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.