Differential RNA aptamer affinity profiling on plasma as a potential diagnostic tool for bladder cancer.

NAR Cancer Pub Date : 2022-08-22 eCollection Date: 2022-09-01 DOI:10.1093/narcan/zcac025
Søren Fjelstrup, Daniel M Dupont, Claus Bus, Jan J Enghild, Jørgen B Jensen, Karin Birkenkamp-Demtröder, Lars Dyrskjøt, Jørgen Kjems
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Abstract

The molecular composition of blood is a signature of human health, reflected in the thousands of blood biomarkers known for human diseases. However, establishing robust disease markers is challenging due to the diversity of individual samples. New sequencing methods have simplified biomarker discovery for circulating DNA and RNA while protein profiling is still laborious and costly. To harness the power of high-throughput sequencing to profile the protein content of a biological sample, we developed a method termed APTASHAPE that uses oligonucleotide aptamers to recognize proteins in complex biofluids. We selected a large pool of 2'Fluoro protected RNA sequences to recognize proteins in human plasma and identified a set of 33 cancer-specific aptamers. Differential enrichment of these aptamers after selection against 1 μl of plasma from individual patients allowed us to differentiate between healthy controls and bladder cancer-diagnosed patients (91% accuracy) and between early non-invasive tumors and late stage tumors (83% accuracy). Affinity purification and mass spectrometry of proteins bound to the predictive aptamers showed the main target proteins to be C4b-binding protein, Complement C3, Fibrinogen, Complement factor H and IgG. The APTASHAPE method thus provides a general, automated and highly sensitive platform for discovering potential new disease biomarkers.

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血浆中的差异 RNA aptamer 亲和谱分析是一种潜在的膀胱癌诊断工具。
血液的分子组成是人类健康的标志,反映在数千种已知的人类疾病血液生物标志物中。然而,由于个体样本的多样性,建立可靠的疾病标志物具有挑战性。新的测序方法简化了循环 DNA 和 RNA 生物标志物的发现,而蛋白质分析仍然费力且昂贵。为了利用高通量测序的优势来分析生物样本中的蛋白质含量,我们开发了一种名为 APTASHAPE 的方法,利用寡核苷酸适配体来识别复杂生物流体中的蛋白质。我们选择了一大批 2'Fluoro 保护的 RNA 序列来识别人血浆中的蛋白质,并确定了一组 33 种癌症特异性适配体。针对 1 μl 患者血浆进行选择后,这些适配体的差异富集使我们能够区分健康对照组和膀胱癌确诊患者(准确率 91%),以及早期非浸润性肿瘤和晚期肿瘤(准确率 83%)。与预测性适配体结合的蛋白质的亲和纯化和质谱分析表明,主要的目标蛋白质是 C4b 结合蛋白、补体 C3、纤维蛋白原、补体因子 H 和 IgG。因此,APTASHAPE 方法为发现潜在的新疾病生物标记物提供了一个通用、自动化和高灵敏度的平台。
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