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RNA processing genes characterize RNA splicing and further stratify lower-grade glioma.

ABSTRACT: BACKGROUND:Aberrant expression of RNA processing genes may drive the alterative RNA profile in lower-grade gliomas (LGGs). Thus, we aimed to further stratify LGGs based on the expression of RNA processing genes. METHODS:This study included 446 LGGs from The Cancer Genome Atlas (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk-signature. The receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to study the prognosis value of the risk-signature. RESULTS:Among the tested 784 RNA processing genes, 276 were significantly correlated with the OS of LGGs. Further LASSO Cox regression identified a 19-gene risk-signature, whose risk score was also an independently prognosis factor (P<0.0001, multiplex Cox regression) in the validation dataset. The signature had better prognostic value than the traditional factors "age", "grade" and "WHO 2016 classification" for 3- and 5-year survival both two datasets (AUCs > 85%). Importantly, the risk-signature could further stratify the survival of LGGs in specific subgroups of WHO 2016 classification. Furthermore, alternative splicing events for genes such as EGFR and FGFR were found to be associated with the risk score. mRNA expression levels for genes, which participated in cell proliferation and other processes, were significantly correlated to the risk score. CONCLUSIONS:Our results highlight the role of RNA processing genes for further stratifying the survival of patients with LGGs and provide insight into the alternative splicing events underlying this role.

SUBMITTER: Chai RC

PROVIDER: S-EPMC6777941 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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RNA processing genes characterize RNA splicing and further stratify lower-grade glioma.

Chai Rui-Chao RC Li Yi-Ming YM Zhang Ke-Nan KN Chang Yu-Zhou YZ Liu Yu-Qing YQ Zhao Zheng Z Wang Zhi-Liang ZL Chang Yuan-Hao YH Li Guan-Zhang GZ Wang Kuan-Yu KY Wu Fan F Wang Yong-Zhi YZ

JCI insight 20190813

<h4>Background</h4>Aberrant expression of RNA processing genes may drive the alterative RNA profile in lower-grade gliomas (LGGs). Thus, we aimed to further stratify LGGs based on the expression of RNA processing genes.<h4>Methods</h4>This study included 446 LGGs from The Cancer Genome Atlas (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a ...[more]

PMID: 31408440

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Project description:BackgroundTo identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system.MethodsWe downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database.ResultsGene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated.ConclusionsWe identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Project description:Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6-methylguanine-DNA methyl-transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

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RNA processing genes characterize RNA splicing and further stratify lower-grade glioma.

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RNA processing genes characterize RNA splicing and further stratify lower-grade glioma.

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