DNA-based molecular classifiers for the profiling of gene expression signatures.
J Nanobiotechnology
; 22(1): 189, 2024 Apr 17.
Article
em En
| MEDLINE
| ID: mdl-38632615
ABSTRACT
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Carcinoma Hepatocelular
/
Neoplasias Hepáticas
Limite:
Humans
Idioma:
En
Revista:
J Nanobiotechnology
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China