School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200437, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 May 5;312:124081. doi: 10.1016/j.saa.2024.124081. Epub 2024 Feb 23.
Intestinal Disease (ID) is often characterized by clinical symptoms such as malabsorption, intestinal dysfunction, and injury. If treatment is not timely, it will increase the risk of cancer. Early diagnosis of ID is the key to cure it. There are certain limitations of the conventional diagnostic methods, such as low sensitivity and specificity. Therefore, development of a highly sensitive, non-invasive diagnostic method for ID is extremely important. Urine samples are easier to collect and more sensitive to changes in biomolecules than other pathological diagnostic samples such as tissue and blood. In this paper, a diagnostic method of ID with urine by surface-enhanced Raman spectroscopy (SERS) is proposed. A classification model between ID patients and healthy controls (HC) and a classification model between different pathological types of ID (i.e., benign intestinal disease (BID) and colorectal cancer (CRC)) are established. Here, 830 urine samples, including 100 HC, 443 BID, and 287 CRC, were investigated by SERS. The ID/HC classification model was developed by analyzing the SERS spectra of 150 ID and 100 HC, while BID/CRC classification model was built with 300 BID and 150 CRC patients by principal component analysis (PCA)-support vector machines (SVM). The two established models were internally verified by leave-one-out-cross-validation (LOOCV). Finally, the BID/CRC classification model was further evaluated by 143 BID and 137 CRC patients as an external test set. It shows that the accuracy of the classification model validated by the LOOCV for ID/HC and BID/CRC is 86.4% and 85.56%, respectively. And the accuracy of the BID/CRC classification model with external test set is 82.14%. It shows that high accuracy can be achieved with these two established classification models. It indicates that ID patients in the general population can be identified and BID and CRC patients can be further classified with measuring urine by SERS. It shows that the proposed diagnostic method and established classification models provide valuable information for clinicians to early diagnose ID patients and analyze different stages of ID.
肠道疾病（ID）常表现为吸收不良、肠道功能障碍和损伤等临床症状。如果不及时治疗，会增加癌症的风险。ID 的早期诊断是治愈它的关键。传统诊断方法存在一定的局限性，如灵敏度和特异性低。因此，开发一种高度敏感、非侵入性的 ID 诊断方法非常重要。与组织和血液等其他病理诊断样本相比，尿液样本更容易采集，对生物分子的变化更敏感。本文提出了一种基于表面增强拉曼光谱（SERS）的 ID 尿液诊断方法。建立了 ID 患者与健康对照（HC）之间的分类模型和不同病理类型 ID（良性肠道疾病（BID）和结直肠癌（CRC））之间的分类模型。在此，对 830 例尿液样本进行了 SERS 分析，其中包括 100 例 HC、443 例 BID 和 287 例 CRC。通过分析 150 例 ID 和 100 例 HC 的 SERS 光谱，建立了 ID/HC 分类模型，通过主成分分析（PCA）-支持向量机（SVM）建立了 300 例 BID 和 150 例 CRC 患者的 BID/CRC 分类模型。通过留一法交叉验证（LOOCV）对两个建立的模型进行内部验证。最后，通过 143 例 BID 和 137 例 CRC 患者作为外部测试集进一步评估 BID/CRC 分类模型。结果表明，LOOCV 验证的 ID/HC 和 BID/CRC 分类模型的准确率分别为 86.4%和 85.56%，外部测试集的 BID/CRC 分类模型的准确率为 82.14%。这表明这两个建立的分类模型可以达到较高的准确率。这表明可以通过测量尿液的 SERS 来识别普通人群中的 ID 患者，并进一步对 BID 和 CRC 患者进行分类。这表明所提出的诊断方法和建立的分类模型为临床医生早期诊断 ID 患者和分析 ID 的不同阶段提供了有价值的信息。
