DNA-based molecular classifiers for the profiling of gene expression signatures.
Journal
Journal of nanobiotechnology
ISSN: 1477-3155
Titre abrégé: J Nanobiotechnology
Pays: England
ID NLM: 101152208
Informations de publication
Date de publication:
17 Apr 2024
17 Apr 2024
Historique:
received:
28
09
2023
accepted:
28
03
2024
medline:
19
4
2024
pubmed:
18
4
2024
entrez:
17
4
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38632615
doi: 10.1186/s12951-024-02445-0
pii: 10.1186/s12951-024-02445-0
pmc: PMC11025223
doi:
Substances chimiques
DNA
9007-49-2
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
189Subventions
Organisme : National Key Research and Development Program of China
ID : 2022YFC2603803
Organisme : National Natural Science Foundation of China
ID : 82172369
Informations de copyright
© 2024. The Author(s).
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