Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

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Development and Verification of Glutamatergic Synapse-Associated Prognosis Signature for Lower-Grade Gliomas

Frontiers in Molecular Neuroscience ◽

10.3389/fnmol.2021.720899 ◽

2021 ◽

Vol 14 ◽

Author(s):

Liguo Ye ◽

Yang Xu ◽

Ping Hu ◽

Long Wang ◽

Ji’an Yang ◽

...

Keyword(s):

Risk Score ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Normal Brain ◽

Lower Grade ◽

Glutamatergic Synapse ◽

Hub Genes ◽

Gene Set ◽

Genome Atlas

Background: Lower-grade glioma (LGG) is the most common histology identified in gliomas, a heterogeneous tumor that may develop into high-grade malignant glioma that seriously shortens patient survival time. Recent studies reported that glutamatergic synapses might play an essential role in the progress of gliomas. However, the role of glutamatergic synapse-related biomarkers in LGG has not been systemically researched yet.Methods: The mRNA expression data of glioma and normal brain tissue were obtained from The Cancer Genome Atlas database and Genotype-Tissue Expression, respectively, and the Chinese Glioma Genome Atlas database was used as a validation set. Difference analysis was performed to evaluate the expression pattern of glutamatergic synapse-related genes (GSRGs) in LGG. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct the glutamatergic synapse-related risk signature (GSRS), and the risk score of each LGG sample was calculated based on the coefficients and expression value of selected GSRGs. Univariate and multivariate Cox regression analyses were used to investigate the prognostic value of risk score. Immunity profile and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the association between risk score and the characters of tumor microenvironment in LGG. Gene set variation analysis (GSVA) was performed to investigate the potential pathways related to GSRS. The HPA database and real-time PCR were used to identify the expression of hub genes identified in GSRS.Results: A total of 22 genes of 39 GSRGs were found differentially expressed among normal and LGG samples. Through the LASSO algorithm, 14-genes GSRS constructed were associated with the prognosis and clinicopathological features of patients with LGG. Furthermore, the risk score level was significantly positively correlated with the infiltrating level of immunosuppressive cells, including M2 macrophages and regulatory T cells. GSVA identified a series of cancer-related pathways related to GSRS, such as P13K-AKT and P53 pathways. Moreover, ATAD1, NLGN2, OXTR, and TNR, hub genes identified in GSRS, were considered as potential prognostic biomarkers in LGG.Conclusion: A 14-genes GSRS was constructed and verified in this study. We provided a novel insight into the role of GSRS in LGG through a series of bioinformatics methods.

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The Solute Carrier Family 7 Genes Are Potential Diagnostic and Prognostic Biomarkers in Lower Grade Glioma

10.21203/rs.3.rs-198130/v1 ◽

2021 ◽

Author(s):

Wentao Liu ◽

Yu Ji ◽

Rijun Ren ◽

Gentang Zhang

Keyword(s):

