Urinary metabolomic fingerprints as a powerful tool for the next generation of prostate cancer diagnosis

• Autor(a) principal: Berenguer, Cristina Viveiros
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.13/4898
• topic: Cancro da próstata; Volatilomica; Urina; Biomarcadores; Prostate cancer; Volatilomics; Urine; Biomarkers; Applied Biochemistry; .; Faculdade de Ciências Exatas e da Engenharia; Domínio/Área Científica::Ciências Naturais::Ciências Químicas

Prostate cancer (PCa) remains the most frequent malignant tumour and a leading cause of oncological death in men, despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, and non-invasive source of data to establish the volatilomic biosignature of PCa. In this context, the primary objective of this thesis was to establish the urinary volatilomic profile of PCa using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS). This non-invasive approach to set putative PCa biomarkers was applied to PCa patients (n = 29), men subjected to a radical prostatectomy (RP, n = 34), and cancer-free individuals (control group, n = 49). A total of 60 VOMs belonging to different chemical families were identified in the groups under study. This included phenolic compounds, terpenes, norisoprenoids, ketones, alcohols, and sulphur containing and furanic compounds. The data matrix obtained was submitted to multivariant analysis, through partial least-squares discriminant analysis (PLS-DA). The results obtained did not show complete discrimination between the groups under study, since more than 50 % of the variability in the outcome data could not be explained by the models. The heatmap according to Pearson’s correlation showed that 2-(1-cyclopentyl)furan, 2-pentanone and 2-bromophenol were more associated with the control group, while carvone, α-corocalene, 2-acetylfuran, and cyclohexanone showed high correlations with the PCa group. In contrast, 2-ethyl-5- methylfuran, methanethiol, o-methoxyphenol, p-cymenene, and 3,4-dehydro-β-ionene, were more associated with the RP group. An exhaustive study of the demographic and clinical characteristics of the patients recruited is crucial to elucidate the changes that occur in their volatilomic profile. Moreover, a larger cohort of samples is necessary to improve the predictive power and reliability of the statistical models developed.