Frontiers in Oncology (May 2020)
Identifying Predictive Gene Expression and Signature Related to Temozolomide Sensitivity of Glioblastomas
Abstract
Temozolomide (TMZ) is considered a standard chemotherapeutic agent for glioblastoma (GBM). Characterizing the biological molecules and signaling pathways involved in TMZ sensitivity would be helpful for selecting therapeutic schemes and evaluating prognosis for GBM. Thus, in the present study, we selected 34 glioma cell lines paired with specific IC50 values of TMZ obtained from CancerRxGene and RNA-seq data downloaded from the Cancer Cell Line Encyclopedia to identify genes related to TMZ sensitivity. The results showed that 1,373 genes were related to the response of GBM cells to TMZ. Biological function analysis indicated that epithelial–mesenchymal transition, Wnt signaling, and immune response were the most significantly activated functions in TMZ-resistant cell lines. Additionally, negative regulation of telomere maintenance via telomerase was enriched in TMZ-sensitive glioma cell lines. We also preliminarily observed a synergistic effect of combination treatment comprising TMZ and a telomerase inhibitor in vitro. We identified six genes (MROH8, BET1, PTPRN2, STC1, NKX3-1, and ARMC10) using the random survival forests variable hunting algorithm based on the minimum error rate of the gene combination and constructed a gene expression signature. The signature was strongly related to GBM clinical characteristics and exhibited good prognosis accuracy for both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets. Patients in the high score group had a shorter survival time than those in the low score group (11.2 vs. 22.2 months, hazard ratio = 7.31, p = 4.59e−11) of the TCGA dataset. The CGGA dataset was selected as a validation group with 40 patients in the high score set and 43 patients in the low score set (12.5 vs. 28.8 months, hazard ratio = 3.42, p = 8.61e−5). Moreover, the signature showed a better prognostic value than MGMT promoter methylation in both datasets. We also developed a nomogram for clinical use that integrated the TMZ response signature and four other risk factors to individually predict patient survival after TMZ chemotherapy. Overall, our study provides promising therapeutic targets and potential guidance for adjuvant therapy of GBM.
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