Correlation Between APOBEC3B Expression and Clinical Characterization in Lower-Grade Gliomas

Zhang, Hao; Chen, Ziyang; Wang, Zeyu; Dai, Ziyu; Hu, Zhengang; Zhang, Xun; Hu, Min; Liu, Zhixiong; Cheng, Quan

doi:10.3389/fonc.2021.625838

Cited by 16 publications

(13 citation statements)

References 41 publications

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“…Somatic mutations and somatic copy number alternations (CNAs) were downloaded from the TCGA database as previously described [27] , [28] . Genomic event enrichment was determined using GISTIC analysis.…”

Section: Methodsmentioning

confidence: 99%

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The molecular feature of macrophages in tumor immune microenvironment of glioma patients

Zhang

Luo

et al. 2021

Computational and Structural Biotechnology Journal

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106

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Section: Methodsmentioning

confidence: 99%

“…Immunohistochemistry (IHC) staining was conducted as previously described [27] , [28] . Paraffin-embedded tissues of 40 glioma samples (25 LGG samples and 15 GBM samples) with the corresponding sequencing data from the Xiangya Neurosurgey (XYNS) cohort were used for immunohistochemistry (IHC).…”

Section: Methodsmentioning

confidence: 99%

The molecular feature of macrophages in tumor immune microenvironment of glioma patients

Zhang

Luo

et al. 2021

Computational and Structural Biotechnology Journal

Self Cite

106

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“…As the most frequent brain malignant tumor, the prognosis of glioma remains unsatisfactory despite the comprehensive application of various treatments [30].…”

Section: Discussionmentioning

confidence: 99%

A Prognostic lncRNA Signature Associated with Lipid Metabolism in Glioma

Duan

Cao

et al. 2021

Preprint

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Background: Long noncoding RNAs (lncRNAs) are promising cancer biomarkers and therapeutic targets . And lipid metabolism reprogramming is a trait of cancer metabolism. However, the relationship between lncRNAs and lipid metabolism and their prognostic value are still unclear in glioma. Hence, we screened for prognostic lncRNAs associated with lipid metabolism and explored their potential biological functions and effects on glioma. Methods: The glioma data were obtained from TCGA and CGGA databases. Correlation analysis was used to identify the lncRNAs associated with lipid metabolism . The Cox and LASSO regression analysis were adopted to find prognostic lncRNAs, and further established ceRNA network, clustering and risk score models. CNV and somatic mutation data were used to explore the correlation between risk score and genomic alterations. GSVA and GSEA was adopted to reveal the potential biological functions of prognostic lncRNAs. The abundance of 22 immune cells was inferred by CIBERSORT algorithm . The TIDE algorithm and GDSC database were used to predict the patient's response to immunotherapy and chemotherapy, respectively. Human glioma cell lines were used for further experimental validation. CCK-8 and colony forming assays were adopted to evaluate the cell viability. The cell proliferation was detected by EdU assay. Transwell assay was used to evaluate the ability of cell invasion and migration. Results: A total of twenty prognostic lncRNAs associated with lipid metabolism were found, and a prognostic lncRNA signature was established. A high risk-score suggested a poor prognosis , more malignant clinicopathological and genomic aberrations features. The risk score was also an independent prognos tic factor. The high-risk patients were more likely to benefit from anti-PD1 treatment. The biological function of signature lncRNAs was mainly to regulate the biosynthesis and transportation of lipid (especially the fatty acid). Among signature lncRNAs, LINC01614 was associated with prognosis, genomic aberrations characteristics, m6A methylation modification, immune infiltration status, and chemotherapy response of patients with glioma. In vitro experiments, silencing the expression of LINC01614 could inhibit the viability, migration , and proliferation of glioma cells. Conclusions: A prognostic lncRNA signature containing twenty lncRNAs associated with lipid metabolism was established in glioma. The signature lncRNAs play an important role in the development of glioma by regulating lipid biosynthesis and transportation. Our study also provides a new perspective for understanding the lipid metabolism and the biological role of lncRNAs in glioma.

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“…Immunohistochemistry (IHC) staining was conducted as previously described ( 31 , 32 ). Paraffin-embedded tissues of 40 glioma samples with the corresponding sequencing data from the Xiangya Neurosurgey (XYNS) cohort were used for performing IHC.…”

Section: Methodsmentioning

confidence: 99%

Immune Infiltrating Cells-Derived Risk Signature Based on Large-scale Analysis Defines Immune Landscape and Predicts Immunotherapy Responses in Glioma Tumor Microenvironment

Zhang

Wang

et al. 2021

Front. Immunol.

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The glioma tumor microenvironment (TME), composed of several noncancerous cells and biomolecules is known for its complexity of cancer-immune system interaction. Given that, novel risk signature is required for predicting glioma patient responses to immunotherapy. In this study, we systematically evaluated the TME infiltration pattern of 2877 glioma samples. TME phenotypes were determined using the Partitioning Around Medoid method. Machine learning including SVM-RFE and Principal component analysis (PCA) were used to construct a TME scoring system. A total of 857 glioma samples from four datasets were used for external validation of the TME-score. The correlation of TME phenotypes and TME-scores with diverse clinicopathologic characteristics, genomic features, and immunotherapeutic efficacy in glioma patients was determined. Immunohistochemistry staining for the M2 macrophage marker CD68 and CD163, mast cell marker CD117, neutrophil marker CD66b, and RNA sequencing of glioma samples from the XYNS cohort were performed. Two distinct TME phenotypes were identified. High TME-score correlated with a high number of immune infiltrating cells, elevated expression of immune checkpoints, increased mutation rates of oncogenes, and poor survival of glioma patients. Moreover, high TME-score exhibited remarkable association with multiple immunomodulators that could potentially mediate immune escape of cancer. Thus, the TME-score showed the potential to predict the efficacy of anti-PD-1 immunotherapy. Univariate and multivariate analyses demonstrated the TME-score to be a valuable prognostic biomarker for gliomas. Our study demonstrated that TME could potentially influence immunotherapy efficacy in melanoma patients whereas its role in immunotherapy of glioma patients remains unknown. Therefore, a better understanding of the TME landscape in gliomas would promote the development of novel immunotherapy strategies against glioma.

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Correlation Between APOBEC3B Expression and Clinical Characterization in Lower-Grade Gliomas

Cited by 16 publications

References 41 publications

The molecular feature of macrophages in tumor immune microenvironment of glioma patients

The molecular feature of macrophages in tumor immune microenvironment of glioma patients

A Prognostic lncRNA Signature Associated with Lipid Metabolism in Glioma

Immune Infiltrating Cells-Derived Risk Signature Based on Large-scale Analysis Defines Immune Landscape and Predicts Immunotherapy Responses in Glioma Tumor Microenvironment

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