scholarly journals Identification of Gender-Specific Molecular Differences in Glioblastoma (GBM) and Low-Grade Glioma (LGG) by the Analysis of Large Transcriptomic and Epigenomic Datasets

2021 ◽  
Vol 11 ◽  
Author(s):  
Md Tipu Khan ◽  
Bharat Prajapati ◽  
Simran Lakhina ◽  
Mridula Sharma ◽  
Sachin Prajapati ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Network Analysis ◽  
Signaling Pathways ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Male And Female ◽  
Males And Females ◽  
Gender Based ◽  
Genome Atlas

Differences in the incidence and outcome of glioma between males and females are well known, being more striking for glioblastoma (GB) than low-grade glioma (LGG). The extensive and well-annotated data in publicly available databases enable us to analyze the molecular basis of these differences at a global level. Here, we have analyzed The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to identify molecular indicators for these gender-based differences by different methods. Based on the nature of data available/accessible, the transcriptomic profile was studied in TCGA by using DeSeq2 and in CGGA by T-test, after correction based. Only IDH1 wild-type tumors were studied in CGGA. Using weighted gene co-expression network analysis (WGCNA), network analysis was done, followed by the assessment of modular differential connectivity. Differentially affected signaling pathways were identified. The gender-based effects of differentially expressed genes on survival were determined. DNA methylation was studied as an indicator of gender-based epigenetic differences. The results clearly showed gender-based differences in both GB and LGG, whatever method or database was used. While there were differences in the results obtained between databases and methods used, some major signaling pathways such as Wnt signaling and pathways involved in immune processes and the adaptive immune response were common to different assessments. There was also a differential gender-based influence of several genes on survival. Also, the autosomal genes NOX, FRG1BP, and AL354714.2 and X-linked genes such as PUDP, KDM6A, DDX3X, and SYAP1 had differential DNA methylation and expression profile in male and female GB, while for LGG, these included autosomal genes such as CNIH3 and ANKRD11 and X-linked genes such as KDM6A, MAOB, and EIF2S3. Some, such as FGF13 and DDX3X, have earlier been shown to have a role in tumor behavior, though their dimorphic effects in males and females have not been identified. Our study thus identifies several crucial differences between male and female glioma, which could be validated further. It also highlights that molecular studies without consideration of gender can obscure critical elements of biology and emphasizes the importance of parallel but separate analyses of male and female glioma.

scholarly journals Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma

2020 ◽  
Vol 11 ◽  
Author(s):  
Yin Qiu Tan ◽  
Yun Tao Li ◽  
Teng Feng Yan ◽  
Yang Xu ◽  
Bao Hui Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Risk Score ◽  
Survival Rates ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Sequencing Analysis ◽  
Six Genes ◽  
Genome Atlas ◽  
Overall Survival Rates

BackgroundThe immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study.MethodsWe collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein–protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients’ overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA).ResultsSix genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG.ConclusionJoint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).

scholarly journals Multi Omics Profiling and Clustering Low Grade Glioma Based on Integrated Stress Status

2021 ◽  
Author(s):  
Xiaolin Ren ◽  
Xin Chen ◽  
Chen Zhu ◽  
Anhua Wu
Keyword(s):  
Stress Response ◽  
High Risk ◽  
Malignant Transformation ◽  
Immune Cell ◽  
High Risk Group ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Integrated Stress Response ◽  
Genome Atlas

Abstract Background: Although the prognosis of low-grade glioma (LGG) is better than that of glioblastoma (GBM), there are still some patients who will develop into high-grade glioma. Integrated stress response contributed to the malignant transformation of tumor. As there is few research focus on the integrated stress status in LGG, it is urgent to profile and re-classify LGG based on integrated stress response (ISR). Methods: Glioma patients were obtained from the Chinese Glioma Genome Atlas ( the Cancer Genome Atlas (TCGA) and GSE16011 cohorts. Statistical 8 analyses were conducted by GraphPad Prism and R language. Results: We quantified four types of integrated stress response respectively. The relationship between the four stress states and the clinical characteristics of LGG was analyzed. Then we re-classified the patients based on these four scores, we found that cluster 1 had the worst prognosis, whereby cluster 3 had the best prognosis. We also established an accurate ISR risk signature to predicting cluster 1. We found that immune response and suppressive immune cell components were more enriched in the high-risk group. We also profiled the genomic difference between low and high risk groups, including the non-missense mutation of drivel genes and the condition of copy number variation (CNV). Conclusion: LGG patients could be divided into four clusters based on the integrated stress status, cluster 1 exhibited malignant transformation trends. ISR signature could reflect the traits of cluster 1 well, high ISR score indicated worse prognosis and enriched inhibitory immune microenvironments.

scholarly journals Characterization of the Clinical Significance of PD-1/PD-Ls Expression and Methylation in Patients With Low-Grade Glioma

