Glioma Tumor Microenvironment Research Articles

DDOST Correlated with Malignancies and Immune Microenvironment in Gliomas.

Among the most common types of brain tumor, gliomas are the most aggressive and have the poorest prognosis. Dolichyl-diphosphooligosaccharide protein glycosyltransferase non-catalytic subunit (DDOST) encodes a component of the oligosaccharide transferase complex and is related to the N-glycosylation of proteins. The role of DDOST in gliomas, however, is not yet known. First, we performed a pan cancer analysis of DDOST in the TCGA cohort. The expression of DDOST was compared between glioma and normal brain tissues in the GEO and Chinese Glioma Genome Atlas (CGGA) databases. In order to explore the role of DDOST in glioma, we analyze the impact of DDOST on the prognosis of glioma patients, with the CGGA 325 dataset as a test set and the CGGA 693 dataset as a validation set. Immunohistochemistry was performed on tissue microarrays to examine whether DDOST has an impact on glioma patient survival. Next, using single-cell sequencing analysis, GSEA, immune infiltration analysis, and mutation analysis, we explored how DDOST affected the glioma tumor microenvironment. Finally, we evaluated the clinical significance of DDOST for glioma treatment by constructing nomograms and decision curve analysis (DCA) curves. We found that DDOST was overexpressed in patients with high grade, IDH wild type, 1p19q non-codel and MGMT un-methylated, which was associated with poor prognosis. Patients with high levels of DDOST, regardless of their clinical characteristics, had a worse prognosis. Immunohistochemical analysis confirmed the results of the above bioinformatics analysis. Mechanistic analysis revealed that DDOST was closely associated with the glioma microenvironment and negatively related to tumor-infiltrating B cells and CD4+ T cells and positively related to CAFs and tumor-associated macrophages. In conclusion, these findings suggested that DDOST mediated the immunosuppressive microenvironment of gliomas and could be an important biomarker in diagnosing and treating gliomas.

Immune landscape of a genetically engineered murine model of glioma compared with human glioma.

Novel therapeutic strategies targeting glioblastoma (GBM) often fail in the clinic, partly because preclinical models in which hypotheses are being tested do not recapitulate human disease. To address this challenge, we took advantage of our previously developed spontaneous Qk/Trp53/Pten (QPP) triple-knockout model of human GBM, comparing the immune microenvironment of QPP mice with that of patient-derived tumors to determine whether this model provides opportunity for gaining insights into tumor physiopathology and preclinical evaluation of therapeutic agents. Immune profiling analyses and single-cell sequencing of implanted and spontaneous tumors from QPP mice and from patients with glioma revealed intratumoral immune components that were predominantly myeloid cells (e.g., monocytes, macrophages, and microglia), with minor populations of T, B, and NK cells. When comparing spontaneous and implanted mouse samples, we found more neutrophils and T and NK cells in the implanted model. Neutrophils and T and NK cells were increased in abundance in samples derived from human high-grade glioma compared with those derived from low-grade glioma. Overall, our data demonstrate that our implanted and spontaneous QPP models recapitulate the immunosuppressive myeloid-dominant nature of the tumor microenvironment of human gliomas. Our model provides a suitable tool for investigating the complex immune compartment of gliomas.

Myeloid Cell Classification and Therapeutic Opportunities Within the Glioblastoma Tumor Microenvironment in the Single Cell-Omics Era.

The glioma tumor microenvironment (TME) is complex and heterogeneous, and multiple emerging and current technologies are being utilized for an improved comprehension and understanding of these tumors. Single cell analysis techniques such as single cell genomic and transcriptomic sequencing analysis are on the rise and play an important role in elucidating the glioma TME. These large datasets will prove useful for patient tumor characterization, including immune configuration that will ultimately influence therapeutic choices and especially immune therapies. In this review we discuss the advantages and drawbacks of these techniques while debating their role in the domain of glioma-infiltrating myeloid cells characterization and function.

