Gene regulatory network perturbations contribute to the development and progression of cancer, however, molecular determinants that mediate transcriptional perturbations remain a fundamental challenge for cancer biology. Researchers at University of Texas MD Anderson Cancer Center show that transcriptional perturbations are widely mediated by long noncoding RNAs (lncRNAs) via integration of genome-wide transcriptional regulation with paired lncRNA and gene expression profiles. Systematic construction of an LncRNA Modulator Atlas in Pan-cancer (LncMAP) reveals distinct types of lncRNA regulatory molecules, which are expressed in multiple tissues, exhibit higher conservation. Strikingly, cancers with similar tissue origin share lncRNA modulators which perturb the regulation of cell cycle and immune response-related functions. Furthermore, the researchers identified a large number of pan-cancer lncRNA modulators with potential clinical significance, which are differentially expressed in cancer or are strongly correlated with drug sensitivity across cell lines. Further stratification of cancer patients based on lncRNA-mediated transcriptional perturbations identifies subtypes with distinct survival rates. Finally, they made a user-friendly web interface available for exploring lncRNA-mediated transcriptional perturbations across cancer types. This study provides a systems-level dissection of lncRNA-mediated regulatory perturbations in cancer, and also presents a valuable tool and resource for investigating the function of lncRNAs in cancer.
An integrative framework identifies widespread lncRNA-mediated
transcriptional network perturbations in pan-cancer
(A) Global transcriptional network perturbations were observed across cancer types. Global lncRNA modulators that mediated the network perturbations were analyzed based on proposed LncMod method. The identified modulators were analyzed for different functional characteristics, including cancer specificity, differential expression, cancer hallmark, drug activity and clinical association. (B) The framework to identify lncRNA modulators across cancer types. Firstly, TF–gene regulation were identified based on ChIP-Seq datasets. Second, regression analysis was used to identify context-specific regulation based on gene expression. Next, lncRNA mediated transcriptional network perturbations in each cancer type were discovered by the modified LncMod method and further classified as six regulatory patterns.
Availability – a user-friendly web interface for LncMAP is availableat: http://www.bio-bigdata.com/LncMAP