Mapping Glioma Progression: Single-Cell RNA Sequencing Illuminates Cell-Cell Interactions and Immune Response Variability

Mapping Glioma Progression: Single-Cell RNA Sequencing Illuminates Cell-Cell Interactions and Immune Response Variability | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mapping Glioma Progression: Single-Cell RNA Sequencing Illuminates Cell-Cell Interactions and Immune Response Variability Xia Li, Shenbo Chen, Ming Ding, Hui Ding, Kun Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4959179/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background: Glioma, the most common primary tumor of the central nervous system, is marked by significant heterogeneity, presenting major challenges for therapeutic approaches and prognostic evaluations. This study explores the interactions between malignant glioma cells and macrophages/monocytes and their influence on tumor progression and treatment responses, using comprehensive single-cell RNA sequencing analysis. Methods: We integrated RNA-seq data from the TCGA and CGGA databases and performed an in-depth analysis of glioma samples using single-cell RNA sequencing, functional enrichment analysis, developmental trajectory analysis, cell-cell communication analysis, and gene regulatory network analysis. Furthermore, we developed a prognostic model based on risk scores and assessed its predictive performance through immune cell infiltration analysis and evaluation of immune treatment responses. Results: We identified 14 distinct glioma cellular subpopulations and 7 primary cell types, alongside 4 macrophage/monocyte subtypes. Developmental trajectory analysis provided insights into the origins and heterogeneity of both malignant cells and macrophages/monocytes. Cell communication analysis revealed that macrophages and monocytes interact with malignant cells through several pathways, including the MIF (Macrophage Migration Inhibitory Factor) and SPP1 (Secreted Phosphoprotein 1) pathways, engaging in key ligand-receptor interactions that influence tumor behavior. Stratification based on these communication characteristics showed a significant correlation with overall survival (OS). Additionally, immune cell infiltration analysis highlighted variations in immune cell abundance across different subgroups, which may be linked to differing responses to immunotherapy. Our predictive model, consisting of 29 prognostic genes, demonstrated high accuracy and robustness across multiple independent cohorts. Conclusion: This study unveils the intricate heterogeneity of the glioma microenvironment, enhancing our understanding of the diverse characteristics of glioma cell subpopulations. It also lays the groundwork for the development of therapeutic strategies and prognostic models that specifically target the glioma microenvironment. Glioma Single-cell RNA Sequencing Cell-cell Communication Prognostic Model Immune Microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Glioma is the most common primary brain tumor in the central nervous system, and it is typically characterized by significant heterogeneity, leading to a generally poor prognosis 1 . Current treatment options for glioma primarily include surgical resection, radiotherapy, and chemotherapy. However, complete surgical removal is often challenging due to the tumor's invasive nature and its critical location within the brain. Radiotherapy uses high-energy radiation to destroy or inhibit the growth of tumor cells, while chemotherapy employs drugs to target and kill these cells 2 . In addition, emerging therapies such as targeted therapy, immunotherapy, and gene therapy are making rapid strides, offering new hope for improving outcomes 3 , 4 . In recent years, with the growing understanding of the tumor microenvironment (TME) in cancer, the complex and crucial roles of immune cells within the TME have come into sharper focus. Macrophages, in particular, exhibit dual functionality within the TME. On one hand, they can exert anti-tumor effects by phagocytosing tumor cells and secreting pro-inflammatory cytokines 5 ; on the other hand, they can be co-opted by tumor cells to adopt an immunosuppressive M2 phenotype, which secretes anti-inflammatory cytokines and other factors that promote tumor growth. Additionally, macrophages contribute to tumor invasion and metastasis through the secretion of enzymes such as matrix metalloproteinases (MMPs) 6 . Monocytes, another pivotal immune cell type, can migrate into the TME and differentiate into macrophages or other immune cells 7 . They play a significant role in inflammatory responses and immune regulation by secreting cytokines and chemokines, which influence the behavior of tumor cells and the recruitment of other immune cells. Immunomodulatory molecules further add to the complexity of the TME. These molecules include a variety of cytokines, chemokines, immune checkpoint molecules, and enzymes, which can be produced by tumor cells, immune cells, or other cells within the microenvironment. These molecules modulate the activation, proliferation, and function of immune cells, thereby regulating the intensity and duration of the immune response. For example, immune checkpoint molecules like PD-L1 can inhibit T-cell-mediated immune responses 8 , while pro-inflammatory cytokines like TNF-α and IL-1β may promote inflammation and tumor progression 9 , 10 . Collectively, macrophages, monocytes, and immunomodulatory molecules play pivotal roles in shaping the tumor and its immune microenvironment, creating a complex regulatory network that deeply impacts tumor development, immune response, and the efficacy of treatments 11 . Therefore, it is crucial to delve deeper into the cell-to-cell communication between malignant glioma cells and macrophages/monocytes, as well as to understand its functional implications. In this study, we first elucidate the heterogeneity of glioma in relation to macrophages and monocytes, further clarifying the complex characteristics of their interactions and identifying key communication pathways. We also classify gliomas to explore the variations across different classifications. Finally, we develop a prognostic model aimed at predicting patient survival, offering a potential tool for improving clinical outcomes. Methods Acquisition of Glioma RNA-seq and scRNA-seq Datasets We obtained glioma RNA-seq data (TCGA-LGG/GBM), which included 704 tumor samples and 5 normal adjacent samples, from the TCGA database using the TCGAbiolinks R package. Additionally, we retrieved two glioma RNA-seq datasets (CGGA-693, CGGA-325) from the CGGA database website ( http://www.cgga.org.cn/ ), consisting of 693 and 325 tumor samples, respectively. We also acquired four public glioma scRNA-seq datasets (GSE103224, GSE131928, GSE138794, GSE139448) from the TISCH2 database ( http://tisch.comp-genomics.org/home/ ). The TCGA-LGG/GBM dataset was used as the training set for model construction, while the CGGA-693 and CGGA-325 datasets served as validation sets for subsequent model validation. Single-Cell RNA Sequencing Analysis Single-cell RNA sequencing analysis was performed using the Seurat R package 12 . Single-cell data quality control was conducted based on the criteria of nFeature_RNA < 9000 & percent.mt < 25, with the remaining parameters set to default. After normalization using the "NormalizeData" function, the top 2000 highly variable features for each dataset were identified using the "FindVariableFeatures" function. Batch effect correction was performed using the harmony R package. Dimensionality reduction and visualization were carried out using the heuristic method and the UMAP algorithm. Malignant cells and macrophages/monocytes were identified and annotated based on the annotation information provided by the TISCH database using the "FindClusters" function. Functional Enrichment Analysis Functional enrichment analysis was conducted using the RunEnrichment R package 13 for Gene Ontology (GO) functional enrichment analysis and the clusterProfiler R package 14 for gene set enrichment analysis (GSEA) to assess significant differences between various tumor subtypes. The GSVA R package 15 was utilized for gene set variation analysis (GSVA) to determine the enrichment of pathways in different scoring groups of the scoring model. Cancer hallmarks were obtained from the msigdb database. Developmental Trajectory Analysis, Cell-cell Communication, and Gene Regulatory Networks (GRNs) After identifying malignant cells and macrophages/monocytes, developmental trajectory analysis was performed using the RunSlingshot R software package ( https://zhanghao-njmu.github.io/SCP/reference/RunSlingshot ). The interactions between cell subpopulations and the active ligands on malignant cells and their target cells were calculated using the CellChat & NicheNet R packages 16 , 17 , based on the expression of known ligands, receptors, and their cofactors. GRN analysis was conducted using the SCENIC R package 18 . Unsupervised Consensus Clustering Based on the genes characteristic of the communication features of glioma malignant cells and macrophages/monocytes, unsupervised consensus clustering was performed on