Lipid-associated macrophage remodeling is present in the tumor microenvironment after high-grade glioma recurrence

Lipid-associated macrophage remodeling is present in the tumor microenvironment after high-grade glioma recurrence | 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 Lipid-associated macrophage remodeling is present in the tumor microenvironment after high-grade glioma recurrence guanchao xie, Hongqing Cai, Fuxing Zuo, Shen Tian, Hongsheng Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7925396/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The inevitable recurrence of most high-grade gliomas (HGG) despite aggressive treatment poses a significant challenge to clinicians. Recently it has been found that exploring the characteristics of cell populations and their biological role in the tumour microenvironment is essential to understand the mechanisms behind tumour recurrence. Focusing on a combination of extensive RNA-seq and single-cell RNA sequencing (scRNA-seq) data, this study explores the mechanisms underlying the process of tumour microenvironment remodelling after HGG recurrence, providing new insights into the recurrence and treatment of HGG Methods Clinical information and bulk RNA-Seq data provided by the Chinese Glioma Genome Atlas (CGGA) data and single-cell sequencing data were analysed to compare the differences in the tumour microenvironments in primary and recurrent gliomas using R Seurat and ssGSEA scoring methods. The ssGSEA scores and HGG key clinical features was used to construct a prognostic model by univariate Cox analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, with different expressions of lipid-associated macrophages (LAMs) were also investigated. Finally, GO and KEGG were performed on LAMs signature gene annotations to look for possible biological functions and pathways. Results We identified a specific macrophage subpopulation defined as lipid-associated macrophages and LAMs cells were significantly increased in patients with HGG recurrence and predicted a poor prognosis. Functionally, LAMs may act as mediators of the immune response, and COX regression analysis revealed that LAMs were found to be an independent prognostic factor for glioma. Conclusion Our comprehensive analysis suggests that there is a remodelling of LAMs in the tumour microenvironment after HGG recurrence. LAMs could serve as a potential prognostic predictor for patients with glioma, and LAMs could play a role by participating in the immune response and glioma-associated immune checkpoints in order to help maintain the malignant phenotype of the neuroglial tumor cells. Lipid-associated macrophages scRNA-seq HGG Bulk RNA-seq Prognosis Figures Figure 1 Figure 2 Figure 3 Introduction Gliomas are the most prevalent primary brain tumors, accounting for 81% of all brain malignancies[ 1 ]. Despite advancements in treatment strategies, such as surgical resection, temozolomide (TMZ) chemotherapy, radiotherapy, and immunotherapy high-grade gliomas frequently recur and develop resistance to these therapies[ 2 ]. Key factors driving glioma recurrence and progression include the inability to fully remove invasive tumor cells, incomplete control of residual cells, and the potential emergence of new lesions. Additionally, glioma cells can undergo rapid clonal evolution, adapting to the tumor microenvironment (TME), thereby exacerbating issues like immune evasion, drug resistance, metastasis, and recurrence[ 3 ]. The TME in recurrent high-grade gliomas is profoundly immunosuppressive, dominated by tumor-associated macrophages (TAMs), which encompass both microglia and monocyte-derived macrophages (MDMs)[ 4 ]. Numerous studies have highlighted the critical role of TAMs in promoting tumor growth, recurrence, invasion, angiogenesis, immunosuppression, and resistance to therapy. However, the complexity and heterogeneity of TAMs shaped by diverse regional microenvironments and their interactions with various cell types and molecular signals, pose significant challenges to fully understanding the glioma TME and its immune responses. Addressing these challenges is essential for elucidating the mechanisms within the TME and developing novel therapeutic strategies targeting these processes[ 5 ]. Che et al. identified four major TAM subgroups based on transcriptional profiles and gene expression, including a lipid-associated macrophage (LAMs) subtype[ 6 ]. LAMs display characteristics akin to M2-like macrophages, such as lipid accumulation and enhanced phagocytic activity, and are predominantly localized at the tumor-adipose interface. Recent studies have identified LAMs in the microenvironments of atherosclerotic plaques, fatty liver, obese adipose tissue, lung metastases, and breast cancer, suggesting that LAMs may play a pivotal role in tumor progression[ 7 , 8 ]. This has made LAMs a promising therapeutic target in high-grade gliomas. In this study, using single-cell sequencing data from glioma samples in the Chinese Glioma Genome Atlas (CGGA) and RNA-seq data from paired glioma samples collected pre- and post-treatment, we investigated the role of LAMs in TME remodeling during glioma recurrence. We further explored the mechanisms of LAM remodeling and conducted a comprehensive analysis of their molecular characteristics, immune features, and prognostic relevance. Our findings offer new insights into TME remodeling in recurrent high-grade gliomas and lay the groundwork for developing LAMs-targeted therapies in glioma treatment. Materials and Methods Bulk RNA-seq data of HGG and clinical information Sequencing data and clinical information for the 192 HGG patients in this study were obtained from the CGGA database (http://www.cgga.org.cn/), specifically from the ‘mRNAseq_325’ [9]dataset, after excluding samples with incomplete information. The glioma scRNA-seq dataset was also sourced from the CGGA database（TABLE 1）, consisting of scRNA-seq data from 14 glioma patients. Sample integration was performed using the anchoring method from the R package ‘Seurat’[10], with core cells identified through filtering of the scRNA-seq data. Cells considered ineligible for analysis included those with three or fewer detected genes, low-quality cells with fewer than 200 or more than 7,500 detected genes, or those containing over 15% mitochondrial genes[11]. In addition, we identified sequencing data and clinical information from 132 (primary/recurrent) matched pairs of patients with high-grade gliomas in the GLASS consortium as an externally validated set of scores for LAMs. Gene expression in the core cells was normalized using a linear