Glycosylation modification identifies novel molecular phenotypes and prognostic stratifications of glioma

Glycosylation modification plays a vital role in tumor progression and is highly associated with glioma prognosis. However, the influence of glycosylation modification on the tumor microenvironment (TME) and omic features of glioma remains unclear. Differentially expressed glycosylation-related genes between adjacent and tumor tissues of The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets were identified. We performed unsupervised clustering to classify patients into different molecular phenotypes, and analyzed their TME heterogeneity, including immunocyte infiltration, immune pathways and tumor purity. Subsequently, we developed a prognostic predicting system named GlycoScore by stepwise least absolute shrinkage and selection operator-Cox regression to evaluate the modification pattern and its association with somatic mutation, clinical significance, immune fractions and drug resistance. Two clustering clusters were identified and presented distinct clinical outcomes and biological functions characterized by hotand cold tumors respectively. Patients with higher GlycoScores exhibited poor prognosis, less mutation counts, and were more sensitive to chemotherapeutics. We also confirmed that the GlycoScore severed as an independent risk factor. Cancer hallmarks such as cell cycle, hippo, and TGFβ were active in the high-GlycoScore group. The combination of tumor mutation burden and the GlycoScore presented an excellent performance in prognostic stratification. Our study suggests that glycosylation is essential for modeling TME of glioma and the GlycoScore is a promising prognostic signature and indicator of immunotherapeutic efficacy.

Abstract LB528: A microglia associated gene signature predicts survival risk in glioma patients

Abstract Background: Gliomas are common tumors that affect the brain and spinal cord. A complex tumor microenvironment is one of the leading reasons for a poor prognosis in glioma patients. Microglia, as part of the immune microenvironment of gliomas, may facilitate glioma growth and invasion. However, the correlation between the microglia abundance and glioma prognosis has not been clarified. Method: Our goal was to examine the relationship between microglia abundance and glioma prognosis. We analyzed the single-cell RNA sequences of human and mouse glioma cells to identify microglia marker genes. Then, we built a LASSO Cox regression model of microglia abundance signatures in gliomas. The Cancer Genome Atlas (TCGA) dataset (Low-grade gliomas, LGG) was used as the training cohort. The Repository of Molecular Brain Neoplasia Data (REMBRANDT) dataset and Chinese Glioma Genome Atlas (CGGA) dataset was used to validate the model as the validation cohorts. Findings: Overall survival was significantly lower in those with a high level of microglia infiltration. Additionally, we found that microglia could interact with the tumor microenvironment and genomic features of gliomas, making microglia abundance negatively associated with the prognosis of glioma patients. Microglia abundance was positively correlated with immune genes expression and immune-related pathways. By applying our LASSO Cox regression model to gliomas, we found that patients with high-risk scores had significantly shorter overall survival (OS) than those with low-risk scores. Conclusions: Taken together, our findings suggest that microglia abundance may be negatively associated with survival in gliomas. Based on the novel microglia abundance signatures, we developed a high accuracy LASSO Cox regression model. In this study, we demonstrated that the model accurately predicted the prognosis of glioma patients and could offer new therapeutic targets for microglial-directed therapies. Keywords: Gliomas; Microglia abundance; LASSO Cox regression; TCGA; Signature.- 1 - Citation Format: Chongming Jiang, Evelien Schaafsma, Yanding Zhao, Thinh Nguyen, Kenneth Zhu, Chao Cheng. A microglia associated gene signature predicts survival risk in glioma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB528.

The Identification of Necroptosis-Related Subtypes, the Construction of a Prognostic Model, and the Characterization of the Tumor Microenvironment in Gliomas.

