The Spectroscopic Similarity between Breast Cancer Tissues and Lymph Nodes Obtained from Patients with and without Recurrence: A Preliminary Study

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Recognition of breast cancer subtypes using FTIR hyperspectral data

Fourier-transform infrared spectroscopy (FTIR) is a powerful, non-destructive, highly sensitive and a promising analytical technique to provide spectrochemical signatures of biological samples, where markers like carbohydrates, proteins, and phosphate groups of DNA can be recognized in biological micro-environment. However, method of measurements of large cells need an excessive time to achieve high quality images, making its clinical use difficult due to speed of data-acquisition and lack of optimized computational procedures. To address such challenges, Machine Learning (ML) based technologies can assist to assess an accurate prognostication of breast cancer (BC) subtypes with high performance. Here, we applied FTIR spectroscopy to identify breast cancer subtypes in order to differentiate between luminal (BT474) and non-luminal (SKBR3) molecular subtypes. For this reason, we tested multivariate classification technique to extract feature information employing three-dimension (3D)-discriminant analysis approach based on 3D-principle component analysis-linear discriminant analysis (3D-PCA-LDA) and 3D-principal component analysis-quadratic discriminant analysis (3D-PCA-QDA), showing an improvement in sensitivity (98%), specificity (94%) and accuracy (98%) parameters compared to conventional unfolded methods. Our results evidence that 3D-PCA-LDA and 3D-PCA-QDA are potential tools for discriminant analysis of hyperspectral dataset to obtain superior classification assessment.

Evaluation of Fourier transform infrared (FTIR) spectroscopy with multivariate analysis as a novel diagnostic tool for lymph node metastasis in gastric cancer

Fourier transform infrared (FTIR) spectroscopy is a vibration spectroscopy that uses infrared radiation to vibrate to absorb the molecular bonds in its absorbed sample. The main purpose of this study was to evaluate FTIR spectroscopy as a novel diagnostic tool for lymph node metastasis (LNM) of gastric cancer. We collected 160 fresh non-metastatic and metastatic lymph nodes (80 each) from 60 patients with gastric cancer for spectral analysis. FTIR spectra of lymph node (LN) samples were obtained in the wavenumber range of 4000 cm^-1 to 900 cm^-1. We calculated the changes in the ratio of spectral intensity (/ I1460). Principal component analysis (PCA) and Fisher's discriminant analysis (FDA) were used to distinguish malignant from normal LN. Four significant bands at 1080 cm^-1, 1640 cm^-1, 1740 cm^-1 and 3260 cm^-1 separated metastatic and non-metastatic LN spectra into two distinct groups by PCA.T-tests showed that, along with the relative intensity ratios (I₁₀₈₀/I₁₄₆₀, I₁₆₄₀/I₁₄₆₀, I₃₂₆₀/I₁₄₆₀, I₁₇₄₀/I₁₄₆₀), these band ratios were also able to differentiate between malignant and benign LN spectra. Six parameters (P1080 cm^-1, P1300 cm^-1, I₁₀₈₀/I₁₄₆₀, I₁₆₄₀/I₁₄₆₀, I₃₂₆₀/I₁₄₆₀, I₁₇₄₀/I₁₄₆₀) were selected as independent factors to set up discriminant functions. The sensitivity of FTIR spectroscopy in diagnosing LNM was 95 % by discriminant analysis. Our study suggested that FTIR spectroscopy can be a useful tool to examine LNM with high sensitivity and specificity for LNM diagnosis. Therefore it can be used in clinical practice as a non-invasive method.