regression model, and the top 2,000 genes exhibiting high variability were selected through ANOVA screening. Principal component analysis (PCA) was applied to the single-cell samples, with the top 20 principal components (PCs) chosen for subsequent analyses[12]. The uniform manifold approximation and projection (UMAP) algorithm was then employed to perform overall dimensionality reduction on the top 20 PCs and to cluster the samples. Marker genes for manual annotation of different clusters were subsequently identified through the CellMarker database and relevant literature sources[13]. TABLE 1 Number of glioma patients engaged in our study was listed. All patients were stratified with age, clinicopathological characteristics options respectively. Characteristics(CGGA) No.of Patients(n=192) PR_type Primary 118 Recurrent 74 Age ＜45 99 ＞45 93 Gender Male 124 Female 68 WHO Grade WHOⅢ 65 WHOⅣ 127 IDH status Mutant 72 Wildtype 120 MGMTp status Methylated 104 Un methylated 88 1p19q status Codel 22 Non Codel 170 Characteristics(GLASS consortium) No.of Patients(n=132) PR_type Primary 132 Recurrent 132 Screening of differential cells and functional enrichment analysis of their marker genes Marker genes were identified for each cluster using the FindAllMarkers function from the Seurat package with parameters set to min.pct=0.25 and only.pos=TRUE to ensure that only significantly upregulated genes were considered. Marker genes with significant differences in each cell type were identified. Based on the significant differences in marker genes for each cell type, scores for each cell (relapse/primary) in the CGGA dataset were calculated using ssGSEA [18], and the difference in scores for each cell type between the primary and recurrent samples was analyzed using Wilcoxon to record the cells that had a significant difference (p<0.05) in the primary and eecurrent groups were recorded as core cells. Marker genes from the significantly different cell types were further analyzed for functional enrichment in GO and KEGG pathways using the DAVID database (https://david.ncifcrf.gov/). Finally, the GO enrichment results for these genes were visualized using a heatmap. Correlation of LAMs with clinical features and prognosis Samples with multiple clinical features were categorized into the following subtypes: age (>45 years, <45 years), gender, radiotherapy status, tumor grade, IDH mutation status, 1q19 co-deletion status, and overall survival (OS) status. For each subtype, the distribution of clinical characteristics was assessed using the Wilcoxon rank test, based on the correlation between LAM expression levels and clinical characteristics in HGG. To further explore the relationship between clinical features and survival, the impact of each clinical variable on overall patient survival was evaluated using univariate and multivariate COX regression models. Clinical variables that were statistically significant in both univariate and multivariate analyses (p<0.05) were considered independent prognostic factors for gliomas. Using these independent prognostic factors, a nomogram was constructed to predict 1-, 3-, and 5-year overall survival rates for high-grade gliomas. Visualization was performed using the ‘survminer’ and ‘ggrisk’ R packages. Additionally, the prognostic model was validated using an independent dataset. Immune Response Analysis To analyze immune cell characteristics, ssGSEA scores for nine tumor-associated immune response genes were calculated for each sample using the GSVA algorithm in the R package. Pearson correlation coefficients were employed to assess the correlation between LAM expression and immune response processes. Gene Ontology (GO) enrichment analysis was also performed on LAM-related genes. The expression levels of six glioma-associated immune checkpoints (PD-1, PD-L1, TIM3, B7-1, B7-2, and Galectin) were extracted, and Spearman correlation analysis results were visualized using a heatmap. Results Identification of LAMs cells Exploring the characteristics of cell populations and their biological roles in the tumor microenvironment is crucial for understanding the mechanisms behind tumor recurrence. In this study, we analyzed 6,148 core cells from the CGGA scRNA-seq database, performing genetic ANOVA on these core cells and principal component analysis (PCA) on 14 single-cell samples. The cells were distributed logically following PCA, and 20 principal components (PCs) with p-values < 0.05 were selected for further analysis. The core cells were then categorized into 15 independent cell clusters (Figure 1A) using the UMAP algorithm. Marker genes for each cluster were identified using the CellMarker database and relevant literature[11], resulting in five primary clusters: tumor cells (SOX2, PTPRZ1), T cells (CD3D, CD3E, G2MK), neutrophils (IL1R2, CXCR2), macrophages (F13A1, APOC1, CD163), and oligodendrocytes (UGT8, FA2H, MOG)(Figure 1C). The expression of key marker genes for each cell type was visualized using bubble plots(Figure 1B). The high expression levels of these marker genes, consistent with previous studies, further validate the accuracy of the cell type classification. By using the FindAllMarkers and Wilcoxon test, 600 significantly different marker genes were obtained to be identified（table S1）. By calculating ssGSEA scores for significantly different marker genes across each cell type, we found that macrophages and neutrophils showed significant downregulation in recurrent HGG (Figure 1D). Consequently, these two cell types were considered core cells for subsequent analyses. We further analyzed macrophages and neutrophils, classifying them into nine distinct cell clusters using UMAP. Marker genes for these clusters were again identified using the CellMarker database and literature[14, 15], resulting in five specific clusters:By calculating ssGSEA scores for significantly different marker genes across each cell type, we found that macrophages and neutrophils showed significant downregulation in recurrent HGG(Figure 1D). Consequently, these two cell types were considered core cells for subsequent analyses. We further analyzed macrophages and neutrophils, classifying them into nine distinct cell clusters using UMAP. Marker genes for these clusters were again identified using the CellMarker database and literature[14, 15], resulting in five specific clusters: LAMs (APOC1, MMP7, MMP9, TREM2, C1QA, C2), neutrophils (IL1R2, CXCR1), Neut1 high-expressing cells (PTGS2, S100A12), Neut2 high-expressing cells (IFIT1), and GSCs (SOX2)(Figure 1E). The distribution of each marker gene across these clusters was examined, and the high