Semillero de Bioquímica, Cáncer y Radiobiología (SiBio)

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Examinando Semillero de Bioquímica, Cáncer y Radiobiología (SiBio) por Autor "Patiño Aldana, Andrés Felipe"

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    Exploration of serum metabolites associated with mammographic density as risk factor for breast cancer
    (2025-09-12) Hernández Rodríguez, Andrea del Pilar; Pardo Rodríguez, Daniel; León Carreño, Lizeth; Patiño Aldana, Andrés Felipe; Guerrero, Jose L.; Velásquez, Ariadna; Bacca, Jose; López Molina, Gabriela; Ramírez Prieto, Juliana; Mena, Harold H.; Pedraza Flechas, Ana María; Gaitán Chaparro, Sandra; Cala, Mónica P.; Ondo Méndez, Alejandro Oyono; Semillero de Bioquímica, Cáncer y Radiobiología (SiBio)
    Assessing the risk of breast cancer is essential for personalized breast cancer detection and reducing morbidity and mortality rates. Mammographic density is a well-known risk factor that can improve the quality of risk-prediction models. However, discriminatory precision remains limited at the individual level. Serum metabolic differences based on the percentage of mammographic density could represent an innovative and useful risk identification tool in clinical practice. Methods: This pilot study explored the serum metabolic determinants of mammographic density as a risk factor for breast cancer in women screened at a reference hospital in Bogotá, Colombia (2021). Based on mammographic density percentages, 60 patients were classified into breast cancer risk groups: low (<25%), intermediate (25–50%), and high (>50%). An untargeted metabolomics approach was employed to investigate metabolic alterations associated with these risk levels, using GC-QTOF-MS (Gas Chromatography–Quadrupole Time-of-Flight Mass Spectrometry) and LC-QTOF-MS (Liquid Chromatography–Quadrupole Time-of-Flight Mass Spectrometry) for metabolomics and LC-QTOF-MS for lipidomics. To determine statistically significant differences among the MD groups, both univariate (UVA) and multivariate (MVA) analyses were performed Results: Among the three density groups, only the comparison between high and low mammographic density revealed significant differences, six metabolites met the quality criteria and demonstrated good discriminatory ability (AUC > 0.78): tyrosine, glycerol, monopalmitin, tetradecanoylcarnitine, prostaglandin PGE2 glyceryl ester, and ketoglutaric acid. Conclusion: These findings suggest that metabolic profiles could be valuable tools for improving risk assessment prediction models. However, its clinical applicability should be evaluated in future prospective studies.