the TCGA-LGG/GBM dataset using the ConsensusClusterPlus R package 19 . A consensus matrix was generated, and samples with high similarity were grouped into a cluster. The optimal number of clusters was determined using the cumulative distribution function (CDF) curve and the partitioning around medoids (PAM) silhouette score. Immune Cell Infiltration and Immune Treatment Response Analysis Immune cell infiltration analysis was conducted on bulk RNA-seq datasets using eight TME deconvolution algorithms built into the IOBR R package ( https://github.com/IOBR/IOBR ): CIBERSORT, TIMER, xCell, MCPcounter, ESTIMATE, EPIC, IPS, quanTIseq. Cytolytic activity (CYT) and IFNG scores were calculated using the ssGSEA algorithm. Additionally, the immune response and scores in the TCGA-LGG/GBM data were predicted using the TIDE online analysis ( http://tide.dfci.harvard.edu/ ). Acquisition of Immunomodulatory Molecules A total of 150 immunomodulators and chemokines were downloaded from the TISIDB database ( http://cis.hku.hk/TISIDB/ ), including 41 chemokines, 24 immunoinhibitors, 46 immunostimulators, 21 MHC molecules, and 18 receptors. Prognostic Model Construction and Validation In the training set, differentially expressed genes (DEGs) were obtained using the RunDEtest R package ( https://zhanghao-njmu.github.io/SCP/reference/RunDEtest ). The number of genes was reduced using LASSO regression analysis, followed by the construction of a predictive model using multivariate Cox regression analysis. Glioma samples were divided into high-risk and low-risk groups based on the risk score. The overall survival (OS) between the two groups was determined using the Kaplan-Meier curve. The performance of the model was estimated using the receiver operating characteristic (ROC) analysis and validated in the validation set using the R packages "survival", "survminer" and "timeROC" 20 , 21 , 22 . Statistical Analysis All statistical analyses were performed using R software (version 4.3.3). For paired independent samples, the Wilcoxon test was used for comparison. The Spearman or Pearson correlation analysis was employed to evaluate the relationship between two continuous variables. Survival differences between the two groups were determined using the Kaplan-Meier curve and the log-rank test. The performance of variables in predicting survival was assessed using the ROC curve. Statistical significance was set at a two-tailed p-value < 0.05. Results In-depth Single-Cell RNA Sequencing Analysis of Gliomas We performed a comprehensive single-cell RNA sequencing analysis of glioma samples by integrating data from four independent datasets (GSE103224, GSE131928, GSE138794, GSE139448), resulting in a compilation of data from 62,670 single cells. After a rigorous standardization and quality control process, we observed that the number of features (nFeature_RNA) and RNA counts (nCount_RNA) ranged from thousands to tens of thousands of transcripts (Fig. 1 A). A heuristic approach was used to determine the optimal dimensionality of the datasets (Fig. 1 B). From the merged dataset, we selected 2,000 highly variable features, with the top eight being MALAT1, TPSB2, CD74, SPP1, TPSAB1, APOE, and CRYGS (Fig. 1 C). UMAP (Uniform Manifold Approximation and Projection) was utilized to display the distribution of single cells from the four datasets in a two-dimensional space (Fig. 1 D). Further dimensionality reduction and clustering of the glioma single-cell data allowed us to successfully identify 28 distinct subpopulations (Fig. 1 E, F). Through detailed annotation of these subpopulations, we identified 14 major cell types, including Malignant cells, Neurons, Oligodendrocytes, Astrocytes, and others (Fig. 1 G). To validate our cell type annotations, we employed violin plots to demonstrate the expression patterns of cell type-specific marker genes. These markers, including CHCHD2P6, CLDN5, C1QB, MDM2, TRBC2, KNG1, MATR3, DLX6-AS1, and CHI3L1, exhibited distinct and high expression levels in their respective cell types (Fig. 1 H). Heterogeneity and Developmental Trajectory Analysis of Malignant Cells and Macrophages/Monocytes in Gliomas In our detailed examination of the heterogeneity within malignant cell populations in gliomas, we discovered that these cells are not uniform but instead comprise multiple subpopulations with distinct developmental trajectories. The developmental trajectory analysis revealed that NB-like Malignant cells are situated at the basal part of the trajectory, suggesting they may serve as the origin for other subpopulations (Fig. 2 A, B). By comparing gene expression across these subpopulations, we identified differentially expressed genes unique to each group. Figure 2 C highlights the top 10 differentially expressed genes for seven identified subpopulations. Additionally, we performed Gene Ontology Biological Process (GO_BP) enrichment analysis for each malignant cell subpopulation. Figure 4 D illustrates the top five most enriched GO_BP terms for four malignant cell subpopulations, which include processes such as wound healing, detoxification of copper ion, and oxidative phosphorylation. We also conducted a comprehensive analysis of macrophages and monocytes within the glioma microenvironment. The UMAP analysis identified four distinct macrophage and monocyte subtypes, labeled as MacroMono_0, MacroMono_1, MacroMono_2, and MacroMono_3 (Fig. 2 E). The developmental trajectory diagram indicated that MacroMono_1 might be the progenitor of the other subtypes (Fig. 2 F, G). Furthermore, we identified characteristic genes specific to each macrophage and monocyte subpopulation (Fig. 2 H). Cell Communication and Key Pathways Between Macrophages/Monocytes and Malignant Cells in Gliomas Our exploration of cell-to-cell communication within the glioma microenvironment revealed a complex network of intercellular signaling pathways between macrophages/monocytes and malignant cells. The number and intensity of these interactions are depicted in Fig. 3 A, where MacroMono_1 shows relatively low activity and fewer interactions with other cells. To further investigate the molecular mechanisms underlying these interactions, we conducted an in-depth analysis of ligand-receptor pairs between macrophages/monocytes and malignant cells. Key ligand-receptor interactions identified include MIF binding with CD74 + CXCR4 or CD74 + CD44, and SPP1 binding with CD44 or ITGAV + ITGB1 (Fig. 3 B). Further pathway analysis highlighted the MIF signaling pathway's differential expression levels among various cell subpopulations within the glioma tumor microenvironment (Fig. 3 C). Figures 3 D and 3 E underscore the significant role and higher intensity of MacroMono_0, MacroMono_2, and MES-like malignant cells in the MIF signaling pathway, suggesting their dominant influence and potential impact on tumor progression and treatment response. Additionally, our study identified the SPP1 signaling network as a critical component of the interaction between macrophages/monocytes and malignant cells, with notable activity in MacroMono_0, MacroMono_3, and MES-like malignant cells (Figs. 3 F, G, H). Ligand-Receptor-Target Networks and Gene Regulatory Networks (GRNs) in the Tumor Microenvironment (TME) of Gliomas Utilizing the NicheNet tool, we analyzed ligands expressed by macrophages and monocytes within the glioma tumor microenvironment. Several ligands, including TGFB1, TGAM, VEGFA, HBEGF, IL1B, and PLXNB2, were identified as highly associated, with their expression levels in the TME potentially having a significant impact on tumor cell behavior. The interactions between ligand-receptor pairs from macrophages/monocytes and malignant cells offer insights into how these immune cells communicate with tumor cells via ligand secretion, potentially influencing the biological functions of the malignant cells. For instance, TGFB1 secreted by macrophages/monocytes can bind to EGFR and ERBB3 receptors on malignant cells, thereby activating downstream signaling pathways that promote tumor progression. A heatmap visualizes the regulatory potential of the top-ranked ligands and their downstream target genes in malignant cells. Notably, TGFB1 not only affects the expression of its direct receptors but also modulates other genes involved in tumor growth and immune regulation, such as SERPING1 and HLA-A (Fig. 4 A). Further investigation into the gene regulatory networks (GRNs) in gliomas revealed distinct regulatory modules, including IKZF1_extended_30g, HDAC2_extended_141g, HDAC2_52g, and ZNF454_extended_69g, which exhibit varying activities and functions across different cell types (Figs. 4 B, C). Subgroup Stratification of the Tumor Microenvironment (TME) in Gliomas and Its Prognostic and Biological Characteristics Based on the identified subpopulation characteristics, we employed an unsupervised consensus clustering method to stratify gliomas into two subpopulations. The optimal number of clusters was determined using the CDF curve and PAC scores (Figs. 5 A, B, C). Survival analysis indicated that the second cluster was associated with a poorer overall survival (OS) (Fig. 5 D). Using the ssGSEA method, we further evaluated