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

• Published in: Scientific reports Vol. 13; no. 1; p. 20772
• Main Authors: Kluz-Barłowska, Marta, Kluz, Tomasz, Paja, Wiesław, Sarzyński, Jaromir, Łączyńska-Madera, Monika, Odrzywolski, Adrian, Król, Paweł, Cebulski, Józef, Depciuch, Joanna
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26-11-2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1517; 692/4028; 692/53; Amides; Female; Genetic markers; Humanities and Social Sciences; Humans; Learning algorithms; Lipids; Machine learning; multidisciplinary; Ovarian cancer; Ovarian Neoplasms - drug therapy; Platinum; Principal components analysis; Proteins; Raman spectroscopy; Science; Science (multidisciplinary); Spectroscopy; Spectrum analysis; Spectrum Analysis, Raman - methods; Vibrations
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-48169-3

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%.