Quantitative Prediction of Steatosis in Patients with Non-Alcoholic Fatty Liver by Means of Hepatic MicroRNAs Present in Serum and Correlating with Hepatic Fat
Por:
Quintás G, Caiment F, Rienda I, Pérez-Rojas J, Pareja E, Castell JV and Jover R
Publicada:
1 ago 2022
Ahead of Print:
18 ago 2022
Resumen:
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent form of chronic liver disease worldwide, but a reliable non-invasive method to quantify liver steatosis in primary healthcare is not available. Circulating microRNAs have been proposed as biomarkers of severe/advanced NAFLD (steatohepatitis and fibrosis). However, the use of circulating miRNAs to quantitatively assess the % of liver fat in suspected NAFLD patients has not been investigated. We performed global miRNA sequencing in two sets of samples: human livers from organ donors (n = 20), and human sera from biopsy-proven NAFLD patients (n = 23), both with a wide range of steatosis quantified in their liver biopsies. Partial least squares (PLS) regression combined with recursive feature elimination (RFE) was used to select miRNAs associated with steatosis. Moreover, regression models with only 2 or 3 miRNAs, with high biological relevance, were built. Comprehensive microRNA sequencing of liver and serum samples resulted in two sets of abundantly expressed miRNAs (418 in liver and 351 in serum). Pearson correlation analyses indicated that 18% of miRNAs in liver and 14.5% in serum were significantly associated with the amount of liver fat. PLS-RFE models demonstrated that 50 was the number of miRNAs providing the lowest error in both liver and serum models predicting steatosis. Comparison of the two miRNA subsets showed 19 coincident miRNAs that were ranked according to biological significance (guide/passenger strand, relative abundance in liver and serum, number of predicted lipid metabolism target genes, correlation significance, etc.). Among them, miR-10a-5p, miR-98-5p, miR-19a-3p, miR-30e-5p, miR-32-5p and miR-145-5p showed the highest biological relevance. PLS regression models with serum levels of 2-3 of these miRNAs predicted the % of liver fat with errors <5%.
Filiaciones:
Quintás G:
Unidad Analítica, Health Research Institute Hospital La Fe, 46026 Valencia, Spain
Health and Biomedicine, LEITAT Technological Centre, 08005 Barcelona, Spain
Caiment F:
Department of Toxicogenomics, School of Oncology and Developmental Biology (GROW), Maastricht University, 6229-ER Maastricht, The Netherlands
Rienda I:
Pathology Department, Hospital Universitario y Politécnico La Fe, 46026 Valencia, Spain
Pérez-Rojas J:
Pathology Department, Hospital Universitario y Politécnico La Fe, 46026 Valencia, Spain
:
Servicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, 46017 Valencia, Spain
Unidad Mixta de Investigación en Hepatología Experimental, Health Research Institute Hospital La Fe, 46026 Valencia, Spain
Castell JV:
Unidad Mixta de Investigación en Hepatología Experimental, Health Research Institute Hospital La Fe, 46026 Valencia, Spain
Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad de Valencia, 46010 Valencia, Spain
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain
Jover R:
Unidad Mixta de Investigación en Hepatología Experimental, Health Research Institute Hospital La Fe, 46026 Valencia, Spain
Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Universidad de Valencia, 46010 Valencia, Spain
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Instituto de Salud Carlos III, 28029 Madrid, Spain
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