expression of marker genes in specific cells further validated the cell type classification. By using the FindAllMarkers and Wilcoxon test, 480 significantly different marker genes were obtained to be identified（table S1）. Calculating ssGSEA scores for significantly different marker genes per cell, we found that LAMs were significantly upregulated in recurrent HGG (Figure 1F). In addition, we compared the expression levels of LAMs in high-grade glioma patients in the GLASS database before and after disease progression and found that LAMs expression was significantly increased after recurrence (Figure 2A). This finding supports the hypothesis that LAMs undergo remodeling in the tumor microenvironment after tumor recurrence. High LAM Expression is Associated with Poor Prognosis To investigate the clinical significance of lipid-associated macrophages (LAMs) in high-grade gliomas, we evaluated the impact of LAMs and various clinical characteristics on patient prognosis, specifically overall survival, using univariate and multivariate Cox regression models(Figure 2D). The univariate analysis revealed significant associations between LAM expression, WHO grade, IDH mutation status, and recurrence status with overall survival. The multivariate analysis confirmed that LAM expression is an independent predictor of survival. These results underscore the role of LAMs as an independent prognostic factor in gliomas. To improve the clinical utility of survival prediction, we incorporated LAMs and three other independent prognostic factors into a nomogram, enabling individualized predictions of overall survival for high-grade glioma (HGG) patients at 1, 2, 3, 5, and 10 years(Figure 2F-G). Calibration plots and actual observations from both training and validation datasets demonstrated close alignment, indicating strong predictive accuracy(Figure 2E). Kaplan-Meier survival curves further supported these findings, showing that higher LAM expression is associated with shorter overall survival, positioning LAMs as a potential malignant biomarker(Figure 2B). Additionally, we found that patients with WHO grade 4 tumors exhibited higher LAM expression compared to those with WHO grade 3 gliomas. Higher LAM expression was also observed in patients with wild-type IDH, non-co-deleted 1p/19q, those Greater than 45 years old, and in patients with unmethylated MGMTp across all datasets[11](Figure2H). LAMs Correlation with Immune Function in Gliomas LAMs play a complex role in gliomas. To better understand their biological function, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses(Figure 3A-D). The results showed that LAM-related genes were predominantly enriched in immune response processes, including the regulation of MHC class II, T-cell activation, interleukin-6 (IL-6), and inflammatory responses. KEGG pathway analysis further demonstrated significant involvement of LAM-related genes in immune-related pathways, such as exosome biogenesis, phagosome function, and antigen processing and presentation. These findings suggest that LAMs play a pivotal role in modulating immune responses within gliomas. We focused on the role of LAMs in modulating immune responses, analyzing their correlation with nine key immune system processes. As expected, LAMs were positively correlated with most tumor-associated immune functions. Tumor cells often upregulate immune checkpoint molecules such as PD-L1, PD-L2, and galectin. Our analysis confirmed that LAMs were positively associated with several tumor-suppressive immune checkpoints(Figure 3E), indicating that LAMs may contribute to tumor immune evasion by regulating the expression of these checkpoints, thus supporting the maintenance of a malignant phenotype in glioma cellsFigure 3G). These findings are consistent with previous studies on TREM2[16]. To further explore the role of LAMs in glioma immune responses, we analyzed their correlation with inflammatory responses and found that LAMs were consistently positively associated with the expression of HCK, interferon, MHC class I, MHC class II,STAT1,andSTAT2(Figure3F). Discussion Gliomas are among the most aggressive and lethal tumors of the central nervous system, with limited therapeutic options available[ 17 , 18 ]. Surgical resection remains the primary treatment for most gliomas, but tumor recurrence is nearly inevitable[ 19 ]. It is believed that the main contributing factors are both postoperative regenerative inhibition and microenvironmental remodeling[ 20 ]. Therefore, understanding the mechanisms through which tumor recurrence induces changes in the molecular landscape and immunosuppressive tumor microenvironment, and developing therapies that exploit these mechanisms, are critical for improving clinical outcomes[ 21 ]. In our study, we identified a specific lipid-associated macrophage (LAM) subtype by downscaling and clustering scRNA-seq data from the CGGA dataset. We then used bulk RNA-seq data and the ssGSEA algorithm to identify key cells involved in the altered tumor microenvironment after recurrence. Notably, LAMs expression was significantly elevated in high-grade glioma patients following recurrence, suggesting that LAMs play a key role in tumor microenvironment remodeling. Our findings highlight the crucial role of LAMs in the reorganization of the tumor microenvironment post-recurrence. Furthermore, we found that LAMs act as a malignant biomarker in high-grade gliomas. By evaluating immune infiltration, immune checkpoints, and associated inflammatory genes, we concluded that LAMs may enhance tumor immune evasion by influencing the expression of immune checkpoints. Recent research has shown that LAMs may regulate inflammatory responses near cell death and lipid accumulation by maintaining metabolic homeostasis in a TREM2-dependent manner[ 22 , 23 ]. They acquire pro-tumorigenic capacity to support the immunosuppressive microenvironment through CAF-driven recruitment of monocytes to tumors via the CXCL12-CXCR4 axis[ 7 ]. Additionally, studies suggest that eliminating LAMs from the tumor microenvironment enhances the efficacy of immunotargeting drugs in preclinical models[ 23 ]. This growing body of evidence positions LAMs as a potential target for reactivating anti-tumor immune responses, particularly in high-grade gliomas. Our analysis of clinical data from the CGGA revealed that high LAMs expression is associated with poor prognosis in glioma patients. Both univariate and multivariate survival analyses indicated that elevated LAMs levels predict reduced survival, suggesting that LAMs could serve as prognostic markers for glioma patients. Despite prior studies