Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection

Author:

D’Ambrosi Silvia1,Giannoukakos Stavros23ORCID,Antunes-Ferreira Mafalda1ORCID,Pedraz-Valdunciel Carlos45ORCID,Bracht Jillian W. P.6ORCID,Potie Nicolas23,Gimenez-Capitan Ana6ORCID,Hackenberg Michael23ORCID,Fernandez Hilario Alberto7ORCID,Molina-Vila Miguel A.6ORCID,Rosell Rafael5,Würdinger Thomas18,Koppers-Lalic Danijela9ORCID

Affiliation:

1. Department of Neurosurgery, Cancer Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands

2. Department of Genetics, Faculty of Science, University of Granada, 18071 Granada, Spain

3. Bioinformatics Laboratory, Biomedical Research Centre (CIBM), PTS, 18100 Granada, Spain

4. Department of Biochemistry, Molecular Biology and Biomedicine, Universitat Autónoma de Barcelona (UAB), 08193 Cerdanyola, Spain

5. Germans Trias i Pujol Health Sciences Institute and Hospital (IGTP), 08916 Barcelona, Spain

6. Pangaea Oncology—Laboratory of Oncology, Quirón Dexeus University Hospital, Sabino Arana 5-19, 08028 Barcelona, Spain

7. Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071 Granada, Spain

8. Brain Tumor Center Amsterdam, Cancer Center Amsterdam, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands

9. Mathematical Institute, Leiden University, 2333 CA Leiden, The Netherlands

Abstract

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.

Funder

European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

Reference49 articles.

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