Systematic identification, development, and validation of prognostic biomarkers involving the tumor-immune microenvironment for glioblastoma

Understanding the Immune System and Biospecimen-Based Response in Glioblastoma: A Practical Guide to Utilizing Signal Redundancy for Biomarker and Immune Signature Discovery

Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15-20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines. CD markers are surface markers expressed on various cells throughout the body, often associated with immune cells. Cytokines are the effector molecules of the immune system. Together, CD markers and cytokines can serve as useful biomarkers to reflect immune status in patients with GBM. However, there are gaps in the understanding of the intricate interactions between GBM and the peripheral immune system and how these interactions change with standard and immune-modulating treatments. The key to understanding the true nature of these interactions is through multi-omic analysis of tumor progression and treatment response. This review aims to identify potential non-invasive blood-based biomarkers that can contribute to an immune signature through multi-omic approaches, leading to a better understanding of immune involvement in GBM.

Characterization of tumor microenvironment in glioblastoma multiforme identifies ITGB2 as a key immune and stromal related regulator in glial cell types

Glioblastoma multiforme (GBM) is the most aggressive form of brain tumor characterized by inter and intra-tumor heterogeneity and complex tumor microenvironment. To uncover the molecular targets in this milieu, we systematically identified immune and stromal interactions at the glial cell type level that leverages on RNA-sequencing data of GBM patients from The Cancer Genome Atlas. The perturbed genes between the high vs low immune and stromal scored patients were subjected to weighted gene co-expression network analysis to identify the glial cell type specific networks in immune and stromal infiltrated patients. The intramodular connectivity analysis identified the highly connected genes in each module. Combining it with univariable and multivariable prognostic analysis revealed common vital gene ITGB2, between the immune and stromal infiltrated patients enriched in microglia and newly formed oligodendrocytes. We found following unique hub genes in immune infiltrated patients; COL6A3 (microglia), ITGAM (oligodendrocyte precursor cells), TNFSF9 (microglia), and in stromal infiltrated patients, SERPINE1 (microglia) and THBS1 (newly formed oligodendrocytes, oligodendrocyte precursor cells). To validate these hub genes, we used external GBM patient single cell RNA-sequencing dataset and this identified ITGB2 to be significantly enriched in microglia, newly formed oligodendrocytes, T-cells, macrophages and adipocyte cell types in both immune and stromal datasets. The tumor infiltration analysis of ITGB2 showed that it is correlated with myeloid dendritic cells, macrophages, monocytes, neutrophils, B-cells, fibroblasts and adipocytes. Overall, the systematic screening of tumor microenvironment components at glial cell types uncovered ITGB2 as a potential target in primary GBM.

Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning

Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms.

Use of Novel m6A Regulator-mediated Methylation Modification Patterns in Distinct Tumor Microenvironment Profiles to Identify and Predict Glioma Prognosis and Progression, T-cell Dysfunction, and Clinical Response to ICI Immunotherapy

BACKGROUND

The specific functions of RNA N6-methyladenosine (m6A) modifications in the glioma tumor microenvironment (TME) and glioma patient prognosis and treatment have not been determined to date.

OBJECTIVE

The objective of the study was to determine the role of m6A modifications in glioma TME.

METHODS

Nonnegative matrix factorization (NMF) methods were used to determine m6A clusters and m6A gene signatures based on 21 genes relating to m6A modifications. TME characteristics for each m6A cluster and m6A gene signature were quantified by established m6A score. The utility of m6A score was validated in immunotherapy and other antiangiogenic treatment cohorts.

RESULTS

Three m6A clusters were identified among 3,395 glioma samples, and they were linked to different biological activities and clinical outcomes. The m6A clusters were highly consistent with immune profiles known as immune-inflamed, immune-excluded, and immune-desert phenotypes. Clusters within individual tumors could predict glioma inflammation, molecular subtypes, TME stromal activity, genetic variation, alternative splicing, and prognosis. As for the m6A score and m6A gene signature, patients with low m6A scores exhibited an increased tumor mutation burden, immune activity, neoantigen load, and prolonged survival. A low m6A score indicated the potential for a low level of T-cell dysfunction, a considerably better treatment response, and durable clinical benefits from immunotherapy, bevacizumab and regorafenib.

