Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Molecular Medicine

Nicolae Leopold¹  Alessio Naccarati²  Barbara Pardini²  Dan Burghelea³  Florin Elec³  Gheorghita Iacob³  Tudor Moisoiu³  Giulio Ferrero⁴  Alessandra Allione⁵  Giuseppe Matullo⁵  Giovanni Birolo⁵  Emilia Licarete⁶  Ramona G. Cozan⁷  Stefania D. Iancu⁷  Zoltán Bálint⁷  Andrei Stefancu⁷  David Horst⁸  Simon Schallenberg⁸  Mihnea P. Dragomir⁸  Radu I. Badea⁹ 

[1]Biomed Data Analytics SRL

[2]Candiolo Cancer Institute-FPO IRCCS

[3]Clinical Institute of Urology and Renal Transplantation

[4]Department of Clinical and Biological Sciences, University of Turin

[5]Department of Medical Sciences, University of Turin

[6]Faculty of Biology, Babeș-Bolyai University

[7]Faculty of Physics, Babeș-Bolyai University

[8]Institute of Pathology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität Zu Berlin and Berlin Institute of Health

[9]Iuliu Hatieganu University of Medicine and Pharmacy

关键词: Bladder cancer;    microRNA;    SERS;    Liquid biopsy;    Molecular subtypes;    Biomarkers;   

DOI  :  10.1186/s10020-022-00462-z

来源: DOAJ

【 摘 要 】

Abstract Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

【 授权许可】

Unknown