the relative infiltration abundance of 28 immune cell subpopulations within the two glioma subpopulations. The results showed that, compared to C1, C2 exhibited a significantly higher number of infiltrating immune cells, all with statistical significance (Fig. 5 E). Additionally, analysis of immune therapy response between the two clusters revealed that the activity of three immune therapy predictive factors—IFN-γ, GEP, and CYT—was significantly elevated in C2 (Fig. 5 F). Subgroup Stratification of the Tumor Microenvironment (TME) in Gliomas and Its Distinct TME Characteristics Utilizing various algorithms, including CIBERSORT, MCP-counter, quanTIseq, EPIC, and TIMER, we assessed the infiltration abundance of immune cell subpopulations within the two glioma subpopulations. The analysis revealed that several immune cell subpopulations, such as macrophages, exhibited high infiltration levels in C2, while NK cells were more prominently expressed in C1 (Fig. 6 A). GSVA was employed to evaluate the activity of the anti-cancer immune cycle and immune therapy predictive pathways between the two subpopulations. The results demonstrated significant differences in the activity of the anti-cancer immune cycle, with C2 showing a markedly higher enrichment score compared to C1. Additionally, there were notable differences in the activity of several immune therapy-related pathways, where C1 had significantly lower enrichment scores than C2, possibly reflecting variations in their anti-tumor immune responses (Figs. 6 B, C). Figure 6 D presents the expression levels of 150 immune regulatory factors across the two subpopulations. Moreover, GSEA identified significant differences in the activity of various cancer-related pathways between the two subpopulations, such as the interferon response, muscle generation, estrogen response, inflammatory response, and KRAS signaling pathway (Fig. 6 E). Glioma Prognostic Model Exhibits High Accuracy and Robust Performance in Predicting Prognosis We further developed a prognostic model for glioma using LASSO regression analysis (Fig. 7 A). The model ultimately incorporated 29 relevant genes, with the coefficients for each gene illustrated in Fig. 7 B. Based on the risk scores, patients from the TCGA dataset were stratified into two groups. Survival analysis indicated that the low-risk group had significantly better survival outcomes. The prognostic model was also validated using the CGGA-693 and CGGA-325 datasets, demonstrating excellent predictive capability and accuracy. The ROC values at 1, 3, and 5 years were all above 0.9, underscoring the high accuracy and robust performance of our prognostic model in predicting patient outcomes (Figs. 7 C, D, E). Association of Risk Score with Immune Checkpoints, Immune Cell Infiltration Levels, and Upregulated and Downregulated Pathways in Glioma Patients A high-risk score was found to be negatively correlated with inhibitory immune checkpoints and the abundance of immune cell infiltration (Figs. 8 A, B). Key immune checkpoints, such as VTCN1, TIGIT, CD200R1, and BTLA, are crucial molecules that modulate immune responses and play significant roles in tumor immune evasion and immunotherapy. The negative correlation between a high-risk score and these immune checkpoints suggests that these checkpoints may have a positive role in the anti-glioma immune response (Fig. 8 A). Additionally, a high-risk score was negatively correlated with the abundance of immune cell infiltration, providing insights into the role of different cell types in glioma progression and patient prognosis (Fig. 8 B). Pathway analysis revealed that in high-risk glioma patients, the upregulated pathways predominantly involved Staphylococcus aureus infection, Systemic Lupus Erythematosus, and ECM-receptor interaction. In contrast, the downregulated pathways were mainly related to Nicotine Addiction, Glutamatergic Synapse, and GABAergic Synapse (Figs. 8 C, D). Discussion In this study, we conducted an in-depth single-cell RNA sequencing analysis to explore the intricate heterogeneity of glioma, focusing particularly on malignant cells and macrophages/monocytes within the tumor microenvironment. Our results not only reaffirm the diversity of immune cells previously observed in the glioma microenvironment but also, through developmental trajectory analysis, suggest possible origins and differentiation pathways for malignant cells, providing new perspectives on the biological behavior of glioma. The subpopulations of malignant cells and the distinct macrophage/monocyte subtypes we identified highlight the significant heterogeneity within the glioma microenvironment. Notably, the identification of the NB-like Malignant cell population suggests its potential role as an initiator in tumorigenesis. This aligns with the cancer stem cell theory, which posits that tumor-initiating cells are pivotal in tumor initiation, progression, and therapeutic resistance 23 . Additionally, our developmental trajectory analysis offers insights into the differentiation pathways of malignant cells, consistent with findings by Verhaak et al., who identified molecular subtypes of glioma through large-scale genomic analysis 24 . Our analysis of cell-to-cell communication uncovered the complex interactions between macrophages/monocytes and malignant cells. The identification of the MIF and SPP1 signaling pathways, in particular, offers new targets for glioma therapy. The immunomodulatory role of MIF in various tumors is well-documented, and our study further underscores its critical function within the glioma microenvironment 25 , 26 . Similarly, SPP1, an important extracellular matrix protein, has been implicated in the adhesion, migration, and invasion of tumor cells 27 , 28 . Our investigation into gene regulatory networks (GRNs) revealed several transcription factors, such as IKZF1 and HDAC2, that may play crucial roles in glioma development. The aberrant activity of these transcription factors is closely linked to the proliferation, survival, and invasion of tumor cells, making them potential therapeutic targets for glioma. Using unsupervised consensus clustering, we stratified gliomas into two subgroups with distinct survival outcomes. The higher levels of immune cell infiltration and enhanced immune treatment response in the C2 subgroup suggest that it may be more responsive to immunotherapy. This observation is consistent with the findings of Economopoulou et al., who emphasized the role of the immune microenvironment in tumor immune evasion and response to immunotherapy 29 . The prognostic model we developed demonstrated high accuracy and robustness in predicting glioma patient survival. This model not only serves as a valuable tool for clinical management but also identifies genes that could be targets for future research and drug development. The abnormal expression of these genes is closely associated with glioma invasiveness, therapeutic resistance, and prognosis 30 , 31 , 32 , 33 , 34 , 35 . Moreover, the negative correlation between high-risk scores and immune checkpoints and immune cell infiltration levels highlights the significant role of the immune microenvironment in glioma prognosis. This finding aligns with the concept of immune editing, which suggests that tumors can evade immune surveillance by modulating the immune microenvironment 36 . Additionally, the expression of immune checkpoint molecules may serve as biomarkers for predicting patient responsiveness to immunotherapy, offering opportunities for personalized treatment. Despite the valuable insights gained from our study on glioma heterogeneity and the interactions within the tumor microenvironment, there are several limitations. Firstly, the sample size in our study is relatively limited, and future research should validate our findings with a larger cohort and across more diverse populations in terms of ethnicity and geographic distribution. Secondly, our results need to be corroborated by prospective clinical trials to ensure their robustness. Future studies should also delve deeper into the interactions between malignant cell subpopulations and macrophage/monocyte subtypes and how these interactions influence glioma progression and treatment response. In conclusion, our study, leveraging single-cell RNA sequencing and comprehensive analysis, has illuminated the complex interactions between malignant cells and immune cells within the glioma microenvironment. The prognostic model we developed, alongside our analysis of cell-cell communication and gene regulatory networks, offers new strategies for personalized glioma treatment. These findings not only enhance our understanding of glioma heterogeneity but also provide valuable information for future research and therapeutic approaches. However, our research does come with certain limitations: (1) The dataset size used in this study was limited, and while we have drawn meaningful conclusions from the available data, a larger dataset would likely yield more robust and generalizable results; (2) The study lacks external validation, as the findings have not been corroborated by additional independent studies, which could impact the strength and reliability of our conclusions; (3) The results may not be broadly generalizable due to the specific characteristics of the dataset, and further research with more diverse samples is needed to confirm the applicability of these findings across different contexts. Declarations ACKNOWLEDGMENTS This work was supported by the South China Sea Rising Star Science and Technology Innovation Talent Platform Project(NHXXRCXM202351). CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. 