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Aydin et al. J Transl Genet Genom. 2025;9:406-26 Journal of Translational
DOI: 10.20517/jtgg.2025.108
Genetics and Genomics

Original Article Open Access

Translational insights into Duchenne muscular
dystrophy: network biomarker identification and

drug repositioning through multi-omics approaches

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Busra Aydin 1 , Hasibe Busra Parmak , Melcenur Ebru İyisoy , Zumre Unal , Hilal Eskicubuk , Keziban
Okutan 3
1
Department of Bioengineering, Faculty of Engineering and Architecture, Konya Food and Agriculture University, Konya 42090,
Turkey.
2
Department of Molecular Biology and Genetics, Faculty of Agriculture and Natural Sciences, Konya Food and Agriculture
University, Konya 42090, Turkey.
3
Department of Biotechnology, Institute of Postgraduate Education, Konya Food and Agriculture University, Konya 42090,
Turkey.
Correspondence to: Busra Aydin, Department of Bioengineering, Faculty of Engineering and Architecture, Konya Food and
Agriculture University, Konya 42090, Turkey. E-mail: busra.aydin@gidatarim.edu.tr
How to cite this article: Aydin B, Parmak HB, İyisoy ME, Unal Z, Eskicubuk H, Okutan K. Translational insights into Duchenne
muscular dystrophy: network biomarker identification and drug repositioning through multi-omics approaches. J Transl Genet
Genom. 2025;9:406-26. https://dx.doi.org/10.20517/jtgg.2025.108

Received: 11 Sep 2025 First Decision: 10 Nov 2025 Revised: 23 Nov 2025 Accepted: 9 Dec 2025 Published: 30 Dec 2025
Academic Editor: Ramón Cacabelos Copy Editor: Ping Zhang Production Editor: Ping Zhang

Abstract
Aim: Duchenne muscular dystrophy (DMD) is a rare genetic condition that results in a lack of dystrophin protein
due to a series of mutations. Current treatment strategies for DMD remain limited, highlighting the urgent need for
novel therapeutic options. This study aimed to identify drugs that can be repositioned using DMD-specific
molecular network signatures and potential diagnostic biomarkers, using a holistic, multi-omics data-integration
approach.

Methods: We have examined messenger RNA expression datasets GSE109178, GSE70955, and GSE38417 to
identify differentially expressed genes (DEGs) using adjusted P-value < 0.001 and |log (fold change)| > 1 as the cut-
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off criteria. A total of 285 DEGs were identified as common across all three datasets. Principal component
analyses were carried out using 33 hub genes identified from three-layered (protein-protein interaction,
transcription factor, and microRNA) biological network constructions.

Results: The discrimination effect of these hub genes was found to be significantly higher between DMD patients

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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