DNA-based molecular classifiers for the profiling of gene expression signatures

作者全名：Zhang, Li; Liu, Qian; Guo, Yongcan; Tian, Luyao; Chen, Kena; Bai, Dan; Yu, Hongyan; Han, Xiaole; Luo, Wang; Feng, Tong; Deng, Shixiong; Xie, Guoming

作者地址：[Zhang, Li; Tian, Luyao; Chen, Kena; Bai, Dan; Yu, Hongyan; Han, Xiaole; Luo, Wang; Feng, Tong; Xie, Guoming] Chongqing Med Univ, Dept Lab Med, Key Lab Lab Med Diagnost, Minist Educ, Chongqing 400016, Peoples R China; [Zhang, Li; Deng, Shixiong] Chongqing Med Univ, Dept Forens Med, Chongqing 400016, Peoples R China; [Liu, Qian] Chongqing Med Univ, Affiliated Hosp 2, Nucl Med Dept, Chongqing 400010, Peoples R China; [Guo, Yongcan] Southwest Med Univ, Tradit Chinese Med Hosp, Clin Lab, Luzhou 646000, Peoples R China

通信作者：Xie, GM (通讯作者)，Chongqing Med Univ, Dept Lab Med, Key Lab Lab Med Diagnost, Minist Educ, Chongqing 400016, Peoples R China.; Deng, SX (通讯作者)，Chongqing Med Univ, Dept Forens Med, Chongqing 400016, Peoples R China.

来源：JOURNAL OF NANOBIOTECHNOLOGY

ESI学科分类：BIOLOGY & BIOCHEMISTRY

WOS号：WOS:001204816800003

JCR分区：Q1

影响因子：10.6

年份：2024

卷号：22

期号：1

开始页：　

结束页：　

文献类型：Article

关键词：　

摘要：Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

基金机构：National Key Research and Development Program of China

基金资助正文：Not applicable.