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Engrailed 2 (EN2) acts as a glioma suppressor by inhibiting tumor proliferation/invasion and enhancing sensitivity to temozolomide. Cancer Cell Int . 2020;20:65. doi:10.1186/s12935-020-1145-y Dunn GP, Bruce AT, Ikeda H, Old LJ, Schreiber RD. Cancer immunoediting: from immunosurveillance to tumor escape. Nat Immunol . 2002;3(11):991-998. doi:10.1038/ni1102-991 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Sep, 2024 Reviews received at journal 18 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviews received at journal 17 Sep, 2024 Reviewers agreed at journal 11 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviewers agreed at journal 10 Sep, 2024 Reviewers agreed at journal 09 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers invited by journal 06 Sep, 2024 Editor assigned by journal 03 Sep, 2024 Submission checks completed at journal 30 Aug, 2024 First submitted to journal 22 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4959179","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356605084,"identity":"b394583b-866f-4631-937c-3c6237f8c7ca","order_by":0,"name":"Xia Li","email":"","orcid":"","institution":"Wanning People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Li","suffix":""},{"id":356605086,"identity":"e11f2b94-e61f-4996-861e-e2a15b1292a7","order_by":1,"name":"Shenbo Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shenbo","middleName":"","lastName":"Chen","suffix":""},{"id":356605087,"identity":"9818a3bc-a2ed-4649-ab5c-2bfbeb42182b","order_by":2,"name":"Ming Ding","email":"","orcid":"","institution":"Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Ding","suffix":""},{"id":356605089,"identity":"d14c70e2-cd32-4ab2-b998-ae28d912e100","order_by":3,"name":"Hui Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACe2YGBiCSkONn73/4IKGihrAWw2aIFmPJnjPMBg/OHCOsxeAAWAtD4oYbPmySD1uYidBynPfw68I2C2ODG7zHKhIb2Bj427sT8Gs5zJdmPbNNQk7ydl/ajcQdMgwSZ85uIKCFx8yYt03CmO/OAbMbiWfYGAwkconTkthwI8GsILGNmSgtxo9BWibcyDFjIEqLYTOPGTPPOVAgH0uWSDhzjIegX+z5zxh/5imrA0Zl88GPPypq5Pjbe/FrAQI2CWQeDyHlIMD8gRhVo2AUjIJRMIIBAOl2SVCYrRTWAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Ding","suffix":""},{"id":356605092,"identity":"793022ce-63ac-4114-ba37-b38ec66e93d8","order_by":4,"name":"Kun Yang","email":"","orcid":"","institution":"The First Affiliated Hospital of Hainan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-08-22 15:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4959179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4959179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65434412,"identity":"385995f0-9647-40a8-83eb-594afefadc80","added_by":"auto","created_at":"2024-09-27 12:10:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1649680,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlioma scRNA-seq datasets integration, processing and annotation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Single-cell sequencing depth and counts and fraction of reads in glioma samples.\u003c/p\u003e\n\u003cp\u003eD: UMAP visualization of 62670 glioma single cells from four scRNA-seq datasets.\u003c/p\u003e\n\u003cp\u003eB: Determine the “dimensionality” of the dataset by a heuristic method.\u003c/p\u003e\n\u003cp\u003eC: The highly variable features in the merged scRNA-seq dataset.\u003c/p\u003e\n\u003cp\u003eE,F: UMAP visualization of 62670 cells from four public glioma scRNA-seq cohorts and a total of 28 subclusters were identified under the resolution of 0.6.\u003c/p\u003e\n\u003cp\u003eG: 14 major cell types were manually annotated.\u003c/p\u003e\n\u003cp\u003eH: Vlnplots illustrating the expression values of cell type-specific markers.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/21448f8f9bf457487a16d03c.png"},{"id":65434473,"identity":"1dbfadf3-4016-4e20-8abc-779ff28346f6","added_by":"auto","created_at":"2024-09-27 12:10:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1280412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003escRNA-seq analysis unravels the heterogeneity of in malignant cells, Macrocytes and Monocytes in glioma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, B: The predicted developmental trajectories of malignant cell subsets.\u003c/p\u003e\n\u003cp\u003eC: The differentially expressed genes of each malignant cell subset.\u003c/p\u003e\n\u003cp\u003eD: Top five enriched GO_BP terms of each malignant cell subset.\u003c/p\u003e\n\u003cp\u003eE: UMAP visualization of four subtypes of macrocytes and monocytes in glioma.\u003c/p\u003e\n\u003cp\u003eF, G: The predicted developmental trajectories of malignant cell subsets.\u003c/p\u003e\n\u003cp\u003eH: Volcano plot showing the differentially expressed genes of each subset.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/28ae6fa2d8f79c30823d93cb.png"},{"id":65434407,"identity":"f2a122cc-410b-4b24-83e0-52012fa42943","added_by":"auto","created_at":"2024-09-27 12:10:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1441362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntercellular communications between Macrocytes/Monocytes and malignant cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: The intercellular interactions between subsets of Macrocytes/Monocytes and malignant cells.\u003c/p\u003e\n\u003cp\u003eB: The ligand-receptor pairs between Macrocytes/Monocytes and malignant cells.\u003c/p\u003e\n\u003cp\u003eC: Expression profiles of MIF signaling pathway in Macrocytes/Monocytes and malignant cells.\u003c/p\u003e\n\u003cp\u003eD: The importance of each subset of Macrocytes/Monocytes and malignant cells in the MIF signaling pathway.\u003c/p\u003e\n\u003cp\u003eE: The incoming/outgoing strength of each subset of Macrocytes/Monocytes and malignant cells in the MIF signaling pathway (left) and the whole signaling pathways (right).\u003c/p\u003e\n\u003cp\u003eF: Expression profiles of SPP1 signaling pathway in Macrocytes/Monocytes and malignant cells.\u003c/p\u003e\n\u003cp\u003eG: The importance of each subset of Macrocytes/Monocytes and malignant cells in the SPP1 signaling pathway.\u003c/p\u003e\n\u003cp\u003eH: The incoming/outgoing strength of each subset of Macrocytes/Monocytes and malignant cells in the SPP1 signaling pathway.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/5da37c4b8244de21b4c990f5.png"},{"id":65437972,"identity":"05284ad7-fc14-4466-bfc9-ab51879122aa","added_by":"auto","created_at":"2024-09-27 12:18:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":383491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ligands-receptors-targets and gene regulatory networks (GRNs) in the TME of glioma according to NicheNet.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Dot plots, Ligand-receptor and Heatmap showing\u003c/p\u003e\n\u003cp\u003eB: UMAP visualization of the five regulons at single-cell level of glioma.\u003c/p\u003e\n\u003cp\u003eC: Heatmap demonstrated the activity of each regulon in macrophage/monocyte and malignant cells.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/4f1ce76154e887a33666523d.png"},{"id":65434469,"identity":"a7672a38-8f8b-46a7-b832-a78030550ae9","added_by":"auto","created_at":"2024-09-27 12:10:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":688925,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignature stratifies glioma TME into two subclusters with distinct prognosis and biological features.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: The consensus score matrix of all samples when k = 2. A higher consensus score denotes higher similarity.\u003c/p\u003e\n\u003cp\u003eB: The CDF curves of the consensus matrix for each k (indicated by colors).\u003c/p\u003e\n\u003cp\u003eC: The PAC score for each k.\u003c/p\u003e\n\u003cp\u003eD: KM survival curves with log-rank test demonstrate survival discrepancies between two clusters.\u003c/p\u003e\n\u003cp\u003eE: Relative infiltration abundances of 28 immune cell subsets determined by ssGSEA method in two clusters. ns: non-significant; * p \u0026lt; 0.05; *** p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eF: The activities of CYT, GFP, and IFNG between two clusters.\u003c/p\u003e","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/a23ec0c80230f5c6c70697a4.png"},{"id":65434341,"identity":"fd0dfbd3-abcd-42ec-9643-6a86985c49ee","added_by":"auto","created_at":"2024-09-27 12:10:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1578970,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignature stratifies glioma TME into two subclusters with distinct TME landscapes and distinct dysregulated pathways.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: The infiltration abundance of immune cell subsets evaluated by CIBERSORT, MCP-counter, quanTIseq, EPIC, and TIMER for two clusters.