Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection
Metadatos
Mostrar el registro completo del ítemAutor
D’Ambrosi, Silvia; Giannoukakos, Stavros Panagiotis; M Potie, Nicolas Thierry; Hackenberg, Michael; Fernández Hilario, Alberto LuisEditorial
MDPI
Materia
Liquid biopsy Biomarkers Circular RNA Messenger RNA Platelets Lung cancer Cancer diagnosis
Fecha
2023-03-02Referencia bibliográfica
D’Ambrosi, S.; Giannoukakos, S.; Antunes-Ferreira, M.; Pedraz-Valdunciel, C.; Bracht, J.W.P.; Potie, N.; Gimenez-Capitan, A.; Hackenberg, M.; Fernandez Hilario, A.; Molina-Vila, M.A.; et al. Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection. Int. J. Mol. Sci. 2023, 24, 4881. [https://doi.org/ 10.3390/ijms24054881]
Patrocinador
European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie 765492.Resumen
Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often
exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in
insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker
approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions
of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer
detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-
circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal
selected signature is then used to generate the predictive classification model using machine learning
algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models
reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial
analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA),
enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified
five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept
study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers,
providing a potential combinatorial diagnostic signature for lung cancer detection.