Metabolite fingerprint analysis of cervical cancer using LC-QTOF/MS and multivariate data analysis

Abstract

Cervical cancer (CC) is the second most common cancer in females worldwide, as yet, the metabolic alterations that are specific for the development of CC have not been fully determined, which also precludes the early diagnosis and prognosis of this pathology. In this pilot study, we determined the metabolic fingerprint of urine samples from women diagnosed with CC and women not diagnosed with CC using LC (Agilent 1290 Infinity LC System) coupled with Q-TOF/MS (Agilent, 6550 iFunnel) and independent variable analysis. Urine fingerprints allowed for the discrimination of women diagnosed with CC from the control subjects. In addition, we identified a set of metabolites with a strong discriminative power, such as 3-methylhistidine, citric acid, cytosine, indoleacetic acid, salicyluric acid, L-methionine, aminomalonic acid, glutaric acid, ursodeoxycholic acid and N-acetylornithine, which are involved in key metabolic pathways such as the citrate cycle, lysine degradation, tryptophan metabolism, cysteine and methionine metabolism, etc. Finally, we provide evidence for the implication of these compounds in metabolic routes that may be associated with the early genesis of CC, which highlights their potential use as prognostic markers for the identification of women at risk of developing CC. Urine fingerprints reveal disease-specific metabolic imbalances in women diagnosed with CC .