miRGTF-net

miRGTF-net constructs miRNA–gene–transcription factor (TF) regulatory networks that integrate transcriptional regulation, post-transcriptional silencing by microRNAs, and co-expression between host genes and their intronic miRNAs for tissue-specific network analysis.

Key Features:

• Integration of interaction types: Models transcriptional regulation by TFs, miRNA-mediated gene silencing, and co-expression relationships between host genes and intronic miRNAs.
• Experimentally validated interactions: Incorporates experimentally validated TF–gene and miRNA–target interaction data.
• Expression-driven, tissue-specific networks: Integrates miRNA and mRNA expression profiles, including paired sequencing data, to produce biologically relevant, tissue-specific networks.
• GraphML export and component analysis: Exports networks in GraphML format to enable analysis of weakly and strongly connected components.
• Compatibility with transcriptomic platforms: Applicable to microarray and RNA sequencing datasets as well as paired miRNA/mRNA-sequencing data.
• Prognostic signature derivation: Facilitates generation of prognostic gene signatures from constructed regulatory networks.

Scientific Applications:

• Intracellular interaction mapping: Identifies and quantifies regulatory interactions among miRNAs, genes, and transcription factors.
• Breast cancer network analysis (TCGA): Applied to paired miRNA/mRNA-sequencing data from The Cancer Genome Atlas (TCGA) breast cancer samples to construct tissue-specific regulatory networks.
• Prognostic signatures in ER-positive breast cancer: Generated prognostic gene signatures with high accuracy across microarray and RNA-seq transcriptomic datasets in ER-positive breast cancer.
• E2F1 and ERα analysis: Revealed that many prognostic genes are direct targets of transcription factor E2F1 and suggested interplay between estrogen receptor alpha and E2F1 as a recurrence factor in tamoxifen-treated patients.

Methodology:

Combines experimentally validated interaction data with sample-specific miRNA and mRNA expression profiles to construct tissue-specific miRNA–gene–TF regulatory networks and exports networks in GraphML for component analysis.