Effect of downregulated lncRNA NBAT1 on the biological behavior of glioblastoma cells

Emerging Role of Long Non-Coding RNAs in the Pathobiology of Glioblastoma

Glioblastoma is the utmost aggressive diffuse kind of glioma which is originated from astrocytes, neural stem cells or progenitors. This malignant tumor has a poor survival rate. A number of genetic aberrations and somatic mutations have been associated with this kind of cancer. In recent times, the impact of long non-coding RNAs (lncRNAs) in glioblastoma has been underscored by several investigations. Up-regulation of a number of oncogenic lncRNAs such as H19, MALAT1, SNHGs, MIAT, UCA, HIF1A-AS2 and XIST in addition to down-regulation of other tumor suppressor lncRNAs namely GAS5, RNCR3 and NBAT1 indicate the role of these lncRNAs in the pathogenesis of glioblastoma. Several in vitro and a number of in vivo studies have demonstrated the contribution of these transcripts in the regulation of cell proliferation and apoptosis, cell survival, invasion and metastasis of glioblastoma cells. Moreover, some lncRNAs such as SBF2-AS1 are involved in conferring resistance to temozolomide. Finally, few circularRNAs have been identified that influence the evolution of glioblastoma. In this paper, we discuss the impacts of lncRNAs in the pathogenesis of glioblastoma, their applications as markers and their implications in the therapeutic responses in this kind of cancer.

MicroRNA-181 inhibits glioblastoma cell growth by directly targeting CCL8

MicroRNAs (miRNAs/miRs), including miR-181, are closely linked to the development and progression of glioblastoma. However, the function of miR-181 in glioblastoma has not been fully clarified. The aim of the present study was to investigate the role of miR-181 in glioblastoma. miR-181 was revealed to be downregulated in glioblastoma tissues and cell lines, and associated with poor prognosis in patients with glioblastoma. Overexpression of miR-181 inhibited glioblastoma cell proliferation, invasion and migration, arrested glioblastoma cell cycle in the G1 phase and induced glioblastoma cell apoptosis. miR-181 was demonstrated to decrease expression of C-C motif chemokine ligand 8 (CCL8) by directly interacting with its 3′-untranslated region. Overexpression of CCL8 inversely reversed the proliferation, invasion and migration-promoting effects of miR-181 in glioblastoma cells. Furthermore, CCL8 was upregulated in glioblastoma tissues and was negatively correlated with miR-181 expression. These results indicate that miR-181 is a potential molecular biomarker or therapeutic target in the clinical management of glioblastoma.

LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation

Evidences increasingly indicate the involvement of long non-coding RNAs (lncRNAs) in various biological processes. As the mutations and abnormalities of lncRNAs are closely related to the progression of complex diseases, the identification of lncRNA-disease associations has become an important step toward the understanding and treatment of diseases. Since only a limited number of lncRNA-disease associations have been validated, an increasing number of computational approaches have been developed for predicting potential lncRNA-disease associations. However, how to predict potential associations precisely through computational approaches remains challenging. In this study, we propose a novel two-side sparse self-representation (TSSR) algorithm for lncRNA-disease association prediction. By learning the self-representations of lncRNAs and diseases from known lncRNA-disease associations adaptively, and leveraging the information provided by known lncRNA-disease associations and the intra-associations among lncRNAs and diseases derived from other existing databases, our model could effectively utilize the estimated representations of lncRNAs and diseases to predict potential lncRNA-disease associations. The experiment results on three real data sets demonstrate that our TSSR outperforms other competing methods significantly. Moreover, to further evaluate the effectiveness of TSSR in predicting potential lncRNAs-disease associations, case studies of Melanoma, Glioblastoma, and Glioma are carried out in this paper. The results demonstrate that TSSR can effectively identify some candidate lncRNAs associated with these three diseases.