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

通过多变量和机器学习分析 FT-Raman 数据作为检测患有铂耐药卵巢癌的女性光谱标记的新方法

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作者:Marta Kluz-Barłowska, Tomasz Kluz, Wiesław Paja, Jaromir Sarzyński, Monika Łączyńska-Madera, Adrian Odrzywolski, Paweł Król, Józef Cebulski, Joanna Depciuch

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-1 and 2713 cm-1 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%.

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