on glioma prognosis, accurate prediction for high-grade glioma patients has been limited due to insufficient long-term follow-up data[ 24 ]. In this study, we developed and validated a prognostic model incorporating LAMs scores, recurrence status, WHO grade, and IDH1 mutation status to identify high-risk patients post-resection[ 25 ]. The nomogram incorporating LAM scores demonstrated superior predictive accuracy compared to models based solely on clinical factors, underscoring its value in assessing prognosis for high-grade glioma patients. LAMs were notably enriched in more malignant phenotypes, such as IDH wild-type status and grade 4 gliomas[ 26 ], and showed a strong positive correlation with tumor immune functions, immune checkpoint markers, and inflammatory response genes. These findings support the hypothesis that LAMs may contribute to immune escape by enhancing immune checkpoint expression and regulating inflammatory responses near cell death and lipid accumulation, thereby maintaining a malignant tumor phenotype[ 16 ]. Future glioma treatments will likely involve combination therapies, integrating neurosurgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy[ 18 , 27 ]. LAMs, as crucial players in tumor microenvironment remodeling and immune evasion, represent a promising target for novel therapeutic strategies. However, this study has limitations, including the relatively small scRNA-seq sample size and the lack of experimental validation. Future research should aim to validate these findings in extended animal models to enhance the accuracy and applicability of our results. Declarations Availability of data and materials The raw data used in this study, in the form of sequencing reads, are available from the Chinese Glioma Genome Atlas (CGGA) repository（http://www.cgga.org.cn/）, Single-cell data were obtained through the scRNA-seq module in CGGA, and RNA-seq data for high-level GBM tumors were obtained from the mRNAseq_325 module in CGGA and From Synapse (https://www.synapse.org/glass) from which we used ‘transcript_count_matrix_all_samples.tsv’ . Author contributions XG: data analysis, laboratory work and manuscript writing. ZH, TS: data collection and organization of CGGA database. FC: data collection and organization of GLASS database. ZF, WJ, CH: conception, supervision, and design of the manuscript. All authors contributed to the article and approved the submitted version. Funding This study was supported by the CAMS Innovation Fund for Medical Sciences (grant number 2022-I2M-C&T-B-063) and the National Natural Science Foundation of China (grant numbers 82103231 and 82072803). Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Reni, M., et al., Central nervous system gliomas. Crit Rev Oncol Hematol, 2017. 113 : p. 213-234. Jiang, T., et al., Clinical practice guidelines for the management of adult diffuse gliomas. Cancer Lett, 2021. 499 : p. 60-72. Mancusi, R. and M. Monje, The neuroscience of cancer. Nature, 2023. 618 (7965): p. 467-479. Quail, D.F. and J.A. Joyce, The Microenvironmental Landscape of Brain Tumors. Cancer Cell, 2017. 31 (3): p. 326-341. Cheng, K., et al., Tumor-associated macrophages in liver cancer: From mechanisms to therapy. Cancer Commun (Lond), 2022. 42 (11): p. 1112-1140. 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06:53:56","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73827,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/f0415671b17fcb02dc6ec6dd.html"},{"id":96790651,"identity":"17c7f320-03bd-4054-b0ad-66bd9271df7a","added_by":"auto","created_at":"2025-11-26 06:53:55","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":266142,"visible":true,"origin":"","legend":"\u003cp\u003eSegmentation and identification of cells (A) The umap algorithm was applied to the first 20 PCs for dimensionality reduction, and 15 cell clusters were classified (B) Expression levels of marker genes for each cell cluster (C) All 5 cell clusters were annotated with singleR and CellMarker based on the composition of marker genes. (D) Differential expression cells of HGG primary versus recurrent patients were obtained by calculating ssGSEA scores for each cluster based on marker genes. (E) Annotation of all core cell clusters with singleR and CellMarker (F) ssGSEA scores for each cluster in core cells to obtain HGG primary vs. recurrent patient differentially expressing cells. By Wilcoxon * indicates p \u0026lt; 0.05, ** represents p \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/43ea612ee63a65e8afc512d5.jpeg"},{"id":96790912,"identity":"63148c6b-9cb0-4bce-b465-2364d5bc6a00","added_by":"auto","created_at":"2025-11-26 06:54:01","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":362327,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between LAMs and clinicopathological features of gliomas(A) GLASS database paired comparisons of LAMs expression between primary and recurrent (B-C) Kaplan-Meier analysis of LAMs expression in the CGGA and Synapse databases, and significance of prognostic values was tested by time-series tes (D) Univariate and multivariate analyses of prognostic parameters in the overall survival (OS) of HGG patients in the CGGA database (F) Construction of nomogram models. (E) Calibration plots showing the comparison between predicted OS and actual OS for the probability of survival at 1, 2, 3, 5 and 10 years in the training group. (G) Predictive effects of individualised predictive models, LAMs and clinical prognostic factors on OS in HGG assessed by the C-index (H) Distribution of LAMs between different clinicopathological features in HGG\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/6407b03d943840cc7bc53b53.jpeg"},{"id":96790765,"identity":"902811c8-8e71-4bfd-b363-6cc043d21cd8","added_by":"auto","created_at":"2025-11-26 06:53:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":348977,"visible":true,"origin":"","legend":"\u003cp\u003eBiological functions associated with LAMs. (A-C) Biological processes and pathways associated with LAMs scores analysed by gene ontology. (D) KEGG analysis reveals biological processes and pathways associated with LAMs (E) Relationship between LAMs and six immune checkpoints in gliomas (F) Relationship between LAMs and inflammation-related genes (G) Relationship between LAMs and tumour immune response processes\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/9ae87e4b91f34de75fdd6f4d.jpeg"},{"id":96922605,"identity":"1a6b78eb-2401-4d34-a892-9229b1135051","added_by":"auto","created_at":"2025-11-27 