Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma
Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma
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DOI：
10.1021/acs.jproteome.0c00663
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发表时间：
2021-01-01
影响因子：
4.4
通讯作者：
Monge, Maria Eugenia
中科院分区：
文献类型：
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作者：
Manzi, Malena;Palazzo, Martin;Monge, Maria Eugenia
使用超高效液相色谱-四极杆飞行时间质谱和机器学习技术对来自一个队列的血清样本进行了一项基于发现的脂质谱研究，该队列包括I、II、III和IV期透明细胞肾细胞癌（ccRCC）患者（n = 112）和对照组（n = 52）。基于支持向量机和LASSO变量选择方法的多变量模型产生了用于ccRCC检测和早期诊断的两个判别脂质组。一个16-脂质面板允许区分ccRCC患者与对照组，在交叉验证下的训练集准确率为95.7%，在独立测试集中准确率为77.1%。第二个模型训练区分早期（I和II）和晚期（III和IV）阶段ccRCC产生了一组26种化合物，这些化合物将I期患者从独立测试集中分类，准确率为82.1%。与对照组相比，在ccRCC患者样本中鉴定为水平1的13种物质（包括胆酸、十一碳烯酸、月桂酸、LPC（16：0/0：0）和PC（18：2/18：2））的水平显著较低。此外，3 α-羟基-5 α-雄甾烷-17-酮3-硫酸盐、顺式-5-十二碳烯酸、花生四烯酸、顺式-13-二十二碳烯酸、PI（16：0/18：1）、PC（16：0/18：2）和PC（0 -16：0/20：4）有助于区分早期和晚期ccRCC阶段患者。在较大和不同队列中验证面板后，结果对于早期ccRCC诊断是有利的。
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, 3 alpha-hydroxy-5 alpha-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.