Comprehensive analysis of lncRNA-TF crosstalks and identification of prognostic regulatory feedback loops of glioblastoma using lncRNA/TF-mediated ceRNA network.

Abstract

Glioblastoma (GBM) has become the most aggressive primary brain tumor in the world. Patients with GBM usually have a poor prognosis. The median survival times of GBM patients retain less than 2 years. Thus, it is urgent to investigate the molecular mechanism of GBM. Recently, studies have demonstrated that transcription factors (TFs) participate in cancer pathology by regulating long noncoding RNAs (lncRNAs). However, the functional and regulatory roles of TF-lncRNA crosstalks are still unclear. In this study, we constructed a global lncRNA-TF network (GLTN) based on competing endogenous RNA. As a result, some topological features of GLTN were identified. A known GBM lncRNA MCM3AP-AS1 showed multiple central topological features in GLTN. Furthermore, we identified hub genes and extracted the hub-hub pairs from GLTN to form a hub associated lncRNA-TF network (HALTN). Results showed that a risk model combined with multiple hubs had a significant effect on prognosis. Additionally, we performed module searching and two functional modules from HALTN were identified, which were confirmed as risk factors of GBM. More importantly, we also identified some core lncRNA-TF crosstalks that might form feedback loops to control the biological processes in GBM. Our results demonstrated that the synergistic, competitive lncRNA-TF crosstalks played an important role in pathological processes of GBM, and had strong effect on prognosis. All these results can help us to uncover the molecular mechanism and provide a new therapeutic target for GBM.