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

Abstract

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 stagedependent, 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.

Non-coding RNAs identification and regulatory networks in pathogen-host interaction in the microsporidia congenital infection

“…Similarly, in the silkworm 1, 5 and 10-day larvae, the lncRNA/circRNA ceRNA networks were constructed following the congenital N. bombycis infection, respectively (Table S10 -15). According to previous reports, ceRNA network has two characteristically distinct types: one is common to all stages, which is de ned as the common ceRNA networks, and other is unique for each stage, which is de ned the unique ceRNA networks [32]. We found that lncRNA/circRNA ceRNA networks are almost the unique ceRNA networks.…”

Non-coding RNAs identification and regulatory networks in pathogen-host interaction in the microsporidia congenital infection

“…Similarly, in the silkworm 1, 5 and 10-day larvae, the lncRNA/circRNA ceRNA networks were constructed following the congenital N. bombycis infection, respectively (Table S 13 - 18 ). According to previous reports, ceRNA network has two characteristically distinct types: one is common to all stages, which is defined as the common ceRNA networks, and other is unique for each stage, which is defined the unique ceRNA networks [ 47 ]. We found that lncRNA/circRNA ceRNA networks are almost the unique ceRNA networks.…”

A powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

“…In lung cancer diagnosis and prognosis, extensive research has been conducted utilizing gene expression profiling to identify lung cancer-related gene pathways for therapeutic research (Weiss and Kingsley, 2008;Shao et al, 2010;Cai and Jiang, 2014;Dancik and Theodorescu, 2014;Chang et al, 2015;Long et al, 2019;Venugopal et al, 2019;Kong et al, 2020). In this application, we aim to identify differentially expressed gene pathways between different stages of lung cancer, the findings of which could provide insights into the underlying genetic mechanisms of lung cancer progression.…”