194d4d244d40d1722fdc6ce1b6827ced pmc_10003255.pdf be27f3d998bec8a33be3d2e0ca3913293d37cdd9 pmc_10003255.pdf f34a748f9db15617b02f2108632ba2fc91a5981693e45cc880d9049aaadf0dab pmc_10003255.pdf Title: Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection Subject: 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. Keywords: liquid biopsy; biomarkers; circular RNA; messenger RNA; platelets; lung cancer;cancer diagnosis Author: Silvia D’Ambrosi, Stavros Giannoukakos, Mafalda Antunes-Ferreira, Carlos Pedraz-Valdunciel, Jillian W. P. Bracht, Nicolas Potie, Ana Gimenez-Capitan, Michael Hackenberg, Alberto Fernandez Hilario, Miguel A. Molina-Vila, Rafael Rosell, Thomas Würdinger and Danijela Koppers-Lalic Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Fri Mar 3 04:52:05 2023 CET ModDate: Fri Mar 3 06:04:03 2023 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 17 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2459175 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- OPZDHV+VnURWPalladioL Type 1 Custom yes yes no 10 0 LNILBI+URWPalladioL-Bold Type 1 Custom yes yes no 15 0 CEERWV+URWPalladioL-Roma Type 1 Custom yes yes no 20 0 KHKBVF+URWPalladioL-Ital Type 1 Custom yes yes no 24 0 IRWHZX+TimesNewRomanPSMT Type 1C WinAnsi yes yes no 42 0 CKUPDE+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 45 0 BCDEEE+PalatinoLinotype-Bold TrueType WinAnsi yes yes no 78 0 BCDFEE+PalatinoLinotype-Roman TrueType WinAnsi yes yes no 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PageMode: UseNone Outlines: Item: Title: Introduction Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@44c8afef Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7b69c6ba Children: Item: Title: Analysis of Blood Platelet-Derived RNA Using NanoString nCounter Technology Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@46daef40 Item: Title: Profiling mRNA and circRNA Derived from Lung Cancer Patients and Non-Cancer Individuals Using Human Immunology V2 Panel and 78-circRNA Custom Panel Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@12f41634 Item: Title: Normalization of the Raw Counts and Differential Gene Expression between Lung Cancer Patients and Non-Cancer Individuals Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@13c27452 Item: Title: ML-Classifier Development and Performance for Detection of Lung Cancer Patients Using Human Immunology V2 Panel and 78-circRNA Custom Panel Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@262b2c86 Item: Title: Combinatorial Analysis: mRNA and circRNA Signature for the Detection of LungCancer Patients Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Early-Stage Lung Cancer Detection Using Combinatorial Signature of mRNA and circRNA Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Item: Title: Materials and Methods Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Children: Item: Title: Sample Collection and Population Study Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Isolation of Blood Platelets Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Total RNA Isolation Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Gene Expression Analysis Using nCounter Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: Data Normalization and Differential Expression Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Item: Title: Feature Selection and Classification Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@63440df3 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@3aeaafa6 Info: Title: Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection Author: Silvia D’Ambrosi, Stavros Giannoukakos, Mafalda Antunes-Ferreira, Carlos Pedraz-Valdunciel, Jillian W. P. Bracht, Nicolas Potie, Ana Gimenez-Capitan, Michael Hackenberg, Alberto Fernandez Hilario, Miguel A. Molina-Vila, Rafael Rosell, Thomas Würdinger and Danijela Koppers-Lalic Subject: 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. 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