CONCLUSION

Glioma m6A clusters and gene signatures have distinctive TME features. The m6A gene signature may guide prognostic assessments and promote the use of effective strategies.

Five-gene signature for the prediction of response to immune checkpoint inhibitors in patients with gastric and urothelial carcinomas

BACKGROUND

Ample evidence supports the potential of programmed death-ligand 1 (PD-L1) expression, detected by immunohistochemistry, as a predictive biomarker for immunotherapy in patients with advanced cancers. To predict the response to immune checkpoint inhibitors in patients with gastric and urothelial carcinomas, we aimed to replace PD-L1 combined positive score (CPS) with CD274 mRNA in the original four-gene signature and PD-L1 CPS model developed by us.

METHOD

We used quantitative real-time polymerase chain reaction (qRT-PCR) to measure the expression levels of five target genes in a cohort of 49 patients (33 with gastric cancer and 16 with urothelial carcinoma) who had received immunotherapy and whose therapeutic responses were available. The predictive performance was evaluated using R package maxstat.

RESULTS

Cutoff values of mRNA expression level were measured using the log-rank statistics for progression-free survival (PFS). Based on these cutoffs, immunotherapy responses were predicted and sorted into responder (n = 12, 24.5%) and non-responder (n = 37, 75.5%) groups. The median PFS values of predicted responders and non-responders were 14.8 months (95% confidence interval [CI]: 0-34.7) and 4.7 months (95% CI: 1.0-8.4, p = 0.02), respectively. Among the 12 predicted responders, 10 had microsatellite-stable tumors with a low tumor mutational burden. The actual clinical responses (complete and partial) were higher in the responder group than those in the non-responder group: 83.3% and 16.2%, respectively.

CONCLUSION

We modified a predictive biomarker for CD274 mRNA expression to predict the response to immunotherapy in patients with gastric or urothelial carcinomas.

Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.

Methods

CIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.

Results

In total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.

Conclusion

The seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.

Comprehensive Characterization of Immune Landscape Based on Tumor Microenvironment for Oral Squamous Cell Carcinoma Prognosis

Objective: This study aims to identify an immune-related signature to predict clinical outcomes of oral squamous cell carcinoma (OSCC) patients. Methods: Gene transcriptome data of both tumor and normal tissues from OSCC and the corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA). Tumor Immune Estimation Resource algorithm (ESTIMATE) was used to calculate the immune/stromal-related scores. The immune/stromal scores and associated clinical characteristics of OSCC patients were evaluated. Univariate Cox proportional hazards regression analyses, least absolute shrinkage, and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses were performed to assess the prognostic prediction capacity. Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) function annotation were used to analysis the functions of TME-related genes. Results: Eleven predictor genes were identified in the immune-related signature and overall survival (OS) in the high-risk group was significantly shorter than in the low-risk group. An ROC analysis showed the TME-related signature could predict the total OS of OSCC patients. Moreover, GSEA and GO function annotation proved that immunity and immune-related pathways were mainly enriched in the high-risk group. Conclusions: We identified an immune-related signature that was closely correlated with the prognosis and immune response of OSCC patients. This signature may have important implications for improving the clinical survival rate of OSCC patients and provide a potential strategy for cancer immunotherapy.

Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations

Glioblastoma multiforme (GBM) is the most common and aggressive form of brain cancer. Prognosis evaluation is of great significance in guiding individualized treatment and monitoring of GBM. By integrating different prognostic variables, nomograms simplify the statistical risk prediction model into numerical estimates for death or recurrence, and are hence widely applied in prognosis prediction. In the past two decades, the application of high-throughput profiling technology and the establishment of TCGA database and other public data deposits have provided opportunities to identify cancer-related molecules and prognostic biomarkers. As a result, both molecular features and clinical characteristics of cancer have been reported to be the key factors in nomogram model construction. This article comprehensively reviewed 35 studies of GBM nomograms, analyzed the present situation of GBM nomograms, and discussed the role and significance of nomograms in personalized risk assessment and clinical treatment decision-making. To facilitate the application of nomograms in the prognostic prediction of GBM patients, a website has been established for the online access of nomograms based on the studies of this review, which is called Consensus Nomogram Spectrum for Glioblastoma (CNSgbm) and is accessible through https://bioinfo.henu.edu.cn/nom/NomList.jsp.

