The Inhibitory Effect of miR-345 on Glioma Progression Is Closely Related to circRNA-hsa_circ_0073237 and HDGF.

Glioma is the most common primary malignant tumor of the central nervous system and has a poor prognosis. Therefore, exploring the key molecular targets is a new opportunity for basic research and clinical treatment of glioma. Previous studies found that circRNA-hsa_circ_0073237 was upregulated in gliomas. Our further analyses of the biological function and molecular mechanism of hsa_circ_0073237 showed that hsa_circ_0073237 was also upregulated in glioma cell lines and could combine with miR-345 to inhibit its expression. miR-345 was downregulated in glioma tissues and cells, and targeted to regulate the expression of hepatoma-derived growth factor (HDGF), while HDGF expression was enhanced in glioma. Hsa_circ_0073237 promoted the expression of HDGF in glioma cells by adsorbing miR-345. Hsa_circ_0073237 siRNA, miR-345, and HDGF siRNA effectively inhibited cell viability and invasion and promoted cell apoptosis. When expression of hsa_circ_0073237 and miR-345 was inhibited simultaneously, cell viability, apoptosis, and invasion did not change significantly; however, after transfection with HDGF overexpression vector, the effects of hsa_circ_0073237 siRNA and miR-345 on glioma cell viability, apoptosis, and invasion were obviously reversed. Further construction of glioma xenograft models in nude mice confirmed that the introduction of miR-345 in vivo effectively inhibited tumor growth, significantly reduced tumor diameter and weight, and obviously decreased the expression of HDGF. Therefore, hsa_circ_0073237 can regulate the biological functions of glioma cells through miR-345/HDGF, thereby affecting the progression of tumors, indicating that the hsa_circ_0073237/miR-345/HDGF pathway may be a key target for the treatment of glioma.

UBE2R2-AS1 Inhibits Xenograft Growth in Nude Mice and Correlates with a Positive Prognosis in Glioma.

Our previous study showed that the lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis through the miR-877-3p/TLR4 pathway. In this study, it was further found that the expression of UBE2R2-AS1 in glioma tissues was decreased significantly, and gradually decreased with increasing clinical stage. Chi-square analysis showed that the expression of UBE2R2-AS1 was significantly correlated with the WHO stage of tumor and epilepsy. Using Kaplan-Meier univariate survival analysis, it was found that the expression of UBE2R2-AS1 correlated positively with the overall survival of patients with glioma, while multiple Cox regression analysis showed that the expression of UBE2R2-AS1 correlated positively with the overall survival of patients with glioma as a protective factor for glioma prognosis. The analysis of data from TCGA also showed that patients with high UBE2R2-AS1 levels or low miR-877-3p expression were more likely to have good survival outcomes. Further construction of a glioma xenograft model in nude mice showed that UBE2R2-AS1 overexpression inhibited the growth of tumors, and the inhibition of miR-877-3p expression had a similar effect. Simultaneous UBE2R2-AS1 overexpression and miR-877-3p inhibition further decreased the growth rate of tumors in nude mice. Taken together, the results of our study suggest that UBE2R2-AS1 is an important tumor suppressor gene in glioma, which may be a good marker and treatment target for the clinical detection of glioma.

PFBNet: a priori-fused boosting method for gene regulatory network inference.

BACKGROUND: Inferring gene regulatory networks (GRNs) from gene expression data remains a challenge in system biology. In past decade, numerous methods have been developed for the inference of GRNs. It remains a challenge due to the fact that the data is noisy and high dimensional, and there exists a large number of potential interactions. RESULTS: We present a novel method, namely priori-fused boosting network inference method (PFBNet), to infer GRNs from time-series expression data by using the non-linear model of Boosting and the prior information (e.g., the knockout data) fusion scheme. Specifically, PFBNet first calculates the confidences of the regulation relationships using the boosting-based model, where the information about the accumulation impact of the gene expressions at previous time points is taken into account. Then, a newly defined strategy is applied to fuse the information from the prior data by elevating the confidences of the regulation relationships from the corresponding regulators. CONCLUSIONS: The experiments on the benchmark datasets from DREAM challenge as well as the E.coli datasets show that PFBNet achieves significantly better performance than other state-of-the-art methods (Jump3, GEINE3-lag, HiDi, iRafNet and BiXGBoost).