Glioma-Associated Stromal Cells Stimulate Glioma Malignancy by Regulating the Tumor Immune Microenvironment

Fusion of glioma-associated mesenchymal stem/stromal cells with glioma cells promotes macrophage recruitment and M2 polarization via m6A modification of CSF1

Malignant glioma is the most common primary malignant tumor of the brain in adults, with glioblastoma (GBM) being the most aggressive subtype. Mesenchymal stem/stromal cells (MSCs) have been shown to fuse with tumor cells in various cancers including glioma, thereby regulating tumor progression. However, there has been no systematic research on the fusion of glioma-associated MSCs (GA-MSCs) with glioma cells. Here, it is shown that GA-MSCs are able to spontaneously fuse with glioma cells both in vitro and in vivo. The hybrid cells display significantly lower levels of N6-methyladenosine (m6A) modification and can modulate the glioma microenvironment by attracting and inducing M2-like polarization of macrophages. Mechanistically, the demethylase fat mass and obesity-associated protein (FTO) mediates demethylation in hybrids and promotes macrophage colony-stimulating factor (CSF1) secretion by increasing its RNA stability in an m6A-YTH domain family 2 (YTHDF2)-dependent manner. Our study reveals a novel crosstalk mechanism between glioma cells, GA-MSCs, and macrophages in glioma microenvironment, offering potential new approaches for glioma therapy.

Targeting stromal cells in tumor microenvironment as a novel treatment strategy for glioma

Glioma is the most common primary malignant tumor of the central nervous system in adults, characterized by high mortality, low cure rate and high recurrence rate. Among gliomas, glioblastoma multiforme (GBM) is the most malignant subtype. Currently, the standard treatment for patients with GBM is maximum surgical excision combined with radiotherapy and chemotherapy. But only a small percentage of patients benefit from this standard treatment. The tumor microenvironment plays an important role in the occurrence and development of most tumors. It is primarily composed of tumor cells, peripheral blood vessels, extracellular matrix, signaling molecules, stromal cells, and immune cells. The role of stromal cells in GBM has emerged as the focus of current research. The interaction among tumor, stromal, and immune cells within the tumor microenvironment can influence tumor development. Traditional research and drug therapy in glioma mainly focus on the tumor cells themselves, but recent studies have found that targeting stromal cells in the tumor microenvironment can also modulate tumor progression in GBM. Here, we review the influence of stromal cells in the tumor microenvironment of GBM on tumor cells and its related mechanism, as well as related molecular targets and signaling pathways, providing new ideas for the treatment and prognosis of GBM.

Integrated analysis reveals the potential of cluster of differentiation 86 as a key biomarker in high-grade glioma

This study aimed to evaluate the potential of cluster of differentiation 86 (CD86) as a biomarker in high-grade glioma (HGG). The TCGA and TCIA databases were used to obtain the CD86 expression value, clinical data, and MRI images of HGG patients. Prognostic values were assessed by the Kaplan-Meier method, Receiver operating characteristic curve (ROC), Cox regression, logistic regression, and nomogram analyses. CD86-associated pathways were also explored. We found that CD86 was significantly upregulated in HGG compared with the normal group. Survival analysis showed a significant association between CD86 high expression and shorter overall survival time. Its independent prognostic value was also confirmed. These results suggested the possibility of CD86 as a biomarker in HGG. We also innovatively established 2 radiomics models with Support Vector Machine (SVM) and Logistic regression (LR) algorithms to predict the CD86 expression. The 2 models containing 5 optimal features by SVM and LR methods showed similar favorable performance in predicting CD86 expression in the training set, and their performance were also confirmed in validation set. These results indicated the successful construction of a radiomics model for non-invasively predicting biomarker in HGG. Finally, pathway analysis indicated that CD86 might be involved in the natural killer cell-mediated cytotoxicity in HGG progression.

Current Knowledge about the Peritumoral Microenvironment in Glioblastoma

Glioblastoma is a deadly disease, with a mean overall survival of less than 2 years from diagnosis. Recurrence after gross total surgical resection and adjuvant chemo-radiotherapy almost invariably occurs within the so-called peritumoral brain zone (PBZ). The aim of this narrative review is to summarize the most relevant findings about the biological characteristics of the PBZ currently available in the medical literature. The PBZ presents several peculiar biological characteristics. The cellular landscape of this area is different from that of healthy brain tissue and is characterized by a mixture of cell types, including tumor cells (seen in about 30% of cases), angiogenesis-related endothelial cells, reactive astrocytes, glioma-associated microglia/macrophages (GAMs) with anti-inflammatory polarization, tumor-infiltrating lymphocytes (TILs) with an "exhausted" phenotype, and glioma-associated stromal cells (GASCs). From a genomic and transcriptomic point of view, compared with the tumor core and healthy brain tissue, the PBZ presents a "half-way" pattern with upregulation of genes related to angiogenesis, the extracellular matrix, and cellular senescence and with stemness features and downregulation in tumor suppressor genes. This review illustrates that the PBZ is a transition zone with a pre-malignant microenvironment that constitutes the base for GBM progression/recurrence. Understanding of the PBZ could be relevant to developing more effective treatments to prevent GBM development and recurrence.