CCL19 has potential to be a potential prognostic biomarker and a modulator of tumor immune microenvironment (TIME) of breast cancer: a comprehensive analysis based on TCGA database

The development of cancer was determined by not only the intrinsic properties of cancer cells, but also the communication between cancer cells and tumor microenvironment (TME). We applied ESTIMATE and CIBERSORT algorithms to calculate the immune/stromal component and tumor-infiltrating immune cells (TICs) in TME of BC. The results showed that immune component in TME predicted patients’ survival and associated with progression of BC. Differentially expressed genes (DEGs) were primarily enriched in immune-related activities. Finally, CCL19 was acquired which shared the leading nodes in PPI network and was associated with patients’ survival. High expression of CCL19 predicted better prognosis and participated in progression of BC. Genes in CCL19 up-regulated group were enriched in immune-related activities and these functions might depend on the communications between CCL19 and multiple TICs in TIME. In conclusion, CCL19 functioned as a potential prognostic biomarker and a modulator of TIME in BC through communicating with various TICs.

Integrated analysis of single-cell RNA-seq dataset and bulk RNA-seq dataset constructs a prognostic model for predicting survival in human glioblastoma

BACKGROUND

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. For patients with GBM, the median overall survival (OS) is 14.6 months and the 5-year survival rate is 7.2%. It is imperative to develop a reliable model to predict the survival probability in new GBM patients. To date, most prognostic models for predicting survival in GBM were constructed based on bulk RNA-seq dataset, which failed to accurately reflect the difference between tumor cores and peripheral regions, and thus show low predictive capability. An effective prognostic model is desperately needed in clinical practice.

METHODS

We studied single-cell RNA-seq dataset and The Cancer Genome Atlas-glioblastoma multiforme (TCGA-GBM) dataset to identify differentially expressed genes (DEGs) that impact the OS of GBM patients. We then applied the least absolute shrinkage and selection operator (LASSO) Cox penalized regression analysis to determine the optimal genes to be included in our risk score prognostic model. Then, we used another dataset to test the accuracy of our risk score prognostic model.

RESULTS

We identified 2128 DEGs from the single-cell RNA-seq dataset and 6461 DEGs from the bulk RNA-seq dataset. In addition, 896 DEGs associated with the OS of GBM patients were obtained. Five of these genes (LITAF, MTHFD2, NRXN3, OSMR, and RUFY2) were selected to generate a risk score prognostic model. Using training and validation datasets, we found that patients in the low-risk group showed better OS than those in the high-risk group. We validated our risk score model with the training and validating datasets and demonstrated that it can effectively predict the OS of GBM patients.

CONCLUSION

We constructed a novel prognostic model to predict survival in GBM patients by integrating a scRNA-seq dataset and a bulk RNA-seq dataset. Our findings may advance the development of new therapeutic targets and improve clinical outcomes for GBM patients.

Glioblastoma: Relationship between Metabolism and Immunosuppressive Microenvironment

Glioblastoma (GBM) is the most aggressive brain tumor in adults and is characterized by an immunosuppressive microenvironment. Different factors shaping this tumor microenvironment (TME) regulate tumor initiation, progression, and treatment response. Genetic alterations and metabolism pathways are two main elements that influence tumor immune cells and TME. In this manuscript, we review how both factors can contribute to an immunosuppressive state and overview the strategies being tested.

