Immune and Clinical Features of CD96 Expression in Glioma by in silico Analysis

Zhang, Qiang; Zhong, Hua; Fan, Yinchun; Liu, Qian; Song, Jiancheng; Yao, Shengtao; Cao, Fang

doi:10.3389/fbioe.2020.00592

Cited by 26 publications

(24 citation statements)

References 39 publications

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“…Zhang et al showed that high expression of CD96 was present in the malignant molecule phenotype, including IDH wild type and mesenchymal subtype. They also stressed that it had a positive association with inflammatory activities (89). Indeed, C D 9 6 s h o w e d a h i g h c o n c o r d a n c e w i t h i m m u n e checkpoints such as PD-1, CTLA-4, TIGIT, TIM-3, NR2F6, and GITR, which would suggest a potential synergism (89).…”

Section: Tigit/cd96mentioning

confidence: 99%

“…They also stressed that it had a positive association with inflammatory activities (89). Indeed, C D 9 6 s h o w e d a h i g h c o n c o r d a n c e w i t h i m m u n e checkpoints such as PD-1, CTLA-4, TIGIT, TIM-3, NR2F6, and GITR, which would suggest a potential synergism (89). In addition to that, they discovered that higher CD96 expression predicted worse survival rates in glioma and GBM patients overall.…”

Section: Tigit/cd96mentioning

confidence: 99%

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Immune Checkpoint Inhibitors in Human Glioma Microenvironment

et al. 2021

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Gliomas are the most common primary brain tumors in adults. Despite the fact that they are relatively rare, they cause significant morbidity and mortality. High-grade gliomas or glioblastomas are rapidly progressing tumors with a very poor prognosis. The presence of an intrinsic immune system in the central nervous system is now more accepted. During the last decade, there has been no major progress in glioma therapy. The lack of effective treatment for gliomas can be explained by the strategies that cancer cells use to escape the immune system. This being said, immunotherapy, which involves blockade of immune checkpoint inhibitors, has improved patients’ survival in different cancer types. This novel cancer therapy appears to be one of the most promising approaches. In the present study, we will start with a review of the general concept of immune response within the brain and glioma microenvironment. Then, we will try to decipher the role of various immune checkpoint inhibitors within the glioma microenvironment. Finally, we will discuss some promising therapeutic pathways, including immune checkpoint blockade and the body’s effective anti-glioma immune response.

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Section: Tigit/cd96mentioning

confidence: 99%

Section: Tigit/cd96mentioning

confidence: 99%

Immune Checkpoint Inhibitors in Human Glioma Microenvironment

et al. 2021

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“…The mesenchymal subtype is characterized by higher percentages of cycling cells and neo-angiogenesis, with a highly invasive nature and poor prognosis (16). Furthermore, biological function analysis revealed that immune checkpoint receptor target was highly enriched in mesenchymal subtype glioma and might be a potential marker of mesenchymal subtype (17,18).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

et al. 2021

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Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

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“…Among them, WHO grade was closely associated with OS. 12 The work flow diagram of this study was depicted in Figure S1 .…”

Section: Methodsmentioning

confidence: 99%

Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis

Cao¹,

Fan²,

Yu³

et al. 2021

CMAR

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Introduction: As one of the most prevalent and malignant brain cancers, glioblastoma multiforme (GBM) presents a poor prognosis and the molecular mechanisms remain poorly understood. Consequently, molecular research, including various biomarkers, is essential to exploit the occurrence and development of glioma. Methods: Weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression modules and networks based on the Chinese Glioma Genome Atlas (CGGA) glioblastoma specimens. Then, protein-protein interaction (PPI) and gene ontology (GO) analyses were performed to mine hub genes. RT-PCR and immunohistochemistry were employed to examine the expression level of GRPR, CXCL5, and CXCL11 in glioma patients. Results: We confirmed two gene modules by protein-protein interaction networks. Functional enrichment analysis was performed to identify the significance of gene modules. Prognostic biomarkers GRPR, CXCL5, and CXCL11 related to the survival time of GBM samples were mined in The Cancer Genome Atlas (TCGA) dataset. qRT-PCR revealed that GRPR, CXCL5, and CXCL11 led to a significant increase in GBM sample compared to control. Conclusion:In this study, we developed and confirmed three mRNA signatures (GRPR, CXCL5, and CXCL11) for evaluating overall survival in GBM patients. Our research assists in existing understanding of GBM diagnosis and prognosis.

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Immune and Clinical Features of CD96 Expression in Glioma by in silico Analysis

Cited by 26 publications

References 39 publications

Immune Checkpoint Inhibitors in Human Glioma Microenvironment

Immune Checkpoint Inhibitors in Human Glioma Microenvironment

Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis

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