\u003c/p\u003e\n\u003cp\u003eB: The activities of anti-cancer immunity between two clusters by GSVA.\u003c/p\u003e\n\u003cp\u003eC: The activities of immunotherapy-predicted pathways between two clusters by GSVA.\u003c/p\u003e\n\u003cp\u003eD: The expression abundances of immunoregulators for two clusters.\u003c/p\u003e\n\u003cp\u003eE: Upregulated cancer hallmarks in the two clusters by GSEA. * p \u0026lt; 0.05, ** p \u0026lt; 0.01, **** p \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/155e6dc9b664d3e08d23f054.png"},{"id":65434485,"identity":"49fc22be-f98a-4dd2-a7b9-8648c0faa922","added_by":"auto","created_at":"2024-09-27 12:10:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":667262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignature-based model demonstrates high accuracy and robust performance in predicting prognosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: The selection of prognostic signature genes based on the optimal parameter λ that was obtained in the LASSO regression analysis.\u003c/p\u003e\n\u003cp\u003eB: Lollipop chart of the coefficients of signature genes.\u003c/p\u003e\n\u003cp\u003eC-E: K-M curves displayed survival outcomes of patients in two risk groups. Time-dependent ROC curves were drawn to assess survival rate at 1-year, 3-year, and 5-year.\u003c/p\u003e","description":"","filename":"fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/1d79d2a59cd8bdc2c9841a34.png"},{"id":65434374,"identity":"29962078-37bf-4b63-b2fc-42645a770362","added_by":"auto","created_at":"2024-09-27 12:10:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":833119,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of Immune Checkpoints, Immune Infiltration, and Pathway among riskscore groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA: Correlations between RiskScore and immune checkpoints\u003c/p\u003e\n\u003cp\u003eB: infiltration levels of 22 immune cell subsets determined by CIBERSROT method\u003c/p\u003e\n\u003cp\u003eC: Upregulated pathways in high-risk glioma patients.\u003c/p\u003e\n\u003cp\u003eD: Downregulated pathways in high-risk glioma patients.\u003c/p\u003e","description":"","filename":"fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/ba003c127deaabef75f3e618.png"},{"id":65437984,"identity":"60cf6500-e8c1-4eee-8e3e-853e6393e219","added_by":"auto","created_at":"2024-09-27 12:18:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9537189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4959179/v1/f650db6f-34f5-4d46-a7f9-a6b68ac44416.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping Glioma Progression: Single-Cell RNA Sequencing Illuminates Cell-Cell Interactions and Immune Response Variability","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioma is the most common primary brain tumor in the central nervous system, and it is typically characterized by significant heterogeneity, leading to a generally poor prognosis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Current treatment options for glioma primarily include surgical resection, radiotherapy, and chemotherapy. However, complete surgical removal is often challenging due to the tumor's invasive nature and its critical location within the brain. Radiotherapy uses high-energy radiation to destroy or inhibit the growth of tumor cells, while chemotherapy employs drugs to target and kill these cells\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In addition, emerging therapies such as targeted therapy, immunotherapy, and gene therapy are making rapid strides, offering new hope for improving outcomes\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn recent years, with the growing understanding of the tumor microenvironment (TME) in cancer, the complex and crucial roles of immune cells within the TME have come into sharper focus. Macrophages, in particular, exhibit dual functionality within the TME. On one hand, they can exert anti-tumor effects by phagocytosing tumor cells and secreting pro-inflammatory cytokines\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e; on the other hand, they can be co-opted by tumor cells to adopt an immunosuppressive M2 phenotype, which secretes anti-inflammatory cytokines and other factors that promote tumor growth. Additionally, macrophages contribute to tumor invasion and metastasis through the secretion of enzymes such as matrix metalloproteinases (MMPs)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Monocytes, another pivotal immune cell type, can migrate into the TME and differentiate into macrophages or other immune cells\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. They play a significant role in inflammatory responses and immune regulation by secreting cytokines and chemokines, which influence the behavior of tumor cells and the recruitment of other immune cells. Immunomodulatory molecules further add to the complexity of the TME. These molecules include a variety of cytokines, chemokines, immune checkpoint molecules, and enzymes, which can be produced by tumor cells, immune cells, or other cells within the microenvironment. These molecules modulate the activation, proliferation, and function of immune cells, thereby regulating the intensity and duration of the immune response. For example, immune checkpoint molecules like PD-L1 can inhibit T-cell-mediated immune responses\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, while pro-inflammatory cytokines like TNF-α and IL-1β may promote inflammation and tumor progression\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Collectively, macrophages, monocytes, and immunomodulatory molecules play pivotal roles in shaping the tumor and its immune microenvironment, creating a complex regulatory network that deeply impacts tumor development, immune response, and the efficacy of treatments\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTherefore, it is crucial to delve deeper into the cell-to-cell communication between malignant glioma cells and macrophages/monocytes, as well as to understand its functional implications. In this study, we first elucidate the heterogeneity of glioma in relation to macrophages and monocytes, further clarifying the complex characteristics of their interactions and identifying key communication pathways. We also classify gliomas to explore the variations across different classifications. Finally, we develop a prognostic model aimed at predicting patient survival, offering a potential tool for improving clinical outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of Glioma RNA-seq and scRNA-seq Datasets\u003c/h2\u003e \u003cp\u003eWe obtained glioma RNA-seq data (TCGA-LGG/GBM), which included 704 tumor samples and 5 normal adjacent samples, from the TCGA database using the TCGAbiolinks R package. Additionally, we retrieved two glioma RNA-seq datasets (CGGA-693, CGGA-325) from the CGGA database website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cgga.org.cn/\u003c/span\u003e\u003cspan address=\"http://www.cgga.org.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), consisting of 693 and 325 tumor samples, respectively. We also acquired four public glioma scRNA-seq datasets (GSE103224, GSE131928, GSE138794, GSE139448) from the TISCH2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/home/\u003c/span\u003e\u003cspan address=\"http://tisch.comp-genomics.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The TCGA-LGG/GBM dataset was used as the training set for model construction, while the CGGA-693 and CGGA-325 datasets served as validation sets for subsequent model validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell RNA Sequencing Analysis\u003c/h2\u003e \u003cp\u003eSingle-cell RNA sequencing analysis was performed using the Seurat R package\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Single-cell data quality control was conducted based on the criteria of nFeature_RNA\u0026thinsp;\u0026lt;\u0026thinsp;9000 \u0026amp; percent.mt\u0026thinsp;\u0026lt;\u0026thinsp;25, with the remaining parameters set to default. After normalization using the \"NormalizeData\" function, the top 2000 highly variable features for each dataset were identified using the \"FindVariableFeatures\" function. Batch effect correction was performed using the harmony R package. Dimensionality reduction and visualization were carried out using the heuristic method and the UMAP algorithm. Malignant cells and macrophages/monocytes were identified and annotated based on the annotation information provided by the TISCH database using the \"FindClusters\" function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis was conducted using the RunEnrichment R package\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e for Gene Ontology (GO) functional enrichment analysis and the clusterProfiler R package\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e for gene set enrichment analysis (GSEA) to assess significant differences between various tumor subtypes. The GSVA R package\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e was utilized for gene set variation analysis (GSVA) to determine the