利用基于表面增强拉曼光谱和 AdaBoost 算法的无标记血清 RNA 快速鉴别宫颈癌和子宫肌瘤

IF 0.8 4区 化学 Q4 SPECTROSCOPY
Ziyun Jiao, Guohua Wu, Jing Wang, Xiangxiang Zheng, Longfei Yin
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引用次数: 0

摘要

我们研究了利用表面增强拉曼散射(SERS)技术结合 AdaBoost 算法快速区分宫颈癌患者和子宫肌瘤患者的可行性。我们使用金胶体作为 SERS 活性基底,对 35 名宫颈癌患者和 30 名子宫肌瘤患者的血清 RNA 样品进行了拉曼信号测量。使用主成分分析法对 RNA SERS 光谱进行分析,然后使用独立样本 t 检验(p <0.05)选出差异显著的三个主成分(PC2、PC11 和 PC24)。结果发现,在 448、519、698、1003 和 1076 cm-1 处测量到的相关物质的独特峰强度与该物质在致癌过程中的变化相关。通过调整参数,开发出了理想的 AdaBoost 分类模型。结果表明,该模型的准确率高达 96.92%,灵敏度高达 94.28%,特异性高达 100%。与线性判别分析和支持向量机模型相比,分类的有效性大大提高。目前的研究结果表明,血清 SERS 技术与 AdaBoost 算法相结合,有望发展成为一种有效的宫颈癌筛查工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid Discrimination of Cervical Cancer from Hysteromyoma Using Label-Free Serum RNA Based on Surface-Enhanced Raman Spectroscopy and AdaBoost Algorithm

We investigated the feasibility of using surface-enhanced Raman scattering (SERS) technology combined with the AdaBoost algorithm to rapidly discriminate cervical cancer patients from hysteromyoma patients. Using Au colloids as the SERS active substrate, we recorded Raman signal measurements on serum RNA samples obtained from 35 patients diagnosed with cervical cancer and 30 patients diagnosed with hysteromyoma. Analysis of RNA SERS spectra using principal component analysis, then three principal components (PC2, PC11, and PC24) with significant differences were chosen using the independent samples t-test (p < 0.05). The distinctive peak intensities of the relevant substance, measured at 448, 519, 698, 1003, and 1076 cm–1, were found to be correlated with the substance’s alterations during the carcinogenesis process. The ideal AdaBoost classification model was developed by fi ne-tuning its parameters. The model showcased an impressive accuracy of 96.92%, exhibiting a high sensitivity of 94.28% and an exceptional specificity of 100%, as reported in the results. Compared to the linear discriminant analysis, support vector machine models, the effectiveness of classification greatly improved. The current findings indicate that serum SERS technology, combined with the AdaBoost algorithm, is anticipated to be developed into a potent screening tool for cervical cancer.

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来源期刊
CiteScore
1.30
自引率
14.30%
发文量
145
审稿时长
2.5 months
期刊介绍: Journal of Applied Spectroscopy reports on many key applications of spectroscopy in chemistry, physics, metallurgy, and biology. An increasing number of papers focus on the theory of lasers, as well as the tremendous potential for the practical applications of lasers in numerous fields and industries.
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