The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning

Zhang, Nan; Dai, Ziyu; Wu, Wantao; Wang, Zeyu; Cao, Hui; Zhang, Yakun; Wang, Zhanchao; Zhang, Hao; Cheng, Quan

doi:10.3389/fimmu.2021.656541

Cited by 38 publications

(28 citation statements)

References 42 publications

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“…However, through this large-scale analysis, the immune characteristics of CD74 were preliminarily demonstrated, which could provide a reference for further investigation of the regulatory association between CD74 and immune cells. The low tumor purity has been implicated to be associated with poor prognosis in glioma patients (Zhang et al, 2017;Xu et al, 2021;Zhang et al, 2021;Kang et al, 2021). Since high expression of CD74 indicated low tumor purity, we further explored the clinical significance of CD74 in glioma patients.…”

Section: Discussionmentioning

confidence: 99%

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CD74 Correlated With Malignancies and Immune Microenvironment in Gliomas

Tang

et al. 2021

Front. Mol. Biosci.

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Background: Cluster of differentiation 74 (CD74) is found to be highly involved in the development of various types of cancers and could affect the activities of infiltrated cells in the tumor microenvironment. However, these studies only focus on a few types of immune cells. Our study aims to comprehensively explore the role of CD74 in glioma prognosis and immune microenvironment.Methods: A total of 40 glioma specimens were collected in this study. We extracted data from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Gene-Expression Omnibus (GEO) databases to explore the expression pattern of CD74 in gliomas. gene sets enrichment analysis and gene set variation analysis analyses were conducted to characterize the immune features of CD74. ESTIMATE, ssGSEA, Tumor IMmune Estimation Resource, and CIBERSORT algorithms were applied to assess the immune infiltration. Kaplan-Meier analysis was used for survival analysis. Receiver operating characteristic analysis was used to evaluate the predictive accuracy of CD74 in glioma diagnosis and prognosis.Results: A total of 2,399 glioma patients were included in our study. CD74 was highly expressed in glioma tissue compared to normal brain tissue and its expression was significantly higher in the high-grade glioma compared to the lower grade glioma at transcriptional and translational levels. Besides, CD74 was positively associated with immune checkpoints and inflammatory cytokines as well as immune processes including cytokine secretion and leukocyte activation. The high expression of CD74 indicated a high infiltration of immune cells such as macrophages, dendritic cells, and neutrophils. Moreover, patients with high expression of CD74 had poor prognoses. CD74 had moderate predictive accuracy in the diagnosis of glioblastoma and prediction of survival.Conclusions: In conclusion, our study revealed that the high expression of CD74 was associated with poor prognosis and high immune infiltration. CD74 could be used as a potential target for glioma treatment and as a biomarker to predict the prognosis of glioma patients.

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Section: Discussionmentioning

confidence: 99%

“…The low tumor purity has been implicated to be associated with poor prognosis in glioma patients ( Zhang et al, 2017 ; Xu et al, 2021 ; Zhang et al, 2021 ; Kang et al, 2021 ). Since high expression of CD74 indicated low tumor purity, we further explored the clinical significance of CD74 in glioma patients.…”

Section: Discussionmentioning

confidence: 99%

CD74 Correlated With Malignancies and Immune Microenvironment in Gliomas

Tang

et al. 2021

Front. Mol. Biosci.

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“…Therefore, the interconnections between glioma and the TIME would be impacted if the energy supply or the cellular context of these cells alters with malignancy progressing. Substantial bioinformatic analyses raise awareness of the autophagy-associated links between glioma and the components of TIME predicting a wide area of intensive basic research ( 214 – 219 ). It has been anticipated by distinct autophagy gene signature-based prognostic risk models that there is more immune cells infiltration in the sets with higher autophagy risk scores, implying latent autophagy-involved associations between autophagy and immunity in glioma ( 214 , 218 , 220 ).…”

Section: Redefine the Interconnection Between Glioma And The Immune Microenvironment From An Autophagic Perspectivementioning

confidence: 99%

Breaking Bad: Autophagy Tweaks the Interplay Between Glioma and the Tumor Immune Microenvironment

et al. 2021

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Though significant strides in tumorigenic comprehension and therapy modality have been witnessed over the past decades, glioma remains one of the most common and malignant brain tumors characterized by recurrence, dismal prognosis, and therapy resistance. Immunotherapy advance holds promise in glioma recently. However, the efficacy of immunotherapy varies among individuals with glioma, which drives researchers to consider the modest levels of immunity in the central nervous system, as well as the immunosuppressive tumor immune microenvironment (TIME). Considering the highly conserved property for sustaining energy homeostasis in mammalian cells and repeatedly reported links in malignancy and drug resistance, autophagy is determined as a cutting angle to elucidate the relations between glioma and the TIME. In this review, heterogeneity of TIME in glioma is outlined along with the reciprocal impacts between them. In addition, controversies on whether autophagy behaves cytoprotectively or cytotoxically in cancers are covered. How autophagy collapses from its homeostasis and aids glioma malignancy, which may depend on the cell type and the cellular context such as reactive oxygen species (ROS) and adenosine triphosphate (ATP) level, are briefly discussed. The consecutive application of autophagy inducers and inhibitors may improve the drug resistance in glioma after overtreatments. It also highlights that autophagy plays a pivotal part in modulating glioma and the TIME, respectively, and the intricate interactions among them. Specifically, autophagy is manipulated by either glioma or tumor-associated macrophages to conform one side to the other through exosomal microRNAs and thereby adjust the interactions. Given that some of the crosstalk between glioma and the TIME highly depend on the autophagy process or autophagic components, there are interconnections influenced by the status and well-being of cells presumably associated with autophagic flux. By updating the most recent knowledge concerning glioma and the TIME from an autophagic perspective enhances comprehension and inspires more applicable and effective strategies targeting TIME while harnessing autophagy collaboratively against cancer.

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“…Bioinformatics analysis has been used to evaluate the abundance of immune infiltrating cells in the TME. Several studies have also explored how TME affects immunotherapeutic response and other clinical outcomes ( 9 , 10 ).…”

Section: Introductionmentioning

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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The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning

Cited by 38 publications

References 42 publications

CD74 Correlated With Malignancies and Immune Microenvironment in Gliomas

CD74 Correlated With Malignancies and Immune Microenvironment in Gliomas

Breaking Bad: Autophagy Tweaks the Interplay Between Glioma and the Tumor Immune Microenvironment

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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