Ja'farawy Muhammad Shalahuddin Al, Linh Vo Thi Nhat, Mun Chaewon, Yang Jun-Yeong, Kim Jun Young, Park Rowoon, Park Sung-Gyu, Kim Dong-Ho, Lee Min-Young, Jung Ho Sang
Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, Gyeongnam, 51508, South Korea.
Advanced Materials Engineering, Korea National University of Science and Technology (UST), Daejeon, 34113, South Korea.
Adv Sci (Weinh). 2025 May 8:e2503257. doi: 10.1002/advs.202503257.
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.
表观遗传DNA甲基化与癌基因激活和肿瘤抑制基因失活有关。一种可靠且无需标记的定量测量DNA甲基化水平的方法对于诊断和监测甲基化相关疾病至关重要。在此，开发了等离子体分子捕获（PME）方法辅助的表面增强拉曼光谱（SERS），作为一种简便的策略，用于捕获和无标记检测DNA甲基化，该方法利用目标分析物存在下等离子体颗粒的原位表面生长。这种高度灵敏且适应性强的技术在目标分析物周围形成热点，克服了不匹配的几何特性，并产生强大的电磁场，从而导致显著的SERS信号增强。PME方法有效地对DNA甲基化进行了分析和定量，展示了强大的DNA分析能力。基于逻辑回归（LR）的机器学习能够以高灵敏度、特异性和准确性对结直肠癌临床血清样本和正常患者的甲基化水平进行准确的定量和分类，突出了该技术的可行性。所开发的PME方法与机器学习相结合，为疾病筛查和诊断提供了有前景的传感技术，标志着疾病检测和患者护理方面的重大进展。
