Systematically Dissecting the Function of RNA-Binding Proteins During Glioma Progression

Construction and Verification of an RNA-Binding Protein-Associated Prognostic Model for Gliomas

OBJECTIVE

To construct and verificate an RNA-binding protein (RBP)-associated prognostic model for gliomas using integrated bioinformatics analysis.

METHODS

RNA-sequencing and clinic pathological data of glioma patients from The Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas database (CGGA) were downloaded. The aberrantly expressed RBPs were investigated between gliomas and normal samples in TCGA database. We then identified prognosis related hub genes and constructed a prognostic model. This model was further validated in the CGGA-693 and CGGA-325 cohorts.

RESULTS

Totally 174 differently expressed genes-encoded RBPs were identified, containing 85 down-regulated and 89 up-regulated genes. We identified five genes-encoded RBPs (ERI1, RPS2, BRCA1, NXT1, and TRIM21) as prognosis related key genes and constructed a prognostic model. Overall survival (OS) analysis revealed that the patients in the high-risk subgroup based on the model were worse than those in the low-risk subgroup. The area under the receiver operator characteristic curve (AUC) of the prognostic model was 0.836 in the TCGA dataset and 0.708 in the CGGA-693 dataset, demonstrating a favorable prognostic model. Survival analyses of the five RBPs in the CGGA-325 cohort validated the findings. A nomogram was constructed based on the five genes and validated in the TCGA cohort, confirming a promising discriminating ability for gliomas.

CONCLUSION

The prognostic model of the five RBPs might serve as an independent prognostic algorithm for gliomas.

Identification of a 6-RBP gene signature for a comprehensive analysis of glioma and ischemic stroke: Cognitive impairment and aging-related hypoxic stress

There is mounting evidence that ischemic cerebral infarction contributes to vascular cognitive impairment and dementia in elderly. Ischemic stroke and glioma are two majorly fatal diseases worldwide, which promote each other's development based on some common underlying mechanisms. As a post-transcriptional regulatory protein, RNA-binding protein is important in the development of a tumor and ischemic stroke (IS). The purpose of this study was to search for a group of RNA-binding protein (RBP) gene markers related to the prognosis of glioma and the occurrence of IS, and elucidate their underlying mechanisms in glioma and IS. First, a 6-RBP (POLR2F, DYNC1H1, SMAD9, TRIM21, BRCA1, and ERI1) gene signature (RBPS) showing an independent overall survival prognostic prediction was identified using the transcriptome data from TCGA-glioma cohort (n = 677); following which, it was independently verified in the CGGA-glioma cohort (n = 970). A nomogram, including RBPS, 1p19q codeletion, radiotherapy, chemotherapy, grade, and age, was established to predict the overall survival of patients with glioma, convenient for further clinical transformation. In addition, an automatic machine learning classification model based on radiomics features from MRI was developed to stratify according to the RBPS risk. The RBPS was associated with immunosuppression, energy metabolism, and tumor growth of gliomas. Subsequently, the six RBP genes from blood samples showed good classification performance for IS diagnosis (AUC = 0.95, 95% CI: 0.902–0.997). The RBPS was associated with hypoxic responses, angiogenesis, and increased coagulation in IS. Upregulation of SMAD9 was associated with dementia, while downregulation of POLR2F was associated with aging-related hypoxic stress. Irf5/Trim21 in microglia and Taf7/Trim21 in pericytes from the mouse cerebral cortex were identified as RBPS-related molecules in each cell type under hypoxic conditions. The RBPS is expected to serve as a novel biomarker for studying the common mechanisms underlying glioma and IS.

Advances in nanoparticle mediated targeting of RNA binding protein for cancer

RNA binding proteins (RBPs) enact a very crucial part in the RNA directive processes. Atypical expression of these RBPs affects many steps of RNA metabolism, majorly altering its expression. Altered expression and dysfunction of RNA binding proteins lead to cancer progression and other diseases. We enumerate various available interventions, and recent findings focused on targeting RBPs for cancer therapy and diagnosis. The treatment, sensitization, chemoprevention, gene-mediated, and virus mediated interventions were studied to treat and diagnose cancer. The application of passively and actively targeted lipidic nanoparticles, polymeric nanoparticles, virus-based particles, and vaccine-based immunotherapy for the delivery of therapeutic agent/s against cancer are discussed. We also discuss the formulation aspect of nanoparticles for achieving delivery at the site of action and ongoing clinical trials targeting RBPs.