Radiomics for characterization of the glioma immune microenvironment

Increasing evidence suggests that besides mutational and molecular alterations, the immune component of the tumor microenvironment also substantially impacts tumor behavior and complicates treatment response, particularly to immunotherapies. Although the standard method for characterizing tumor immune profile is through performing integrated genomic analysis on tissue biopsies, the dynamic change in the immune composition of the tumor microenvironment makes this approach not feasible, especially for brain tumors. Radiomics is a rapidly growing field that uses advanced imaging techniques and computational algorithms to extract numerous quantitative features from medical images. Recent advances in machine learning methods are facilitating biological validation of radiomic signatures and allowing them to "mine" for a variety of significant correlates, including genetic, immunologic, and histologic data. Radiomics has the potential to be used as a non-invasive approach to predict the presence and density of immune cells within the microenvironment, as well as to assess the expression of immune-related genes and pathways. This information can be essential for patient stratification, informing treatment decisions and predicting patients' response to immunotherapies. This is particularly important for tumors with difficult surgical access such as gliomas. In this review, we provide an overview of the glioma microenvironment, describe novel approaches for clustering patients based on their tumor immune profile, and discuss the latest progress on utilization of radiomics for immune profiling of glioma based on current literature.

Worldwide productivity and research trend of publications concerning glioma-associated macrophage/microglia: A bibliometric study

Glioma-associated macrophage/microglia (GAM) represents a key player in shaping a unique glioma ecosystem to facilitate tumor progression and therapeutic resistance. Numerous studies have been published concerning GAM, but no relevant bibliometric study has been performed yet. Our bibliometric study aimed to comprehensively summarize and analyze the global scientific output, research hotspots, and trendy topics of publications on GAM over time. Data on publications on GAM were collected using the Web of Science (WoS). The search date was 16 January 2022, and the publications were collected from 2002 to 2021. Totally, 1,224 articles and reviews were incorporated and analyzed in the current study. It showed that the annual publications concerning GAM kept increasing over the past 20 years. The United States had the largest number of publications and total citations. Holland, Kettenmann, and Gutmann were the top three authors in terms of citation frequency. Neuro-oncology represented the most influential journal in GAM studies, with the highest H-index, total citations, and publication numbers. The paper published by Hambardzumyan in 2016 had the highest local citations. Additionally, the analysis of keywords implied that "prognosis," "tumor microenvironment," and "immunotherapy" might become research hotspots. Furthermore, trendy topics in GAM studies suggested that "immune infiltration," "immune microenvironment," "bioinformatics," "prognosis," and "immunotherapy" deserved additional attention. In conclusion, this bibliometric study comprehensively analyzed the publication trend of GAM studies for the past 20 years, in which the research hotspots and trendy topics were also uncovered. This information offered scholars critical references for conducting in-depth studies on GAM in the future.

Prognostic model and immunotherapy prediction based on molecular chaperone-related lncRNAs in lung adenocarcinoma

Introduction: Molecular chaperones and long non-coding RNAs (lncRNAs) have been confirmed to be closely related to the occurrence and development of tumors, especially lung cancer. Our study aimed to construct a kind of molecular chaperone-related long non-coding RNAs (MCRLncs) marker to accurately predict the prognosis of lung adenocarcinoma (LUAD) patients and find new immunotherapy targets.

Methods: In this study, we acquired molecular chaperone genes from two databases, Genecards and molecular signatures database (MsigDB). And then, we downloaded transcriptome data, clinical data, and mutation information of LUAD patients through the Cancer Genome Atlas (TCGA). MCRLncs were determined by Spearman correlation analysis. We used univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to construct risk models. Kaplan-meier (KM) analysis was used to understand the difference in survival between high and low-risk groups. Nomogram, calibration curve, concordance index (C-index) curve, and receiver operating characteristic (ROC) curve were used to evaluate the accuracy of the risk model prediction. In addition, we used gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses to explore the potential biological functions of MCRLncs. Immune microenvironmental landscapes were constructed by using single-sample gene set enrichment analysis (ssGSEA), tumor immune dysfunction and exclusion (TIDE) algorithm, “pRRophetic” R package, and “IMvigor210” dataset. The stem cell index based on mRNAsi expression was used to further evaluate the patient’s prognosis.