14:19:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1575608,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/8c3c3dba-b741-4482-b3cb-287902801595.pdf"},{"id":96790920,"identity":"18a4f835-44fa-4c73-95a7-f83048c7f0b6","added_by":"auto","created_at":"2025-11-26 06:54:02","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":114823,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/3e6bd9e8b2a2455e6338d6e0.docx"},{"id":96790914,"identity":"6ce52589-4713-4478-8576-3ecee25909de","added_by":"auto","created_at":"2025-11-26 06:54:01","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":106361,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7925396/v1/684147031032da8673fe9d14.docx"}],"financialInterests":"","formattedTitle":"Lipid-associated macrophage remodeling is present in the tumor microenvironment after high-grade glioma recurrence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGliomas are the most prevalent primary brain tumors, accounting for 81% of all brain malignancies[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in treatment strategies, such as surgical resection, temozolomide (TMZ) chemotherapy, radiotherapy, and immunotherapy high-grade gliomas frequently recur and develop resistance to these therapies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Key factors driving glioma recurrence and progression include the inability to fully remove invasive tumor cells, incomplete control of residual cells, and the potential emergence of new lesions. Additionally, glioma cells can undergo rapid clonal evolution, adapting to the tumor microenvironment (TME), thereby exacerbating issues like immune evasion, drug resistance, metastasis, and recurrence[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe TME in recurrent high-grade gliomas is profoundly immunosuppressive, dominated by tumor-associated macrophages (TAMs), which encompass both microglia and monocyte-derived macrophages (MDMs)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Numerous studies have highlighted the critical role of TAMs in promoting tumor growth, recurrence, invasion, angiogenesis, immunosuppression, and resistance to therapy. However, the complexity and heterogeneity of TAMs shaped by diverse regional microenvironments and their interactions with various cell types and molecular signals, pose significant challenges to fully understanding the glioma TME and its immune responses. Addressing these challenges is essential for elucidating the mechanisms within the TME and developing novel therapeutic strategies targeting these processes[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChe et al. identified four major TAM subgroups based on transcriptional profiles and gene expression, including a lipid-associated macrophage (LAMs) subtype[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. LAMs display characteristics akin to M2-like macrophages, such as lipid accumulation and enhanced phagocytic activity, and are predominantly localized at the tumor-adipose interface. Recent studies have identified LAMs in the microenvironments of atherosclerotic plaques, fatty liver, obese adipose tissue, lung metastases, and breast cancer, suggesting that LAMs may play a pivotal role in tumor progression[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This has made LAMs a promising therapeutic target in high-grade gliomas.\u003c/p\u003e\u003cp\u003eIn this study, using single-cell sequencing data from glioma samples in the Chinese Glioma Genome Atlas (CGGA) and RNA-seq data from paired glioma samples collected pre- and post-treatment, we investigated the role of LAMs in TME remodeling during glioma recurrence. We further explored the mechanisms of LAM remodeling and conducted a comprehensive analysis of their molecular characteristics, immune features, and prognostic relevance. Our findings offer new insights into TME remodeling in recurrent high-grade gliomas and lay the groundwork for developing LAMs-targeted therapies in glioma treatment.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eBulk RNA-seq data of HGG and clinical information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing data and clinical information for the 192 HGG patients in this study were obtained from the CGGA database (http://www.cgga.org.cn/), specifically from the \u0026lsquo;mRNAseq_325\u0026rsquo; [9]dataset, after excluding samples with incomplete information. The glioma scRNA-seq dataset was also sourced from the CGGA database（TABLE 1）, consisting of scRNA-seq data from 14 glioma patients. Sample integration was performed using the anchoring method from the R package \u0026lsquo;Seurat\u0026rsquo;[10], with core cells identified through filtering of the scRNA-seq data. Cells considered ineligible for analysis included those with three or fewer detected genes, low-quality cells with fewer than 200 or more than 7,500 detected genes, or those containing over 15% mitochondrial genes[11]. In addition, we identified sequencing data and clinical information from 132 (primary/recurrent) matched pairs of patients with high-grade gliomas in the GLASS consortium as an externally validated set of scores for LAMs.\u003c/p\u003e\n\u003cp\u003eGene expression in the core cells was normalized using a linear regression model, and the top 2,000 genes exhibiting high variability were selected through ANOVA screening. Principal component analysis (PCA) was applied to the single-cell samples, with the top 20 principal components (PCs) chosen for subsequent analyses[12]. The uniform manifold approximation and projection (UMAP) algorithm was then employed to perform overall dimensionality reduction on the top 20 PCs and to cluster the samples. Marker genes for manual annotation of different clusters were subsequently identified through the CellMarker database and relevant literature sources[13].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;TABLE 1 Number of glioma patients engaged in our study was listed. All patients were stratified with age, clinicopathological characteristics options respectively.