Identification of Immune-Related Risk Characteristics and Prognostic Value of Immunophenotyping in TNBC

Background: Triple-negative breast cancer (TNBC) is not sensitive to targeted therapy with HER-2 monoclonal antibody and endocrine therapy due to lack of ER, PR, and HER-2 receptors. TNBC is a breast cancer subtype with the worst prognosis and the highest mortality rate compared with other subtypes. Materials and Methods: Breast cancer-related data were retrieved from The Cancer Genome Atlas (TCGA) database, and 116 cases of triple-negative breast cancer were identified from the data. GSE31519 dataset was retrieved from Gene Expression Omnibus (GEO) database, comprising a total of 68 cases with TNBC. Survival analysis was performed based on immune score, infiltration score and mutation score to explore differences in prognosis of different immune types. Analysis of differentially expressed genes was conducted and GSEA analysis based on these genes was conducted to explore the potential mechanism. Results: The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that MMP9, CXCL9, CXCL10, CXCL11 and CD7 are key genes that may affect immune typing of TNBC patients and play an important role in prediction of prognosis in TNBC patients. Conclusion: The current study presents an evaluation system based on immunophenotyping, which provides a more accurate prognostic evaluation tool for TNBC patients. Differentially expressed genes can be targeted to improve treatment of TNBC.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Construction and Validation of an Immune-Based Prognostic Model for Pancreatic Adenocarcinoma Based on Public Databases

Background

Pancreatic adenocarcinoma (PAAD) is a highly lethal and aggressive tumor with poor prognoses. The predictive capability of immune-related genes (IRGs) in PAAD has yet to be explored. We aimed to explore prognostic-related immune genes and develop a prediction model for indicating prognosis in PAAD.

Methods

The messenger (m)RNA expression profiles acquired from public databases were comprehensively integrated and differentially expressed genes were identified. Univariate analysis was utilized to identify IRGs that related to overall survival. Whereafter, a multigene signature in the Cancer Genome Atlas cohort was established based on the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Moreover, a transcription factors regulatory network was constructed to reveal potential molecular processes in PAAD. PAAD datasets downloaded from the Gene Expression Omnibus database were applied for the validations. Finally, correlation analysis between the prognostic model and immunocyte infiltration was investigated.

Results

Totally, 446 differentially expressed immune-related genes were screened in PAAD tissues and normal tissues, of which 43 IRGs were significantly related to the overall survival of PAAD patients. An immune-based prognostic model was developed, which contained eight IRGs. Univariate and multivariate Cox regression revealed that the risk score model was an independent prognostic indicator in PAAD (HR > 1, P < 0.001). Besides, the sensitivity of the model was evaluated by the receiver operating characteristic curve analysis. Finally, immunocyte infiltration analysis revealed that the eight-gene signature possibly played a pivotal role in the status of the PAAD immune microenvironment.

Conclusion

A novel prognostic model based on immune genes may serve to characterize the immune microenvironment and provide a basis for PAAD immunotherapy.

The expression and prognostic value of the epidermal growth factor receptor family in glioma

BACKGROUND

The epidermal growth factor receptor (EGFR) family belongs to the transmembrane protein receptor of the tyrosine kinase I subfamily and has 4 members: EGFR/ERBB1, ERBB2, ERBB3, and ERBB4. The EGFR family is closely related to the occurrence and development of a variety of cancers.

MATERIALS/METHODS

In this study, we used multiple online bioinformatics websites, including ONCOMINE, TCGA, CGGA, TIMER, cBioPortal, GeneMANIA and DAVID, to study the expression profiles, prognostic values and immune infiltration correlations of the EGFR family in glioma.

RESULTS

We found that EGFR and ERBB2 mRNA expression levels were higher in glioblastoma (GBM, WHO IV) than in other grades (WHO grade II & III), while the ERBB3 and ERBB4 mRNA expression levels were the opposite. EGFR and ERBB2 were notably downregulated in IDH mutant gliomas, while ERBB3 and ERBB4 were upregulated, which was associated with a poor prognosis. In addition, correlation analysis between EGFR family expression levels and immune infiltrating levels in glioma showed that EGFR family expression and immune infiltrating levels were significantly correlated. The PPI network of the EGFR family in glioma and enrichment analysis showed that the EGFR family and its interactors mainly participated in the regulation of cell motility, involving integrin receptors and Rho family GTPases.

CONCLUSIONS

In summary, the results of this study indicate that the EGFR family members may become potential therapeutic targets and new prognostic markers for glioma.

MYD88 Is a Potential Prognostic Gene and Immune Signature of Tumor Microenvironment for Gliomas

Purpose

To explore the profiles of immune and stromal components of the tumor microenvironment (TME) and their related key genes in gliomas.

