Exploring a Circulating <scp>miRNA</scp> Signature for <scp>PMM2</scp> ‐ <scp>CDG</scp> : Initial Insights Toward Diagnosis, Stratification, and Monitoring

Phosphomannomutase deficiency (PMM2-CDG) is the most common congenital disorder of glycosylation, characterized by variable early-onset neurological (hypotonia, cerebellar syndrome, developmental delay) and multi-organ manifestations. Although several clinical trials are ongoing, current biomarkers lack prognostic or monitoring utility. Emerging transcriptomic studies suggest dysregulated pathways in PMM2-CDG, but miRNAs, key gene expression regulators, remain unexplored. This cross-sectional study aims to investigate a circulating miRNA signature that may distinguish PMM2-CDG patients from unaffected controls, providing an initial framework for future studies on potential predictive and monitoring tools. Differential gene expression analysis was used to identify significant differentially expressed (DE) miRNAs, while machine learning models (LASSO, XGBoost) were applied to create an miRNA predictive signature. Dysregulated miRNA pathways analysis provided insights into affected tissues and cellular mechanisms. An optimized protocol addressing challenges in pediatric blood samples was implemented. miRNA profiles from blood samples of 28 PMM2-CDG patients and 67 unaffected controls were analyzed, identifying six DE miRNAs. Regarding machine learning models, XGBoost achieved the best performance (AUC 0.917). Biological analysis revealed that DE miRNAs influence neurological, endocrinological, immunological, and cellular pathways related to the PMM2-CDG phenotype. Notably, miR-122-5p emerged as a highly predictive marker, indicating liver and neurological involvement. Circulating miRNAs represent a promising, minimally invasive avenue for further investigation. While preliminary evidence of their potential diagnostic utility is provided, additional validation in larger and more diverse populations is required to determine their relevance for clinical stratification or monitoring in PMM2-CDG, contributing to future biomarker-driven personalized medicine efforts in this disease.

Authors

Institutions

Publication Details

Journal
Journal of Inherited Metabolic Disease
Published
2025-10-13
DOI
https://doi.org/10.1002/jimd.70104
Primary Topic
RNA modifications and cancer
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring a Circulating miRNA Signature for PMM2 ‐ CDG : Initial Insights Toward Diagnosis, Stratification, and Monitoring

Esther Lizano, Adrián Alcalá, Lluc Cabús, Cristina Hernando‐Davalillo et al.
Journal of Inherited Metabolic Disease
RNA modifications and cancer
article

Exploring a Circulating miRNA Signature for PMM2 ‐ CDG : Initial Insights Toward Diagnosis, Stratification, and Monitoring

Esther Lizano, Adrián Alcalá, Lluc Cabús, Cristina Hernando‐Davalillo, João Curado, Julien Lagarde, Gabriel Márquez, Mercè Bolasell, Jennifer Pérez‐Boza, Gregorio Nolasco, Sílvia Carbonell Sala, Mercedes Serrano, Florencia Epifani, P. B. Fernandez, S. Belmonte
article en

Abstract

Phosphomannomutase deficiency (PMM2-CDG) is the most common congenital disorder of glycosylation, characterized by variable early-onset neurological (hypotonia, cerebellar syndrome, developmental delay) and multi-organ manifestations. Although several clinical trials are ongoing, current biomarkers lack prognostic or monitoring utility. Emerging transcriptomic studies suggest dysregulated pathways in PMM2-CDG, but miRNAs, key gene expression regulators, remain unexplored. This cross-sectional study aims to investigate a circulating miRNA signature that may distinguish PMM2-CDG patients from unaffected controls, providing an initial framework for future studies on potential predictive and monitoring tools. Differential gene expression analysis was used to identify significant differentially expressed (DE) miRNAs, while machine learning models (LASSO, XGBoost) were applied to create an miRNA predictive signature. Dysregulated miRNA pathways analysis provided insights into affected tissues and cellular mechanisms. An optimized protocol addressing challenges in pediatric blood samples was implemented. miRNA profiles from blood samples of 28 PMM2-CDG patients and 67 unaffected controls were analyzed, identifying six DE miRNAs. Regarding machine learning models, XGBoost achieved the best performance (AUC 0.917). Biological analysis revealed that DE miRNAs influence neurological, endocrinological, immunological, and cellular pathways related to the PMM2-CDG phenotype. Notably, miR-122-5p emerged as a highly predictive marker, indicating liver and neurological involvement. Circulating miRNAs represent a promising, minimally invasive avenue for further investigation. While preliminary evidence of their potential diagnostic utility is provided, additional validation in larger and more diverse populations is required to determine their relevance for clinical stratification or monitoring in PMM2-CDG, contributing to future biomarker-driven personalized medicine efforts in this disease.

Journal of Inherited Metabolic DiseaseVol. 48(6)
Universitat Autònoma de Barcelona (ES), Hospital Sant Joan de Déu Barcelona (ES), Consejo Superior de Investigaciones Científicas (ES), Universitat Pompeu Fabra (ES), SOM Biotech (Spain) (ES), Instituto de Salud Carlos III (ES), Centre for Biomedical Network Research on Rare Diseases (ES), Centre for Genomic Regulation (ES), Institut Català de Paleontologia Miquel Crusafont (ES), Anaxomics (Spain) (ES), Institut de Recerca Sant Joan de Déu
Centres de Recerca de Catalunya, European Commission, Generalitat de Catalunya, Ministerio de Ciencia e Innovación, National Institutes of Health, Agència de Gestió d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III, Agencia Estatal de Investigación
Openalex Percentile: Top 74%
RNA modifications and cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.