enrichment of pathways in different scoring groups of the scoring model. Cancer hallmarks were obtained from the msigdb database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDevelopmental Trajectory Analysis, Cell-cell Communication, and Gene Regulatory Networks (GRNs)\u003c/h2\u003e \u003cp\u003eAfter identifying malignant cells and macrophages/monocytes, developmental trajectory analysis was performed using the \u003cb\u003eRunSlingshot\u003c/b\u003e R software package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zhanghao-njmu.github.io/SCP/reference/RunSlingshot\u003c/span\u003e\u003cspan address=\"https://zhanghao-njmu.github.io/SCP/reference/RunSlingshot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The interactions between cell subpopulations and the active ligands on malignant cells and their target cells were calculated using the CellChat \u0026amp; NicheNet R packages\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, based on the expression of known ligands, receptors, and their cofactors. GRN analysis was conducted using the SCENIC R package\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eUnsupervised Consensus Clustering\u003c/h2\u003e \u003cp\u003eBased on the genes characteristic of the communication features of glioma malignant cells and macrophages/monocytes, unsupervised consensus clustering was performed on the TCGA-LGG/GBM dataset using the ConsensusClusterPlus R package\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. A consensus matrix was generated, and samples with high similarity were grouped into a cluster. The optimal number of clusters was determined using the cumulative distribution function (CDF) curve and the partitioning around medoids (PAM) silhouette score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmune Cell Infiltration and Immune Treatment Response Analysis\u003c/h2\u003e \u003cp\u003eImmune cell infiltration analysis was conducted on bulk RNA-seq datasets using eight TME deconvolution algorithms built into the IOBR R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/IOBR/IOBR\u003c/span\u003e\u003cspan address=\"https://github.com/IOBR/IOBR\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e): CIBERSORT, TIMER, xCell, MCPcounter, ESTIMATE, EPIC, IPS, quanTIseq.\u0026nbsp;Cytolytic activity (CYT) and IFNG scores were calculated using the ssGSEA algorithm. Additionally, the immune response and scores in the TCGA-LGG/GBM data were predicted using the TIDE online analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of Immunomodulatory Molecules\u003c/h2\u003e \u003cp\u003eA total of 150 immunomodulators and chemokines were downloaded from the TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including 41 chemokines, 24 immunoinhibitors, 46 immunostimulators, 21 MHC molecules, and 18 receptors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Model Construction and Validation\u003c/h2\u003e \u003cp\u003eIn the training set, differentially expressed genes (DEGs) were obtained using the RunDEtest R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zhanghao-njmu.github.io/SCP/reference/RunDEtest\u003c/span\u003e\u003cspan address=\"https://zhanghao-njmu.github.io/SCP/reference/RunDEtest\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The number of genes was reduced using LASSO regression analysis, followed by the construction of a predictive model using multivariate Cox regression analysis. Glioma samples were divided into high-risk and low-risk groups based on the risk score. The overall survival (OS) between the two groups was determined using the Kaplan-Meier curve. The performance of the model was estimated using the receiver operating characteristic (ROC) analysis and validated in the validation set using the R packages \"survival\", \"survminer\" and \"timeROC\"\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.3). For paired independent samples, the Wilcoxon test was used for comparison. The Spearman or Pearson correlation analysis was employed to evaluate the relationship between two continuous variables. Survival differences between the two groups were determined using the Kaplan-Meier curve and the log-rank test. The performance of variables in predicting survival was assessed using the ROC curve. Statistical significance was set at a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIn-depth Single-Cell RNA Sequencing Analysis of Gliomas\u003c/h2\u003e \u003cp\u003eWe performed a comprehensive single-cell RNA sequencing analysis of glioma samples by integrating data from four independent datasets (GSE103224, GSE131928, GSE138794, GSE139448), resulting in a compilation of data from 62,670 single cells. After a rigorous standardization and quality control process, we observed that the number of features (nFeature_RNA) and RNA counts (nCount_RNA) ranged from thousands to tens of thousands of transcripts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). A heuristic approach was used to determine the optimal dimensionality of the datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). From the merged dataset, we selected 2,000 highly variable features, with the top eight being MALAT1, TPSB2, CD74, SPP1, TPSAB1, APOE, and CRYGS (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). UMAP (Uniform Manifold Approximation and Projection) was utilized to display the distribution of single cells from the four datasets in a two-dimensional space (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Further dimensionality reduction and clustering of the glioma single-cell data allowed us to successfully identify 28 distinct subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). Through detailed annotation of these subpopulations, we identified 14 major cell types, including Malignant cells, Neurons, Oligodendrocytes, Astrocytes, and others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). To validate our cell type annotations, we employed violin plots to demonstrate the expression patterns of cell type-specific marker genes. These markers, including CHCHD2P6, CLDN5, C1QB, MDM2, TRBC2, KNG1, MATR3, DLX6-AS1, and CHI3L1, exhibited distinct and high expression levels in their respective cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity and Developmental Trajectory Analysis of Malignant Cells and Macrophages/Monocytes in Gliomas\u003c/h2\u003e \u003cp\u003eIn our detailed examination of the heterogeneity within malignant cell populations in gliomas, we discovered that these cells are not uniform but instead comprise multiple subpopulations with distinct developmental trajectories. The developmental trajectory analysis revealed that NB-like Malignant cells are situated at the basal part of the trajectory, suggesting they may serve as the origin for other subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). By comparing gene expression across these subpopulations, we identified differentially expressed genes unique to each group. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC highlights the top 10 differentially expressed genes for seven identified subpopulations. Additionally, we performed Gene Ontology Biological Process (GO_BP) enrichment analysis for each malignant cell subpopulation. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD illustrates the top five most enriched GO_BP terms for four malignant cell subpopulations, which include processes such as wound healing, detoxification of copper ion, and oxidative phosphorylation. We also conducted a comprehensive analysis of macrophages and monocytes within the glioma microenvironment. The UMAP analysis identified four distinct macrophage and monocyte subtypes, labeled as MacroMono_0, MacroMono_1, MacroMono_2, and MacroMono_3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The developmental trajectory diagram indicated that MacroMono_1 might be the progenitor of the other subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, G). Furthermore, we identified characteristic genes specific to each macrophage and monocyte subpopulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCell Communication and Key Pathways Between Macrophages/Monocytes and Malignant Cells in Gliomas\u003c/h2\u003e \u003cp\u003eOur exploration of cell-to-cell communication within the glioma microenvironment revealed a complex network of intercellular signaling pathways between macrophages/monocytes and malignant cells. The number and intensity of these interactions are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, where MacroMono_1 shows relatively low activity and fewer interactions with other cells. To further investigate the molecular mechanisms underlying these interactions, we conducted an in-depth analysis of ligand-receptor pairs between macrophages/monocytes and malignant cells. Key ligand-receptor interactions identified include MIF binding with CD74\u0026thinsp;+\u0026thinsp;CXCR4 or CD74\u0026thinsp;+\u0026thinsp;CD44, and SPP1 binding with CD44 or ITGAV\u0026thinsp;+\u0026thinsp;ITGB1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Further pathway analysis highlighted the MIF signaling pathway's differential expression levels among various cell subpopulations within the glioma tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Figures\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE underscore the significant role and higher intensity of MacroMono_0, MacroMono_2, and MES-like malignant cells in the MIF signaling pathway, suggesting their dominant influence and potential impact on tumor progression and treatment response. Additionally, our study identified the SPP1 signaling network as a critical component of the interaction between macrophages/monocytes and malignant cells, with notable activity in MacroMono_0, MacroMono_3, and MES-like malignant cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, G, H).