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eCharacteristics(CGGA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eNo.of \u0026nbsp;Patients(n=192)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003ePR_type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eRecurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e＜45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003e＞45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003eWHO Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eWHOⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eWHOⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003eIDH status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eMutant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eWildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003eMGMTp status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eMethylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eUn methylated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 557px;\"\u003e\n \u003cp\u003e1p19q status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eCodel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eNon Codel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eCharacteristics(GLASS consortium)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003eNo.of \u0026nbsp;Patients(n=132)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003ePR_type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 302px;\"\u003e\n \u003cp\u003eRecurrent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 255px;\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eScreening of differential cells and functional enrichment analysis of their marker genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarker genes were identified for each cluster using the FindAllMarkers function from the Seurat package with parameters set to min.pct=0.25 and only.pos=TRUE to ensure that only significantly upregulated genes were considered. Marker genes with significant differences in each cell type were identified. Based on the significant differences in marker genes for each cell type, scores for each cell (relapse/primary) in the CGGA dataset were calculated using ssGSEA [18], and the difference in scores for each cell type between the primary and recurrent samples was analyzed using Wilcoxon to record the cells that had a significant difference (p\u0026lt;0.05) in the primary and eecurrent groups were recorded as core cells.\u003c/p\u003e\n\u003cp\u003eMarker genes from the significantly different cell types were further analyzed for functional enrichment in GO and KEGG pathways using the DAVID database (https://david.ncifcrf.gov/). Finally, the GO enrichment results for these genes were visualized using a heatmap.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation of LAMs with clinical features and prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples with multiple clinical features were categorized into the following subtypes: age (\u0026gt;45 years, \u0026lt;45 years), gender, radiotherapy status, tumor grade, IDH mutation status, 1q19 co-deletion status, and overall survival (OS) status. For each subtype, the distribution of clinical characteristics was assessed using the Wilcoxon rank test, based on the correlation between LAM expression levels and clinical characteristics in HGG. To further explore the relationship between clinical features and survival, the impact of each clinical variable on overall patient survival was evaluated using univariate and multivariate COX regression models. Clinical variables that were statistically significant in both univariate and multivariate analyses (p\u0026lt;0.05) were considered independent prognostic factors for gliomas.\u003c/p\u003e\n\u003cp\u003eUsing these independent prognostic factors, a nomogram was constructed to predict 1-, 3-, and 5-year overall survival rates for high-grade gliomas. Visualization was performed using the \u0026lsquo;survminer\u0026rsquo; and \u0026lsquo;ggrisk\u0026rsquo; R packages. Additionally, the prognostic model was validated using an independent dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Response Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze immune cell characteristics, ssGSEA scores for nine tumor-associated immune response genes were calculated for each sample using the GSVA algorithm in the R package. Pearson correlation coefficients were employed to assess the correlation between LAM expression and immune response processes. Gene Ontology (GO) enrichment analysis was also performed on LAM-related genes. The expression levels of six glioma-associated immune checkpoints (PD-1, PD-L1, TIM3, B7-1, B7-2, and Galectin) were extracted, and Spearman correlation analysis results were visualized using a heatmap.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of LAMs cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploring the characteristics of cell populations and their biological roles in the tumor microenvironment is crucial for understanding the mechanisms behind tumor recurrence. In this study, we analyzed 6,148 core cells from the CGGA scRNA-seq database, performing genetic ANOVA on these core cells and principal component analysis (PCA) on 14 single-cell samples. The cells were distributed logically following PCA, and 20 principal components (PCs) with p-values \u0026lt; 0.05 were selected for further analysis. The core cells were then categorized into 15 independent cell clusters (Figure 1A) using the UMAP algorithm. Marker genes for each cluster were identified using the CellMarker database and relevant literature[11], resulting in five primary clusters: tumor cells (SOX2, PTPRZ1), T cells (CD3D, CD3E, G2MK), neutrophils (IL1R2, CXCR2), macrophages (F13A1, APOC1, CD163), and oligodendrocytes (UGT8, FA2H, MOG)(Figure 1C). The expression of key marker genes for each cell type was visualized using bubble plots(Figure 1B). The high expression levels of these marker genes, consistent with previous studies, further validate the accuracy of the cell type classification.\u003c/p\u003e\n\u003cp\u003eBy using the FindAllMarkers and Wilcoxon test, 600 significantly different marker genes were obtained to be identified（table S1）. By calculating ssGSEA scores for significantly different marker genes across each cell type, we found that macrophages and neutrophils showed significant downregulation in recurrent HGG (Figure 1D). Consequently, these two cell types were considered core cells for subsequent analyses. We further analyzed macrophages and neutrophils, classifying them into nine distinct cell clusters using UMAP. Marker genes for these clusters were again identified using the CellMarker database and literature[14, 15], resulting in five specific clusters:By calculating ssGSEA scores for significantly different marker genes across each cell type, we found that macrophages and neutrophils showed significant downregulation in recurrent HGG(Figure 1D). Consequently, these two cell types were considered core cells for subsequent analyses. We further analyzed macrophages and neutrophils, classifying them into nine distinct cell clusters using UMAP. Marker genes for these clusters were again identified using the CellMarker database and literature[14, 15], resulting in five