MDM2 promotes the proliferation and inhibits the apoptosis of pituitary adenoma cells by directly interacting with p53

Introduction: Pituitary adenomas constitute one of the most common intracranial tumours. The mouse double minute 2 homologue (MDM2) is considered as an important oncogene in many tumours, but it has been little studied in pituitary adenomas and the mechanism is not well understood. The purpose of this study was to investigate the function of MDM2 and its primary mechanism of action in pituitary adenoma cells. Material and methods: The expression of MDM2 in pituitary adenoma cell lines and normal cells was determined by real-time polymerase chain reaction (RT-PCR). The proliferation and apoptosis of pituitary adenoma cells after inhibition of MDM2 expression were detected by MTS and flow cytometry, respectively. The protein expressions of MDM2 and p53 were detected by western blot. Co-IP was used to detect the direct binding between MDM2 and p53. Results: The results of RT-PCR showed that MDM2 was significantly up-regulated in pituitary adenoma cell lines. Inhibition of MDM2 suppressed the proliferation and promoted apoptosis of pituitary adenoma cells. However, inhibiting the expression of MDM2 can promotethe protein expression of p53. The results of co-IP showed that MDM2 interacted with p53 by direct combination. Then, we inhibited the expressions of p53 and MDM2 simultaneously in the pituitary adenoma cells by co-transfecting siRNAs, and the results showed that, compared with the group that inhibited MDM2 alone, cell proliferation of the co-transfected group increased and apoptosis of the cotransfected group decreased, which was similar to the NC group. Conclusions: Taken together, these results suggest that MDM2 promoted the proliferation and inhibited the apoptosis of pituitary adenoma cells by directly interacting with p53 in pituitary adenoma cells. Therefore, MDM2-p53 may serve as a novel marker and therapeutic target for pituitary adenomas

A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM