The Sociobiology of Brain Tumors

Gutmann, David H.

doi:10.1007/978-3-030-35727-6_8

Cited by 6 publications

(5 citation statements)

References 52 publications

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“…Chemokines involved in leucocyte tra cking, angiogenesis, tumor growth, invasion, metastasis, and survival 5 . Previous studies 3,16 have reported a correlation among CXC chemokines, the tumor microenvironment, and cancer immunotherapy, suggesting that CXC chemokines may modulate tumor progression and immunotherapeutic effect. However, integrative bioinformatics analysis of CXC chemokines to evaluate the progress in GMB has not been well reported.…”

Section: Discussionmentioning

confidence: 94%

Identification of Prognostic Biomarkers and Immunotherapeutic Targets with CXC Chemokines in Glioblastoma Multiforme Using Integrated Bioinformatic Analysis

Liu¹,

Zhu²,

Wang³

2020

Preprint

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Background Glioblastoma multiforme (GBM) is the most malignant central nervous system tumour bearing a dismal prognosis. The study aimed to explore the potential biomarkers and therapeutic targets with CXC chemokines in GBM by integrated bioinformatics analysis.Methods Differentially expressed CXC Chemokines were identified in GBM using GEPIA and UALCAN databases,and Kaplan–Meier analyses were performed by GEPIA subsequently. Protein -protein interaction (PPI) network was established in STRING database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were utilized to analyze differentially expressed CXC Chemokines and their similar genes gained from GEPIA. Then, we conducted transcription factors, kinase targets, and immune cells infiltration using TRRUST, LinkedOmics, and TIMER, respectively.Results The mRNA expression levels of CXCL3/5/6/9/10/11/12/13/16 in GMB were significantly elevated compared to normal tissues. GBM patients with higher transcriptional levels of CXCL5/6 were significantly associated with worse disease-free survival, while higher transcriptional levels of CXCL3/5/8 were significantly related to worse overall survival. The functions of CXC chemokines were enriched in Chemokine signaling pathway, Cytokine-cytokine receptor interaction, IL-17 signaling pathway, et.al. RELA and NFKB1were key transcription factors of CXC chemokines. The kinase targets of CXC chemokine contained CDK1, CDK2, PRKCD, MAPK14, ATM, LCK, MTOR, and GRK3, which are involved in oncogenesis, migration, and survival. Moreover, we revealed significant correlations between the expression of CXC chemokines and the infiltration immune cells, especially for dendritic cells.Conclusion The significant CXC chemokines and related pathways may provide a novel possibility for prognostic biomarkers and immunotherapeutic treatment in GMB.Short title: CXC Chemokines with prognosis in GBM

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Section: Discussionmentioning

confidence: 94%

Identification of Prognostic Biomarkers and Immunotherapeutic Targets with CXC Chemokines in Glioblastoma Multiforme Using Integrated Bioinformatic Analysis

Liu¹,

Zhu²,

Wang³

2020

Preprint

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“…Macrophages critically influence glioma formation, maintenance, and progression ( Gutmann, 2020 ), and CD74 is the master regulator of macrophage functions in glioblastoma ( Alban et al, 2020 ; Quail and Joyce, 2017 ; Zeiner et al, 2015 ). To examine the function of CD74 in macrophages in gliomas, we used CD74 as the target gene and selected 50 genes from genes expressed in >50% of macrophages isolated from glioblastoma patients ( Neftel et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Applying causal discovery to single-cell analyses using CausalCell

Wen

Huang

Guo

et al. 2023

eLife

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Correlation between objects is prone to occur coincidentally, and exploring correlation or association in most situations does not answer scientific questions rich in causality. Causal discovery (also called causal inference) infers causal interactions between objects from observational data. Inferred causal interactions in single cells provide valuable clues for investigating molecular interaction and gene regulation, identifying critical diagnostic and therapeutic targets, and designing experimental and clinical interventions. The report of causal discovery methods and generation of single-cell data make applying causal discovery to single-cells a promising direction. However, how to evaluate and choose causal discovery methods and how to develop workflow and platform remain challenges. We report the workflow and platform CausalCell (http://www.gaemons.net/causalcell/causalDiscovery/) for performing single-cell causal discovery. The workflow/platform is developed upon benchmarking four kinds of causal discovery methods and is examined by analysing multiple scRNA-seq datasets. Our results suggest that different situations call for different methods and the constraint-based PC algorithm plus kernel-based conditional independence tests suit for most situations. Relevant issues are discussed and tips for best practices are recommended.

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“…In recent years, with the development of bioinformatics and the multi-omics researches of tumor, we have a deeper understanding of the process of tumorigenesis and development. Immune in ltration plays an important role in the development of glioma cells, which recruits immune cells for their growth by secreting chemokines [22], although the speci c roles of immune cells in glioma still remains unclear. In line with the recent success of immunotherapy in multiple solid tumor [23], it is meaningful to explore the immune related genes of glioma and clarify the in ltration of immune cells in tumors.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

Zhong

Luo

Yang

et al. 2021

Preprint

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Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.

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The Sociobiology of Brain Tumors

Cited by 6 publications

References 52 publications

Identification of Prognostic Biomarkers and Immunotherapeutic Targets with CXC Chemokines in Glioblastoma Multiforme Using Integrated Bioinformatic Analysis

Identification of Prognostic Biomarkers and Immunotherapeutic Targets with CXC Chemokines in Glioblastoma Multiforme Using Integrated Bioinformatic Analysis

Applying causal discovery to single-cell analyses using CausalCell

Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

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