The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis
Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic and underdiagnosed; consequently, there is a demand for simple, non-invasive diagnostic tools. In this study, we developed a method to quantify liver steatosis based on miRNAs, present in liver and serum, that cor...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Biomolecules |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2218-273X/14/11/1423 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850267771570487296 |
|---|---|
| author | Polina Soluyanova Guillermo Quintás Álvaro Pérez-Rubio Iván Rienda Erika Moro Marcel van Herwijnen Marcha Verheijen Florian Caiment Judith Pérez-Rojas Ramón Trullenque-Juan Eugenia Pareja Ramiro Jover |
| author_facet | Polina Soluyanova Guillermo Quintás Álvaro Pérez-Rubio Iván Rienda Erika Moro Marcel van Herwijnen Marcha Verheijen Florian Caiment Judith Pérez-Rojas Ramón Trullenque-Juan Eugenia Pareja Ramiro Jover |
| author_sort | Polina Soluyanova |
| collection | DOAJ |
| description | Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic and underdiagnosed; consequently, there is a demand for simple, non-invasive diagnostic tools. In this study, we developed a method to quantify liver steatosis based on miRNAs, present in liver and serum, that correlate with liver fat. The miRNAs were analyzed by miRNAseq in liver samples from two cohorts of patients with a precise quantification of liver steatosis. Common miRNAs showing correlation with liver steatosis were validated by RT-qPCR in paired liver and serum samples. Multivariate models were built using partial least squares (PLS) regression to predict the percentage of liver steatosis from serum miRNA levels. Leave-one-out cross validation and external validation were used for model selection and to estimate predictive performance. The miRNAseq results disclosed (a) 144 miRNAs correlating with triglycerides in a set of liver biobank samples (<i>n</i> = 20); and (b) 124 and 102 miRNAs correlating with steatosis by biopsy digital image and MRI analyses, respectively, in liver samples from morbidly obese patients (<i>n</i> = 24). However, only 35 miRNAs were common in both sets of samples. RT-qPCR allowed to validate the correlation of 10 miRNAs in paired liver and serum samples. The development of PLS models to quantitatively predict steatosis demonstrated that the combination of serum miR-145-3p, 122-5p, 143-3p, 500a-5p, and 182-5p provided the lowest root mean square error of cross validation (RMSECV = 1.1, <i>p</i>-value = 0.005). External validation of this model with a cohort of mixed MASLD patients (<i>n</i> = 25) showed a root mean squared error of prediction (RMSEP) of 5.3. In conclusion, it is possible to predict the percentage of hepatic steatosis with a low error rate by quantifying the serum level of five miRNAs using a cost-effective and easy-to-implement RT-qPCR method. |
| format | Article |
| id | doaj-art-1dcd1bb9d2c84a1c82d29ae0c29430b1 |
| institution | OA Journals |
| issn | 2218-273X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj-art-1dcd1bb9d2c84a1c82d29ae0c29430b12025-08-20T01:53:40ZengMDPI AGBiomolecules2218-273X2024-11-011411142310.3390/biom14111423The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver SteatosisPolina Soluyanova0Guillermo Quintás1Álvaro Pérez-Rubio2Iván Rienda3Erika Moro4Marcel van Herwijnen5Marcha Verheijen6Florian Caiment7Judith Pérez-Rojas8Ramón Trullenque-Juan9Eugenia Pareja10Ramiro Jover11Unidad Mixta de Investigación en Hepatología Experimental, IIS Hospital La Fe, 46026 Valencia, SpainHealth and Biomedicine, LEITAT Technological Center, 08225 Terrassa, SpainServicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, 46017 Valencia, SpainPathology Department, Hospital Universitario y Politécnico La Fe, 46026 Valencia, SpainUnidad Mixta de Investigación en Hepatología Experimental, IIS Hospital La Fe, 46026 Valencia, SpainDepartment of Translational Genomics, Research Institute of Oncology and Developmental Biology (GROW), Maastricht University, 6229-ER Maastricht, The NetherlandsDepartment of Translational Genomics, Research Institute of Oncology and Developmental Biology (GROW), Maastricht University, 6229-ER Maastricht, The NetherlandsDepartment of Translational Genomics, Research Institute of Oncology and Developmental Biology (GROW), Maastricht University, 6229-ER Maastricht, The NetherlandsPathology Department, Hospital Universitario y Politécnico La Fe, 46026 Valencia, SpainServicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, 46017 Valencia, SpainServicio de Cirugía General y Aparato Digestivo, Hospital Universitario Dr. Peset, 46017 Valencia, SpainUnidad Mixta de Investigación en Hepatología Experimental, IIS Hospital La Fe, 46026 Valencia, SpainMetabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic and underdiagnosed; consequently, there is a demand for simple, non-invasive diagnostic tools. In this study, we developed a method to