specific clusters: LAMs (APOC1, MMP7, MMP9, TREM2, C1QA, C2), neutrophils (IL1R2, CXCR1), Neut1 high-expressing cells (PTGS2, S100A12), Neut2 high-expressing cells (IFIT1), and GSCs (SOX2)(Figure 1E). The distribution of each marker gene across these clusters was examined, and the high expression of marker genes in specific cells further validated the cell type classification. By using the FindAllMarkers and Wilcoxon test, 480 significantly different marker genes were obtained to be identified（table S1）. Calculating ssGSEA scores for significantly different marker genes per cell, we found that LAMs were significantly upregulated in recurrent HGG (Figure 1F). In addition, we compared the expression levels of LAMs in high-grade glioma patients in the GLASS database before and after disease progression and found that LAMs expression was significantly increased after recurrence (Figure 2A). This finding supports the hypothesis that LAMs undergo remodeling in the tumor microenvironment after tumor recurrence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh LAM Expression is Associated with Poor Prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the clinical significance of lipid-associated macrophages (LAMs) in high-grade gliomas, we evaluated the impact of LAMs and various clinical characteristics on patient prognosis, specifically overall survival, using univariate and multivariate Cox regression models(Figure 2D). The univariate analysis revealed significant associations between LAM expression, WHO grade, IDH mutation status, and recurrence status with overall survival. The multivariate analysis confirmed that LAM expression is an independent predictor of survival. These results underscore the role of LAMs as an independent prognostic factor in gliomas.\u003c/p\u003e\n\u003cp\u003eTo improve the clinical utility of survival prediction, we incorporated LAMs and three other independent prognostic factors into a nomogram, enabling individualized predictions of overall survival for high-grade glioma (HGG) patients at 1, 2, 3, 5, and 10 years(Figure 2F-G). Calibration plots and actual observations from both training and validation datasets demonstrated close alignment, indicating strong predictive accuracy(Figure 2E). Kaplan-Meier survival curves further supported these findings, showing that higher LAM expression is associated with shorter overall survival, positioning LAMs as a potential malignant biomarker(Figure 2B).\u003c/p\u003e\n\u003cp\u003eAdditionally, we found that patients with WHO grade 4 tumors exhibited higher LAM expression compared to those with WHO grade 3 gliomas. Higher LAM expression was also observed in patients with wild-type IDH, non-co-deleted 1p/19q, those Greater than 45 years old, and in patients with unmethylated MGMTp across all datasets[11](Figure2H).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eLAMs Correlation with Immune Function in Gliomas\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLAMs play a complex role in gliomas. To better understand their biological function, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses(Figure 3A-D). The results showed that LAM-related genes were predominantly enriched in immune response processes, including the regulation of MHC class II, T-cell activation, interleukin-6 (IL-6), and inflammatory responses. KEGG pathway analysis further demonstrated significant involvement of LAM-related genes in immune-related pathways, such as exosome biogenesis, phagosome function, and antigen processing and presentation. These findings suggest that LAMs play a pivotal role in modulating immune responses within gliomas.\u003c/p\u003e\n\u003cp\u003eWe focused on the role of LAMs in modulating immune responses, analyzing their correlation with nine key immune system processes. As expected, LAMs were positively correlated with most tumor-associated immune functions. Tumor cells often upregulate immune checkpoint molecules such as PD-L1, PD-L2, and galectin. Our analysis confirmed that LAMs were positively associated with several tumor-suppressive immune checkpoints(Figure 3E), indicating that LAMs may contribute to tumor immune evasion by regulating the expression of these checkpoints, thus supporting the maintenance of a malignant phenotype in glioma cellsFigure 3G). These findings are consistent with previous studies on TREM2[16].\u003c/p\u003e\n\u003cp\u003eTo further explore the role of LAMs in glioma immune responses, we analyzed their correlation with inflammatory responses and found that LAMs were consistently positively associated with the expression of HCK, interferon, MHC class I, MHC class II,STAT1,andSTAT2(Figure3F).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGliomas are among the most aggressive and lethal tumors of the central nervous system, with limited therapeutic options available[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Surgical resection remains the primary treatment for most gliomas, but tumor recurrence is nearly inevitable[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is believed that the main contributing factors are both postoperative regenerative inhibition and microenvironmental remodeling[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Therefore, understanding the mechanisms through which tumor recurrence induces changes in the molecular landscape and immunosuppressive tumor microenvironment, and developing therapies that exploit these mechanisms, are critical for improving clinical outcomes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn our study, we identified a specific lipid-associated macrophage (LAM) subtype by downscaling and clustering scRNA-seq data from the CGGA dataset. We then used bulk RNA-seq data and the ssGSEA algorithm to identify key cells involved in the altered tumor microenvironment after recurrence. Notably, LAMs expression was significantly elevated in high-grade glioma patients following recurrence, suggesting that LAMs play a key role in tumor microenvironment remodeling. Our findings highlight the crucial role of LAMs in the reorganization of the tumor microenvironment post-recurrence.