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A LC-MS based method, which utilizes both reversed-performance (RP) chromatography and hydrophilic interaction chromatography (HILIC) separations, has been carried out in conjunction with multivariate data analysis to discriminate the global serum profiles of renal cell carcinoma (RCC) patients and healthy controls. The HILIC was found necessary for a comprehensive serum metabonomic profiling as well as RP separation. The feasibility of using serum metabonomics for the diagnosis and staging of RCC has been evaluated. One-hundred percent sensitivity in detection has been achieved, and a satisfactory clustering between the early stage and advanced-stage patients is observed. The results suggest that the combination of LC-MS analysis with multivariate statistical analysis can be used for RCC diagnosis and has potential in the staging of RCC. The MS/MS experiments have been carried out to identify the biomarker patterns that made great contribution to the discrimination. As a result, 30 potential biomarkers for RCC are identified. It is possible that the current biomarker patterns are not unique to RCC but just the result of any malignancy disease. To further elucidate the pathophysiology of RCC, related metabolic pathways have been studied. RCC is found to be closely related to disturbed phospholipid catabolism, sphingolipid metabolism, phenylalanine metabolism, tryptophan metabolism, fatty acid beta-oxidation, cholesterol metabolism, and arachidonic acid metabolism.
Lin et al. (Tue,) studied this question.