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLigand-Receptor-Target Networks and Gene Regulatory Networks (GRNs) in the Tumor Microenvironment (TME) of Gliomas\u003c/h2\u003e \u003cp\u003eUtilizing the NicheNet tool, we analyzed ligands expressed by macrophages and monocytes within the glioma tumor microenvironment. Several ligands, including TGFB1, TGAM, VEGFA, HBEGF, IL1B, and PLXNB2, were identified as highly associated, with their expression levels in the TME potentially having a significant impact on tumor cell behavior. The interactions between ligand-receptor pairs from macrophages/monocytes and malignant cells offer insights into how these immune cells communicate with tumor cells via ligand secretion, potentially influencing the biological functions of the malignant cells. For instance, TGFB1 secreted by macrophages/monocytes can bind to EGFR and ERBB3 receptors on malignant cells, thereby activating downstream signaling pathways that promote tumor progression. A heatmap visualizes the regulatory potential of the top-ranked ligands and their downstream target genes in malignant cells. Notably, TGFB1 not only affects the expression of its direct receptors but also modulates other genes involved in tumor growth and immune regulation, such as SERPING1 and HLA-A (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Further investigation into the gene regulatory networks (GRNs) in gliomas revealed distinct regulatory modules, including IKZF1_extended_30g, HDAC2_extended_141g, HDAC2_52g, and ZNF454_extended_69g, which exhibit varying activities and functions across different cell types (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Stratification of the Tumor Microenvironment (TME) in Gliomas and Its Prognostic and Biological Characteristics\u003c/h2\u003e \u003cp\u003eBased on the identified subpopulation characteristics, we employed an unsupervised consensus clustering method to stratify gliomas into two subpopulations. The optimal number of clusters was determined using the CDF curve and PAC scores (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, C). Survival analysis indicated that the second cluster was associated with a poorer overall survival (OS) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Using the ssGSEA method, we further evaluated the relative infiltration abundance of 28 immune cell subpopulations within the two glioma subpopulations. The results showed that, compared to C1, C2 exhibited a significantly higher number of infiltrating immune cells, all with statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Additionally, analysis of immune therapy response between the two clusters revealed that the activity of three immune therapy predictive factors\u0026mdash;IFN-γ, GEP, and CYT\u0026mdash;was significantly elevated in C2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup Stratification of the Tumor Microenvironment (TME) in Gliomas and Its Distinct TME Characteristics\u003c/h2\u003e \u003cp\u003eUtilizing various algorithms, including CIBERSORT, MCP-counter, quanTIseq, EPIC, and TIMER, we assessed the infiltration abundance of immune cell subpopulations within the two glioma subpopulations. The analysis revealed that several immune cell subpopulations, such as macrophages, exhibited high infiltration levels in C2, while NK cells were more prominently expressed in C1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). GSVA was employed to evaluate the activity of the anti-cancer immune cycle and immune therapy predictive pathways between the two subpopulations. The results demonstrated significant differences in the activity of the anti-cancer immune cycle, with C2 showing a markedly higher enrichment score compared to C1. Additionally, there were notable differences in the activity of several immune therapy-related pathways, where C1 had significantly lower enrichment scores than C2, possibly reflecting variations in their anti-tumor immune responses (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD presents the expression levels of 150 immune regulatory factors across the two subpopulations. Moreover, GSEA identified significant differences in the activity of various cancer-related pathways between the two subpopulations, such as the interferon response, muscle generation, estrogen response, inflammatory response, and KRAS signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGlioma Prognostic Model Exhibits High Accuracy and Robust Performance in Predicting Prognosis\u003c/h2\u003e \u003cp\u003eWe further developed a prognostic model for glioma using LASSO regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The model ultimately incorporated 29 relevant genes, with the coefficients for each gene illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB. Based on the risk scores, patients from the TCGA dataset were stratified into two groups. Survival analysis indicated that the low-risk group had significantly better survival outcomes. The prognostic model was also validated using the CGGA-693 and CGGA-325 datasets, demonstrating excellent predictive capability and accuracy. The ROC values at 1, 3, and 5 years were all above 0.9, underscoring the high accuracy and robust performance of our prognostic model in predicting patient outcomes (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D, E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAssociation of Risk Score with Immune Checkpoints, Immune Cell Infiltration Levels, and Upregulated and Downregulated Pathways in Glioma Patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA high-risk score was found to be negatively correlated with inhibitory immune checkpoints and the abundance of immune cell infiltration (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, B). Key immune checkpoints, such as VTCN1, TIGIT, CD200R1, and BTLA, are crucial molecules that modulate immune responses and play significant roles in tumor immune evasion and immunotherapy. The negative correlation between a high-risk score and these immune checkpoints suggests that these checkpoints may have a positive role in the anti-glioma immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Additionally, a high-risk score was negatively correlated with the abundance of immune cell infiltration, providing insights into the role of different cell types in glioma progression and patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Pathway analysis revealed that in high-risk glioma patients, the upregulated pathways predominantly involved Staphylococcus aureus infection, Systemic Lupus Erythematosus, and ECM-receptor interaction. In contrast, the downregulated pathways were mainly related to Nicotine Addiction, Glutamatergic Synapse, and GABAergic Synapse (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted an in-depth single-cell RNA sequencing analysis to explore the intricate heterogeneity of glioma, focusing particularly on malignant cells and macrophages/monocytes within the tumor microenvironment. Our results not only reaffirm the diversity of immune cells previously observed in the glioma microenvironment but also, through developmental trajectory analysis, suggest possible origins and differentiation pathways for malignant cells, providing new perspectives on the biological behavior of glioma.\u003c/p\u003e \u003cp\u003eThe subpopulations of malignant cells and the distinct macrophage/monocyte subtypes we identified highlight the significant heterogeneity within the glioma microenvironment. Notably, the identification of the NB-like Malignant cell population suggests its potential role as an initiator in tumorigenesis. This aligns with the cancer stem cell theory, which posits that tumor-initiating cells are pivotal in tumor initiation, progression, and therapeutic resistance\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Additionally, our developmental trajectory analysis offers insights into the differentiation pathways of malignant cells, consistent with findings by Verhaak et al., who identified molecular subtypes of glioma through large-scale genomic analysis\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur analysis of cell-to-cell communication uncovered the complex interactions between macrophages/monocytes and malignant cells. The identification of the MIF and SPP1 signaling pathways, in particular, offers new targets for glioma therapy. The immunomodulatory role of MIF in various tumors is well-documented, and our study further underscores its critical function within the glioma microenvironment\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Similarly, SPP1, an important extracellular matrix protein, has been implicated in the adhesion, migration, and invasion of tumor cells\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur investigation into gene regulatory networks (GRNs) revealed several transcription factors, such as IKZF1 and HDAC2, that may play crucial roles in glioma development. The aberrant activity of these transcription factors is closely linked to the proliferation, survival, and invasion of tumor cells, making them potential therapeutic targets for glioma.