Gene Expression ◽

Amino Acid ◽

The Cancer Genome Atlas ◽

Normal Brain ◽

Lower Grade ◽

Lower Grade Glioma ◽

Family Gene ◽

Solute Carrier ◽

Genome Atlas ◽

Brain Tissues

Abstract Background: The solute carrier (SLC) 7 family genes are a group of cationic amino acid/glycoprotein transporters and of importance to the maintenance of amino acid nutrition and survival of tumour cells. This study was to investigate the diagnostic values of SLC7 family genes and their associations with overall survival (OS) and relapse-free survival (RFS) in Lower grade glioma (LGG). Methods: SLC7 family gene expression and clinical data were retrieved from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas database. The expression difference of SLC7 family genes was compared between 523 LGG and 1141 normal brain tissues. The associations between gene expression, clinicopathologic factors, patients’ OS and RFS were analysed by various statistical methods in the two datasets. Results: As compared to normal brain tissues, SLC7A10 expression was significantly down-regulated, while SLC7A5, SLC7A7 expression was significantly up-regulated in LGG tissues. Multivariate analysis and validation analysis confirmed that increased SLC7A7 expression was associated with increased mortality (P≤0.001, Odd ratio [OR]:2.66, 95% Confidence interval [CI]: 1.56–4.6). While, increased SLC7A4 and SLC7A14 expression was associated with reduced mortality (P=0.02, OR:0.38, 95% CI: 0.16–0.81; P≤0.001, OR:0.38, 95% CI: 0.21–0.67; respectively). Increased SLC7A11 expression was associated with decreased RFS (P=0.01, OR:0.61, 95% CI: 0.43–0.88). Conclusion: SLC7A5, SLC7A7, SLC7A10 might serve as diagnostic biomarkers in LGG. High SLC7A4, SLC7A7 and SLC7A14 expression is significantly associated with OS. SLC7 family gene expression represents a potentially diagnostic and prognostic biomarker to predict survival in LGG.

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A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

Technology in Cancer Research & Treatment ◽

10.1177/1533033821992084 ◽

2021 ◽

Vol 20 ◽

pp. 153303382199208

Author(s):

Wentao Liu ◽

Jiaxuan Zou ◽

Rijun Ren ◽

Jingping Liu ◽

Gentang Zhang ◽

...

Keyword(s):

Risk Score ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Signature ◽

Low Grade Glioma ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Normal Brain ◽

Cox Regression Analysis ◽

Genome Atlas

Aim: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. Methods: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. Results: We identified a total of 1489 genes significantly correlated with patients’ prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. Conclusions: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

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GENE-49. RNA PROCESSING GENES CHARACTERIZE RNA SPLICING AND FURTHER STRATIFY LOWER-GRADE GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noz175.451 ◽

2019 ◽

Vol 21 (Supplement_6) ◽

pp. vi108-vi108

Author(s):

Rui-Chao Chai ◽

tao jiang ◽

Yong-Zhi Wang

Keyword(s):

Alternative Splicing ◽

Risk Score ◽

Rna Processing ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Validation Dataset ◽

Lower Grade ◽

Aberrant Expression ◽

Alternative Splicing Events ◽

Genome Atlas

Abstract 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. 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 ROC curves and Kaplan–Meier curves were used to study the prognosis value of the risk-signature. 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. RNA expression levels for genes, which participated in cell proliferation and other processes, were significantly correlated to the risk score. 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.

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CTNI-39. IDENTIFICATION OF AN AUTOPHAGY-RELATED GENE SIGNATURE AND ESTABLISHMENT OF A NOMOGRAM PREDICTING SURVIVAL OF LOWER-GRADE GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.264 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi68-vi68

Author(s):

Lei Wen ◽

hui Wang ◽

Mingyao Lai ◽

Changguo Shan ◽

Linbo Cai

Keyword(s):

Risk Stratification ◽

Cox Regression ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Survival Prediction ◽

Lower Grade ◽

Related Gene ◽

Prognostic Signature ◽

Genome Atlas

Abstract OBJECTIVE The aim of our study was to establish an autophagy-related signature for individualized risk stratification and prognosis prediction in LGG. METHODS RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. The 232 ARGs were obtained from the Human Autophagy Database (HADb). Univariate and Lasso regression were employed to identify differentially expressed autophagy-related genes (ARGs) and establish a prognostic signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index) and calibration curve. RESULTS Fifty-three autophagy-related DEGs were identified. Four autophagy-related genes (DIRAS3, GNAI3, PTK6, and BIRC5) were selected to establish the prognostic signature and verified in the CGGA validation cohorts. Univariate and multivariate Cox regression indicated that the autophagy signature (HR, 95%CI, P) was an independent predictor of prognosis in LGG. Finally, a prognostic nomogram incorporating age, grade, targeted therapy, new event, tumor status and autophagy signature achieved excellent predicative performance (AUC 0.907, 0.865 and 0.858 for 1-year, 3-year and 5-year survival, respectively) verified by Time-dependent ROC, C-index (0.844, 95% CI, 0.799 to 0.889; P = 1.01e-12) and calibration plots. CONCLUSION The present study constructed a robust four autophagy-related gene signature. A prognostic nomogram in risk stratification and prediction of overall survival in LGG was established. The findings may be beneficial to individualized survival prediction and medical decision-making for LGG.

