Dechan Lu,Yanping Chen,Longfeng Ke,Zufang Huang,Yudong Lu,Jing Wang
DNA methylation is a promising biomarker for early cancer detection; however, traditional detection methods require bisulfite treatment and nucleic acid amplification, which are costly and impractical for point-of-care use. We propose a facile method that leverages immunoassays and surface-enhanced Raman scattering (SERS) readouts for the multiplex, sensitive, and portable detection of lung cancer-related DNA methylation biomarkers. This approach employs 5-methylcytosine antibody-modified magnetic probes to enrich methylated DNA sequences and uses core–shell nanoparticles with Probe DNA as SERS nanotags for multiplex detection, eliminating the need for bisulfite treatment and nucleic acid amplification. The multiplex SERS immunoassay detects synthetic methylated SHOX2 and RASSF1A with detection limits of 0.52 pM and 0.66 pM, respectively. It shows a strong correlation with quantitative PCR results in analyzing methylated SHOX2 and RASSF1A from cell lines and formalin-fixed paraffin-embedded tissue samples (n = 35). Additionally, the random forest analysis based on methylated SHOX2 and RASSF1A expression distinguishes lung cancer from benign lung diseases with a clinical sensitivity of 96 %, specificity of 90 %, and an AUC value of 0.972. This method offers a significant advancement for DNA methylation research and clinical applications.
This study employed headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) technology combined with multivariate statistical analysis methods to analyze the flavor compounds in flue-cured tobacco from five different regions in China: Henan, Hunan, Yunnan, Chongqing, and Fujian. A total of 98 volatile aroma compounds were identified through HS-GC-IMS analysis, including esters, ketones, aldehydes, acids, alcohols, heterocyclic compounds, sulfur-containing compounds, other types of compounds, and 8 uncharacterized compounds. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were utilized to conduct dimensionality reduction and distinguish the samples, effectively recognizing differences in volatile compounds among tobacco leaves from various origins. A Random Forest (RF) classification model was constructed, and its reliability was validated through ROC (Receiver Operating Characteristic) analysis, achieving an AUC (Area Under the Curve) value of 0.980, which demonstrates exceptional predictive performance. PCA revealed distinct separations of tobacco leaf samples from different regions on the PCA score plot, and OPLS-DA analysis further validated these differences and confirmed the model's validity through permutation testing. Twenty key aroma compounds with VIP > 1.0 were screened by integrating OPLS-DA with the Random Forest classification model. These compounds showed significant differences in content among different samples, suggesting their potential as chemical markers for distinguishing the origin of flue-cured tobacco. This study not only provides a new method for identifying volatile compounds in tobacco but also offers novel insights into the geographical identification of flue-cured tobacco.
危险品化学品经营许可证（带存储）