2021 ◽  
Vol 20 ◽  
pp. 153303382110119
Author(s):  
Jie Mei ◽  
Yun Cai ◽  
Rui Xu ◽  
Xuejing Yang ◽  
Weijian Zhou ◽  
...  
Keyword(s):  
Survival Data ◽  
Immune Escape ◽  
Checkpoint Inhibitors ◽  
Expression Patterns ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Low Grade ◽  
Programmed Death ◽  
Genome Atlas

Background: Immune checkpoints play crucial roles in the immune escape of cancer cells. However, the exact prognostic values of expression and methylation of programmed-death 1 (PD-1), programmed-death-ligand 1 (PD-L1) and PD-L2 in low-grade glioma (LGG) have not been well-defined yet. Methods: A total 514 LGG samples from the Cancer Genome Atlas (TCGA) dataset containing gene expression, DNA methylation, and survival data were enrolled in our study. Besides, a total of 137 primary LGG samples from the Chinese Glioma Genome Atlas (CGGA) database were also extracted for the survival analysis of the prognostic values of PD-1/PD-Ls expression. Results: PD-1/PD-Ls had distinct co-expression patterns in LGG tissues. The expression and methylation level of PD-1/PD-Ls seemed to be various in different LGG subtypes. Besides, overexpression and hypo-methylation of PD-1/PD-Ls were associated with worse prognosis. In addition, PD-1/PD-Ls expression was positively associated with TIICs infiltration, while their methylation was negatively associated with TIICs infiltration. Moreover, PD-1/PD-Ls and their positively correlated gene mainly participated in immune response related biological processes. Conclusion: To conclude, overexpression and hypo-methylation of PD-1/PD-Ls predicted unfavorable prognosis in LGG patients, suggesting those patients may benefit from PD1/PD-Ls checkpoint inhibitors treatment.

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

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.

scholarly journals Downregulation of LUZP2 Is Correlated with Poor Prognosis of Low-Grade Glioma

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Yong Li ◽  
Gang Deng ◽  
Yangzhi Qi ◽  
Huikai Zhang ◽  
Hongxiang Jiang ◽  
...  
Keyword(s):  
Nervous System ◽  
Brain Tumors ◽  
System Development ◽  
Tumor Grade ◽  
Nervous System Development ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Biological Behavior ◽  
Low Grade ◽  
Genome Atlas

Background. LUZP2 is a protein limitedly expressed in the brain and spinal cord, while there are few studies on it in brain tumors. Low-grade glioma (LGG) is one of the most common brain tumors. However, the biological behavior of LGG is not very clear at present. This study was aimed at exploring the role of LUZP2 in LGG. Methods. By data mining in The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), the expression, clinical characteristics, and potential regulatory mechanism of LUZP2 in LGG were assessed. The regulatory miRNAs of LUZP2 were predicted using miRDB, TargetScan, and miRTarBase. Meanwhile, the potential biological function of coexpressed genes was investigated by GO and KEGG analyses. Results. LUZP2 expression was downregulated with the increase of tumor grade (p<0.05). Low LUZP2 expression independently predicted poor OS in LGG in TCGA cohort and the CGGA part B and part C cohorts (all p<0.001). Additionally, LUZP2 was targeted by miR-142-5p according to 2 prediction databases and 1 validated database, which was negatively related to LUZP2 mRNA expression (p<0.001). Kaplan-Meier analyses demonstrated that low miR-142-5p expression was significantly associated with poor OS (p<0.001). Furthermore, coexpression genes of LUZP2 were significantly involved in nervous system development and metabolic pathways.Conclusions. LUZP2 may be crucial for nervous system extracellular matrix development and serve as an important clinical biomarker for LGG patients. miR-142-5p upregulation could be the upstream regulator that contributed to LUZP2 downregulation.

scholarly journals Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy

2021 ◽  
Vol 9 ◽  
Author(s):  
Ganglei Li ◽  
Zhanxiong Wu ◽  
Jun Gu ◽  
Yu Zhu ◽  
Tiesong Zhang ◽  
...  
Keyword(s):  
R Package ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Response Patterns ◽  
Survival Times ◽  
Characteristic Index ◽  
Metabolic Characteristics ◽  
Different Response ◽  
Genome Atlas

Metabolic signatures are frequently observed in cancer and are starting to be recognized as important regulators for tumor progression and therapy. Because metabolism genes are involved in tumor initiation and progression, little is known about the metabolic genomic profiles in low-grade glioma (LGG). Here, we applied bioinformatics analysis to determine the metabolic characteristics of patients with LGG from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We also performed the ConsensusClusterPlus, the CIBERSORT algorithm, the Estimate software, the R package “GSVA,” and TIDE to comprehensively describe and compare the characteristic difference between three metabolic subtypes. The R package WGCNA helped us to identify co-expression modules with associated metabolic subtypes. We found that LGG patients were classified into three subtypes based on 113 metabolic characteristics. MC1 patients had poor prognoses and MC3 patients obtained longer survival times. The different metabolic subtypes had different metabolic and immune characteristics, and may have different response patterns to immunotherapy. Based on the metabolic subtype, different patterns were exhibited that reflected the characteristics of each subtype. We also identified eight potential genetic markers associated with the characteristic index of metabolic subtypes. In conclusion, a comprehensive understanding of metabolism associated characteristics and classifications may improve clinical outcomes for LGG.

