InDePTH: detection of hub genes for developing gene expression networks under anticancer drug treatment
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Masaru Koido1, Yuri Tani1, Satomi Tsukahara1, Yuka Okamoto1 and Akihiro Tomida1
1Cancer Chemotherapy Center, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
Akihiro Tomida, email: email@example.com
Keywords: drug-induced gene expression change; transcriptome; network analysis; data mining; LINCS
Received: March 06, 2018 Accepted: May 19, 2018 Published: June 26, 2018
It has been difficult to elucidate the structure of gene regulatory networks under anticancer drug treatment. Here, we developed an algorithm to highlight the hub genes that play a major role in creating the upstream and downstream relationships within a given set of differentially expressed genes. The directionality of the relationships between genes was defined using information from comprehensive collections of transcriptome profiles after gene knockdown and overexpression. As expected, among the drug-perturbed genes, our algorithm tended to derive plausible hub genes, such as transcription factors. Our validation experiments successfully showed the anticipated activity of certain hub gene in establishing the gene regulatory network that was associated with cell growth inhibition. Notably, giving such top priority to the hub gene was not achieved by ranking fold change in expression and by the conventional gene set enrichment analysis of drug-induced transcriptome data. Thus, our data-driven approach can facilitate to understand drug-induced gene regulatory networks for finding potential functional genes.
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