Extracellular vesicles and particles (EVPs) are recognized as ideal liquid biopsy tools for cancer detection, and membrane proteins are commonly used EVP biomarkers. However, bulk analysis of EVP membrane protein biomarkers typically fails to meet the clinical requirement for diagnostic accuracy. We investigated the correlation between the membrane protein expression level, the binding kinetics to aptamers and the sizes of EVPs with interferometric plasmonic microscopy (iPM), and demonstrated the implementation of the correlative signature to determine cancer types. Using EVPs collected from both cell model and clinical plasma samples with liver, lung, breast, or prostate cancer, we found that the selective set of membrane protein expression levels of five protein markers and their binding kinetics were highly heterogeneous across various sizes of EVPs, resulting in the low overall accuracy (<50%) in cancer classification with bulk analysis of all populations. By grouping the EVPs into three subpopulations according to their sizes, the overall accuracy could be increased to about 70%. We further grouped the EVPs into subpopulations with a 10 nm interval in sizes and analysed the correlation between the membrane proteins and sizes with a machine learning algorithm. The results show that the overall accuracy to discriminate cancer types could be improved to 85%. Therefore, this work highlights the significance of size-dependent subtyping of EVPs and suggests that the correlation between the selective set of membrane proteins and sizes of EVP can serve as a signature for clinical cancer diagnosis.

中文翻译：

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膜蛋白与细胞外囊泡和颗粒大小之间的相关性：癌症诊断的潜在特征

细胞外囊泡和颗粒（EVP）被认为是癌症检测的理想液体活检工具，膜蛋白是常用的 EVP 生物标志物。然而，EVP 膜蛋白生物标志物的批量分析通常无法满足诊断准确性的临床要求。我们利用干涉等离子体显微镜 (iPM) 研究了膜蛋白表达水平、适体结合动力学和 EVP 大小之间的相关性，并演示了如何使用相关特征来确定癌症类型。使用从肝癌、肺癌、乳腺癌或前列腺癌的细胞模型和临床血浆样本中收集的 EVP，我们发现五种蛋白质标记物的选择性膜蛋白表达水平及其结合动力学在不同大小的 EVP 中具有高度异质性，导致对所有人群进行批量分析时癌症分类的总体准确性较低（<50%）。通过根据 EVP 的大小将其分为三个子群，总体准确率可以提高到 70% 左右。我们进一步将 EVP 分成大小间隔为 10 nm 的亚群，并使用机器学习算法分析膜蛋白和大小之间的相关性。结果表明，区分癌症类型的总体准确率可以提高到85%。因此，这项工作强调了 EVP 大小依赖性亚型的重要性，并表明选择性膜蛋白集与 EVP 大小之间的相关性可以作为临床癌症诊断的标志。