Methods

We applied bioinformatic techniques to identify the core gene that participated in the regulation of the TME of the gliomas. And immunohistochemistry staining was used to calculate the gene expressions in clinical cases.

Results

The CIBERSORT and ESTIMATE were used to figure out the composition of TME in 698 glioma cases from The Cancer Genome Atlas (TCGA) database. Differential expression analysis identified 2103 genes between the high and the low-score group. Then the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, univariate Cox regression analysis, and protein–protein interaction (PPI) network construction were conducted based on these genes. MYD88 was identified as the key gene by the combination univariate Cox and PPI analysis. Furthermore, MYD88 expression was significantly associated with the overall survival and WHO grade of glioma patients. The genes in the high-expression MYD88 group were mainly in immune-related pathways in the Gene Set Enrichment Analysis (GSEA). We found that macrophage M2 accounted for the largest portion with an average of 27.6% in the glioma TIICs and was associated with high expression of MYD88. The results were verified in CGGA database and clinical cases in our hospital. Furthermore, we also found the MYD88 expression was higher in IDH1 wild types. The methylation rate was lower in high grade gliomas.

Conclusion

MYD88 had predictive prognostic value in glioma patients by influencing TIICs dysregulation especially the M2-type macrophages.

A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state.

Research Supporting a Pilot Study of Metronomic Dapsone during Glioblastoma Chemoirradiation

This short note presents previous research data supporting a pilot study of metronomic dapsone during the entire course of glioblastoma treatment. The reviewed data indicate that neutrophils are an integral part of human glioblastoma pathophysiology, contributing to or facilitating glioblastoma growth and treatment resistance. Neutrophils collect within glioblastoma by chemotaxis along several chemokine/cytokine gradients, prominently among which is interleukin-8. Old data from dermatology research has shown that the old and inexpensive generic drug dapsone inhibits neutrophils' chemotaxis along interleukin-8 gradients. It is on that basis that dapsone is used to treat neutrophilic dermatoses, for example, dermatitis herpetiformis, bullous pemphigoid, erlotinib-related rash, and others. The hypothesis of this paper is that dapsone will reduce glioblastomas' neutrophil accumulations by the same mechanisms by which it reduces dermal neutrophil accumulations in the neutrophilic dermatoses. Dapsone would thereby reduce neutrophils' contributions to glioblastoma growth. Dapsone is not an ideal drug, however. It generates methemoglobinemia that occasionally is symptomatic. This generation is reduced by concomitant use of the antacid drug cimetidine. Given the uniform lethality of glioblastoma as of 2020, the risks of dapsone 100 mg twice daily and cimetidine 400 mg twice daily is low enough to warrant a judicious pilot study.

Six Immune Associated Genes Construct Prognostic Model Evaluate Low-Grade Glioma

Background

The immunotherapy of Glioma has always been a research hotspot. Although tumor associated microglia/macrophages (TAMs) proves to be important in glioma progression and drug resistance, our knowledge about how TAMs influence glioma remains unclear. The relationship between glioma and TAMs still needs further study.

Methods

We collected the data of TAMs in glioma from NCBI Gene Expression Omnibus (GEO) that included 20 glioma samples and 15 control samples from four datasets. Six genes were screened from the Differential Expression Gene through Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network and single-cell sequencing analysis. A risk score was then constructed based on the six genes and patients' overall survival rates of 669 patients from The Cancer Genome Atlas (TCGA). The efficacy of the risk score in prognosis and prediction was verified in Chinese Glioma Genome Atlas (CGGA).

Results

Six genes, including CD163, FPR3, LPAR5, P2ry12, PLAUR, SIGLEC1, that participate in signal transduction and plasma membrane were selected. Half of them, like CD163, FPR3, SIGLEC1, were mainly expression in M2 macrophages. FPR3 and SIGLEC1 were high expression genes in glioma associated with grades and IDH status. The overall survival rates of the high risk score group was significantly lower than that of the low risk score group, especially in LGG.

Conclusion

Joint usage of the 6 candidate genes may be an effective method to diagnose and evaluate the prognosis of glioma, especially in Low-grade glioma (LGG).