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FT-Raman 数据分析的多元和机器学习作为一种新的方法来检测光谱标志物铂耐药的女性患有卵巢癌。

FT-Raman data analyzed by multivariate and machine learning as a new methods for detection spectroscopy marker of platinum-resistant women suffering from ovarian cancer.

机构信息

Department of Pathology, Fryderyk Chopin University Hospital, F. Szopena 2, 35-055, Rzeszow, Poland.

Department of Gynecology, Gynecology Oncology and Obstetrics, Fryderyk Chopin University Hospital, F.Szopena 2, 35-055, Rzeszow, Poland.

出版信息

Sci Rep. 2023 Nov 26;13(1):20772. doi: 10.1038/s41598-023-48169-3.

Abstract

The phenomenon of platinum resistance is a very serious problem in the treatment of ovarian cancer. Unfortunately, no molecular, genetic marker that could be used in assigning women suffering from ovarian cancer to the platinum-resistant or platinum-sensitive group has been discovered so far. Therefore, in this study, for the first time, we used FT-Raman spectroscopy to determine chemical differences and chemical markers presented in serum, which could be used to differentiate platinum-resistant and platinum-sensitive women. The result obtained showed that in the serum collected from platinum-resistant women, a significant increase of chemical compounds was observed in comparison with the serum collected from platinum-sensitive woman. Moreover, a decrease in the ratio between amides vibrations and shifts of peaks, respectively, corresponding to C-C/C-N stretching vibrations from proteins, amide III, amide II, C = O and CH lipids vibrations suggested that in these compounds, structural changes occurred. The Principal Component Analysis (PCA) showed that using FT-Raman range, where the above-mentioned functional groups were present, it was possible to differentiate the serum collected from both analyzed groups. Moreover, C5.0 decision tree clearly showed that Raman shifts at 1224 cm and 2713 cm could be used as a marker of platinum resistance. Importantly, machine learning methods showed that the accuracy, sensitivity and specificity of the FT-Raman spectroscopy were from 95 to 100%.

摘要

铂耐药现象是卵巢癌治疗中一个非常严重的问题。不幸的是,目前尚未发现任何可用于将患有卵巢癌的女性分配到铂耐药或铂敏感组的分子遗传标志物。因此,在这项研究中,我们首次使用傅里叶变换拉曼光谱(FT-Raman spectroscopy)来确定血清中存在的化学差异和化学标志物,这些标志物可用于区分铂耐药和铂敏感的女性。结果表明,与铂敏感女性的血清相比,铂耐药女性的血清中观察到化学化合物的显著增加。此外,酰胺振动和相应的峰位移之间的比率降低,分别对应于蛋白质的 C-C/C-N 伸缩振动、酰胺 III、酰胺 II、C=O 和 CH 脂质振动,表明这些化合物中发生了结构变化。主成分分析(PCA)表明,使用存在上述官能团的 FT-Raman 范围,可以区分来自两个分析组的血清。此外,C5.0 决策树清楚地表明,在 1224 cm 和 2713 cm 处的 Raman 位移可作为铂耐药的标志物。重要的是,机器学习方法表明 FT-Raman 光谱的准确性、灵敏度和特异性从 95%到 100%不等。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ed9b/10679116/63e97e0ba75e/41598_2023_48169_Fig1_HTML.jpg

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