Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer.

Non-coding RNAs are fundamental to the competing endogenous RNA (CeRNA) hypothesis in oncology. Previous work focused on static CeRNA networks. We construct and analyze CeRNA networks for four sequential stages of lung adenocarcinoma (LUAD) based on multi-omics data of long non-coding RNAs (lncRNAs), microRNAs and mRNAs. We find that the networks possess a two-level bipartite structure: common competing endogenous network (CCEN) composed of an invariant set of microRNAs over all the stages and stage-dependent, unique competing endogenous networks (UCENs). A systematic enrichment analysis of the pathways of the mRNAs in CCEN reveals that they are strongly associated with cancer development. We also find that the microRNA-linked mRNAs from UCENs have a higher enrichment efficiency. A key finding is six microRNAs from CCEN that impact patient survival at all stages, and four microRNAs that affect the survival from a specific stage. The ten microRNAs can then serve as potential biomarkers and prognostic tools for LUAD. Author summary: Lung cancer is the leading cause of cancer-related human deaths worldwide. Lung adenocarcinoma is one of the most common subtypes, and has more pronounced genomic variations than other lung cancer subtypes. A milestone discovery in cancer research is the roles played by non-coding RNAs which have been identified as the oncogenic drivers and tumor suppressors. In cancer development, non-coding RNAs form an inseparable unity of RNA-level regulating networks in the intracellular environment, and the dynamical interplay and competition among different types of RNAs are playing a pivotal role. We have developed a quantitative approach to reconstructing the the mutual regulation networks of RNAs for the progressive stages of lung adenocarcinoma at the post-transcriptional level. Our analysis revealed the emergence of two characteristically distinct types of networks that possess a two-level bipartite structure, and we uncovered a number of key genes that affect or even determine the survival of patients at each stage. Our work establishes a more comprehensive gene-data analysis framework than previous ones, not only providing a tool to probe more deeply into the mechanisms of cancer evolution than previously possible but also having the potential to lead to more effective biomarkers and drug targets for lung cancer. [ABSTRACT FROM AUTHOR]