Identification of Immune Cell Infiltration and Immune-Related Genes in the Tumor Microenvironment of Glioblastomas

Huang, Sicong; Song, Zijun; Zhang, Tiesong; He, Xuyan; Huang, Kejie; Zhang, Qihui; Shen, Ju; Pan, Jianwei

doi:10.3389/fimmu.2020.585034

Cited by 52 publications

(35 citation statements)

References 56 publications

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“…Recently, computational analyses of RNA-seq data have demonstrated that a glioblastoma microenvironment lacking central memory CD4 T cells or natural killer (NK) cells is further correlated with better prognosis, and the expression levels of ICOS and TNFSF14 were negatively associated with clinical outcome (21). It has also been reported that the glioblastoma microenvironment contains hub genes (CCT3, OLIG2, PSMB9, TRIM21) that were associated with distinct immune cell infiltration characterized by the expression of immune checkpoints and are further involved in cancer development and progression in patients with high-grade glioblastoma (22)(23)(24). In low-grade glioma, the expression level of transforming growth factor beta 1 (TGF-b) and programmed cell death ligand 1 (PDL1) was positively correlated with immune risk score, and prognostic hub genes that were positively correlated with immune cell infiltration have also been revealed (25,26).…”

Section: Introductionmentioning

confidence: 99%

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

et al. 2021

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BackgroundGlioma is the most common type of primary brain tumor in adults. Patients with the most malignant form have an overall survival time of <16 months. Although considerable progress has been made in defining the adapted therapeutic strategies, measures to counteract tumor escape have not kept pace, due to the developed resistance of malignant glioma. In fact, identifying the nature and role of distinct tumor-infiltrating immune cells in glioma patients would decipher potential mechanisms behind therapy failure.MethodsWe integrated into our study glioma transcriptomic datasets from the Cancer Genome Atlas (TCGA) cohort (154 GBM and 516 LGG patients). LM22 immune signature was built using CIBERSORT. Hierarchical clustering and UMAP dimensional reduction algorithms were applied to identify clusters among glioma patients either in an unsupervised or supervised way. Furthermore, differential gene expression (DGE) has been performed to unravel the top expressed genes among the identified clusters. Besides, we used the least absolute shrinkage and selection operator (LASSO) and Cox regression algorithm to set up the most valuable prognostic factor.ResultsOur study revealed, following gene enrichment analysis, the presence of two distinct groups of patients. The first group, defined as cluster 1, was characterized by the presence of immune cells known to exert efficient antitumoral immune response and was associated with better patient survival, whereas the second group, cluster 2, which exhibited a poor survival, was enriched with cells and molecules, known to set an immunosuppressive pro-tumoral microenvironment. Interestingly, we revealed that gene expression signatures were also consistent with each immune cluster function. A strong presence of activated NK cells was revealed in cluster 1. In contrast, potent immunosuppressive components such as regulatory T cells, neutrophils, and M0/M1/M2 macrophages were detected in cluster 2, where, in addition, inhibitory immune checkpoints, such as PD-1, CTLA-4, and TIM-3, were also significantly upregulated. Finally, Cox regression analysis further corroborated that tumor-infiltrating cells from cluster 2 exerted a significant impact on patient prognosis.ConclusionOur work brings to light the tight implication of immune components on glioma patient prognosis. This would contribute to potentially developing better immune-based therapeutic approaches.