Necroptosis is a recently discovered form of cell death that plays a vital role in the progression of cancer, the spread of metastases, and the immunologic response to tumors. Due to the dual role of necrotic apoptotic processes in tumor pathogenesis and the heterogeneity of gliomas, the function of necroptosis in the glioma microenvironment is still poorly understood. We characterized the expression of necroptosis-related genes (NRGs) within glioma samples at both the genetic and transcriptional levels, identifying three distinct subtypes. Additionally, we constructed a risk score, which is capable of accurately predicting patient prognosis, correlates with tumor mutation burden (TMB), tumor stem cell index (CSC), immune checkpoints, and predicts tumor drug sensitivity. To facilitate its application in the clinic, we developed a nomogram and demonstrated that it predicts the prognosis of glioma patients with good accuracy and reliability using multiple datasets. We examined the function of necroptosis in the tumor microenvironment (TME) and the prognosis of gliomas, which may be useful for guiding individualized treatment plans for gliomas targeting necroptosis.

The Role of Myeloid Cells in GBM Immunosuppression.

Gliomas are intrinsic brain tumors that originate from glial cells. Glioblastoma (GBM) is the most aggressive glioma type and resistant to immunotherapy, mainly due to its unique immune environment. Dimensional data analysis reveals that the intra-tumoral heterogeneity of immune cell populations in the glioma microenvironment is largely made up of cells of myeloid lineage. Conventional therapies of combined surgery, chemotherapy and radiotherapy have achieved limited improvements in the prognosis of glioma patients, as myeloid cells are prominent mediators of immune and therapeutic responses—like immunotherapy resistance—in glioma. Myeloid cells are frequently seen in the tumor microenvironment (TME), and they are polarized to promote tumorigenesis and immunosuppression. Reprogramming myeloid cells has emerged as revolutionary, new types of immunotherapies for glioma treatment. Here we detail the current advances in classifying epigenetic, metabolic, and phenotypic characteristics and functions of different populations of myeloid cells in glioma TME, including myeloid-derived suppressor cells (MDSCs), glioma-associated microglia/macrophages (GAMs), glioma-associated neutrophils (GANs), and glioma-associated dendritic cells (GADCs), as well as the mechanisms underlying promotion of tumorigenesis. The final goal of this review will be to provide new insights into novel therapeutic approaches for specific targeting of myeloid cells to improve the efficacy of current treatments in glioma patients.

Glioma-Associated Microglia Characterization in the Glioblastoma Microenvironment through a 'Seed-and Soil' Approach: A Systematic Review.

Background and aim: Ever since the discovery of tumor-associated immune cells, there has been growing interest in the understanding of the mechanisms underlying the crosstalk between these cells and tumor cells. A “seed and soil” approach has been recently introduced to describe the glioblastoma (GBM) landscape: tumor microenvironments act as fertile “soil” and interact with the “seed” (glial and stem cells compartment). In the following article, we provide a systematic review of the current evidence pertaining to the characterization of glioma-associated macrophages and microglia (GAMs) and microglia and macrophage cells in the glioma tumor microenvironment (TME). Methods: An online literature search was launched on PubMed Medline and Scopus using the following research string: “((Glioma associated macrophages OR GAM OR Microglia) AND (glioblastoma tumor microenvironment OR TME))”. The last search for articles pertinent to the topic was conducted in February 2022. Results: The search of the literature yielded a total of 349 results. A total of 235 studies were found to be relevant to our research question and were assessed for eligibility. Upon a full-text review, 58 articles were included in the review. The reviewed papers were further divided into three categories based on their focus: (1) Microglia maintenance of immunological homeostasis and protection against autoimmunity; (2) Microglia crosstalk with dedifferentiated and stem-like glioblastoma cells; (3) Microglia migratory behavior and its activation pattern. Conclusions: Aggressive growth, inevitable recurrence, and scarce response to immunotherapies are driving the necessity to focus on the GBM TME from a different perspective to possibly disentangle its role as a fertile ‘soil’ for tumor progression and identify within it feasible therapeutic targets. Against this background, our systematic review confirmed microglia to play a paramount role in promoting GBM progression and relapse after treatments. The correct and extensive understanding of microglia–glioma crosstalk could help in understanding the physiopathology of this complex disease, possibly opening scenarios for improvement of treatments.