\u003c/p\u003e \u003cp\u003eUsing unsupervised consensus clustering, we stratified gliomas into two subgroups with distinct survival outcomes. The higher levels of immune cell infiltration and enhanced immune treatment response in the C2 subgroup suggest that it may be more responsive to immunotherapy. This observation is consistent with the findings of Economopoulou et al., who emphasized the role of the immune microenvironment in tumor immune evasion and response to immunotherapy\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe prognostic model we developed demonstrated high accuracy and robustness in predicting glioma patient survival. This model not only serves as a valuable tool for clinical management but also identifies genes that could be targets for future research and drug development. The abnormal expression of these genes is closely associated with glioma invasiveness, therapeutic resistance, and prognosis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e,\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Moreover, the negative correlation between high-risk scores and immune checkpoints and immune cell infiltration levels highlights the significant role of the immune microenvironment in glioma prognosis. This finding aligns with the concept of immune editing, which suggests that tumors can evade immune surveillance by modulating the immune microenvironment\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Additionally, the expression of immune checkpoint molecules may serve as biomarkers for predicting patient responsiveness to immunotherapy, offering opportunities for personalized treatment.\u003c/p\u003e \u003cp\u003eDespite the valuable insights gained from our study on glioma heterogeneity and the interactions within the tumor microenvironment, there are several limitations. Firstly, the sample size in our study is relatively limited, and future research should validate our findings with a larger cohort and across more diverse populations in terms of ethnicity and geographic distribution. Secondly, our results need to be corroborated by prospective clinical trials to ensure their robustness. Future studies should also delve deeper into the interactions between malignant cell subpopulations and macrophage/monocyte subtypes and how these interactions influence glioma progression and treatment response.\u003c/p\u003e \u003cp\u003eIn conclusion, our study, leveraging single-cell RNA sequencing and comprehensive analysis, has illuminated the complex interactions between malignant cells and immune cells within the glioma microenvironment. The prognostic model we developed, alongside our analysis of cell-cell communication and gene regulatory networks, offers new strategies for personalized glioma treatment. These findings not only enhance our understanding of glioma heterogeneity but also provide valuable information for future research and therapeutic approaches. However, our research does come with certain limitations: (1) The dataset size used in this study was limited, and while we have drawn meaningful conclusions from the available data, a larger dataset would likely yield more robust and generalizable results; (2) The study lacks external validation, as the findings have not been corroborated by additional independent studies, which could impact the strength and reliability of our conclusions; (3) The results may not be broadly generalizable due to the specific characteristics of the dataset, and further research with more diverse samples is needed to confirm the applicability of these findings across different contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the South China Sea Rising Star Science and Technology Innovation Talent Platform Project(NHXXRCXM202351).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNicholson JG, Fine HA. 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Cancer immunoediting: from immunosurveillance to tumor escape. \u003cem\u003eNat Immunol\u003c/em\u003e. 2002;3(11):991-998. doi:10.1038/ni1102-991\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Glioma, Single-cell RNA Sequencing, Cell-cell Communication, Prognostic Model, Immune Microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4959179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4959179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Glioma, the most common primary tumor of the central nervous system, is marked by significant heterogeneity, presenting major challenges for therapeutic approaches and prognostic evaluations. This study explores the interactions between malignant glioma cells and macrophages/monocytes and their influence on tumor progression and treatment responses, using comprehensive single-cell RNA sequencing analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We integrated RNA-seq data from the TCGA and CGGA databases and performed an in-depth analysis of glioma samples using single-cell RNA sequencing, functional enrichment analysis, developmental trajectory analysis, cell-cell communication analysis, and gene regulatory network analysis. Furthermore, we developed a prognostic model based on risk scores and assessed its predictive performance through immune cell infiltration analysis and evaluation of immune treatment responses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified 14 distinct glioma cellular subpopulations and 7 primary cell types, alongside 4 macrophage/monocyte subtypes. Developmental trajectory analysis provided insights into the origins and heterogeneity of both malignant cells and macrophages/monocytes. Cell communication analysis revealed that macrophages and monocytes interact with malignant cells through several pathways, including the MIF (Macrophage Migration Inhibitory Factor) and SPP1 (Secreted Phosphoprotein 1) pathways, engaging in key ligand-receptor interactions that influence tumor behavior. Stratification based on these communication characteristics showed a significant correlation with overall survival (OS). Additionally, immune cell infiltration analysis highlighted variations in immune cell abundance across different subgroups, which may be linked to differing responses to immunotherapy. Our predictive model, consisting of 29 prognostic genes, demonstrated high accuracy and robustness across multiple independent cohorts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study unveils the intricate heterogeneity of the glioma microenvironment, enhancing our understanding of the diverse characteristics of glioma cell subpopulations. It also lays the groundwork for the development of therapeutic strategies and prognostic models that specifically target the glioma microenvironment.\u003c/p\u003e","manuscriptTitle":"Mapping Glioma Progression: Single-Cell RNA Sequencing Illuminates Cell-Cell Interactions and Immune Response Variability","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-27 11:05:39","doi":"10.21203/rs.3.rs-4959179/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-20T05:06:03+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-18T13:20:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-18T03:17:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-17T22:01:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"32441916240864918986488243246146512589","date":"2024-09-11T22:46:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-11T11:53:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159646730296629027425323989597043975620","date":"2024-09-10T07:41:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128483219866691590445610984342948967096","date":"2024-09-09T07:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45754702532502048632747539477729262684","date":"2024-09-06T19:14:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27425211605857370662614444087694347936","date":"2024-09-06T09:03:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-06T08:47:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-03T09:10:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-30T05:21:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-08-22T15:35:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"329df971-6cce-4f2f-b011-8a1364a37e48","owner":[],"postedDate":"September 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T18:08:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-27 11:05:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4959179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4959179","identity":"rs-4959179","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}