Plasmonic Molecular Entrapment for Label‐Free Methylated DNA Detection and Machine‐Learning Assisted Quantification

Plasmonic Molecular Entrapment for Label‐Free Methylated DNA Detection and Machine‐Learning Assisted Quantification

Abstract Epigenetic DNA methylations are linked to the activation of oncogenes and inactivation of tumor suppressor genes. A reliable and label‐free method to quantitatively measure DNA methylation levels is essential for diagnosing and monitoring methylation‐related diseases. Herein, plasmonic mole...

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Main Authors: Muhammad Shalahuddin Al Ja'farawy, Vo Thi Nhat Linh, Chaewon Mun, Jun‐Yeong Yang, Jun Young Kim, Rowoon Park, Sung‐Gyu Park, Dong‐Ho Kim, Min‐Young Lee, Ho Sang Jung
Format: Article
Language:English
Published: Wiley 2025-08-01
Series:Advanced Science
Subjects:
DNA methylation
hotspot engineering
label‐free diagnosis
plasmonic materials
surface‐enhanced Raman scattering
Online Access:https://doi.org/10.1002/advs.202503257
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Summary:Abstract Epigenetic DNA methylations are linked to the activation of oncogenes and inactivation of tumor suppressor genes. A reliable and label‐free method to quantitatively measure DNA methylation levels is essential for diagnosing and monitoring methylation‐related diseases. Herein, plasmonic molecular entrapment (PME) method assisted SERS as facile strategy for trapping and label‐free sensing of DNA methylation, utilizing in situ surface growth of plasmonic particle in the presence of target analytes, are developed. This highly sensitive and adaptable technique forms hotspot sites around target analytes, overcoming mismatch geometrical properties and producing a strong electromagnetic field that leads to significant SERS signal enhancement. The PME method effectively profiles and quantifies DNA methylation, demonstrating robust capabilities for DNA analysis. A logistic regression (LR)‐based machine learning accurately quantifies and classifies methylation levels in clinical serum samples of colorectal cancer and normal patients with high sensitivity, specificity, and accuracy, highlighting the feasibility of this technique. The developed PME method combined with machine learning offers promising sensing techniques for disease screening and diagnosis, marking a significant advancement in disease detection and patient care.
ISSN:2198-3844

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