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

• Published in: International journal of molecular sciences Vol. 24; no. 5; p. 4881
• Main Authors: D'Ambrosi, Silvia, Giannoukakos, Stavros, Antunes-Ferreira, Mafalda, Pedraz-Valdunciel, Carlos, Bracht, Jillian W P, Potie, Nicolas, Gimenez-Capitan, Ana, Hackenberg, Michael, Fernandez Hilario, Alberto, Molina-Vila, Miguel A, Rosell, Rafael, Würdinger, Thomas, Koppers-Lalic, Danijela
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2023; MDPI
• Subjects: Analysis; Asymptomatic; Bioinformatics; Biomarkers; Biomarkers, Tumor - genetics; Biopsy; Blood; Blood platelets; Blood Platelets - pathology; Cancer; circular RNA; Combinatorial analysis; Diagnosis; Diagnostic systems; Gene expression; Humans; Immunology; liquid biopsy; Lung cancer; Lung Neoplasms - genetics; Machine learning; Messenger RNA; Metastasis; Mortality; platelets; Prediction models; RNA, Circular - genetics; RNA, Messenger - genetics; Transcriptomics; Netherlands; Spain; lung cancer; messenger RNA; biomarkers; circular RNA; cancer diagnosis; liquid biopsy; platelets
• ISSN: 1422-0067; 1661-6596; 1422-0067
• DOI: 10.3390/ijms24054881

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.