A Novel Risk Score Model Based on Eleven Extracellular Matrix-Related Genes for Predicting Overall Survival of Glioma Patients.

Gliomas are the most common lethal primary brain tumors with variable survival outcomes for patients. The extracellular matrix (ECM) is linked with clinical prognosis of glioma patients, but it is not commonly used as a clinical indicator. Herein, we investigated changes in ECM-related genes (ECMRGs) via analyzing the transcriptional data of 938 gliomas from TCGA and CGGA datasets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, a 11-ECMRG signature that is strongly linked with overall survival (OS) in glioma patients was identified. This signature was characterized by high-risk and low-risk score patterns. We found that the patients in the high-risk group are significantly linked with malignant molecular features and worse outcomes. Univariate and multivariate Cox regression analyses suggested that the signature is an independent indicator for glioma prognosis. The prediction accuracy of the signature was verified through time-dependent receiver operating characteristic (ROC) curves and calibration plots. Further bioinformatics analyses implied that the ECMRG signature is strongly associated with the activation of multiple oncogenic and metabolic pathways and immunosuppressive tumor microenvironment in gliomas. In addition, we confirmed that the high-risk score is an indicator for a therapy-resistant phenotype. In addition to bioinformatics analyses, we functionally verified the oncogenic role of bone morphogenetic protein 1 (BMP1) in gliomas in vitro.

Cell-directed aptamer therapeutic targeting for cancers including those within the central nervous system

ABSTRACT Osteopontin (OPN) is produced by tumor cells as well as by myeloid cells and is enriched in the tumor microenvironment (TME) of many cancers. Given the roles of OPN in tumor progression and immune suppression, we hypothesized that targeting OPN with aptamers that have high affinity and specificity could be a promising therapeutic strategy. Bi-specific aptamers targeting ligands for cellular internalization were conjugated to siRNAs to suppress OPN were created, and therapeutic leads were selected based on target engagement and in vivo activity. Aptamers as carriers for siRNA approaches were created including a cancer targeting nucleolin aptamer Ncl-OPN siRNA and a myeloid targeting CpG oligodeoxynucleotide (ODN)-OPN siRNA conjugate. These aptamers were selected as therapeutic leads based on 70–90% OPN inhibition in cancer (GL261, 344SQ, 4T1B2b) and myeloid (DC2.4) cells relative to scramble controls. In established immune competent 344SQ lung cancer and 4T1B2b breast cancer models, these aptamers, including in combination, demonstrate therapeutic activity by inhibiting tumor growth. The Ncl-OPN siRNA aptamer demonstrated efficacy in an immune competent orthotopic glioma model administered systemically secondary to the ability of the aptamer to access the glioma TME. Therapeutic activity was demonstrated using both aptamers in a breast cancer brain metastasis model. Targeted inhibition of OPN in tumor cells and myeloid cells using bifunctional aptamers that are internalized by specific cell types and suppress OPN expression once internalized may have clinical potential in cancer treatment.

Immune Characteristics of LYN in Tumor Microenvironment of Gliomas.

The prognosis of gliomas is poor and there are limited therapeutic approaches. Immunotherapy has become a promising treatment for gliomas. Here, we explored the expression pattern of Lck/yes-related protein tyrosine kinase (LYN) in gliomas and assessed its value as an immunotherapy biomarker. Transcriptional data was mined from two publicly available datasets, TCGA and CGGA, and used to investigate the correlation between LYN and clinical characteristics including patient prognosis, somatic mutation, and immune infiltrating features in gliomas. Besides, the correlation between LYN and classical immune checkpoint molecules was explored. Glioma samples obtained from the Xiangya Hospital cohort were used for immunohistochemistry staining. High expression level of LYN was observed in advanced gliomas and other cancer types, which predicted a worse prognosis. LYN stratified patients’ survival in the Xiangya cohort and was also significantly associated with infiltrating immune cell types and inflammatory activities in the tumor microenvironment. LYN was involved in tumor mutation, correlated with the regulation of oncogenic genes, and also showed a significant positive correlation with PD-L1. LYN can be a potential diagnostic marker and immunotherapy marker in gliomas.

