Zibo Municipal Hospital, Zibo, Shandong, 255400, China.
Department of clinical laboratory, Central Hospital of Xiangtan, Xiangtan, Hunan, 411100, China.
BMC Cancer. 2022 Feb 28;22(1):214. doi: 10.1186/s12885-022-09318-5.
Bladder cancer (BC) is one of the most frequent cancer in the world, and its incidence is rising worldwide, especially in developed countries. Urine metabolomics is a powerful approach to discover potential biomarkers for cancer diagnosis. In this study, we applied an ultra-performance liquid chromatography coupled to mass spectrometry (UPLC-MS) method to profile the metabolites in urine from 29 bladder cancer patients and 15 healthy controls. The differential metabolites were extracted and analyzed by univariate and multivariate analysis methods. Together, 19 metabolites were discovered as differently expressed biomarkers in the two groups, which mainly related to the pathways of phenylacetate metabolism, propanoate metabolism, fatty acid metabolism, pyruvate metabolism, arginine and proline metabolism, glycine and serine metabolism, and bile acid biosynthesis. In addition, a subset of 11 metabolites of those 19 ones were further filtered as potential biomarkers for BC diagnosis by using logistic regression model. The results revealed that the area under the curve (AUC) value, sensitivity and specificity of receiving operator characteristic (ROC) curve were 0.983, 95.3% and 100%, respectively, indicating an excellent discrimination power for BC patients from healthy controls. It was the first time to reveal the potential diagnostic markers of BC by metabolomics, and this will provide a new sight for exploring the biomarkers of the other disease in the future work.
膀胱癌（BC）是世界上最常见的癌症之一，其发病率在全球范围内呈上升趋势，尤其是在发达国家。尿液代谢组学是发现癌症诊断潜在生物标志物的有力方法。在这项研究中，我们应用超高效液相色谱-质谱联用（UPLC-MS）方法分析了 29 例膀胱癌患者和 15 例健康对照者尿液中的代谢物。通过单变量和多变量分析方法提取和分析差异代谢物。共有 19 种代谢物被发现为两组之间差异表达的生物标志物，主要与苯乙酸代谢途径、丙酸代谢途径、脂肪酸代谢途径、丙酮酸代谢途径、精氨酸和脯氨酸代谢途径、甘氨酸和丝氨酸代谢途径以及胆汁酸生物合成途径有关。此外，通过逻辑回归模型进一步筛选出这 19 种代谢物中的 11 种作为膀胱癌诊断的潜在生物标志物。结果表明，曲线下面积（AUC）值、ROC 曲线的灵敏度和特异性分别为 0.983、95.3%和 100%，表明对膀胱癌患者和健康对照者具有极好的区分能力。这是首次通过代谢组学揭示膀胱癌的潜在诊断标志物，这将为未来探索其他疾病的生物标志物提供新的视角。
