Protein Research Unit Ruhr within Europe (PURE), Ruhr-University Bochum, Department of Biophysics ND04-596, Universitätsstrasse 150, 44780 Bochum, Germany.
Analyst. 2013 Jul 21;138(14):4092-102. doi: 10.1039/c3an00337j. Epub 2013 May 28.
The extraction of disease specific information from Fourier transform infrared (FTIR) spectra of human body fluids demands the highest standards of accuracy and reproducibility of measurements because the expected spectral differences between healthy and diseased subjects are very small in relation to a large background absorbance of the whole sample. Here, we demonstrate that with the increased sensitivity of modern FTIR spectrometers, automatisation of sample preparation and modern bioinformatics, it is possible to identify and validate spectral biomarker candidates for distinguishing between urinary bladder cancer (UBC) and inflammation in suspected bladder cancer patients. The current dataset contains spectra of blood serum and plasma samples of 135 patients. All patients underwent cytology and pathological biopsy characterization to distinguish between patients without UBC (46) and confirmed UBC cases (89). A minimally invasive blood test could spare control patients a repeated cystoscopy including a transurethral biopsy, and three-day stationary hospitalisation. Blood serum, EDTA and citrate plasma were collected from each patient and processed following predefined strict standard operating procedures. Highly reproducible dry films were obtained by spotting sub-nanoliter biofluid droplets in defined patterns, which were compared and optimized. Particular attention was paid to the automatisation of sample preparation and spectral preprocessing to exclude errors by manual handling. Spectral biomarker candidates were identified from absorbance spectra and their 1(st) and 2(nd) derivative spectra using an advanced Random Forest (RF) approach. It turned out that the 2(nd) derivative spectra were most useful for classification. Repeat validation on 21% of the dataset not included in predictor training with Linear Discriminant Analysis (LDA) classifiers and Random Forests (RFs) yielded a sensitivity of 93 ± 10% and a specificity of 46 ± 18% for bladder cancer. The low specificity can be most likely attributed to the unbalanced and small number of control samples. Using this approach, spectral biomarker candidates in blood-derived biofluids were identified, which allow us to distinguish between cancer and inflammation, but the observed differences were tiny. Obviously, a much larger sample number has to be investigated to reliably validate such candidates.
从人体体液的傅里叶变换红外（FTIR）光谱中提取疾病特异性信息需要最高标准的测量准确性和可重复性，因为健康和患病受试者之间预期的光谱差异与整个样本的大背景吸收率相比非常小。在这里，我们证明了，随着现代 FTIR 光谱仪的灵敏度提高、样品制备自动化和现代生物信息学的发展，有可能识别和验证区分膀胱癌（UBC）和疑似膀胱癌患者炎症的光谱生物标志物候选物。当前数据集包含 135 名患者的血清和血浆样本光谱。所有患者均接受细胞学和病理活检特征分析，以区分无 UBC（46 例）和确诊 UBC 病例（89 例）。微创血液检测可以使对照患者免于重复进行包括经尿道活检在内的膀胱镜检查，以及为期三天的住院治疗。从每位患者采集血清、EDTA 和柠檬酸盐血浆，并按照预先规定的严格标准操作程序进行处理。通过以预定模式点出亚纳升级生物流体液滴，获得高度可重复的干燥膜，并对其进行比较和优化。特别注意通过手动处理排除错误的样品制备和光谱预处理的自动化。使用先进的随机森林（RF）方法从吸光度光谱及其一阶和二阶导数光谱中识别光谱生物标志物候选物。结果表明，二阶导数光谱最有利于分类。在不包括预测器训练的数据集的 21%上进行重复验证，使用线性判别分析（LDA）分类器和随机森林（RF）进行分类，膀胱癌的灵敏度为 93%±10%，特异性为 46%±18%。特异性低很可能归因于对照样本的不平衡和数量较少。使用这种方法，在血液衍生的生物流体中识别了光谱生物标志物候选物，这些候选物可用于区分癌症和炎症，但观察到的差异很小。显然，必须研究更多的样本数量来可靠地验证这些候选物。
