Abstract
Purpose: Glioblastoma multiforme (GBM) is the most widely occurring brain malignancy. It is modulated by a variety of genes, and patients with GBM have a low survival ratio and an unsatisfactory treatment effect. The irregular regulation of RNA binding proteins (RBPs) is implicated in several malignant neoplasms and reported to exhibit an association with the occurrence and development of carcinoma. Thus, it is necessary to build a stable, multi-RBPs signature-originated model for GBM prognosis and treatment response prediction. Methods: Differentially expressed RBPs (DERBPs) were screened out based on the RBPs data of GBM and normal brain tissues from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Program (GTEx) datasets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses on DERBPs were performed, followed by an analysis of the Protein-Protein Interaction network. Survival analysis of the DERBPs was conducted by univariate and multivariate Cox regression. Then, a risk score model was created on the basis of the gene signatures in various survival-associated RBPs, and its prognostic and predictive values were evaluated through Kaplan-Meier analysis and log-rank test. A nomogram on the basis of the hub RBPs signature was applied to estimate GBM patients’ survival rates. Moreover, western blot was for the detection of the proteins. Results: BICC1, GNL3L, and KHDRBS2 were considered as prognosis-associated hub RBPs and then were applied in the construction of a prognostic model. Poor survival results appeared in GBM patients with a high-risk score. The area under the time-dependent ROC curve of the prognostic model was 0.723 in TCGA and 0.707 in Chinese Glioma Genome Atlas (CGGA) cohorts, indicating a good prognostic model. What was more, the survival duration of the high-risk group receiving radiotherapy or temozolomide chemotherapy was shorter than that of the low-risk group. The nomogram showed a great discriminating capacity for GBM, and western blot experiments demonstrated that the proteins of these 3 RBPs had different expressions in GBM cells. Conclusion: The identified 3 hub RBPs-derived risk score is effective in the prediction of GBM prognosis and treatment response, and benefits to the treatment of GBM patients.
Highlights
As the most widespread malignant neoplasm in human brain, glioblastoma multiforme (GBM) has retained a severe incidence ratio and prognosis (Johnson et al, 2013; Davis, 2016)
BICC1, G Protein Nucleolar 3 Like (GNL3L), and KHDRBS2 were considered as prognosis-associated hub RNA binding proteins (RBPs) and were applied in the construction of a prognostic model
The nomogram showed a great discriminating capacity for GBM, and western blot experiments demonstrated that the proteins of these 3 RBPs had different expressions in GBM cells
Summary
As the most widespread malignant neoplasm in human brain, glioblastoma multiforme (GBM) has retained a severe incidence ratio and prognosis (Johnson et al, 2013; Davis, 2016). Diagnosis of GBM mainly relies on examination of histopathology, neoplasm molecular biomarkers and imaging assessments, which is certainly not applicable to early diagnosis (Batash et al, 2017). RNA binding proteins (RBPs) refer to the proteins that interact with several RNAs, and participate in most posttranscriptional modulation processes and cellular homeostasis (Hentze et al, 2018; Gebauer et al, 2020). RBPs mediate the modulation of RNA splicing, polyadenylation, stability, localization, translation, and degradation via binding to targeted RNAs and forming ribonucleoprotein complexes (Wende et al, 2019). Taking post-transcriptional modulation into consideration, no doubt abnormally dysregulated RBPs are intimately associated with the incidence and development of many diseases, such as cancers (Pereira et al, 2017; Neelamraju et al, 2018). A lot of attention has turned to the roles of RBPs in cancers
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