{"title":"Exosomal Proteome from Hepatocellular Carcinoma Patient-Derived Xenograft Mice Serves as Identity of Liver Cancer","authors":"Tao Zuo, , , Peiru Chen, , , Zhenpeng Zhang, , , Mingsong Mao, , , Yuan Li, , , Zilin Li, , , Xin Feng, , , Yihao Wang, , , Zhen Sun, , , Fenglong Jiao, , , Fangyuan Gao, , , Tao Zhang, , , Yanchang Li, , , Yao Zhang, , , Fengsong Liu, , , Yangjun Zhang, , , Yuanyuan Ruan, , , Lei Chang*, , , Lianghai Hu*, , , Yali Zhang*, , and , Ping Xu*, ","doi":"10.1021/acs.jproteome.5c00307","DOIUrl":null,"url":null,"abstract":"<p >Hepatocellular carcinoma (HCC) constitutes approximately 90% of liver cancers, yet its early detection remains challenging due to the low sensitivity of current diagnostic methods and the difficulty in identifying minimal cancer cells within the body. This study employed a patient-derived xenograft (PDX) mouse model to screen for biomarkers, leveraging its advantage of low background interference compared to human serum exosome studies. Using a novel microextraction technique, exosomes were isolated from just one microliter of serum from HCC PDX mice, followed by proteomic profiling. Analysis revealed that serum exosomal proteins from PDX mice were enriched in cancer-related pathways. The exosomal proteome not only distinguished PDX mice from controls but also differentiated between individual PDX groups, supporting its use as a tumor-specific identifier (tumor ID) and a noninvasive tool for monitoring tumors. Bioinformatics identified a panel of serum exosomal protein biomarkers predictive of HCC patient outcomes, which were validated via targeted proteomics and array-based high-throughput detection. These upregulated biomarkers in HCC patients positively correlate with disease severity and poor prognosis, underscoring their clinical potential.</p>","PeriodicalId":48,"journal":{"name":"Journal of Proteome Research","volume":"24 10","pages":"4977–4987"},"PeriodicalIF":3.6000,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Proteome Research","FirstCategoryId":"99","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.jproteome.5c00307","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
引用次数: 0
Abstract
Hepatocellular carcinoma (HCC) constitutes approximately 90% of liver cancers, yet its early detection remains challenging due to the low sensitivity of current diagnostic methods and the difficulty in identifying minimal cancer cells within the body. This study employed a patient-derived xenograft (PDX) mouse model to screen for biomarkers, leveraging its advantage of low background interference compared to human serum exosome studies. Using a novel microextraction technique, exosomes were isolated from just one microliter of serum from HCC PDX mice, followed by proteomic profiling. Analysis revealed that serum exosomal proteins from PDX mice were enriched in cancer-related pathways. The exosomal proteome not only distinguished PDX mice from controls but also differentiated between individual PDX groups, supporting its use as a tumor-specific identifier (tumor ID) and a noninvasive tool for monitoring tumors. Bioinformatics identified a panel of serum exosomal protein biomarkers predictive of HCC patient outcomes, which were validated via targeted proteomics and array-based high-throughput detection. These upregulated biomarkers in HCC patients positively correlate with disease severity and poor prognosis, underscoring their clinical potential.
期刊介绍:
Journal of Proteome Research publishes content encompassing all aspects of global protein analysis and function, including the dynamic aspects of genomics, spatio-temporal proteomics, metabonomics and metabolomics, clinical and agricultural proteomics, as well as advances in methodology including bioinformatics. The theme and emphasis is on a multidisciplinary approach to the life sciences through the synergy between the different types of "omics".