scholarly journals DNA Methylation-Driven Genes for Developing Survival Nomogram for Low-Grade Glioma

2022 ◽  
Vol 11 ◽  
Author(s):  
Yingyun Guo ◽  
Yuan Li ◽  
Jiao Li ◽  
Weiping Tao ◽  
Weiguo Dong
Keyword(s):  
Dna Methylation ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Low Grade ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cancer Genome Atlas ◽  
Auc Value ◽  
User Friendly ◽  
Genome Atlas

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes, ARL9, CMYA5, and STEAP3, were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients.

scholarly journals Multiomics Profiling and Clustering of Low-Grade Gliomas Based on the Integrated Stress Status

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaolin Ren ◽  
Xin Chen ◽  
Chen Zhu ◽  
Anhua Wu
Keyword(s):  
Stress Response ◽  
High Risk ◽  
Stress Responses ◽  
Copy Number Variations ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Integrated Stress Response ◽  
Cluster 2 ◽  
Genome Atlas

Background. Although the prognosis of low-grade glioma is better than that of glioblastoma, there are still some groups with poor prognosis. The integrated stress response contributes to the malignant progress of tumors. As there had limited research focused on the integrated stress status in LGG, it is urgent to profile and reclassify LGG based on the integrated stress response. Methods. Information of glioma patients was obtained from the Chinese Glioma Genome Atlas, The Cancer Genome Atlas, and the GSE16011 cohorts. Statistical analyses were conducted using GraphPad Prism 8 and R language. Results. We summarized and quantified four types of integrated stress responses. Relationships between these four types of stress states and the clinical characteristics were analyzed in low-grade glioma. We then reclassified the patients based on these four scores and found that cluster 2 had the worst prognosis, while cluster 1 had the best prognosis. We also established an accurate integrated stress response risk signature for predicting cluster 2. We found that immune response and suppressive immune cell components were more enriched in the high-risk group. We also profiled the genomic differences between the low- and high-risk groups, including the nonmissense mutation of driver genes and the copy number variations. Conclusion. Low-grade glioma patients were divided into three clusters based on the integrated stress status, with cluster 2 exhibiting malignant transformation trends. The signature adequately reflected the traits of cluster 2, while a high risk score indicated a worse prognosis and an enriched inhibitory immune microenvironment.

Comprehensive assessment of PD-L1 and PD-L2 dysregulation in gastrointestinal cancers

Epigenomics ◽  
2020 ◽  
Author(s):  
Qijie Zhao ◽  
Jinan Guo ◽  
Yueshui Zhao ◽  
Jing Shen ◽  
Parham Jabbarzadeh Kaboli ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Signaling Pathways ◽  
Underlying Mechanism ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Gastrointestinal Cancers ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background: PD-L1 and PD-L2 are ligands of PD-1. Their overexpression has been reported in different cancers. However, the underlying mechanism of PD-L1 and PD-L2 dysregulation and their related signaling pathways are still unclear in gastrointestinal cancers. Materials & methods: The expression of PD-L1 and PD-L2 were studied in The Cancer Genome Atlas and Genotype-Tissue Expression databases. The gene and protein alteration of PD-L1 and PD-L2 were analyzed in cBioportal. The direct transcription factor regulating PD-L1/ PD-L2 was determined with ChIP-seq data. The association of PD-L1/PD-L2 expression with clinicopathological parameters, survival, immune infiltration and tumor mutation burden were investigated with data from The Cancer Genome Atlas. Potential targets and pathways of PD-L1 and PD-L2 were determined by protein enrichment, WebGestalt and gene ontology. Results: Comprehensive analysis revealed that PD-L1 and PD-L2 were significantly upregulated in most types of gastrointestinal cancers and their expressions were positively correlated. SP1 was a key transcription factor regulating the expression of PD-L1. Conclusion: Higher PD-L1 or PD-L2 expression was significantly associated with poor overall survival, higher tumor mutation burden and more immune and stromal cell populations. Finally, HIF-1, ERBB and mTOR signaling pathways were most significantly affected by PD-L1 and PD-L2 dysregulation. Altogether, this study provided comprehensive analysis of the dysregulation of PD-L1 and PD-L2, its underlying mechanism and downstream pathways, which add to the knowledge of manipulating PD-L1/PD-L2 for cancer immunotherapy.

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