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Section: Introductionmentioning

confidence: 99%

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

et al. 2021

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“…In recent years, studies have found that immune cells in the TME can influence the survival of patients with glioma (17,20,34). Immune checkpoint proteins PD-L1, CTLA-4, TIGIT and TIM-3 are significantly upregulated with the hypomethylation of promoters.…”

Section: Discussionmentioning

confidence: 99%

“…Treatment with immune checkpoint inhibitors, including programmed deathligand (PD-L1), has improved glioma patient outcomes (18,19). In the glioma microenvironment, infiltrating immune cells have been demonstrated to benefit tumor progression (20). For example, cluster of differentiation (CD)30 ligand deficiency accelerates glioma progression b y p r o m o t i n g t h e f o r m a t i o n o f t u m o r i m m u n e microenvironment (21).…”

Section: Introductionmentioning

confidence: 99%

High expression of PYCARD is an independent predictor of unfavorable prognosis and chemotherapy resistance in glioma

Liang

Zhong

et al. 2021

Ann Transl Med

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Background: PYD and CARD domain-containing (PYCARD) was upregulated in TMZ-resistant cell lines and glioma tissue and was correlated with poor prognosis, its role in glioma is unclear known. The aim of this study was to elucidate the relationship between PYCARD and glioma based on Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA) databases.Methods: Glioma-resistant cells were compared with parental cells based on the GSE53014 and GSE113510 data sets. The relationship between PYCARD, tumor microenvironment, and long noncoding RNAs (lncRNAs) was assessed using logistic regression. Moreover, Kaplan-Meier and Cox regression were used to analyze the relationship between PYCARD expression and survival rate. Gene set enrichment analysis (GSEA) was also used to determine the biological function of PYCARD and lncRNAs. Cell viability and cell migration assays were used to evaluate the ability of cells to migrate and proliferate. Finally, we analyzed the expression patterns of PYCARD genes in a wide range of cancers.Results: Elevated expression of PYCARD promoted glioma cell proliferation and migration. PYCARD expression was significantly positively associated with gamma delta T cells but negatively correlated with M2 macrophages in glioblastoma multiforme (GBM). Likewise, PYCARD expression was significantly positively associated with monocytes but negatively associated with activated mast cells in low grade glioma (LGG).We also found that 3 PYCARD-related lncRNAs in GBM and 4 PYCARD-related lncRNAs in LGG had a predictive value for glioma patients. The pan-cancer analysis showed that PYCARD expression was higher in most cancer groups.Conclusions: High expression of PYCARD is an independent predictor of unfavorable prognosis and chemotherapy resistance in glioma.

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“…Imaging and time-lapse were performed at 24h or 48h post transplantation (4). Image analysis was achieved using Fiji and Imaris software (5). B-K. Each cell population is highlighted with a coded color; cancer cells in blue or white, macrophages in magenta or white, and vessels in green.…”

Section: Conflict Of Interestmentioning

confidence: 99%

“…Similarly, the recruitment and infiltration of several types of immune cells in the tumour such as macrophages, neutrophils and lymphocytes have been described as having a direct impact on cancer progression 4 . Finally, the composition of the tumour niche is nowadays used as a mean of diagnosis and indicator of good or bad prognosis 5 6 . Therefore, the 3D dynamics visualization of the tumour microenvironment appears as a predictive tool of cancer cell behaviour in terms of intravasation, invasiveness and metastasis.…”

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confidence: 99%

Modelling 3D tumour microenvironment in vivo: a tool to predict cancer fate

Marines¹,

Lorenzini²,

Kissa

et al. 2021

Preprint

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Recently, many studies demonstrated the fundamental role of tumour microenvironment (TME) in cancer progression. Here, we describe a state-of-the-art method to visualize in 3D the behaviour of tumours in zebrafish embryos. We highlight two major actors of TME, macrophages and vessels. This valuable tool is transposable to Patients Derived Xenograft imaging in order to predict the fate of malignant tumours according to the dynamics of their TME.

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Identification of Immune Cell Infiltration and Immune-Related Genes in the Tumor Microenvironment of Glioblastomas

Cited by 52 publications

References 56 publications

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

Prognostic Gene Expression Signature in Patients With Distinct Glioma Grades

High expression of PYCARD is an independent predictor of unfavorable prognosis and chemotherapy resistance in glioma

Modelling 3D tumour microenvironment in vivo: a tool to predict cancer fate

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