Artículo
Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma
Manzi, Malena
; Palazzo, Martín; Knott, María Elena
; Beauseroy, Pierre; Yankilevich, Patricio
; Giménez, María Isabel; Monge, Maria Eugenia
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Fecha de publicación:
11/2020
Editorial:
American Chemical Society
Revista:
Journal of Proteome Research
ISSN:
1535-3893
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
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Articulos(CIBION)
Articulos de CENTRO DE INVESTIGACIONES EN BIONANOCIENCIAS "ELIZABETH JARES ERIJMAN"
Articulos de CENTRO DE INVESTIGACIONES EN BIONANOCIENCIAS "ELIZABETH JARES ERIJMAN"
Articulos(IBIOBA - MPSP)
Articulos de INST. D/INV.EN BIOMED.DE BS AS-CONICET-INST. PARTNER SOCIEDAD MAX PLANCK
Articulos de INST. D/INV.EN BIOMED.DE BS AS-CONICET-INST. PARTNER SOCIEDAD MAX PLANCK
Citación
Manzi, Malena; Palazzo, Martín; Knott, María Elena; Beauseroy, Pierre; Yankilevich, Patricio; et al.; Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma; American Chemical Society; Journal of Proteome Research; 20; 1; 11-2020; 841-857
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