Integrative Transcriptome Analysis Of Psoriasis Disease And Treatment Datasets For Developing Therapeutic Intervention Strategies | 9357
Journal of Computer Science & Systems Biology
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Psoriasis is a chronic inflammatory skin disease with autoimmune origin and complex pathological features. The goal of our
study is to develop strategies for therapeutic intervention by exploring underlying molecular mechanisms of psoriasis, and
the subsequent alterations following therapeutic interventions through an integrative transcriptomics analysis. To this effect, we
conducted a meta-analysis on ten genome-wide transcriptomics studies consisting of over 700 human skin biopsies from normal
and psoriatic skins including non-lesional, lesional, and treated, as well as samples from
human epidermal keratinocyte
model. We identified a meta-set of 595 genes with high concordance across disease sets and selected an 87-gene disease signature
for psoriasis. We present potential therapeutic intervention strategies, based on functional enrichment analysis of disease- and
therapeutically intervened data. Additionally, we share our strategies on identifying new drug targets and drug repurposing
opportunities for psoriasis. To the best of our knowledge, this is the first comprehensive analysis integrating both disease and
treated datasets from psoriasis at such a scale. We anticipate that approaches similar to the one that we outline here, would help
elucidate underlying mechanisms and help in developing therapeutic intervention strategies for psoriasis.
Angela Qu has Master degree in Biomedical Informatics from Columbia University. She has completed her Ph.D. in Bioinformatics/Biomedical
Engineering from University of Cincinnati and postdoctoral studies from Columbia Genome Center. She is currently a Senior Scientific Investigator
in Quantitative Science Division at GlaxoSmithKline. She has published over 20 papers in peer-reviewed journals and book chapters in areas of
systems biology, bioinformatics, and drug discovery and development.
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