Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients

Abstract

Despite numerous advances in "omics" research, early detection of ovarian cancer still remains a challenge. The aim of this study was to determine whether attenuated total reflection Fourier-transform infrared (ATR-FTIR) or Raman spectroscopy could characterise alterations in the biomolecular signatures of human blood plasma/serum obtained from ovarian cancer patients compared to non-cancer controls. Blood samples isolated from ovarian cancer patients (n = 30) and healthy controls (n = 30) were analysed using ATR-FTIR spectroscopy. For comparison, a smaller cohort of samples (n = 8) were analysed using an InVia Renishaw Raman spectrometer. Resultant spectra were pre-processed prior to being inputted into principal component analysis (PCA) and linear discriminant analysis (LDA). Statistically significant differences (P < 0.001) were observed between spectra of ovarian cancer versus control subjects for both biospectroscopy methods. Using a support vector machine classifier for Raman spectra of blood plasma, a diagnostic accuracy of 74% was achieved, while the same classifier showed 93.3% accuracy for IR spectra of blood plasma. These observations suggest that a biospectroscopy approach could be applied to identify spectral alterations associated with the presence of insidious ovarian cancer.

Using Fourier transform IR spectroscopy to analyze biological materials

“…5). This data set consisted of blood serum and plasma samples of patients with ovarian cancer or endometrial cancer ( n = 30 for both) and control patients without ovarian cancer ( n = 30) analyzed with ATR-FTIR spectroscopy (7 spectra per sample) 41,131 . The classification rate, defined as the average between sensitivity and specificity, was used as a classification performance measure to class patients on the basis of their disease status (i.e., ‘normal’ versus ‘cancer’).…”

Ultra-filtration of human serum for improved quantitative analysis of low molecular weight biomarkers using ATR-IR spectroscopy

“…The IR spectrum contains rich and specific information about the molecular composition of the human serum which, coupled to advanced multivariate analysis tools, could deliver accurate diagnostics. Recent systematic studies have demonstrated the potential of IR serum based diagnosis with different data mining approaches such as Principal Components Analysis (PCA), PCA coupled to Linear Discriminant Analysis (PCA-LDA), Random Forest or Support Vector Machine (SVM) [26][27][28][29][30] . However, the development of spectroscopic technologies with medical perspectives needs to align with clinical requirements to enable clinical translation of these promising technologies 31 .…”

Towards SERS based applications in food analytics: Lipophilic sensor layers for the detection of Sudan III in food matrices

“…Due to its high specificity Raman spectroscopy is a suitable and widely used method for a high number of analytical tasks [31][32][33][34][35][36]. However, the Raman-effect is a very weak scattering process.…”