血清细胞外囊泡携带Trop2蛋白作为前列腺癌的潜在分子指标。

Prima Dewi Sinawang, Mehmet O. Ozen, Shiqin Liu, En-Chi Hsu, Demir Akin, Emily Ding, Rosalie Nolley, James D. Brooks, Tanya Stoyanova, Utkan Demirci
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引用次数: 0

摘要

细胞外囊泡(EVs)是一种脂质纳米到微尺寸的囊泡,因其在细胞间通讯中的作用以及作为各种疾病的微创分子指标的潜力而越来越受到研究。然而,由于货物的异质性和缺乏方便的量化方法,在表征电动汽车的特定表面分子方面仍然存在挑战。在这项研究中,我们展示了前列腺癌患者血清中携带trop2的ev (EV-Trop2)的分离、表征、检测和定量。这项工作结合了我们的EV分离方法与ELISA的独特优势,可以直接从血清中进行表面蛋白特异性EV分析。据我们所知,这是首次从前列腺癌患者血清中分离和量化EV-Trop2,以研究其表达模式与前列腺癌状态的关系。对高危前列腺癌(n = 22)、低危前列腺癌(n = 23)和无癌组(n = 21)三组患者的血清样本进行分析,发现EV-Trop2表达水平在高危组和低危组之间(p = 0.0015)、高危组和无癌组之间(p = 0.0015)有显著差异
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Extracellular Vesicles in Serum Carry Trop2 Protein as a Potential Molecular Indicator in Prostate Cancer

Extracellular Vesicles in Serum Carry Trop2 Protein as a Potential Molecular Indicator in Prostate Cancer

Extracellular vesicles (EVs) are lipid nano-to-micro-sized vesicles increasingly studied for their role in intercellular communication and their potential as minimally invasive molecular indicators in various diseases. However, challenges remain in characterizing specific surface molecules on EVs due to cargo heterogeneity and the lack of convenient quantification methods. In this study, we show the isolation, characterization, detection, and quantification of Trop2-carrying EVs (EV-Trop2) in serum of prostate cancer patients. This work combines the unique advantages of our EV isolation method with ELISA to enable surface-protein-specific EV analysis directly from serum. This is, to our knowledge, the first demonstration to isolate and quantify EV-Trop2 from prostate cancer patient serum to study its expression patterns in relation to prostate cancer status. Analysis of serum samples from three patient groups: high-risk prostate cancer (n = 22), low-risk prostate cancer (n = 23), and cancer-free groups (n = 21), revealed significantly different levels of EV-Trop2 expression between the high-risk and low-risk patient groups (p = 0.0015) and between high-risk patient and cancer-free groups (p < 0.0001). Multivariate modeling further showed that EV-Trop2 contributed to improved classifier metrics across the three sample groups. These findings highlight a strategy for probing EV-associated surface targets and suggest broader applicability of this approach across multiple cancers.

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