The genes significantly associated with an improved prognosis and long-term survival of glioblastoma

Abstract

Background and purpose Glioblastoma multiforme (GBM) is the most devastating brain tumor with less than 5% of patients surviving 5 years following diagnosis. Many studies have focused on the genetics of GBM with the aim of improving the prognosis of GBM patients. We investigated specific genes whose expressions are significantly related to both the length of the overall survival and the progression-free survival in patients with GBM. Methods We obtained data for 12,042 gene mRNA expressions in 525 GBM tissues from the Cancer Genome Atlas (TCGA) database. Among those genes, we identified independent genes significantly associated with the prognosis of GBM. Receiver operating characteristic (ROC) curve analysis was performed to determine the genes significant for predicting the long-term survival of patients with GBM. Bioinformatics analysis was also performed for the significant genes. Results We identified 33 independent genes whose expressions were significantly associated with the prognosis of 525 patients with GBM. Among them, the expressions of five genes were independently associated with an improved prognosis of GBM, and the expressions of 28 genes were independently related to a poorer prognosis of GBM. The expressions of the ADAM22, ATP5C1, RAC3, SHANK1, AEBP1, C1RL, CHL1, CHST2, EFEMP2, and PGCP genes were either positively or negatively related to the long-term survival of GBM patients. Conclusions Using a large-scale and open database, we found genes significantly associated with both the prognosis and long-term survival of patients with GBM. We believe that our findings may contribute to improving the understanding of the mechanisms underlying GBM.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

“…These results revealed that Model-new1 was not a robust and effective model for PFS prediction of GBM patients. On the other hand, a recent study identified genes associated with the PFI using a linear correlation method (Pearson correlation coefficient) without the information from the GBM patients at recurrence and suggested the contribution of these genes to predicting PFI [ 42 ]. We employed the same criteria to identify genes associated with the TTR and constructed another new model for predicting TTR (“Model-new2”; Additional file 5 : Fig.…”

“…Model-new1 with four prognostic markers was then constructed through a multivariate stepwise regression analysis (Additional file 4 : Table S4 ). For Model-new2 construction, TTR-associated genes were identified if the genes simultaneously satisfied: (1) the gene expression levels of the primary GBMs were highly correlated with the PFI values (absolute value of Pearson’s correlation coefficient ( r ) ≥ 0.2 with P < 0.01 according to a previous study [ 42 ]); and (2) the third and fourth rules stated in the previous section. Model-new2 with five prognostic markers was constructed through a multivariate stepwise regression analysis (Additional file 4 : Table S4 ).…”

Does limited-margin radiotherapy change the recurrence pattern and survival of patients with high-grade gliomas? Analysis and validation of a different approach

“…Similarly, central and in-eld recurrences often develop early during the follow-up period, but some long-surviving patients also experience central and in-eld recurrences years later. Recurrences that occur after a relatively long time may be caused by the molecular or genomic characteristics of the tumor, but a de nitive comment cannot be made on this issue in this study [21,22]. The majority of recurrences in this study which involved the delineation of CTVs in dual volumes, manifested in the central area approximately 12-13 mm from the GTV.…”