Co-regulations of miRNAs have been much less studied than the research on regulations between miRNAs and their target genes, although these two problems are equally important for understanding the entire mechanisms of complex post-transcriptional regulations. The difficulty to construct a miRNA–miRNA co-regulation network lies in how to determine reliable miRNA pairs from various resources of data related to the same disease such as expression levels, gene ontology (GO) databases, and protein–protein interactions. Here we take a novel integrative approach to the discovery of miRNA–miRNA co-regulation networks. This approach can progressively refine the various types of data and the computational analysis results. Applied to three lung cancer miRNA expression data sets of different subtypes, our method has identified a miRNA–miRNA co-regulation network and co-regulating functional modules common to lung cancer. An example of these functional modules consists of genes SMAD2, ACVR1B, ACVR2A and ACVR2B. This module is synergistically regulated by let-7a/b/c/f, is enriched in the same GO category, and has a close proximity in the protein interaction network. We also find that the co-regulation network is scale free and that lung cancer related miRNAs have more synergism in the network. According to our literature survey and database validation, many of these results are biologically meaningful for understanding the mechanism of the complex post-transcriptional regulations in lung cancer. •A progressive data refining approach is proposed for the identification of miRNA co-regulation networks.•The network of co-regulating miRNAs is scale free and its degree distribution follows a power law.•Lung cancer related miRNAs have more synergism in the network.•The miRNAs from the same family tend to have similar functions and high correlation.