TY - GEN
T1 - Identification of a gene expression core signature for Duchenne Muscular Dystrophy (DMD) via integrative analysis reveals novel potential compounds for treatment
AU - Moreno Pérez, Norú Ichim
AU - Aranda, Manuel
AU - Voolstra, Christian R.
N1 - KAUST Repository Item: Exported on 2020-10-01
PY - 2010/5
Y1 - 2010/5
N2 - Duchenne muscular dystrophy (DMD) is a recessive X-linked form of muscular dystrophy and one of the most prevalent genetic disorders of childhood. DMD is characterized by rapid progression of muscle degeneration, and ultimately death. Currently, glucocorticoids are the only available treatment for DMD, but they have been shown to result in serious side effects. The purpose of this research was to define a core signature of gene expression related to DMD via integrative analysis of mouse and human datasets. This core signature was subsequently used to screen for novel potential compounds that antagonistically affect the expression of signature genes. With this approach we were able to identify compounds that are 1) already used to treat DMD, 2) currently under investigation for treatment, and 3) so far unknown but promising candidates. Our study highlights the potential of meta-analyses through the combination of datasets to unravel previously unrecognized associations and reveal new relationships. © IEEE.
AB - Duchenne muscular dystrophy (DMD) is a recessive X-linked form of muscular dystrophy and one of the most prevalent genetic disorders of childhood. DMD is characterized by rapid progression of muscle degeneration, and ultimately death. Currently, glucocorticoids are the only available treatment for DMD, but they have been shown to result in serious side effects. The purpose of this research was to define a core signature of gene expression related to DMD via integrative analysis of mouse and human datasets. This core signature was subsequently used to screen for novel potential compounds that antagonistically affect the expression of signature genes. With this approach we were able to identify compounds that are 1) already used to treat DMD, 2) currently under investigation for treatment, and 3) so far unknown but promising candidates. Our study highlights the potential of meta-analyses through the combination of datasets to unravel previously unrecognized associations and reveal new relationships. © IEEE.
UR - http://hdl.handle.net/10754/564277
UR - http://ieeexplore.ieee.org/document/5510485/
UR - http://www.scopus.com/inward/record.url?scp=77955608592&partnerID=8YFLogxK
U2 - 10.1109/CIBCB.2010.5510485
DO - 10.1109/CIBCB.2010.5510485
M3 - Conference contribution
SN - 9781424467662
SP - 82
EP - 87
BT - 2010 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology
PB - Institute of Electrical and Electronics Engineers (IEEE)
ER -