基于蛋白酶活性的纳米生物传感器早期检测胰腺癌。

Obdulia Covarrubias-Zambrano, Deepesh Agarwal, Madumali Kalubowilage, Sumia Ehsan, Asanka S Yapa, Jose Covarrubias, Anup Kasi, Balasubramaniam Natarajan, Stefan H Bossmann
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引用次数: 0

摘要

胰腺癌患者在最初诊断后的五年生存率已上升至12.8%,仍是最致命的癌症类型之一。这种疾病被称为“沉默杀手”,因为由于胰腺在体内的位置和非特异性临床症状,早期发现具有挑战性。Bossmann团队开发了用于蛋白酶/精氨酸酶检测的超灵敏纳米生物传感器,该传感器由Fe/Fe3O4纳米颗粒、菁氨酸5.5和与TCPP相关的设计肽序列组成。从基因表达分析和血清蛋白酶/精氨酸酶活性检测中获得的初步数据表明早期胰腺癌检测的可行性。几种基质金属蛋白酶(MMPs, -1, -3和-9),组织蛋白酶(CTS) B和E,中性粒细胞弹性蛋白酶和尿激酶蛋白原激活剂(uPA)已被确定为近端生物标志物的候选物。在这项研究中,我们确认了2018年的初步结果,使用更大的组样本量(n= 159)进行血清样本分析,其中包括局部(n=33)和转移性胰腺癌(n=50),胰腺炎(n=26),以及年龄匹配的健康对照组(n=50)。通过优化的基于信息融合的分层决策结构,对8个具有超灵敏蛋白酶和精氨酸酶活性测量能力的纳米生物传感器获得的数据进行分析。这允许建模早期检测胰腺癌作为一个多类分类问题。最引人注目的结果是,这种方法允许从血清分析中检测局部胰腺癌,准确率约为96%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Protease activity-based nanobiosensors for early detection of pancreatic cancer.

Five-year survival rate for pancreatic cancer patients has increased to 12.8% afterthe initial diagnosis, still making it one of the deadliest cancertypes. This disease is known as the "silent killer" because early detection is challenging due to the location of the pancreas in the body and the nonspecific clinical symptoms. The Bossmann group has developed ultrasensitive nanobiosensors for protease/arginase detection comprised of Fe/Fe3O4 nanoparticles, cyanine 5.5, and designer peptide sequences linked to TCPP. Initial data obtained from both gene expression analysis and protease/arginase activity detection in serum indicated the feasibility of early pancreatic cancer detection. Several matrix metalloproteinases (MMPs, -1, -3, and -9), cathepsins (CTS) B and E, neutrophil elastase, and urokinase plaminogen activator (uPA) have been identified as candidates for proximal biomarkers. In this study, we have confirmed our initial results from 2018 performing serum sample analysis assays using a larger group sample size (n = 159), which included localized (n=33) and metastatic pancreatic cancer (n=50), pancreatitis (n=26), and an age-matched healthy control group (n=50). The data obtained from the eight nanobiosensors capable of ultrasensitive protease and arginase activity measurements were analyzed by means of an optimized information fusion-based hierarchical decision structure. This permits the modeling of early-stage detection of pancreatic cancer as a multi-class classification problem. The most striking result is that this methodology permits the detection of localized pancreatic cancers from serum analyses with around 96% accuracy.

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