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RUNX1 and REXO2 are associated with the heterogeneity and prognosis of IDH wild type lower grade glioma

Scientific Reports ◽

10.1038/s41598-021-91382-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Haiwei Wang ◽

Xinrui Wang ◽

Liangpu Xu ◽

Ji Zhang ◽

Hua Cao

Keyword(s):

Transcription Factor ◽

Low Frequency ◽

Tumor Grade ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Wild Type ◽

Lower Grade Glioma ◽

Cancer Genome Atlas ◽

Worse Prognosis ◽

Genome Atlas

AbstractBased on isocitrate dehydrogenase (IDH) alterations, lower grade glioma (LGG) is divided into IDH mutant and wild type subgroups. However, the further classification of IDH wild type LGG was unclear. Here, IDH wild type LGG patients in The Cancer Genome Atlas and Chinese Glioma Genome Atlas were divided into two sub-clusters using non-negative matrix factorization. IDH wild type LGG patients in sub-cluster2 had prolonged overall survival and low frequency of CDKN2A alterations and low immune infiltrations. Differentially expressed genes in sub-cluster1 were positively correlated with RUNX1 transcription factor. Moreover, IDH wild type LGG patients with higher stromal score or immune score were positively correlated with RUNX1 transcription factor. RUNX1 and its target gene REXO2 were up-regulated in sub-cluster1 and associated with the worse prognosis of IDH wild type LGG. RUNX1 and REXO2 were associated with the higher immune infiltrations. Furthermore, RUNX1 and REXO2 were correlated with the worse prognosis of LGG or glioma. IDH wild type LGG in sub-cluster2 was hyper-methylated. REXO2 hyper-methylation was associated with the favorable prognosis of LGG or glioma. At last, we showed that, age, tumor grade and REXO2 expression were independent prognostic factors in IDH wild type LGG.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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A comprehensive analysis of the angiogenesis-related genes in glioblastoma multiforme vs. brain lower grade glioma

Arquivos de Neuro-Psiquiatria ◽

10.1590/0004-282x20190131 ◽

2020 ◽

Vol 78 (1) ◽

pp. 34-38

Author(s):

Burcu BITERGE-SUT

Keyword(s):

Glioblastoma Multiforme ◽

Malignant Gliomas ◽

Genetic Alterations ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Nonsense Mutations ◽

Lower Grade Glioma ◽

Cancer Genome Atlas ◽

Genome Atlas

Abstract Brain tumors are one of the most common causes of cancer-related deaths around the world. Angiogenesis is critical in high-grade malignant gliomas, such as glioblastoma multiforme. Objective: The aim of this study is to comparatively analyze the angiogenesis-related genes, namely VEGFA, VEGFB, KDR, CXCL8, CXCR1 and CXCR2 in LGG vs. GBM to identify molecular distinctions using datasets available on The Cancer Genome Atlas (TCGA). Methods: DNA sequencing and mRNA expression data for 514 brain lower grade glioma (LGG) and 592 glioblastoma multiforme (GBM) patients were acquired from The Cancer Genome Atlas (TCGA), and the genetic alterations and expression levels of the selected genes were analyzed. Results: We identified six distinct KDR mutations in the LGG patients and 18 distinct KDR mutations in the GBM patients, including missense and nonsense mutations, frame shift deletion and altered splice region. Furthermore, VEGFA and CXCL8 were significantly overexpressed within GBM patients. Conclusions: VEGFA and CXCL8 are important factors for angiogenesis, which are suggested to have significant roles during tumorigenesis. Our results provide further evidence that VEGFA and CXCL8 could induce angiogenesis and promote LGG to progress into GBM. These findings could be useful in developing novel targeted therapeutics approaches in the future.

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