Author, as appears in the article.: Gutierrez-Tordera, Laia; Papandreou, Christopher; Novau-Ferre, Nil; Garcia-Gonzalez, Pablo; Rojas, Melina; Marquie, Marta; Chapado, Luis A; Papagiannopoulos, Christos; Fernandez-Castillo, Noelia; Valero, Sergi; Folch, Jaume; Ettcheto, Miren; Camins, Antoni; Boada, Merce; Ruiz, Agustin; Bullo, Monica
Department: Bioquímica i Biotecnologia
URV's Author/s: Bulló Bonet, Mònica / Folch Lopez, Jaume / GUTIERREZ TORDERA, LAIA / Novau Ferré, Nil / Rojas Criollo, Melina Isabella
Keywords: Small non-coding rna Potential biomarkers Nested case–control study Mild cognitive impairment Gene regulatory networks Biomarkers Atn Alzheimer’s disease Alzheimer's disease tau small non-coding rna performance nested case-control study mirnas mild cognitive impairment gene regulatory networks expression diagnosis biomarkers atn
Abstract: Alzheimer's disease (AD) diagnosis relies on clinical symptoms complemented with biological biomarkers, the Amyloid Tau Neurodegeneration (ATN) framework. Small non-coding RNA (sncRNA) in the blood have emerged as potential predictors of AD. We identified sncRNA signatures specific to ATN and AD, and evaluated both their contribution to improving AD conversion prediction beyond ATN alone.This nested case-control study was conducted within the ACE cohort and included MCI patients matched by sex. Patients free of type 2 diabetes underwent cerebrospinal fluid (CSF) and plasma collection and were followed-up for a median of 2.45-years. Plasma sncRNAs were profiled using small RNA-sequencing. Conditional logistic and Cox regression analyses with elastic net penalties were performed to identify sncRNA signatures for A+(T|N)+ and AD. Weighted scores were computed using cross-validation, and the association of these scores with AD risk was assessed using multivariable Cox regression models. Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis of the identified signatures were performed.The study sample consisted of 192 patients, including 96 A+(T|N)+ and 96 A-T-N- patients. We constructed a classification model based on a 6-miRNAs signature for ATN. The model could classify MCI patients into A-T-N- and A+(T|N)+ groups with an area under the curve of 0.7335 (95% CI, 0.7327 to 0.7342). However, the addition of the model to conventional risk factors did not improve the prediction of AD beyond the conventional model plus ATN status (C-statistic: 0.805 [95% CI, 0.758 to 0.852] compared to 0.829 [95% CI, 0.786, 0.872]). The AD-related 15-sncRNAs signature exhibited better predictive performance than the conventional model plus ATN status (C-statistic: 0.849 [95% CI, 0.808 to 0.890]). When ATN was included in this model, the prediction further improved to 0.875 (95% CI, 0.840 to 0.910). The miRNA-target interaction network and functional analysis, including GO and KEGG pathway enrichment analysis, suggested that the miRNAs in both signatures are involved in neuronal pathways associated with AD.The AD-related sncRNA signature holds promise in predicting AD conversion, providing insights into early AD development and potential targets for prevention.© 2024. The Author(s).
Thematic Areas: Medicina ii General biochemistry,genetics and molecular biology Biochemistry, genetics and molecular biology (miscellaneous) Biochemistry, genetics and molecular biology (all) Biochemistry & molecular biology
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: nil.novau@urv.cat melina.rojas@estudiants.urv.cat laia.gutierrez@iispv.cat monica.bullo@urv.cat jaume.folch@urv.cat
Author identifier: 0000-0002-0218-7046 0000-0002-5051-8858
Record's date: 2024-12-07
Papper version: info:eu-repo/semantics/publishedVersion
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Cell And Bioscience. 14 (1): 8-8
APA: Gutierrez-Tordera, Laia; Papandreou, Christopher; Novau-Ferre, Nil; Garcia-Gonzalez, Pablo; Rojas, Melina; Marquie, Marta; Chapado, Luis A; Papagianno (2024). Exploring small non-coding RNAs as blood-based biomarkers to predict Alzheimer's disease. Cell And Bioscience, 14(1), 8-8. DOI: 10.1186/s13578-023-01190-5
Entity: Universitat Rovira i Virgili
Journal publication year: 2024
Publication Type: Journal Publications
Repositori URV