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.
Sci Rep. 2024 May 14;14(1):11025. doi: 10.1038/s41598-024-61775-z.
Platinum-resistant phenomena in ovarian cancer is very dangerous for women suffering from this disease, because reduces the chances of complete recovery. Unfortunately, until now there are no methods to verify whether a woman with ovarian cancer is platinum-resistant. Importantly, histopathology images also were not shown differences in the ovarian cancer between platinum-resistant and platinum-sensitive tissues. Therefore, in this study, Fourier Transform InfraRed (FTIR) and FT-Raman spectroscopy techniques were used to find chemical differences between platinum-resistant and platinum-sensitive ovarian cancer tissues. Furthermore, Principal Component Analysis (PCA) and machine learning methods were performed to show if it possible to differentiate these two kind of tissues as well as to propose spectroscopy marker of platinum-resistant. Indeed, obtained results showed, that in platinum-resistant ovarian cancer tissues higher amount of phospholipids, proteins and lipids were visible, however when the ratio between intensities of peaks at 1637 cm (FTIR) and at 2944 cm (Raman) and every peaks in spectra was calculated, difference between groups of samples were not noticed. Moreover, structural changes visible as a shift of peaks were noticed for C-O-C, C-H bending and amide II bonds. PCA clearly showed, that PC1 can be used to differentiate platinum-resistant and platinum-sensitive ovarian cancer tissues, while two-trace two-dimensional correlation spectra (2T2D-COS) showed, that only in amide II, amide I and asymmetric CH lipids vibrations correlation between two analyzed types of tissues were noticed. Finally, machine learning algorithms showed, that values of accuracy, sensitivity and specificity were near to 100% for FTIR and around 95% for FT-Raman spectroscopy. Using decision tree peaks at 1777 cm, 2974 cm (FTIR) and 1714 cm, 2817 cm (FT-Raman) were proposed as spectroscopy marker of platinum-resistant.
铂耐药现象在卵巢癌中对患有这种疾病的女性非常危险，因为这降低了完全康复的机会。不幸的是，到目前为止，还没有方法来验证患有卵巢癌的女性是否对铂耐药。重要的是，组织病理学图像也没有显示铂耐药和铂敏感组织之间的卵巢癌的差异。因此，在这项研究中，傅里叶变换红外（FTIR）和 FT-Raman 光谱技术被用于寻找铂耐药和铂敏感卵巢癌组织之间的化学差异。此外，还进行了主成分分析（PCA）和机器学习方法，以显示是否有可能区分这两种组织，并提出铂耐药的光谱标志物。事实上，研究结果表明，在铂耐药的卵巢癌组织中，可见到更高量的磷脂、蛋白质和脂质，但当计算强度峰比（FTIR 的 1637 cm-1 和 Raman 的 2944 cm-1）和光谱中每个峰时，未观察到组间的差异。此外，观察到的结构变化表现为峰的移动，如 C-O-C、C-H 弯曲和酰胺 II 键。PCA 清楚地表明，PC1 可用于区分铂耐药和铂敏感的卵巢癌组织，而二维相关光谱（2T2D-COS）表明，只有在酰胺 II、酰胺 I 和不对称 CH 脂质振动中，才能观察到两种分析类型的组织之间的相关性。最后，机器学习算法表明，FTIR 的准确率、灵敏度和特异性接近 100%，FT-Raman 光谱的准确率约为 95%。使用决策树，FTIR 的 1777 cm 和 2974 cm 以及 FT-Raman 的 1714 cm 和 2817 cm 的峰被提出作为铂耐药的光谱标志物。
