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Fig. 1 | Breast Cancer Research

Fig. 1

From: MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice

Fig. 1

MicroRNA (miRNA) sequencing data analysis. a Identification of stage-specific differentially expressed miRNA-messenger RNA (mRNA) regulatory network. At each time point, for a differentially expressed miRNA and one of its mRNA targets predicted based on their sequence complementarity from the m3RNA database, we calculated the correlation between their expression levels (reads per kilobase million (RPKM)) from all 24 samples. Any miRNA-mRNA interaction with positive correlation was filtered out. Thus, only miRNA-mRNA pairs with opposite differential expression were included in the regulatory network for each cancer developmental stage. b Identification of overall transition pattern-specific miRNA-mRNA regulatory network. We classified miRNAs into 27 groups based on the overall expression patterns of the three consecutive stage transitions. In each group, only mRNA-miRNA pairs with opposite transition patterns were considered. c Identification of miRNA regulatory modules. We first annotated genes in a miRNA-mRNA regulatory network with functional and structural terms from Gene Ontology Biological Process, Kyoto Encyclopedia of genes and genomes (KEGG) pathways, Panther pathways, and Interpro domains. Using a maximal biclique analysis followed by bi-clustering, we then identified sets of coherently related genes and annotation terms. The miRNA regulatory modules were formed by adding miRNAs to corresponding sets that they potentially regulate

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