Results: Sixteen MCRLncs were identified as independent prognostic indicators in patients with LUAD. Patients in the high-risk group had significantly worse overall survival (OS). ROC curve suggested that the prognostic features of MCRLncs had a good predictive ability for OS. Immune system activation was more pronounced in the high-risk group. Prognostic features of the high-risk group were strongly associated with exclusion and cancer-associated fibroblasts (CAF). According to this prognostic model, a total of 15 potential chemotherapeutic agents were screened for the treatment of LUAD. Immunotherapy analysis showed that the selected chemotherapeutic drugs had potential application value. Stem cell index mRNAsi correlates with prognosis in patients with LUAD.

Conclusion: Our study established a kind of novel MCRLncs marker that can effectively predict OS in LUAD patients and provided a new model for the application of immunotherapy in clinical practice.

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.

HOTAIRM1 Maintained the Malignant Phenotype of tMSCs Transformed by GSCs via E2F7 by Binding to FUS

Objective. Mesenchymal stromal/stem cells (MSCs) are an important part of the glioma microenvironment and are involved in the malignant progression of glioma. In our previous study, we showed that MSCs can be induced to a malignant phenotype (tMSCs) by glioma stem cells (GSCs) in the microenvironment. However, the potential mechanism by which tMSCs maintain their malignant phenotype after malignant transformation has not been fully clarified. Methods. The expression of HOTAIRM1, FUS, and E2F7 was detected by qRT-PCR. Clone formation, EdU, and Transwell assay were used to explore the role of HOTAIRM1, FUS, and E2F7 on the proliferation, migration, and invasion of tMSCs. Bioinformatics analysis and RNA immunoprecipitation were used to explore the relation among HOTAIRM1, FUS, and E2F7. Results. HOTAIRM1 was upregulated in tMSCs compared with MSCs. Loss- and gain-of-function assays showed that HOTAIRM1 promoted the proliferation, migration, and invasion of tMSCs. qRT-PCR and functional assays revealed that E2F7 might be the downstream target of HOTAIRM1. A further study of the mechanism showed that HOTAIRM1 could bind to FUS, an RNA-binding protein (RBP), and thus regulate E2F7, which could promote the malignant phenotype of tMSCs. Conclusion. Our study revealed that the HOTAIRM1/FUS/E2F7 axis is involved in the malignant progression of tMSCs transformed by GSCs in the glioma microenvironment and may function as a novel target for glioma therapy.

Development and Validation of a Novel Prognostic Model for Lower-Grade Glioma Based on Enhancer RNA-Regulated Prognostic Genes

Enhancer RNAs (eRNAs) are present specifically in tumors, where they affect the expression of eRNA-regulated genes (ERGs). Owing to this characteristic, ERGs were hypothesized to improve prognosis of overall survival in heterogeneous low-grade and intermediate-grade gliomas. This study aimed to construct and validate an ERG prognostic tool to facilitate clinical management, and offer more effective diagnostic and therapeutic biomarkers for glioma. Survival-related eRNAs were identified, and their ERGs were selected based on eRNA and target gene information. The ERG prognostic model was constructed and validated using internal and external validation cohorts. Finally, biological differences related to the ERG signature were analysed to explore the potential mechanisms influencing survival outcomes. Thirteen ERGs were identified and used to build an ERG risk signature, which included five super-enhancer RNA (seRNA)-regulated genes and five LGG-specific eRNA-regulated genes. The prognostic nomogram established based on combining the ERG score, age, and sex was evaluated by calibration curves, clinical utility, Harrell’s concordance index (0.86; 95% CI: 0.83-0.90), and time-dependent receiver operator characteristic curves. We also explored potential immune-related mechanisms that might cause variation in survival. The established prognostic model displayed high validity and robustness. Several immune-related genes regulated by seRNAs or specific eRNAs were identified, indicating that these transcripts or their genes were potential targets for improving immunotherapeutic/therapeutic outcomes. The functions of an important specific eRNA-regulated gene (USP28) were validated in robust vitro experiments. In addition, the ERG risk signature was significantly associated with the immune microenvironment and other immune-related features.

