Department of Urology, La Croix du Sud Hospital, 52 Chemin de Ribaute St., 31130 Quint Fonsegrives, France.
Department of Urology, Clinical Municipal Hospital, 11 Tabacarilor St., 400139 Cluj-Napoca, Romania.
Int J Mol Sci. 2024 Mar 31;25(7):3891. doi: 10.3390/ijms25073891.
The advent of Surface-Enhanced Raman Scattering (SERS) has enabled the exploration and detection of small molecules, particularly in biological fluids such as serum, blood plasma, urine, saliva, and tears. SERS has been proposed as a simple diagnostic technique for various diseases, including cancer. Renal cell carcinoma (RCC) ranks as the sixth most commonly diagnosed cancer in men and is often asymptomatic, with detection occurring incidentally. The onset of symptoms typically aligns with advanced disease, aggressive histology, and unfavorable prognosis, and therefore new methods for an early diagnosis are needed. In this study, we investigated the utility of label-free SERS in urine, coupled with two multivariate analysis approaches: Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM), to discriminate between 50 RCC patients and 44 healthy donors. Employing LDA-PCA, we achieved a discrimination accuracy of 100% using 13 principal components, and an 88% accuracy in discriminating between different RCC stages. The SVM approach yielded a training accuracy of 100%, a validation accuracy of 99% for discriminating between RCC and controls, and an 80% accuracy for discriminating between stages. The comparative analysis of raw and normalized SERS spectral data shows that while raw data disclose relative concentration variations in urine metabolites between the two classes, the normalization of spectral data significantly improves the accuracy of discrimination. Moreover, the selection of principal components with markedly distinct scores between the two classes serves to alleviate overfitting risks and reduces the number of components employed for discrimination. We obtained the accuracy of the discrimination between the RCC patients cases and healthy donors of 90% for three PCs and a linear discrimination function, and a 88% accuracy of discrimination between stages using six PCs, mitigating practically the risk of overfitting and increasing the robustness of our analysis. Our findings underscore the potential of label-free SERS of urine in conjunction with chemometrics for non-invasive and early RCC detection.
表面增强拉曼散射（SERS）的出现使得对小分子的探索和检测成为可能，特别是在血清、血浆、尿液、唾液和眼泪等生物流体中。SERS 已被提议作为各种疾病的简单诊断技术，包括癌症。肾细胞癌（RCC）是男性中第六种最常见的癌症，通常无症状，偶然发现。症状的出现通常与晚期疾病、侵袭性组织学和不良预后一致，因此需要新的早期诊断方法。在这项研究中，我们研究了无标记 SERS 在尿液中的应用，结合两种多元分析方法：主成分分析结合线性判别分析（PCA-LDA）和支持向量机（SVM），以区分 50 名 RCC 患者和 44 名健康供体。使用 LDA-PCA，我们使用 13 个主成分实现了 100%的区分准确率，对不同 RCC 阶段的区分准确率为 88%。SVM 方法的训练准确率为 100%，对 RCC 和对照组的区分准确率为 99%，对不同阶段的区分准确率为 80%。原始和归一化 SERS 光谱数据的比较分析表明，虽然原始数据揭示了两类尿液代谢物之间的相对浓度变化，但光谱数据的归一化显著提高了区分的准确性。此外，选择两类之间得分差异显著的主成分可以减轻过拟合风险，并减少用于区分的成分数量。我们获得了 RCC 患者和健康供体之间的 90%区分准确率，使用三个主成分和一个线性判别函数，以及使用六个主成分的 88%区分准确率，减轻了过拟合的风险，提高了我们分析的稳健性。我们的研究结果突出了无标记 SERS 与化学计量学相结合在非侵入性和早期 RCC 检测中的潜力。
