Background: Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide, emphasizing the urgent need for early, non-invasive, and accessible diagnostic tools. This study aimed to evaluate the effectiveness of a microelectromechanical systems (MEMS)-based electronic nose (E-nose) in combination with gas chromatography–mass spectrometry (GC-MS) for CRC detection through sweat volatile organic compounds (VOCs). Methods: A total of 136 sweat samples were collected from 68 volunteer participants. Samples were processed using solid-phase microextraction (SPME) and analyzed by GC-MS, while a custom-designed E-nose system comprising 14 gas sensors captured real-time VOC profiles. Data were analyzed using multivariate statistical techniques, including PCA and PLS-DA, and classified with machine learning algorithms (LDA, LR, SVM, k-NN). Results: GC-MS analysis revealed statistically significant differences between CRC patients and healthy controls (COs). Cross-validation showed that the highest classification accuracy for GC-MS data was 81% with the k-NN classifier, whereas E-nose data achieved up to 97% accuracy using the LDA classifier. Conclusions: Sweat volatilome analysis, supported by advanced data processing and complementary use of E-nose technology and GC-MS, demonstrates strong potential as a reliable, non-invasive approach for early CRC detection.
背景：结直肠癌（CRC）仍是全球癌症相关死亡的主要原因之一，凸显了对早期、无创且易于获取的诊断工具的迫切需求。本研究旨在评估基于微机电系统（MEMS）的电子鼻（E-nose）结合气相色谱-质谱联用技术（GC-MS）通过汗液挥发性有机化合物（VOCs）检测CRC的有效性。方法：共收集68名志愿者参与者的136份汗液样本。样本采用固相微萃取（SPME）处理，并通过GC-MS进行分析，同时使用包含14个气体传感器的定制电子鼻系统实时捕获VOCs图谱。数据采用多元统计技术（包括主成分分析PCA和偏最小二乘判别分析PLS-DA）进行分析，并运用机器学习算法（线性判别分析LDA、逻辑回归LR、支持向量机SVM、k近邻k-NN）进行分类。结果：GC-MS分析显示CRC患者与健康对照者（COs）之间存在统计学显著差异。交叉验证表明，GC-MS数据在k-NN分类器中最高分类准确率为81%，而电子鼻数据使用LDA分类器准确率高达97%。结论：汗液挥发性组分析在先进数据处理及电子鼻技术与GC-MS互补应用的支持下，展现出作为早期CRC检测可靠、无创方法的强大潜力。
肿瘤(癌症)患者之家