Bioinformatics Study On Transcriptional Factor And MiRNA Co-Regulation Networks In Complex Diseases

Complex diseases result from a complex interplay between multiple genetic and environmental factors,which are difficult problems threatening human health.Bioinformatics,the study of information contained and concealed in biomedical big data,is an indispensable force in the study of complex diseases.With the development of bioinformatics,biological networks have become an effective strategy to interpret complex diseases from a systematic perspective.Gene regulatory network(GRN)is a kind of biological network which focuses on the regulatory mechanism of gene expression.Gene expression regulation is a complex biological process involving various regulators across multiple levels that controls organism development and cell homeostasis.In gene expression regulation system,transcription factors(TFs)and microRNAs(miRNAs)have been recognized to play important roles at transcriptional level and posttranscriptional level respectively.Moreover,increasing evidence suggests that TFs and miRNAs are able to work together.Specifically,TFs and miRNAs have been shown to regulate shared target genes in feed-forward loops(FFLs)and co-regulating pairs.At the network level,TF-miRNA FFLs and co-regulating pairs are major network motifs(i.e.genetic interconnection patterns that occur more often by chance in biological networks),forming the TF-miRNA co-regulatory network and serving as basic building blocks of a complex regulatory system.Perturbations of the interwoven regulatory patterns involving TFs and miRNAs trigger global alterations in gene expression and have close connection with the initiation,progression and prognosis of complex diseases.Therefore,it is of great significance to explore the TF-miRNA synergistic regulation for the understanding of complex diseases.Cancer is a complex,heterogeneous disease which has been always of great concern in biomedical study.Indeed,analyses of TF-miRNA co-regulatory patterns have already revealed an essential role of their combined regulatory influence in some well-studied cancers indicating the significance of TF-miRNA co-regulation in complex diseases.Several web resources have been developed to unravel how TFs and miRNAs interact with genes,which also serve as data basis for studying TF-miRNA co-regulation.Nevertheless,online applications for TF-miRNA co-regulatory analysis,especially with a focus on cancers,are lacking.In addition,the study of cancer prognosis serves as an important part of cancer research.TFs and miRNAs cooperatively orchestrate gene expression;their dysregulation also affects cancer prognosis.Despite substantial efforts to identify the prognostic signatures,few related studies have investigated prognostic signatures and the regulatory mechanism behind them from a system-level perspective,especially considering the cooperation between TFs and miRNAs.In the first part of the study,we designed and developed a user-friendly platform.The platform integrated multi-dimensional data and utilized network motif algorithm to construct TF-miRNA co-regulatory networks,which is able to conduct comprehensive analyses within the context of particular cancer type.The R-Shiny based platform supports queries and visualization of cancer-specific TF-miRNA co-regulatory information,network topology analysis and enrichment analysis.In the second part of the study,we established prognosis-related TF-miRNA coregulatory networks for 12 major cancers based on the framework of the first study.We characterized the emergent properties and behaviors of TFs,miRNAs,and their joint target genes at a systems level,in the context of cancer prognosis.We found that these joint target genes and associated co-regulatory patterns exhibit cancer-specific properties.Some TFs and miRNAs are highly conserved across cancers,which also maintain the structure of co-regulatory nets.We designed a novel model to illustrate the complex TFmiRNA co-regulation mechanism in the context of cancer prognosis.Finally,we conducted a comprehensive survey including literature consultation to validate our findings.To sum up,we took the TF-miRNA co-regulation as the starting point of research.Based on the bioinformatics method and the perspective of biological network,we developed a web tool for investigating cancer-specific TF-miRNA co-regulatory networks and analyzed prognosis-related TF-miRNA co-regulatory networks for 12 major cancers.The above work provides methods and tools for the study of TF-miRNA co-regulatory networks in different cancers and contributes to the comprehension of regulatory mechanisms,bringing novel and powerful insights into cancer study at systemlevel.