Fluorescence Liquid Biopsy for Cancer Detection Is Improved by Using Cationic Dendronized Hyperbranched Polymer
Resumen: (1) Background: Biophysical techniques applied to serum samples characterization could promote the development of new diagnostic tools. Fluorescence spectroscopy has been previously applied to biological samples from cancer patients and differences from healthy individuals were observed. Dendronized hyperbranched polymers (DHP) based on bis(hydroxymethyl)propionic acid (bis-MPA) were developed in our group and their potential biomedical applications explored. (2) Methods: A total of 94 serum samples from diagnosed cancer patients and healthy individuals were studied (20 pancreatic ductal adenocarcinoma, 25 blood donor, 24 ovarian cancer, and 25 benign ovarian cyst samples). (3) Results: Fluorescence spectra of serum samples (fluorescence liquid biopsy, FLB) in the presence and the absence of DHP-bMPA were recorded and two parameters from the signal curves obtained. A secondary parameter, the fluorescence spectrum score (FSscore), was calculated, and the diagnostic model assessed. For pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer, the classification performance was improved when including DHP-bMPA, achieving high values of statistical sensitivity and specificity (over 85% for both pathologies). (4) Conclusions: We have applied FLB as a quick, simple, and minimally invasive promising technique in cancer diagnosis. The classification performance of the diagnostic method was further improved by using DHP-bMPA, which interacted differentially with serum samples from healthy and diseased subjects. These preliminary results set the basis for a larger study and move FLB closer to its clinical application, providing useful information for the oncologist during patient diagnosis.
Idioma: Inglés
DOI: 10.3390/ijms22126501
Año: 2021
Publicado en: International Journal of Molecular Sciences 22, 12 (2021), 6501 [22 pp.]
ISSN: 1661-6596

Factor impacto JCR: 6.208 (2021)
Categ. JCR: BIOCHEMISTRY & MOLECULAR BIOLOGY rank: 69 / 297 = 0.232 (2021) - Q1 - T1
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 50 / 179 = 0.279 (2021) - Q2 - T1

Factor impacto CITESCORE: 6.9 - Computer Science (Q1) - Chemical Engineering (Q1) - Medicine (Q1) - Biochemistry, Genetics and Molecular Biology (Q2)

Factor impacto SCIMAGO: 1.176 - Computer Science Applications (Q1) - Inorganic Chemistry (Q1) - Spectroscopy (Q1) - Organic Chemistry (Q1) - Physical and Theoretical Chemistry (Q1) - Molecular Biology (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/B25-17R
Financiación: info:eu-repo/grantAgreement/ES/DGA/E45-17R
Financiación: info:eu-repo/grantAgreement/ES/DGA/E47-20R
Financiación: info:eu-repo/grantAgreement/ES/ISCIII-ERDF-ESF/PI15-00663-Investing in your future
Financiación: info:eu-repo/grantAgreement/ES/ISCIII-ERDF-ESF/PI18-00349-Investing in your future
Financiación: info:eu-repo/grantAgreement/ES/ISCIII-FIS/FI19-00146
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/BFU2016-78232-P
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)
Área (Departamento): Area Medicina (Dpto. Medicina, Psiqu. y Derm.)
Área (Departamento): Área Bioquímica y Biolog.Mole. (Dpto. Bioq.Biolog.Mol. Celular)


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Este artículo se encuentra en las siguientes colecciones:
Artículos > Artículos por área > Estadística e Investigación Operativa
Artículos > Artículos por área > Bioquímica y Biología Molecular
Artículos > Artículos por área > Medicina



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