP alone.

Conclusion: This novel serum autoantibody panel serves as a valuable diagnostic biomarker. Its combination with AFP significantly reduces missed diagnoses, offering a promising strategy to optimize HCC screening.

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一种新的自身抗体组作为肝细胞癌的潜在诊断标记物。

目的：肝细胞癌（HCC）是全球主要的健康负担。肿瘤相关自身抗体（TAAs）是一种很有前途的癌症检测生物标志物。本研究旨在评价自身抗体检测在HCC中的诊断价值。患者和方法：通过多组学筛选（基因表达综合分析（GEO）、基因表达谱交互分析（GEPIA）、临床蛋白质组学肿瘤分析联盟（CPTAC）、人类蛋白图谱（HPA））确定候选抗原，并通过酶联免疫吸附试验（ELISA）在280例HCC患者和280例对照组中进行验证。使用八种机器学习算法构建诊断模型。结果：共鉴定出10个taa， auc范围为0.610 ~ 0.729。Logistic回归（LR）被确定为最优模型。LR模型预测早期HCC阳性率（62.39%）明显高于AFP阳性率（47.71%）。值得注意的是，该模型对afp阴性HCC的预测能力较好（AUC = 0.751）。LR模型联合AFP诊断阳性率为96.36%，明显高于单独AFP诊断阳性率64.78%。结论：这种新的血清自身抗体是一种有价值的诊断性生物标志物。联合AFP可显著减少漏诊，为优化HCC筛查提供了一种有前景的策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biomarkers in medicine 医学-医学：研究与实验

CiteScore

3.80

自引率

4.50%

发文量

审稿时长

6-12 weeks

期刊介绍： Biomarkers are physical, functional or biochemical indicators of physiological or disease processes. These key indicators can provide vital information in determining disease prognosis, in predicting of response to therapies, adverse events and drug interactions, and in establishing baseline risk. The explosion of interest in biomarker research is driving the development of new predictive, diagnostic and prognostic products in modern medical practice, and biomarkers are also playing an increasingly important role in the discovery and development of new drugs. For the full utility of biomarkers to be realized, we require greater understanding of disease mechanisms, and the interplay between disease mechanisms, therapeutic interventions and the proposed biomarkers. However, in attempting to evaluate the pros and cons of biomarkers systematically, we are moving into new, challenging territory. Biomarkers in Medicine (ISSN 1752-0363) is a peer-reviewed, rapid publication journal delivering commentary and analysis on the advances in our understanding of biomarkers and their potential and actual applications in medicine. The journal facilitates translation of our research knowledge into the clinic to increase the effectiveness of medical practice. As the scientific rationale and regulatory acceptance for biomarkers in medicine and in drug development become more fully established, Biomarkers in Medicine provides the platform for all players in this increasingly vital area to communicate and debate all issues relating to the potential utility and applications. Each issue includes a diversity of content to provide rounded coverage for the research professional. Articles include Guest Editorials, Interviews, Reviews, Research Articles, Perspectives, Priority Paper Evaluations, Special Reports, Case Reports, Conference Reports and Company Profiles. Review coverage is divided into themed sections according to area of therapeutic utility with some issues including themed sections on an area of topical interest. Biomarkers in Medicine provides a platform for commentary and debate for all professionals with an interest in the identification of biomarkers, elucidation of their role and formalization and approval of their application in modern medicine. The audience for Biomarkers in Medicine includes academic and industrial researchers, clinicians, pathologists, clinical chemists and regulatory professionals.