Journal
NUCLEIC ACIDS RESEARCH
Volume 40, Issue 17, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/nar/gks395
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Funding
- National Institutes of Health [R01 GM077122, DP1 DA28994]
- Hundred Talents Program of the Chinese Academy of Sciences
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We describe here a novel method for integrating gene and miRNA expression profiles in cancer using feed-forward loops (FFLs) consisting of transcription factors (TFs), miRNAs and their common target genes. The dChip-GemiNI (Gene and miRNA Network-based Integration) method statistically ranks computationally predicted FFLs by their explanatory power to account for differential gene and miRNA expression between two biological conditions such as normal and cancer. GemiNI integrates not only gene and miRNA expression data but also computationally derived information about TF-target gene and miRNA-mRNA interactions. Literature validation shows that the integrated modeling of expression data and FFLs better identifies cancer-related TFs and miRNAs compared to existing approaches. We have utilized GemiNI for analyzing six data sets of solid cancers (liver, kidney, prostate, lung and germ cell) and found that top-ranked FFLs account for similar to 20% of transcriptome changes between normal and cancer. We have identified common FFL regulators across multiple cancer types, such as known FFLs consisting of MYC and miR-15/miR-17 families, and novel FFLs consisting of ARNT, CREB1 and their miRNA partners. The results and analysis web server are available at ext-link-type=uri xlink:href=http://www.canevolve.org/dChip-GemiNi xmlns:xlink=http://www.w3.org/1999/xlink>http://www.canevolve.org/dChip-GemiNi.
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