Circadian Regulation Patterns With Distinct Immune Landscapes in Gliomas Aid in the Development of a Risk Model to Predict Prognosis and Therapeutic Response.

Circadian disruption in tumorigenesis has been extensively studied, but how circadian rhythm (CR) affects the formation of tumor microenvironment (TME) and the crosstalk between TME and cancer cells is largely unknown, especially in gliomas. Herein, we retrospectively analyzed transcriptome data and clinical parameters of glioma patients from public databases to explore circadian rhythm-controlled tumor heterogeneity and characteristics of TME in gliomas. Firstly, we pioneered the construction of a CR gene set collated from five datasets and review literatures. Unsupervised clustering was used to identify two CR clusters with different CR patterns on the basis of the expression of CR genes. Remarkably, the CR cluster-B was characterized by enriched myeloid cells and activated immune-related pathways. Next, we applied principal component analysis to construct a CRscore to quantify CR patterns of individual tumors, and the function of the CRscore in prognostic prediction was further verified by univariate and multivariate regression analyses in combination with a nomogram. The CRscore could not only be an independent factor to predict prognosis of glioma patients but also guide patients to choose suitable treatment strategies: immunotherapy or chemotherapy. A glioma patient with a high CRscore might respond to immune checkpoint blockade, whereas one with a low CRscore could benefit from chemotherapy. In this study, we revealed that circadian rhythms modulated tumor heterogeneity, TME diversity, and complexity in gliomas. Evaluating the CRscore of an individual tumor would contribute to gaining a greater understanding of the tumor immune status of each patient, enhancing the accuracy of prognostic prediction, and suggesting more effective treatment options.

Construction of a prognostic classifier and prediction of the immune landscape and immunosuppressive molecules in gliomas based on combination of inflammatory response-related genes and angiogenesis-associated genes

Objective:We aimed to combine inflammatory response-related genes (IRRGs) and angiogenesis-associated genes (AAGs) to build a prognostic classifier and to predict immune landscapes and immunosuppressive molecules in gliomas. Introduction: Gliomas, the commonest primary brain tumors, account for about 80% of cancerous tumors in the central nervous system (CNS), featuring rapid progression, high malignancy, and extremely poor prognosis. The induction of inflammatory responses and angiogenesis have been considered to be closely related to tumors. However, there are little publications systematically elaborating on their impacts on gliomas. Methods: We downloaded the data of IRRGs and AAGs from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and retrieved 68 differentially expressed genes (DEGs), of which 13 DEGs pertained to the prognosis of glioma cases. Next, 9 DEGs were screened from the 13 major DEGs with predictive significance and utilized to build a 9-gene signature as a prognostic risk score model (PRSM) with the aid of univariate Cox regression analyses (CRA) and least absolute shrinkage and selection operator (LASSO)-CRA. On this basis, glioma patients fell into high-risk (HR) group and low-risk (LR) group. Later, we implemented Gene Set Enrichment Analysis (GSEA, Gene Set: WP_ANGIOGENESIS) and calculate the scores of cell infiltration and immune-associated function by harnessing single-sample GSEA (ssGSEA). Results: The prognosis was compared between the two groups by introducing Kaplan-Meier (KM) analysis, which yielded that HR group exhibited poorer prognosis. Additionally, the predictive capacity and independent characteristics were proven by the receiver operating characteristic curve (ROC) and multivariate CRA. Further, We took an evaluation of immune profiles, which unraveled that immunosuppressive cell count was distinctively larger in HS group. Finally, a protein-protein interaction (PPI) network of DEGs was built, and 10 hub genes were obtained, of which epidermal growth factor receptor (EGFR) was closely related to poor prognosis. Conclusion: A 9-gene signature was established on the strength of IRRGs and AAGs for predicting glioma prognosis, tumor microenvironment (TME), immune landscapes and immunosuppressive molecules. However, the molecular mechanism developed by this signature to function in tumor immunity needs further experimental research in the future and is expected to be a research target for glioma immunotherapy strategies.

