Potential roles for Gfi1 in the pathogenesis and proliferation of glioma

Chaperone protein P4HB predicts temozolomide response and prognosis in malignant glioma

Prolyl 4-hydroxylase beta polypeptide (P4HB) is a chaperone protein associated with temozolomide (TMZ) resistance through the unfolded protein response. Cancer cells with constitutive activation of endoplasmic reticulum stress and upregulation of P4HB have been observed to show resistance against chemotherapies. The present study focused on the evaluation of the prognostic value of P4HB in subtypes of glioma with or without O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. P4HB expression was assessed by immunohistochemical staining in 73 grade I-IV gliomas and its association with the clinicopathological data was determined. It was indicated that P4HB expression was significantly associated with several parameters, including age, tumour grade and the number of TMZ treatment cycles received. In the Kaplan-Meier analysis, P4HB expression was positively associated with risk of mortality and disease progression. In patients treated with TMZ, high P4HB expression was significantly associated with poor overall survival (OS) and progression-free survival (PFS). The association between MGMT promoter methylation and P4HB expression was also assessed. Patients with MGMT^MethP4HB^Low tumours had the most favourable PFS (48 months) among cases with various combinations of MGMT methylation status and P4HB expression. Multivariate analysis revealed that P4HB may be used as an independent prognostic indicator of OS, particularly in high-grade gliomas. The present study uncovered the potential role of P4HB in a nuanced pathological stratification during clinical decision-making with respect to MGMT promoter methylation status and TMZ treatment.

Using evolutional properties of gene networks in understanding survival prognosis of glioblastoma

Previously, we investigated survival prognosis of glioblastoma by applying a gene regulatory approach to a human glioblastoma dataset. Here, we further extend our understanding of survival prognosis of glioblastoma by refining the network inference technique we apply to the glioblastoma dataset with the intent of uncovering further topological properties of the networks. For this study, we modify the approach by specifically looking at both positive and negative correlations separately, as opposed to absolute correlations. There is great interest in applying mathematical modeling approaches to cancer cell line datasets to generate network models of gene regulatory interactions. Analysis of these networks using graph theory metrics can identify genes of interest. The principal approach for modeling microarray datasets has been to group all the cell lines together into one overall network, and then, analyze this network as a whole. As per the previous study, we categorize a human glioblastoma cell line dataset into five categories based on survival data, and analyze each category separately using both negative and positive correlation networks constructed using a modified version of the WGCNA algorithm. Using this approach, we identified a number of genes as being important across different survival stages of the glioblastoma cell lines.