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基于深度学习的 CRISPR 增强光电流极性切换用于双重 lncRNA 检测在癌症诊断中的应用。

CRISPR-Enhanced Photocurrent Polarity Switching for Dual-lncRNA Detection Combining Deep Learning for Cancer Diagnosis.

机构信息

College of Public Health, Zhengzhou University, Zhengzhou 450001, China.

College of Chemistry, Zhengzhou University, Zhengzhou 450001, China.

出版信息

Anal Chem. 2024 Aug 13;96(32):13278-13284. doi: 10.1021/acs.analchem.4c02617. Epub 2024 Aug 2.

DOI:10.1021/acs.analchem.4c02617
PMID:39092917
Abstract

Abnormal expression in long noncoding RNAs (lncRNAs) is closely associated with cancers. Herein, a novel CRISPR/Cas13a-enhanced photocurrent-polarity-switching photoelectrochemical (PEC) biosensor was engineered for the joint detection of dual lncRNAs, using deep learning (DL) to assist in cancer diagnosis. After target lncRNA-activated CRISPR/Cas13a cleaves to induce DNAzyme bidirectional walkers with the help of cofactor Mg, nitrogen-doped carbon-Cu/CuO octahedra are introduced into the biosensor, producing a photocurrent in the opposite direction of CdS quantum dots (QDs). The developed PEC biosensor shows high specificity and sensitivity with limits of detection down to 25.5 aM for lncRNA HOTAIR and 53.1 aM for lncRNA MALAT1. More importantly, this platform for the lncRNA joint assay in whole blood can successfully differentiate cancers from healthy people. Furthermore, the DL model is applied to explore the potential pattern hidden in data of the established technology, and the accuracy of DL cancer diagnosis can acquire 93.3%. Consequently, the developed platform offers a new avenue for lncRNA joint detection and early intelligent diagnosis of cancer.

摘要

长非编码 RNA(lncRNAs)的异常表达与癌症密切相关。在此,设计了一种新型的 CRISPR/Cas13a 增强型光电流极性转换光电化学(PEC)生物传感器,用于联合检测双重 lncRNA,同时利用深度学习(DL)辅助癌症诊断。在靶 lncRNA 激活 CRISPR/Cas13a 后,在辅助因子 Mg 的帮助下诱导 DNA 酶双向行走器,将掺氮碳-Cu/CuO 八面体引入生物传感器中,在 CdS 量子点(QDs)的相反方向产生光电流。所开发的 PEC 生物传感器具有高特异性和灵敏度,对 lncRNA HOTAIR 的检测限低至 25.5 aM,对 lncRNA MALAT1 的检测限低至 53.1 aM。更重要的是,该平台可用于全血中的 lncRNA 联合检测,成功地区分癌症患者和健康人群。此外,DL 模型被应用于探索所建立技术数据中隐藏的潜在模式,DL 癌症诊断的准确性可达到 93.3%。因此,所开发的平台为 lncRNA 联合检测和癌症的早期智能诊断提供了新途径。

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