Prognostic Potential of Secreted Modular Calcium-Binding Protein 1 in Low-Grade Glioma.

Background: Secreted modular calcium-binding protein 1 (SMOC1) belongs to a family of matricellular proteins; it was involved in embryo development, endothelial cell proliferation, angiogenesis, integrin–matrix interactions, cell adhesion, and regulation of glucose metabolism. Previous studies showed that the expression of SMOC1 was increased in some tumors. However, the prognostic value and the biological function of SMOC1 in tumor remain unclear. Methods: In this study, we explored the expression profile and prognostic value of SMOC1 in pan-cancers, especially glioma, via multiple databases, including Oncomine, Gene Expression Profiling Interactive 2, PrognoScan, Kaplan–Meier plotter, and the Chinese Glioma Genome Atlas database. Furthermore, LinkedOmics was used to identify the genes coexpressed with SMOC1 and to perform Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology analysis in low-grade glioma (LGG). Also, the Cancer Single-Cell State Atlas database was used to evaluate the correlation between SMOC1 expression and functional state activities in glioma cells. In addition, the Tumor Immune Estimation Resource and TISIDB databases were used to evaluate the correlations between SMOC1 expression and tumor-infiltrating immune cells in the tumor microenvironment. Results: Compared with normal brain tissues, the expression of SMOC1 was increased in LGG tissues. The higher expression of SMOC1 was significantly correlated with better survival of LGG patients. Additionally, functional analyses showed that the SMOC1 coexpressed genes were inhibited in processes such as response to type I interferon and interferon-gamma, lymphocyte-mediated immunity, leukocyte migration, adaptive immune response, neutrophil-mediated immunity, T cell activation, and pathways including EMC–receptor interaction, Th17 cell differentiation, and leukocyte trans-endothelial migration in LGG. Moreover, the expression of SMOC1 was correlated with stemness, hypoxia, EMT, and metastasis of glioma cells. Additionally, the expression of SMOC1 expression was negatively correlated with levels of infiltrating B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells, and gene markers of most immune cells in LGG. Conclusion: Our results suggest that SMOC1 could be a potential biomarker to determine prognosis and might play a specific role in the tumor microenvironment of glioma, thereby influencing the development and progression of glioma. These findings provide some new insights for further investigation.

COL5A1 Serves as a Biomarker of Tumor Progression and Poor Prognosis and May Be a Potential Therapeutic Target in Gliomas.

Glioma is the most common malignancy of the central nervous system. Although advances in surgical resection, adjuvant radiotherapy, and chemotherapy have been achieved in the last decades, the prognosis of gliomas is still dismal. COL5A1 is one of the collagen members with minor content but prominent functions. The present study examined the biological functions, prognostic value, and gene-associated tumor-infiltrating immune cells of COL5A1 through experiments and bioinformatics analysis. We found that the overexpression of COL5A1 was positively correlated with the increasing tumor malignancies and indicated poor prognosis in gliomas. Moreover, downregulation of COL5A1 could inhibit proliferation and migration of glioma cells and enhance their temozolomide sensitivities in vitro. Further bioinformatic analysis revealed that COL5A1 and its co-expressed genes participated in a number of pathways and biological processes involved in glioma progression. Finally, we evaluated the tumor-infiltrating immune cells of gliomas depending on COL5A1 and found that the percentages of the dendritic cells, which were known as the central mediator of tumor microenvironment in gliomas, were positively associated with the expression levels of COL5A1. Taken together, COL5A1 is an important biomarker and potential therapeutic target of gliomas.

