Cargando…
D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients
The study describes a protocol for methylation analysis integrated with Machine Learning (ML) algorithms developed to classify Facio-Scapulo-Humeral Dystrophy (FSHD) subjects. The DNA methylation levels of two D4Z4 regions (DR1 and DUX4-PAS) were assessed by an in-house protocol based on bisulfite s...
Autores principales: | , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777431/ https://www.ncbi.nlm.nih.gov/pubmed/36552879 http://dx.doi.org/10.3390/cells11244114 |
_version_ | 1784856102349832192 |
---|---|
author | Caputo, Valerio Megalizzi, Domenica Fabrizio, Carlo Termine, Andrea Colantoni, Luca Bax, Cristina Gimenez, Juliette Monforte, Mauro Tasca, Giorgio Ricci, Enzo Caltagirone, Carlo Giardina, Emiliano Cascella, Raffaella Strafella, Claudia |
author_facet | Caputo, Valerio Megalizzi, Domenica Fabrizio, Carlo Termine, Andrea Colantoni, Luca Bax, Cristina Gimenez, Juliette Monforte, Mauro Tasca, Giorgio Ricci, Enzo Caltagirone, Carlo Giardina, Emiliano Cascella, Raffaella Strafella, Claudia |
author_sort | Caputo, Valerio |
collection | PubMed |
description | The study describes a protocol for methylation analysis integrated with Machine Learning (ML) algorithms developed to classify Facio-Scapulo-Humeral Dystrophy (FSHD) subjects. The DNA methylation levels of two D4Z4 regions (DR1 and DUX4-PAS) were assessed by an in-house protocol based on bisulfite sequencing and capillary electrophoresis, followed by statistical and ML analyses. The study involved two independent cohorts, namely a training group of 133 patients with clinical signs of FSHD and 150 healthy controls (CTRL) and a testing set of 27 FSHD patients and 25 CTRL. As expected, FSHD patients showed significantly reduced methylation levels compared to CTRL. We utilized single CpG sites to develop a ML pipeline able to discriminate FSHD subjects. The model identified four CpGs sites as the most relevant for the discrimination of FSHD subjects and showed high metrics values (accuracy: 0.94, sensitivity: 0.93, specificity: 0.96). Two additional models were developed to differentiate patients with lower D4Z4 size and patients who might carry pathogenic variants in FSHD genes, respectively. Overall, the present model enables an accurate classification of FSHD patients, providing additional evidence for DNA methylation as a powerful disease biomarker that could be employed for prioritizing subjects to be tested for FSHD. |
format | Online Article Text |
id | pubmed-9777431 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97774312022-12-23 D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients Caputo, Valerio Megalizzi, Domenica Fabrizio, Carlo Termine, Andrea Colantoni, Luca Bax, Cristina Gimenez, Juliette Monforte, Mauro Tasca, Giorgio Ricci, Enzo Caltagirone, Carlo Giardina, Emiliano Cascella, Raffaella Strafella, Claudia Cells Article The study describes a protocol for methylation analysis integrated with Machine Learning (ML) algorithms developed to classify Facio-Scapulo-Humeral Dystrophy (FSHD) subjects. The DNA methylation levels of two D4Z4 regions (DR1 and DUX4-PAS) were assessed by an in-house protocol based on bisulfite sequencing and capillary electrophoresis, followed by statistical and ML analyses. The study involved two independent cohorts, namely a training group of 133 patients with clinical signs of FSHD and 150 healthy controls (CTRL) and a testing set of 27 FSHD patients and 25 CTRL. As expected, FSHD patients showed significantly reduced methylation levels compared to CTRL. We utilized single CpG sites to develop a ML pipeline able to discriminate FSHD subjects. The model identified four CpGs sites as the most relevant for the discrimination of FSHD subjects and showed high metrics values (accuracy: 0.94, sensitivity: 0.93, specificity: 0.96). Two additional models were developed to differentiate patients with lower D4Z4 size and patients who might carry pathogenic variants in FSHD genes, respectively. Overall, the present model enables an accurate classification of FSHD patients, providing additional evidence for DNA methylation as a powerful disease biomarker that could be employed for prioritizing subjects to be tested for FSHD. MDPI 2022-12-18 /pmc/articles/PMC9777431/ /pubmed/36552879 http://dx.doi.org/10.3390/cells11244114 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Caputo, Valerio Megalizzi, Domenica Fabrizio, Carlo Termine, Andrea Colantoni, Luca Bax, Cristina Gimenez, Juliette Monforte, Mauro Tasca, Giorgio Ricci, Enzo Caltagirone, Carlo Giardina, Emiliano Cascella, Raffaella Strafella, Claudia D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients |
title | D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients |
title_full | D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients |
title_fullStr | D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients |
title_full_unstemmed | D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients |
title_short | D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients |
title_sort | d4z4 methylation levels combined with a machine learning pipeline highlight single cpg sites as discriminating biomarkers for fshd patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777431/ https://www.ncbi.nlm.nih.gov/pubmed/36552879 http://dx.doi.org/10.3390/cells11244114 |
work_keys_str_mv | AT caputovalerio d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT megalizzidomenica d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT fabriziocarlo d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT termineandrea d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT colantoniluca d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT baxcristina d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT gimenezjuliette d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT monfortemauro d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT tascagiorgio d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT riccienzo d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT caltagironecarlo d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT giardinaemiliano d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT cascellaraffaella d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients AT strafellaclaudia d4z4methylationlevelscombinedwithamachinelearningpipelinehighlightsinglecpgsitesasdiscriminatingbiomarkersforfshdpatients |