Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma

Wu, Yin; Jiang, Xingjun; Tan, Jun; Xin, Zhaoqi; Zhou, Quanwei; Zhan, Chaohong; Fu, Xianyong; Wu, Zhaoping; Guo, Youwei; Jiang, Zhipeng; Ren, Chao; Tang, Guihua

doi:10.3389/fonc.2020.01409

Cited by 51 publications

(42 citation statements)

References 40 publications

(49 reference statements)

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“…Further, we formulated a nomogram to guide clinical practice in an individualized manner, and its predictive performance was validated across different datasets. Although many previous studies have adopted nomogram models in predicting overall survival of LGG patients, most of them (37)(38)(39) suffered from a lack of external validation. Our study, on the other hand, offered solid external validation with ROC analysis and calibration plot and the nomogram demonstrated to be clinically relevant, discriminant and accurate in predicting survival outcomes.…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Qiu

Tian

et al. 2021

Front. Oncol.

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BackgroundCD86 has great potential to be a new target of immunotherapy by regulating cancer immune response. However, it remains unclear whether CD86 is a friend or foe in lower-grade glioma (LGG).MethodsThe prognostic value of CD86 expression in pan-cancer was analyzed using Cox regression and Kaplan-Meier analysis with data from the cancer genome atlas (TCGA). Cancer types where CD86 showed prognostic value in overall survival and disease-specific survival were identified for further analyses. The Chinese Glioma Genome Atlas (CGGA) dataset were utilized for external validation. Quantitative real-time PCR (qRT-PCR), Western blot (WB), and Immunohistochemistry (IHC) were conducted for further validation using surgical samples from Jiangsu Province hospital. The correlations between CD86 expression and tumor immunity were analyzed using the Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm, Tumor IMmune Estimation Resource (TIMER) database, and expressions of immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) was performed using clusterprofiler r package to reveal potential pathways.ResultsPan-cancer survival analysis established CD86 expression as an unfavorable prognostic factor in tumor progression and survival for LGG. CD86 expression between Grade-II and Grade-III LGG was validated using qRT-PCR and WB. Additionally, CD86 expression in LGG with unmethylated O(6)-methylguanine-DNA-methyltransferase (MGMT) promoter was significantly higher than those with methylated MGMT (P<0.05), while in LGG with codeletion of 1p/19q it was significantly downregulated as opposed to those with non-codeletion (P<2.2*10-16). IHC staining validated that CD86 expression was correlated with MGMT status and X1p/19q subtypes, which was independent of tumor grade. Multivariate regression validated that CD86 expression acts as an unfavorable prognostic factor independent of clinicopathological factors in overall survival of LGG patients. Analysis of tumor immunity and GSEA revealed pivotal role of CD86 in immune response for LGG.ConclusionsIntegrated analysis shows that CD86 is an unfavorable prognostic biomarker in LGG patients. Targeting CD86 may become a novel approach for immunotherapy of LGG.

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Section: Discussionmentioning

confidence: 99%

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Qiu

Tian

et al. 2021

Front. Oncol.

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“…Thus, looking for more immune targets is still needed. Recently, many methods have emerged to predict glioma prognosis based on immune and stromal scores (15)(16)(17)(18). Meanwhile, similar methods have been used in many other solid tumor studies to predict prognosis of patients (19)(20)(21).…”

Section: Discussionmentioning

confidence: 99%

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

Tan

Yan

et al. 2020

Front. Immunol.

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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).

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“…Tumor-derived cytokines and chemokines are able to reprogram those immune cells into tumor-associated phenotypes which therefore have profound effects on progression and therapeutic resistance by inducing inflammatory or anti-inflammatory responses [ 4 ]. The multiple therapeutic antibodies that block immune checkpoints, such as cytotoxic programmed cell death protein 1 (PD1, PDCD1) and T lymphocyte associated antigen 4 (CTLA4), showed great effects in treating non-small-cell lung cancer, kidney cancer, and melanoma [ 5 ]. Yet the clinical application of checkpoint inhibitors in glioma is up against tremendous challenges because of the “cold phenotype” of glioma characterized by a paucity of T-cell infiltration but robust macrophage infiltration [ 4 , 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients

Tian

Liu

et al. 2021

Cancers

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The prognosis of patients with glioma is largely related to both the tumor-infiltrating immune cells and the expression of RNA-binding proteins (RBPs) that are able to regulate various pro-inflammatory and oncogenic mediators. However, immune-associated RBPs in glioma remain unexplored. In this study, we captured patient data from The Cancer Genome Atlas (TCGA) and divided them into two immune subtype groups according to the difference in infiltration of immune cells. After differential expression and co-expression analysis, we identified 216 RBPs defined as immune-associated RBPs. After narrowing down processes, eight RBPs were selected out to construct a risk signature that proven to be a novel and independent prognostic factor. The patients were divided into high- and low-risk groups on the basis of risk score. Higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints such as PD1 and CTLA4. In addition, analyses of pathway enrichment, somatic mutation, copy number variations and immuno-/chemotherapeutic response prediction were performed in high- and low-risk groups and compared with each other. For the first time, we demonstrated a novel signature composed of eight immune-associated RBPs that was valuable in predicting the survival of glioma patients and directing immunotherapy and chemotherapy.

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Development and Validation of a Tumor Mutation Burden–Related Immune Prognostic Model for Lower-Grade Glioma

Cited by 51 publications

References 40 publications

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

Integrated Analysis Reveals Prognostic Value and Immune Correlates of CD86 Expression in Lower Grade Glioma

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

Identification and Validation of an Immune-Associated RNA-Binding Proteins Signature to Predict Clinical Outcomes and Therapeutic Responses in Glioma Patients

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