Identification of IGF2BP3 as an Adverse Prognostic Biomarker of Gliomas

N6-methyladenosine (m⁶A) RNA modification can alter gene expression and function by regulating RNA splicing, stability, translocation, and translation. It is involved in various types of cancer. However, its role in gliomas is not well known. This study aimed to determine the prognostic value of the m⁶A RNA methylation regulator in gliomas and investigate the underlying mechanisms of the aberrant expression of m⁶A-related genes.mRNA expression profiles and clinical information of 448 glioma samples were obtained from The Cancer Genome Atlas and cBioportal. The expression of m⁶A-related genes in normal controls and low-grade glioma and glioblastoma was obtained from Gene Expression Profiling Interactive Analysis. Further, m⁶A-related gene expression and its relationship with prognosis were obtained through The Chinese Glioma Genome Atlas (CGGA). Multivariate Cox regression analyses were performed, and a nomogram was built with potential risk factors based on a multivariate Cox analysis to predict survival probability. Online tools such as Gene Set Enrichment Analysis, STRING, Cytoscape, and Molecular Complex Detection were applied for bioinformatics analysis and to investigate the underlying mechanisms of the aberrant expression of m⁶A-related genes. The multivariate Cox regression analysis found that higher expression levels of YTHDC2 and insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3, also called IMP3) were independent negative and positive prognostic factors for overall survival (OS), respectively. Data from the CGGA database showed that IGF2BP3 expression increased when the tumor grade increased. Receiver operating characteristic (ROC) curve was used to evaluate the predictive specificity and sensitivity. The area under the ROC curve indicated that the OS prediction was 0.92 (1-year) and 0.917 (3-years), indicating that m⁶A-related genes could predict patient survival. In addition, IGF2BP3 was closely related to the shorter survival period of patients. Copy number variation and DNA methylation, but not somatic mutations, might contribute to the abnormal upregulation of IGF2BP3 in gliomas. Significantly altered genes were identified, and the protein–protein interaction network was constructed. Based on the data presented, our study identified several m⁶A-related genes, especially IGF2BP3, that could be potential prognostic biomarkers of gliomas. The study unveiled the potential regulatory mechanism of IGF2BP3 in gliomas.

Development of a gene expression-based prognostic signature for IDH wild-type glioblastoma

BACKGROUND

We aimed to develop a gene expression-based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations.

METHODS

Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as "ATE"), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net-selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts.

RESULTS

NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status.

CONCLUSIONS

The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

Development and Multi-Data Set Verification of an RNA Binding Protein Signature for Prognosis Prediction in Glioma

Objective: Increasing evidence emphasizes the clinical implications of RNA binding proteins (RBPs) in cancers. This study aimed to develop a RBP signature for predicting prognosis in glioma.

Methods: Two glioma datasets as training (n = 693) and validation (n = 325) sets were retrieved from the CGGA database. In the training set, univariate Cox regression analysis was conducted to screen prognosis-related RBPs based on differentially expressed RBPs between WHO grade II and IV. A ten-RBP signature was then established. The predictive efficacy was evaluated by ROCs. The applicability was verified in the validation set. The pathways involving the risk scores were analyzed by ssGSEA. scRNA-seq was utilized for evaluating their expression in different glioma cell types. Moreover, their expression was externally validated between glioma and control samples.

Results: Based on 39 prognosis-related RBPs, a ten RBP signature was constructed. High risk score distinctly indicated a poorer prognosis than low risk score. AUCs were separately 0.838 and 0.822 in the training and validation sets, suggesting its well performance for prognosis prediction. Following adjustment of other clinicopathological characteristics, the signature was an independent risk factor. Various cancer-related pathways were significantly activated in samples with high risk score. The scRNA-seq identified that risk RBPs were mainly expressed in glioma malignant cells. Their high expression was also found in glioma than control samples.

Conclusion: This study developed a novel RBP signature for robustly predicting prognosis of glioma following multi-data set verification. These RBPs may affect the progression of glioma.