Dipartimento di Medicina Clinica e Sperimentale, Università di Foggia, 71122 Foggia, Italy.
Dipartimento di Medicina Clinica e Sperimentale, Università di Foggia, 71122 Foggia, Italy.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Nov 15;321:124683. doi: 10.1016/j.saa.2024.124683. Epub 2024 Jun 19.
Colorectal cancer is one of the most diagnosed types of cancer in developed countries. Current diagnostic methods are partly dependent on pathologist experience and laboratories instrumentation. In this study, we used Fourier Transform Infrared (FTIR) spectroscopy in transflection mode, combined with Principal Components Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares - Discriminant Analysis (PLS-DA), to build a classification algorithm to diagnose colon cancer in cell samples, based on absorption spectra measured in two spectral ranges of the mid-infrared spectrum. In particular, PCA technique highlights small biochemical differences between healthy and cancerous cells: these are related to the larger lipid content in the former compared with the latter and to the larger relative amount of protein and nucleic acid components in the cancerous cells compared with the healthy ones. Comparison of the classification accuracy of PCA-LDA and PLS-DA methods applied to FTIR spectra measured in the 1000-1800 cm (low wavenumber range, LWR) and 2700-3700 cm (high wavenumber range, HWR) remarks that both algorithms are able to classify hidden class FTIR spectra with excellent accuracy (100 %) in both spectral regions. This is a hopeful result for clinical translation of infrared spectroscopy: in fact, it makes reliable the predictions obtained using FTIR measurements carried out only in the HWR, in which the glass slides used in clinical laboratories are transparent to IR radiation.
结直肠癌是发达国家最常见的癌症类型之一。目前的诊断方法部分依赖于病理学家的经验和实验室仪器。在这项研究中，我们使用反射模式下的傅里叶变换红外（FTIR）光谱，结合主成分分析（PCA），然后是线性判别分析（LDA）和偏最小二乘判别分析（PLS-DA），建立了一种分类算法，用于诊断细胞样本中的结肠癌，该算法基于中红外光谱两个光谱范围内测量的吸收光谱。特别是，PCA 技术突出了健康细胞和癌细胞之间的微小生化差异：这些差异与前者的脂质含量较大而后者的脂质含量较小有关，与前者的蛋白质和核酸成分的相对含量较大而后者的蛋白质和核酸成分的相对含量较小有关。比较 PCA-LDA 和 PLS-DA 方法应用于在 1000-1800 cm（低波数范围，LWR）和 2700-3700 cm（高波数范围，HWR）测量的 FTIR 光谱的分类准确性表明，这两种算法都能够以优异的准确性（100%）对隐藏类 FTIR 光谱进行分类，在这两个光谱区域都是如此。这是红外光谱学临床转化的一个有希望的结果：事实上，它使仅在 HWR 中进行的 FTIR 测量获得的预测变得可靠，在临床实验室中使用的载玻片对 IR 辐射是透明的。
