Screening of cancer-specific biomarkers for hepatitis B-related hepatocellular carcinoma based on a proteome microarray.

IF 6.1 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Molecular & Cellular Proteomics Pub Date : 2024-11-01 DOI:10.1016/j.mcpro.2024.100872

Wudi Hao, Danyang Zhao, Yuan Meng, Mei Yang, Meichen Ma, Jingwen Hu, Jianhua Liu, Xiaosong Qin

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引用次数: 0

Abstract

Hepatocellular carcinoma (HCC) is associated with one of the highest mortality rates among cancers, rendering its early diagnosis clinically invaluable. Serum biomarkers, specifically alpha-fetoprotein (AFP), represent the most promising and widely used diagnostic biomarkers for HCC. However, its detection rate is low in the early stages of HCC progression, and distinguishing specific false positives for other liver-related diseases, such as cirrhosis and acute hepatitis, remains challenging. Therefore, this study was conducted to identify biomarkers for hepatitis B (HBV)-related liver diseases by screening differentially expressed autoantibodies against tumor-associated antigens (TAAbs). We designed a large-scale multistage investigation, encompassing initial screening, HCC-focused, and ELISA validation cohorts to identify potential TAAbs in HBV-related liver diseases, spanning from healthy control (HC) individuals to patients with chronic hepatitis B (CHB), hepatitis B-related cirrhosis (HBC), and HCC, using protein microarray technology. The differential biological characteristics of TAAbs were analyzed using bioinformatics analysis. Validation of tumor-specific biomarkers for HCC was performed using ELISA. In the screening cohort, 547 candidate TAAbs were identified in the HCC group compared to those in the HC group. In the HCC-focused cohort, 64, 61, and 65 candidate TAAbs were identified in the CHB, HBC, and HCC groups, respectively, compared to those in the HC group. Thirty-four proteins exhibited continuously elevated expression from HCs to patients with CHB, HBC, and HCC. Among these, nine were identified as cancer-specific proteins. In the validation cohort, UBE2Z, CNOT3, and EID3 were correlated with liver function indicators in patients with hepatitis B-related HCC. Overall, UBE2Z, CNOT3, and EID3 emerged as cancer-specific biomarkers for HBV-related liver disease, providing a scientific basis for clinical application.

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基于蛋白质组芯片筛选乙型肝炎相关肝细胞癌的癌症特异性生物标记物。

肝细胞癌（HCC）是死亡率最高的癌症之一，因此早期诊断在临床上非常重要。血清生物标志物，特别是甲胎蛋白（AFP），是最有希望且应用最广泛的 HCC 诊断生物标志物。然而，在 HCC 进展的早期阶段，甲胎蛋白的检出率较低，而且区分其他肝脏相关疾病（如肝硬化和急性肝炎）的特异性假阳性仍具有挑战性。因此，本研究通过筛选针对肿瘤相关抗原（TAAbs）的不同表达的自身抗体，来确定乙型肝炎（HBV）相关肝病的生物标记物。我们设计了一项大规模的多阶段调查，包括初步筛选、以 HCC 为重点的队列和 ELISA 验证队列，利用蛋白质微阵列技术从健康对照（HC）个体到慢性乙型肝炎（CHB）、乙型肝炎相关肝硬化（HBC）和 HCC 患者中识别潜在的乙型肝炎相关肝病 TAAbs。利用生物信息学分析方法分析了 TAAbs 的不同生物学特征。利用酶联免疫吸附法对 HCC 的肿瘤特异性生物标记物进行了验证。在筛选队列中，与HC组相比，HCC组发现了547个候选TAAbs。在以HCC为重点的队列中，与HC组相比，CHB组、HBC组和HCC组分别发现了64、61和65个候选TAAbs。从 HC 到 CHB、HBC 和 HCC 患者，有 34 种蛋白质的表达量持续升高。其中，9 个蛋白被确定为癌症特异性蛋白。在验证队列中，UBE2Z、CNOT3 和 EID3 与乙肝相关 HCC 患者的肝功能指标相关。总之，UBE2Z、CNOT3 和 EID3 成为 HBV 相关肝病的癌症特异性生物标志物，为临床应用提供了科学依据。

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

Molecular & Cellular Proteomics 生物-生化研究方法

CiteScore

11.50

自引率

4.30%

发文量

131

审稿时长

84 days

期刊介绍： The mission of MCP is to foster the development and applications of proteomics in both basic and translational research. MCP will publish manuscripts that report significant new biological or clinical discoveries underpinned by proteomic observations across all kingdoms of life. Manuscripts must define the biological roles played by the proteins investigated or their mechanisms of action. The journal also emphasizes articles that describe innovative new computational methods and technological advancements that will enable future discoveries. Manuscripts describing such approaches do not have to include a solution to a biological problem, but must demonstrate that the technology works as described, is reproducible and is appropriate to uncover yet unknown protein/proteome function or properties using relevant model systems or publicly available data. Scope: -Fundamental studies in biology, including integrative "omics" studies, that provide mechanistic insights -Novel experimental and computational technologies -Proteogenomic data integration and analysis that enable greater understanding of physiology and disease processes -Pathway and network analyses of signaling that focus on the roles of post-translational modifications -Studies of proteome dynamics and quality controls, and their roles in disease -Studies of evolutionary processes effecting proteome dynamics, quality and regulation -Chemical proteomics, including mechanisms of drug action -Proteomics of the immune system and antigen presentation/recognition -Microbiome proteomics, host-microbe and host-pathogen interactions, and their roles in health and disease -Clinical and translational studies of human diseases -Metabolomics to understand functional connections between genes, proteins and phenotypes