Synergic integration of the mirnome, machine learning and bioinformatics for the identification of potential disease-modifying agents in obstructive sleep apnea

• Thalia Belmonte ^[4] ; Iván D. Benitez ^[4] ; María C. García-Hidalgo ^[4] ; Marta Molinero ^[4] ; Lucía Pinilla ^[5] ; Olga Mínguez ^[4] ; Rafaela Vaca ^[4] ; Maria Aguilà ^[4] ; Anna Moncusí-Moix ^[4] ; Gerard Torres ^[5] ; Olga Mediano ^[6] ; Juan F. Masa ^[7] ; Maria J. Masdeu ^[1] ; Blanca Montero-San-Martín ^[2] ; Mercè Ibarz ^[2] ; Pablo Martinez-Camblor ^[8] ; Alberto Gómez-Carballa ^[3] ; Antonio Salas ^[3] ; Federico Martinón-Torres ^[3] ; Ferran Barbé ^[4] ; Manuel Sánchez-de-la-Torre ^[5] ; David de Gonzalo-Calvo ^[4]
  1. [1] Universitat Autònoma de Barcelona

     Universitat Autònoma de Barcelona

     Barcelona, España

     
  2. [2] Hospital Universitario Arnau de Vilanova

     Hospital Universitario Arnau de Vilanova

     Lérida, España

     
  3. [3] Universidade de Santiago de Compostela

     Universidade de Santiago de Compostela

     Santiago de Compostela, España

     
  4. [4] Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain
  5. [5] Precision Medicine in Chronic Diseases, University Hospital Arnau de Vilanova and Santa Maria, IRB Lleida, Department of Nursing and Physiotherapy, Faculty of Nursing and Physiotherapy, University of Lleida, Lleida, Spain
  6. [6] Pneumology Department, University Hospital of Guadalajara, Guadalajara, Spain
  7. [7] San Pedro de Alcantara Hospital, Instituto Universitario de Investigación Biosanitaria de Extremadura, Cáceres, Spain
  8. [8] Anesthesiology Department, Geisel School of Medicine at Dartmouth, Hanover, NH, USA

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• Localización: Archivos de bronconeumología: Organo oficial de la Sociedad Española de Neumología y Cirugía Torácica SEPAR y la Asociación Latinoamericana de Tórax ( ALAT ), ISSN 0300-2896, Vol. 61, Nº. 6 (June), 2025, págs. 348-358
• Idioma: inglés
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• Dialnet Métricas: 2 Citas
• Resumen

  • Introduction Understanding the diverse pathogenetic pathways in obstructive sleep apnea (OSA) is crucial for improving outcomes. microRNA (miRNA) profiling is a promising strategy for elucidating these mechanisms.

    Objective To characterize the pathogenetic pathways linked to OSA through the integration of miRNA profiles, machine learning (ML) and bioinformatics.

    Methods This multicenter study involved 525 patients with suspected OSA who underwent polysomnography. Plasma miRNAs were quantified via RNA sequencing in the discovery phase, with validation in two subsequent phases using RT-qPCR. Supervised ML feature selection methods and comprehensive bioinformatic analyses were employed. The associations among miRNA targets, OSA and OSA treatment were further explored using publicly available external datasets.

    Results Following the discovery and technical validation phases in a subset of patients with and without confirmed OSA (n = 53), eleven miRNAs were identified as candidates for the subsequent feature selection process. These miRNAs were then quantified in the remaining population (n = 472). Feature selection methods revealed that the miRNAs let-7d-5p, miR-15a-5p and miR-107 were the most informative of OSA. The predominant mechanisms linked to these miRNAs were closely related to cellular events such as cell death, cell differentiation, extracellular remodeling, autophagy and metabolism. One target of let-7d-5p and miR-15a-5p, the TFDP2 gene, exhibited significant differences in gene expression between subjects with and without OSA across three independent databases.

    Conclusion Our study identified three plasma miRNAs that, in conjunction with their target genes, provide new insights into OSA pathogenesis and reveal novel regulators and potential drug targets.