The acquisition of temozolomide resistance is a major clinical challenge for glioblastoma treatment. Chemoresistance in glioblastoma is largely attributed to repair of temozolomide-induced DNA lesions by O6-methylguanine-DNA methyltransferase (MGMT). However, some MGMT-deficient glioblastomas are still resistant to temozolomide, and the underlying molecular mechanisms remain unclear. We found that DYNC2H1 (DHC2) was expressed more in MGMT-deficient recurrent glioblastoma specimens and its expression strongly correlated to poor progression-free survival in MGMT promotor methylated glioblastoma patients. Furthermore, silencing DHC2, both in vitro and in vivo, enhanced temozolomide-induced DNA damage and significantly improved the efficiency of temozolomide treatment in MGMT-deficient glioblastoma. Using a combination of subcellular proteomics and in vitro analyses, we showed that DHC2 was involved in nuclear localization of the DNA repair proteins, namely XPC and CBX5, and knockdown of either XPC or CBX5 resulted in increased temozolomide-induced DNA damage. In summary, we identified the nuclear transportation of DNA repair proteins by DHC2 as a critical regulator of acquired temozolomide resistance in MGMT-deficient glioblastoma. Our study offers novel insights for improving therapeutic management of MGMT-deficient glioblastoma.

BackgroundPreviously developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma. Although the role of alternative splicing (AS) in cancer, especially in glioma, has been validated, a comprehensive analysis of AS in glioma has not yet been conducted. In this study, we aimed at classifying glioma based on prognostic AS.MethodsUsing the TCGA glioblastoma (GBM) and low-grade glioma (LGG) datasets, we analyzed prognostic splicing events. Consensus clustering analysis was conducted to classified glioma samples and correlation analysis was conducted to characterize regulatory network of splicing factors and splicing events.ResultsWe analyzed prognostic splicing events and proposed novel splicing classifications across pan-glioma samples (labeled pST1–7) and across GBM samples (labeled ST1–3). Distinct splicing profiles between GBM and LGG were observed, and the primary discriminator for the pan-glioma splicing classification was tumor grade. Subtype-specific splicing events were identified; one example is AS of zinc finger proteins, which is involved in glioma prognosis. Furthermore, correlation analysis of splicing factors and splicing events identified SNRPB and CELF2 as hub splicing factors that upregulated and downregulated oncogenic AS, respectively.ConclusionA comprehensive analysis of AS in glioma was conducted in this study, shedding new light on glioma heterogeneity and providing new insights into glioma diagnosis and treatment.

TMZ resistance remains one of the main reasons why treatment of glioblastoma (GBM) fails. In order to investigate the underlying proteins and pathways associated with TMZ resistance, we conducted a cytoplasmic proteome research of U87 cells treated with TMZ for 1 week, followed by differentially expressed proteins (DEPs) screening, KEGG pathway analysis, protein–protein interaction (PPI) network construction, and validation of key candidate proteins in TCGA dataset. A total of 161 DEPs including 65 upregulated proteins and 96 downregulated proteins were identified. Upregulated DEPs were mainly related to regulation in actin cytoskeleton, focal adhesion, and phagosome and PI3K-AKT signaling pathways which were consistent with our previous studies. Further, the most significant module consisted of 28 downregulated proteins that were filtered from the PPI network, and 9 proteins (DHX9, HNRNPR, RPL3, HNRNPA3, SF1, DDX5, EIF5B, BTF3, and RPL8) among them were identified as the key candidate proteins, which were significantly associated with prognosis of GBM patients and mainly involved in ribosome and spliceosome pathway. Taking the above into consideration, we firstly identified candidate proteins and pathways associated with TMZ resistance in GBM using proteomics and bioinformatic analysis, and these proteins could be potential biomarkers for prevention or prediction of TMZ resistance in the future.