Dataset Information

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma.

ABSTRACT: Glioblastoma (GBM) is the most aggressive malignant brain tumour, with high morbidity and mortality rates. Currently, there is a lack of systematic and comprehensive analysis on the prognostic significance of alternative splicing (AS) profiling for GBM. The GBM data, including RNA-sequencing, corresponding clinical information and the expression levels of splicing factor genes, were downloaded from The Cancer Genome Atlas and the SpliceAid2 database. The prognostic models were assessed by the least absolute shrinkage and selection operator Cox regression analysis. The correlation network between survival-associated AS events and splicing factors was plotted. Prognostic models were built for every AS event type and performed well for risk stratification in patients with GBM. The final prognostic signature served as an independent prognostic factor [hazard ratio (HR), 4.61; 95% confidence interval (CI), 2.97-7.16; P=9.66×10^-12] for several clinical parameters, including age, sex, isocitrate dehydrogenase mutation, O⁶-methylguanine-DNA methyltransferase promoter methylation and risk score. The HR for risk score with GBM was 1.0063 (95% CI, 1.0024-1.0103). The splicing regulatory network indicated that heat shock protein b-1, protein arginine N-methyltransferase 5, protein FAM50B and endoplasmic reticulum chaperone BiP genes were independent prognostic factors for GBM. The results of the present study support the ongoing effort in developing novel genomic models and providing potentially more effective treatment options for patients with GBM.

SUBMITTER: Qiu J

PROVIDER: S-EPMC7798022 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma.

Qiu Jiting J Wang Chunhui C Hu Hongkang H Chen Sarah S Ding Xuehua X Cai Yu Y

Oncology letters 20201220 2

Glioblastoma (GBM) is the most aggressive malignant brain tumour, with high morbidity and mortality rates. Currently, there is a lack of systematic and comprehensive analysis on the prognostic significance of alternative splicing (AS) profiling for GBM. The GBM data, including RNA-sequencing, corresponding clinical information and the expression levels of splicing factor genes, were downloaded from The Cancer Genome Atlas and the SpliceAid2 database. The prognostic models were assessed by the le ...[more]

PMID: 33552257

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Project description:BackgroundGlioblastoma multiforme (GBM) is a common malignant brain tumor with high mortality. It is urgently necessary to develop a new treatment because traditional approaches have plateaued.PurposeHere, we identified an immune-related gene (IRG)-based prognostic signature to comprehensively define the prognosis of GBM.MethodsGlioblastoma samples were selected from the Chinese Glioma Genome Atlas (CGGA). We retrieved IRGs from the ImmPort data resource. Univariate Cox regression and LASSO Cox regression analyses were used to develop our predictive model. In addition, we constructed a predictive nomogram integrating the independent predictive factors to determine the one-, two-, and 3-year overall survival (OS) probabilities of individuals with GBM. Additionally, the molecular and immune characteristics and benefits of ICI therapy were analyzed in subgroups defined based on our prognostic model. Finally, the proteins encoded by the selected genes were identified with liquid chromatography-tandem mass spectrometry and western blotting (WB).ResultsSix IRGs were used to construct the predictive model. The GBM patients were categorized into a high-risk group and a low-risk group. High-risk group patients had worse survival than low-risk group patients, and stronger positive associations with multiple tumor-related pathways, such as angiogenesis and hypoxia pathways, were found in the high-risk group. The high-risk group also had a low IDH1 mutation rate, high PTEN mutation rate, low 1p19q co-deletion rate and low MGMT promoter methylation rate. In addition, patients in the high-risk group showed increased immune cell infiltration, more aggressive immune activity, higher expression of immune checkpoint genes, and less benefit from immunotherapy than those in the low-risk group. Finally, the expression levels of TNC and SSTR2 were confirmed to be significantly associated with patient prognosis by protein mass spectrometry and WB.ConclusionHerein, a robust predictive model based on IRGs was developed to predict the OS of GBM patients and to aid future clinical research.

Project description:BackgroundCuproptosis, a newly reported type of programmed cell death, takes part in the regulation of tumor progression, treatment response, and prognosis. But the specific effect of cuproptosis-related genes (CRGs) on glioblastoma (GBM) is still unclear.MethodsThe transcriptome data and corresponding clinical data of GBM samples were downloaded from the TCGA and GEO databases. R software and R packages were used to perform statistical analysis, consensus cluster analysis, survival analysis, Cox regression analysis, Lasso regression analysis, and tumor microenvironment analysis. The mRNA and protein expression levels of model-related genes were detected by RT-qPCR and Western blot assays, respectively.ResultsThe expression profile of CRGs in 209 GBM samples from two separate datasets was obtained. Two cuproptosis subtypes, CRGcluster A and CRGcluster B, were identified by consensus cluster analysis. There were apparent differences in prognosis, tumor microenvironment, and immune checkpoint expression levels between the two subtypes, and there were 79 prognostic differentially expressed genes (DEGs). According to the prognostic DEGs, two gene subtypes, geneCluster A and geneCluster B, were identified, and a prognostic risk score model was constructed and validated. This model consists of five prognostic DEGs, including PDIA4, DUSP6, PTPRN, PILRB, and CBLN1. Ultimately, to improve the applicability of the model, a nomogram was established. Patients with GBM in the low-risk cluster have a higher mutation burden and predict a longer OS than in the high-risk group. Moreover, the risk score was related to drug sensitivity and negatively correlated with the CSC index.ConclusionWe successfully constructed a cuproptosis-related prognostic model, which can independently predict the prognosis of GBM patients. These results further complement the understanding of cuproptosis and provide new theoretical support for developing a more effective treatment strategy.

Project description:Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Project description:The present study aimed to construct and evaluate a novel experiment-based hypoxia signature to help evaluations of GBM patient status. First, the 426 proteins, which were previously found to be differentially expressed between normal and hypoxia groups in glioblastoma cells with statistical significance, were converted into the corresponding genes, among which 212 genes were found annotated in TCGA. Second, after evaluated by single-variable Cox analysis, 19 different expressed genes (DEGs) with prognostic value were identified. Based on λ value by LASSO, a gene-based survival risk score model, named RiskScore, was built by 7 genes with LASSO coefficient, which were FKBP2, GLO1, IGFBP5, NSUN5, RBMX, TAGLN2 and UBE2V2. Kaplan-Meier (K-M) survival curve analysis and the area under the curve (AUC) were plotted to further estimate the efficacy of this risk score model. Furthermore, the survival curve analysis was also plotted based on the subtypes of age, IDH, radiotherapy and chemotherapy. Meanwhile, immune infiltration, GSVA, GSEA and chemo drug sensitivity of this risk score model were evaluated. Third, the 7 genes expression were evaluated by AUC, overall survival (OS) and IDH subtype in datasets, importantly, also experimentally verified in GBM cell lines exposed to hypoxic or normal oxygen condition, which showed significant higher expression in hypoxia than in normal group. Last, combing the hypoxia RiskScore with clinical and molecular features, a prognostic composite nomogram was generated, showing the good sensitivity and specificity by AUC and OS. Meanwhile, univariate analysis and multivariate analysis were used for performed to identify variables in nomogram that were significant in independently predicting duration of survival. It is a first time that we successfully established and validated an independent prognostic risk model based on hypoxia microenvironment from glioblastoma cells and public database. The 7 key genes may provide potential directions for future biochemical and pharmaco-therapeutic research.

Dataset Information

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma.

Publications

Transcriptome analysis and prognostic model construction based on splicing profiling in glioblastoma.

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