quantify liver steatosis based on miRNAs, present in liver and serum, that correlate with liver fat. The miRNAs were analyzed by miRNAseq in liver samples from two cohorts of patients with a precise quantification of liver steatosis. Common miRNAs showing correlation with liver steatosis were validated by RT-qPCR in paired liver and serum samples. Multivariate models were built using partial least squares (PLS) regression to predict the percentage of liver steatosis from serum miRNA levels. Leave-one-out cross validation and external validation were used for model selection and to estimate predictive performance. The miRNAseq results disclosed (a) 144 miRNAs correlating with triglycerides in a set of liver biobank samples (<i>n</i> = 20); and (b) 124 and 102 miRNAs correlating with steatosis by biopsy digital image and MRI analyses, respectively, in liver samples from morbidly obese patients (<i>n</i> = 24). However, only 35 miRNAs were common in both sets of samples. RT-qPCR allowed to validate the correlation of 10 miRNAs in paired liver and serum samples. The development of PLS models to quantitatively predict steatosis demonstrated that the combination of serum miR-145-3p, 122-5p, 143-3p, 500a-5p, and 182-5p provided the lowest root mean square error of cross validation (RMSECV = 1.1, <i>p</i>-value = 0.005). External validation of this model with a cohort of mixed MASLD patients (<i>n</i> = 25) showed a root mean squared error of prediction (RMSEP) of 5.3. In conclusion, it is possible to predict the percentage of hepatic steatosis with a low error rate by quantifying the serum level of five miRNAs using a cost-effective and easy-to-implement RT-qPCR method.https://www.mdpi.com/2218-273X/14/11/1423Metabolic dysfunction-associated steatotic liver diseasemicroRNAserum biomarkerliver steatosisquantitative predictionpartial least squares regression |
| spellingShingle | Polina Soluyanova Guillermo Quintás Álvaro Pérez-Rubio Iván Rienda Erika Moro Marcel van Herwijnen Marcha Verheijen Florian Caiment Judith Pérez-Rojas Ramón Trullenque-Juan Eugenia Pareja Ramiro Jover The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis Biomolecules Metabolic dysfunction-associated steatotic liver disease microRNA serum biomarker liver steatosis quantitative prediction partial least squares regression |
| title | The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis |
| title_full | The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis |
| title_fullStr | The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis |
| title_full_unstemmed | The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis |
| title_short | The Development of a Non-Invasive Screening Method Based on Serum microRNAs to Quantify the Percentage of Liver Steatosis |
| title_sort | development of a non invasive screening method based on serum micrornas to quantify the percentage of liver steatosis |
| topic | Metabolic dysfunction-associated steatotic liver disease microRNA serum biomarker liver steatosis quantitative prediction partial least squares regression |
| url | https://www.mdpi.com/2218-273X/14/11/1423 |
| work_keys_str_mv | AT polinasoluyanova thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT guillermoquintas thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT alvaroperezrubio thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT ivanrienda thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT erikamoro thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT marcelvanherwijnen thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT marchaverheijen thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT floriancaiment thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT judithperezrojas thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT ramontrullenquejuan thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT eugeniapareja thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT ramirojover thedevelopmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT polinasoluyanova developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT guillermoquintas developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT alvaroperezrubio developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT ivanrienda developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT erikamoro developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT marcelvanherwijnen developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT marchaverheijen developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT floriancaiment developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT judithperezrojas developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT ramontrullenquejuan developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT eugeniapareja developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis AT ramirojover developmentofanoninvasivescreeningmethodbasedonserummicrornastoquantifythepercentageofliversteatosis |