a3bd80c95144525796bdea5e36ed041d 292099.pdf 0456e8aa9226f3e9742997e885f73c1262089a6b 292099.pdf 5baadf02b733f510fde4b240dfe39184c2457cdc5a9687bd1620a13fbe59a52d 292099.pdf Title: Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer Subject: Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 L of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures. Keywords: circRNAs; extracellular vesicles; nCounter; lung cancer; NSCLC; liquid biopsies Author: Carlos Pedraz-Valdunciel, Stavros Giannoukakos, Ana Giménez-Capitán, Diogo Fortunato, Martyna Filipska, Jordi Bertran-Alamillo, Jillian W. P. Bracht, Ana Drozdowskyj, Joselyn Valarezo, Natasa Zarovni, Alberto Fernández-Hilario, Michael Hackenberg, Andrés Aguilar-Hernández, Miguel Ángel Molina-Vila and Rafael Rosell Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Sep 27 10:46:21 2022 CEST ModDate: Fri Sep 30 12:57:32 2022 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 4622761 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- JDWIZJ+EURM10 Type 1 Builtin yes yes no 10 0 RUBGLA+URWPalladioL-Bold Type 1 Custom yes yes no 14 0 DSDZOI+URWPalladioL-Roma Type 1 Custom yes yes no 19 0 ZBJUSL+URWPalladioL-Ital Type 1 Custom yes yes no 23 0 AXFIVA+VnURWPalladioL-Bold Type 1 Custom yes yes no 27 0 IAFAOG+TimesNewRomanPS-BoldItalicMT Type 1C 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APJDBG+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 425 0 APJDBI+PalatinoLinotype TrueType WinAnsi yes yes no 428 0 APJDBK+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 431 0 APJEFE+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 434 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-05-18 02:35:49 CEST RepresentationInformation: 292099.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-05-17 17:43:28 CEST Size: 4622761 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 676 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Materials and Methods Destination: section.2 Children: Item: Title: Patient Samples Destination: subsection.2.1 Item: Title: Plasma Processing Destination: subsection.2.2 Item: Title: Enrichment of EVs Destination: subsection.2.3 Item: Title: Transmission Electron Microscopy (TEM) Destination: subsection.2.4 Item: Title: Nano-Flow Cytometry Measurements Destination: subsection.2.5 Item: Title: RNA Isolation and DNase Treatment Destination: subsection.2.6 Item: Title: RT-qPCR and Sanger Sequencing Analysis Destination: subsection.2.7 Item: Title: nCounter Processing Destination: subsection.2.8 Item: Title: Differential Expression Analysis Destination: subsection.2.9 Item: Title: Data Pre-Processing and Normalization for Signature Development Destination: subsection.2.10 Item: Title: Machine Learning (ML) for Signature Development Destination: subsection.2.11 Item: Title: Univariant and Multivariant Analyses Destination: subsection.2.12 Item: Title: Results Destination: section.3 Children: Item: Title: Enrichment of Plasma EVs and Workflow Development for nCounter CircRNA Analysis Destination: subsection.3.1 Item: Title: CircRNA Expression in Plasma EV Samples Destination: subsection.3.2 Item: Title: Development of a CircRNA-Signature Associated with Early-Stage NSCLC Destination: subsection.3.3 Item: Title: Discussion Destination: section.4 Item: Title: Conclusions Destination: section.5 Item: Title: References Destination: section.5 Info: Title: Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer Author: Carlos Pedraz-Valdunciel, Stavros Giannoukakos, Ana Giménez-Capitán, Diogo Fortunato, Martyna Filipska, Jordi Bertran-Alamillo, Jillian W. P. Bracht, Ana Drozdowskyj, Joselyn Valarezo, Natasa Zarovni, Alberto Fernández-Hilario, Michael Hackenberg, Andrés Aguilar-Hernández, Miguel Ángel Molina-Vila and Rafael Rosell Subject: Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 L of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures. 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