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Pathway analysis for identification of potential biomarkers in severe cutaneous drug hypersensitivity reactions
Elbakkoush, Abdallah Ahmed; Atique, Suleman; Khaleel, Anas & Liu, Chien- Tsai
Abstract
Purpose:To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using
pathways analysis in order to identify some potential biomarkers that may be used for early detection of
SJS and epidermal necrolysis (TEN) manifestations.
Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus
database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes
to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the
gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which
are online software and databases.
Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for
topological analysis. It was found by geneMANIA database search that majority of these genes were coexpressed
yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions
comprising almost 14.33 %. There were < 1 % pathway and genetic interactions with values of 0.97 and
0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13
genes were shown to be in evident relationship of interaction with regards to hypersensitivity.
Conclusion: Analysis of differential gene expressions by topological and database approaches in the
current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A,
CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions
and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and
a genetic model to predict SJS and TEN early incidence using these biomarker genes has been
developed.
Keywords
Steven-Johnson Syndrome; Toxic epidermal necrolysis; Differentially expressed genes; Hypersensitivity reactions; Genes; Biomarkers
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