Dataset Information

DSigDB: drug signatures database for gene set analysis.

ABSTRACT: We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22?527 gene sets, consists of 17?389 unique compounds covering 19?531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research.DSigDB is freely available for non-commercial use at http://tanlab.ucdenver.edu/DSigDB.Supplementary data are available at Bioinformatics online.aikchoon.tan@ucdenver.edu.

SUBMITTER: Yoo M

PROVIDER: S-EPMC4668778 | biostudies-other | 2015 Sep

REPOSITORIES: biostudies-other

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DSigDB: drug signatures database for gene set analysis.

Yoo Minjae M Shin Jimin J Kim Jihye J Ryall Karen A KA Lee Kyubum K Lee Sunwon S Jeon Minji M Kang Jaewoo J Tan Aik Choon AC

Bioinformatics (Oxford, England) 20150519 18

<h4>Unlabelled</h4>We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22 527 gene sets, consists of 17 389 unique compounds covering 19 531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene ...[more]

PMID: 25990557

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Project description:Background: Epidemiological studies have found that triple-negative breast cancer (TNBC) and TN inflammatory breast cancer (IBC) are associated with lower frequency and duration of breast-feeding compared to non-TNBC and non-TN IBC, respectively. Limited breast-feeding could reflect abrupt or premature involution and contribute to a "primed" stroma that is permissive to the migration of cancer cells typical of IBC. We hypothesized that gene expression related to abrupt mammary gland involution after forced weaning may be enriched in the tissues of IBC patients and, if so, provide a potential correlation between limited breast-feeding and the development of aggressive breast cancer.Methods: We utilized the Short Time-series Expression Miner (STEM) program to cluster significant signatures from two independent studies that analyzed gene expression at multiple time-points of mouse mammary gland involution. Using 10 significant signatures, we performed gene ontology analysis and gene set enrichment analysis (GSEA) on training and validation sets from human breast cancer gene expression data to identify specific genes that are enriched in IBC compared to non-IBC and in TN compared to non-TN in IBC and non-IBC groups.Results: Examining the combined data, we identified 10 involution gene clusters (Inv1-10) that share time-dependent regulation after forced weaning. Inv5 was the only cluster significantly enriched in IBC in the training and validation set (nominal p-values <0.05) and only by unadjusted p-values (FDR q-values 0.26 and 0.46 respectively). Eight genes in Inv5 are upregulated in both the training and validation sets in IBC. Combining the training and validation sets, both Inv5 and Inv6 have nominal p-values <0.05 and q-values 0.39 and 0.20, respectively. The time course for both clusters includes genes that change within 12 hours after forced weaning.Conclusions: Results from this in silico study suggest correlation between molecular events during abrupt involution and aggressive breast cancer. Specifically, candidate genes from Inv5 merit functional investigation regarding the role of limited breast-feeding in IBC development.

Dataset Information

DSigDB: drug signatures database for gene set analysis.

Publications

DSigDB: drug signatures database for gene set analysis.

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