Iancu Stefania D, Cozan Ramona G, Stefancu Andrei, David Maria, Moisoiu Tudor, Moroz-Dubenco Cristiana, Bajcsi Adel, Chira Camelia, Andreica Anca, Leopold Loredana F, Eniu Daniela, Staicu Adelina, Goidescu Iulian, Socaciu Carmen, Eniu Dan T, Diosan Laura, Leopold Nicolae
Faculty of Physics, Babeș-Bolyai University, 400084 Cluj-Napoca, Romania.
Faculty of Chemistry and Chemical Engineering, Babeș-Bolyai University, 400028 Cluj-Napoca, Romania.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 May 15;273:120992. doi: 10.1016/j.saa.2022.120992. Epub 2022 Feb 4.
SERS analysis of biofluids, coupled with classification algorithms, has recently emerged as a candidate for point-of-care medical diagnosis. Nonetheless, despite the impressive results reported in the literature, there are still gaps in our knowledge of the biochemical information provided by the SERS analysis of biofluids. Therefore, by a critical assignment of the SERS bands, our work aims to provide a systematic analysis of the molecular information that can be achieved from the SERS analysis of serum and urine obtained from breast cancer patients and controls. Further, we compared the relative performance of five different machine learning algorithms for breast cancer and control samples classification based on the serum and urine SERS datasets, and found comparable classification accuracies in the range of 61-89%. This result is not surprising since both biofluids show striking similarities in their SERS spectra providing similar metabolic information, related to purine metabolites. Lastly, by carefully comparing the two datasets (i.e., serum and urine) we show that it is possible to link the misclassified samples to specific metabolic imbalances, such as carotenoid levels, or variations in the creatinine concentration.
生物流体的表面增强拉曼光谱（SERS）分析与分类算法相结合，最近已成为即时医疗诊断的候选方法。尽管如此，尽管文献中报道了令人印象深刻的结果，但我们对生物流体SERS分析所提供的生化信息的了解仍存在差距。因此，通过对SERS谱带的关键赋值，我们的工作旨在对从乳腺癌患者和对照者的血清和尿液的SERS分析中获得的分子信息进行系统分析。此外，我们基于血清和尿液SERS数据集比较了五种不同机器学习算法对乳腺癌和对照样本分类的相对性能，发现分类准确率在61%-89%范围内相当。这个结果并不奇怪，因为两种生物流体在其SERS光谱中显示出惊人的相似性，提供了与嘌呤代谢物相关的相似代谢信息。最后，通过仔细比较两个数据集（即血清和尿液），我们表明有可能将误分类的样本与特定的代谢失衡联系起来，如类胡萝卜素水平或肌酐浓度的变化。
