Integrating isothermal amplification techniques and LNA-based AI-assisted electrochemical bioassay for analysis of KRAS G12V point mutation
Language English Country Netherlands Media print-electronic
Document type Journal Article
PubMed
39961243
DOI
10.1016/j.talanta.2025.127709
PII: S0039-9140(25)00195-X
Knihovny.cz E-resources
- Keywords
- DNA point mutation, Electrochemistry, KRAS gene, Locked nucleic acid, Rolling circle amplification,
- MeSH
- Biological Assay * methods MeSH
- Point Mutation * MeSH
- Electrochemical Techniques * methods MeSH
- Humans MeSH
- DNA Mutational Analysis methods MeSH
- Cell Line, Tumor MeSH
- Oligonucleotides * chemistry genetics MeSH
- Proto-Oncogene Proteins p21(ras) * genetics MeSH
- Nucleic Acid Amplification Techniques * methods MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- KRAS protein, human MeSH Browser
- locked nucleic acid MeSH Browser
- Oligonucleotides * MeSH
- Proto-Oncogene Proteins p21(ras) * MeSH
The KRAS mutation is a crucial biomarker for determining targeted cancer therapies, making its accurate and cost-effective detection vital for precision oncology. However, current methodologies, such as next-generation sequencing (NGS) or PCR-based methods, are often expensive and technically complex, limiting their accessibility. Here, we present a novel bioassay for KRAS G12V mutation analysis that combines rolling circle amplification (RCA) with locked nucleic acid (LNA)-modified magnetic beads, electrochemical detection using carbon electrode chips, and AI-assisted analysis via a logistic regression classifier. Our platform demonstrated exceptional selectivity in distinguishing the KRAS G12V mutation from wild-type (wt) sequences, enabling analysis <1 % of mutated DNA in a wt sample. We validated the bioassay on 7 cancer cell lines and 11 patient-derived samples, achieving results that perfectly correlated with NGS data. This innovative approach simplifies the workflow, reduces costs, and offers high sensitivity and specificity, making it a promising tool for clinical diagnostics and personalized cancer treatment strategies.
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