Constructing methylation-driven ceRNA networks unveil tumor heterogeneity and predict patient prognosis

Cancer development involves a complex interplay between genetic and epigenetic factors, with emerging evidence highlighting the pivotal role of competitive endogenous RNA (ceRNA) networks in regulating gene expression. However, the influence of ceRNA networks by aberrant DNA methylation remains incompletely understood. In our study, we proposed DMceNet, a computational method to characterize the effects of DNA methylation on ceRNA regulatory mechanisms and apply it across eight prevalent cancers. By integrating methylation and transcriptomic data, we constructed methylation-driven ceRNA networks and identified a dominant role of lncRNAs within these networks in two key ways: (i) 17 cancer-shared differential methylation lncRNAs (DMlncs), including PVT1 and CASC2, form a Common Cancer Network (CCN) affecting key pathways such as the G2/M checkpoint, and (ii) 24 cancer-specific DMlncs construct unique ceRNA networks for each cancer type. For instance, in LUAD and STAD, hypomethylation drives DMlncs like PCAT6 and MINCR, disrupting the Wnt signaling pathway and apoptosis. We further investigated the characteristics of these methylation-driven ceRNA networks at the cellular level, revealing how methylation-driven dysregulation varies across distinct cell populations within the tumor microenvironment. Our findings also demonstrate the prognostic potential of cancer-specific ceRNA relationships, highlighting their relevance in predicting patient survival outcomes. This integrated transcriptomic and epigenomic analysis provides new insights into cancer biology and regulatory mechanisms.