Immunolocalization of CD80 and CD86 in Non-Small Cell Lung Carcinoma: CD80 as a Potent Prognostic Factor

It has been demonstrated that tumor cells express programed cell death protein 1 (PD-L1) to escape T lymphocytes that express programed cell protein 1 (PD-1), and PD-1/PD-L1 immune checkpoint inhibitors have been regarded in lung cancer patients. CD80 and CD86 are members of B7 superfamily which regulates T lymphocyte activation and tolerance. However, immunolocalization of CD80 and CD86 has not been examined in the lung carcinoma tissues and their clinical significance remains unknown. Therefore, to clarify clinical significance of CD80 and CD86, we immunolocalized these in 75 non-small cell lung carcinomas (NSCLC) in this study. Immunoreactivities of CD80 and CD86 were mainly detected in tumor-infiltrating macrophages. Immunohistochemical CD80 status was high in 56% of NSCLC, and it was positively associated with stage, pathological T factor, distant metastasis, histological type and PD-L1 status. Moreover, multivariate analysis turned out that the CD80 status was an independent worse prognostic factor. CD86 status was high in 53% of the cases, but it was not significantly associated with any clinicopathological parameters. These findings suggest that CD80 is a potent worse prognostic factor possibly in association with escape from immune attack in NSCLC.

Differential expression of PD‑L1 and PD‑L2 is associated with the tumor microenvironment of TILs and M2 TAMs and tumor differentiation in non‑small cell lung cancer

To improve the treatment strategy of immune-checkpoint inhibitors for non-small cell lung cancer (NSCLC), a comprehensive analysis of programmed death-ligand (PD-L)1 and PD-L2 expression is clinically important. The expression of PD-L1 and PD-L2 on both tumor cells (TCs) and tumor-infiltrating immune cells (ICs) was investigated, with respect to tumor-infiltrating lymphocytes (TILs) and M2 tumor-associated macrophages (TAMs), which are key components of the tumor microenvironment, in 175 patients with resected NSCLC. The TIL and M2 TAM densities were associated with the expression of PD-L1 on the two TCs (both P<0.0001) and ICs (both P<0.0001). The TIL and M2 TAM densities were also associated with the expression of PD-L2 on both TCs (P=0.0494 and P=0.0452, respectively) and ICs (P=0.0048 and P=0.0125, respectively). However, there was no correlation between the percentage of PD-L1-positive TCs and the percentage of PD-L2-positive TCs (r=0.019; P=0.8049). Meanwhile, tumor differentiation was significantly associated with the PD-L1 expression on TCs and ICs (P=0.0002 and P<0.0001, respectively). By contrast, tumor differentiation was inversely associated with the PD-L2 expression on both TCs and ICs (P=0.0260 and P=0.0326, respectively). In conclusion, the combined evaluation of PD-L1 and PD-L2 expression could be clinically important in the treatment strategy of immune-checkpoint inhibitors in patients with NSCLC. In particular, the evaluation of PD-L2 expression may be necessary for patients with PD-L1-negative NSCLC.

A Novel Prognostic Tool for Glioma Based on Enhancer RNA-Regulated Immune Genes

Background: Gliomas are the most malignant tumors of the nervous system. Even though their survival outcome is closely affected by immune-related genes (IRGs) in the tumor microenvironment (TME), the corresponding regulatory mechanism remains poorly characterized.

Methods: Specific enhancer RNAs (eRNAs) can be found in tumors, where they control downstream genes. The present study aimed to identify eRNA-regulated IRGs, evaluate their influence on the TME, and use them to construct a novel prognostic model for gliomas.

Results: Thirteen target genes (ADCYAP1R1, BMP2, BMPR1A, CD4, DDX17, ELN, FGF13, MAPT, PDIA2, PSMB8, PTPN6, SEMA6C, and SSTR5) were identified and integrated into a comprehensive risk signature, which distinguished two risk subclasses. Discrepancies between these subclasses were compared to explore potential mechanisms attributed to eRNA-regulated genes, including immune cell infiltration, clinicopathological features, survival outcomes, and chemotherapeutic drug sensitivity. Furthermore, the risk signature was used to construct a prognostic tool that was evaluated by calibration curve, clinical utility, Harrell’s concordance index (0.87; 95% CI: 0.84–0.90), and time-dependent receiver operator characteristic curves (AUCs: 0.93 and 0.89 at 3 and 5 years, respectively). The strong reliability and robustness of the established prognostic tool were validated in another independent cohort. Finally, potential subtypes were explored in patients with grade III tumors.

Conclusion: Overall, eRNAs were associated with immune-related dysfunctions in the TME. Targeting of IRGs regulated by eRNAs could improve immunotherapeutic/therapeutic outcomes.

Identification and Validation of the Pyroptosis-Related Molecular Subtypes of Lung Adenocarcinoma by Bioinformatics and Machine Learning

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.