EXTH-22. NEOADJUVANT IMMUNOTHERAPY WITH ANTI-PD1 ALTERS THE TUMOR MICROENVIRONMENT POST SURGERY IN A MODEL OF RECURRENT GLIOBLASTOMA

Abstract SIGNIFICANCE New promising clinical trials for glioblastoma are evaluating the efficacy of neoadjuvant immunotherapy in the context of recurrent tumor surgery. OBJECTIVE We investigated the effects of neoadjuvant PD1 blockade on the glioma tumor microenvironment in a clinically relevant murine model of recurrent tumor. RESULTS Using an orthotopic mouse KR158 resection model of glioblastoma that we have established, we show that neoadjuvant anti-PD1 and surgery enhance animal survival and increase the recruitment of CD8+ and CD4+ T cells at recurrent tumor sites following bulk tumor resection compared to surgery followed by adjuvant immunotherapy. Transcriptome and spatial genomic analyses reveal alterations in immune exhaustion and activation pathway signaling after neoadjuvant anti-PD1treatment when compared with adjuvant anti-PD1-treatment or surgery alone. CONCLUSIONS These results provide insights into the effects of neoadjuvant PD1 blockade on the tumor microenvironment and uncover additional treatment targets.

Prognosis and Immunotherapy Significances of a Cancer-Associated Fibroblasts-Related Gene Signature in Gliomas.

As a cold tumor, malignant glioma has strong immunosuppression and immune escape characteristics. The tumor microenvironment (TME) provides the “soil” for the survival of malignant tumors, and cancer-associated fibroblasts (CAFs) are the architects of matrix remodeling in TME. Therefore, CAFs have potent regulatory effects on the recruitment and functional differentiation of immune cells, whereby they synthesize and secrete numerous collagens, cytokines, chemokines, and other soluble factors whose interaction with tumor cells creates an immunosuppressive TME. This consequently facilitates the immune escape of tumor cells. Targeting CAFs would improve the TME and enhance the efficacy of immunotherapy. Thus, regulation of CAFs and CAFs-related genes holds promise as effective immunotherapies for gliomas. Here, by analyzing the Chinese Glioma Genome Atlas and the Cancer Genome Atlas database, the proportion of CAFs in the tumor was revealed to be associated with clinical and immune characteristics of gliomas. Moreover, a risk model based on the expression of CAFs-related six-gene for the assessment of glioma patients was constructed using the least absolute shrinkage and selection operator and the results showed that a high-risk group had a higher expression of the CAFs-related six-genes and lower overall survival rates compared with those in the low-risk group. Additionally, patients in the high-risk group exhibited older age, high tumor grade, isocitrate dehydrogenase wildtype, 1p/19q non-codeletion, O-6-methylguanine-DNA methyltransferase promoter unmethylation and poor prognosis. The high-risk subtype had a high proportion CAFs in the TME of glioma, and a high expression of immune checkpoint genes. Analysis of the Submap algorithm indicated that the high-risk patients could show potent response to anti-PD-1 therapy. The established risk prediction model based on the expression of six CAFs-related genes has application prospects as an independent prognostic indicator and a predictor of the response of patients to immunotherapy.

Immunomodulatory Effects of Tryptophan Metabolism in the Glioma Tumor Microenvironment.

Gliomas are the most common primary malignant tumor in adults’ central nervous system. While current research on glioma treatment is advancing rapidly, there is still no breakthrough in long-term treatment. Abnormalities in the immune regulatory mechanism in the tumor microenvironment are essential to tumor cell survival. The alteration of amino acid metabolism is considered a sign of tumor cells, significantly impacting tumor cells and immune regulation mechanisms in the tumor microenvironment. Despite the fact that the metabolism of tryptophan in tumors is currently discussed in the literature, we herein focused on reviewing the immune regulation of tryptophan metabolism in the tumor microenvironment of gliomas and analyzed possible immune targets. The objective is to identify potential targets for the treatment of glioma and improve the efficiency of immunotherapy.

Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma.

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6‐methylguanine‐DNA methyl‐transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

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

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.