Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC­(16:0/0:0), and PC­(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI­(16:0/18:1), PC­(16:0/18:2), and PC­(O–16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

本研究针对纳入I、II、III、IV期透明细胞肾细胞癌（clear cell renal cell carcinoma, ccRCC）患者（n=112）与健康对照（n=52）的队列血清样本，开展发现式脂质组学分析研究，采用超高效液相色谱-四极杆-飞行时间质谱联用技术结合机器学习方法完成实验检测。基于支持向量机与最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）变量选择方法构建的多变量模型，得到两套可用于ccRCC检测与早期诊断的判别脂质特征组。其中16脂质特征组在训练集的交叉验证中实现95.7%的区分准确率，在独立测试集上的准确率达77.1%，可有效区分ccRCC患者与健康对照。第二个模型旨在区分早期（I、II期）与晚期（III、IV期）ccRCC患者，构建了包含26种化合物的特征组，该模型在独立测试集中区分I期患者与晚期患者的准确率为82.1%。经一级置信度鉴定（level 1）确证的13种代谢物，包括胆酸、十一烯酸、月桂酸、溶血磷脂酰胆碱（lysophosphatidylcholine, LPC）(16:0/0:0)与磷脂酰胆碱（phosphatidylcholine, PC）(18:2/18:2)，在ccRCC患者血清样本中的表达水平显著低于健康对照。此外，3α-羟基-5α-雄烷-17-酮-3-硫酸酯、顺式-5-十二碳烯酸、花生四烯酸、顺式-13-二十二碳烯酸、磷脂酰肌醇（phosphatidylinositol, PI）(16:0/18:1)、PC(16:0/18:2)与PC(O–16:0/20:4)可用于区分早期与晚期ccRCC患者。本研究结果显示，在更大规模及不同队列中验证该特征组后，有望为ccRCC的早期诊断提供有力支撑。