\u003c/p\u003e\u003cp\u003eFurthermore, we found that LAMs act as a malignant biomarker in high-grade gliomas. By evaluating immune infiltration, immune checkpoints, and associated inflammatory genes, we concluded that LAMs may enhance tumor immune evasion by influencing the expression of immune checkpoints. Recent research has shown that LAMs may regulate inflammatory responses near cell death and lipid accumulation by maintaining metabolic homeostasis in a TREM2-dependent manner[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. They acquire pro-tumorigenic capacity to support the immunosuppressive microenvironment through CAF-driven recruitment of monocytes to tumors via the CXCL12-CXCR4 axis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, studies suggest that eliminating LAMs from the tumor microenvironment enhances the efficacy of immunotargeting drugs in preclinical models[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This growing body of evidence positions LAMs as a potential target for reactivating anti-tumor immune responses, particularly in high-grade gliomas.\u003c/p\u003e\u003cp\u003eOur analysis of clinical data from the CGGA revealed that high LAMs expression is associated with poor prognosis in glioma patients. Both univariate and multivariate survival analyses indicated that elevated LAMs levels predict reduced survival, suggesting that LAMs could serve as prognostic markers for glioma patients. Despite prior studies on glioma prognosis, accurate prediction for high-grade glioma patients has been limited due to insufficient long-term follow-up data[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, we developed and validated a prognostic model incorporating LAMs scores, recurrence status, WHO grade, and IDH1 mutation status to identify high-risk patients post-resection[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The nomogram incorporating LAM scores demonstrated superior predictive accuracy compared to models based solely on clinical factors, underscoring its value in assessing prognosis for high-grade glioma patients.\u003c/p\u003e\u003cp\u003eLAMs were notably enriched in more malignant phenotypes, such as IDH wild-type status and grade 4 gliomas[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and showed a strong positive correlation with tumor immune functions, immune checkpoint markers, and inflammatory response genes. These findings support the hypothesis that LAMs may contribute to immune escape by enhancing immune checkpoint expression and regulating inflammatory responses near cell death and lipid accumulation, thereby maintaining a malignant tumor phenotype[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFuture glioma treatments will likely involve combination therapies, integrating neurosurgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. LAMs, as crucial players in tumor microenvironment remodeling and immune evasion, represent a promising target for novel therapeutic strategies. However, this study has limitations, including the relatively small scRNA-seq sample size and the lack of experimental validation. Future research should aim to validate these findings in extended animal models to enhance the accuracy and applicability of our results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data used in this study, in the form of sequencing reads, are available from the Chinese Glioma Genome Atlas (CGGA) repository（http://www.cgga.org.cn/）, Single-cell data were obtained through the scRNA-seq module in CGGA, and RNA-seq data for high-level GBM tumors were obtained from the mRNAseq_325 module in CGGA and From Synapse (https://www.synapse.org/glass) from which we used \u0026nbsp; \u0026lsquo;transcript_count_matrix_all_samples.tsv\u0026rsquo; .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXG: data analysis, laboratory work and manuscript writing. ZH, TS: data collection and organization of CGGA database. FC: data collection and organization of GLASS database. ZF, WJ, CH: conception, supervision, and design of the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the CAMS Innovation Fund for Medical Sciences (grant number 2022-I2M-C\u0026amp;T-B-063) and the National Natural Science Foundation of China (grant numbers 82103231 and 82072803).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eReni, M., et al., \u003cem\u003eCentral nervous system gliomas.\u003c/em\u003e Crit Rev Oncol Hematol, 2017. \u003cstrong\u003e113\u003c/strong\u003e: p. 213-234.\u003c/li\u003e\n\u003cli\u003eJiang, T., et al., \u003cem\u003eClinical practice guidelines for the management of adult diffuse gliomas.\u003c/em\u003e Cancer Lett, 2021. \u003cstrong\u003e499\u003c/strong\u003e: p. 60-72.\u003c/li\u003e\n\u003cli\u003eMancusi, R. and M. 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Zhang, \u003cem\u003eThe history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications.\u003c/em\u003e Cell Mol Immunol, 2020. \u003cstrong\u003e17\u003c/strong\u003e(8): p. 807-821. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lipid-associated macrophages, scRNA-seq, HGG, Bulk RNA-seq, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7925396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7925396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe inevitable recurrence of most high-grade gliomas (HGG) despite aggressive treatment poses a significant challenge to clinicians. Recently it has been found that exploring the characteristics of cell populations and their biological role in the tumour microenvironment is essential to understand the mechanisms behind tumour recurrence. Focusing on a combination of extensive RNA-seq and single-cell RNA sequencing (scRNA-seq) data, this study explores the mechanisms underlying the process of tumour microenvironment remodelling after HGG recurrence, providing new insights into the recurrence and treatment of HGG\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eClinical information and bulk RNA-Seq data provided by the Chinese Glioma Genome Atlas (CGGA) data and single-cell sequencing data were analysed to compare the differences in the tumour microenvironments in primary and recurrent gliomas using R Seurat and ssGSEA scoring methods. The ssGSEA scores and HGG key clinical features was used to construct a prognostic model by univariate Cox analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, with different expressions of lipid-associated macrophages (LAMs) were also investigated. Finally, GO and KEGG were performed on LAMs signature gene annotations to look for possible biological functions and pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified a specific macrophage subpopulation defined as lipid-associated macrophages and LAMs cells were significantly increased in patients with HGG recurrence and predicted a poor prognosis. Functionally, LAMs may act as mediators of the immune response, and COX regression analysis revealed that LAMs were found to be an independent prognostic factor for glioma.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur comprehensive analysis suggests that there is a remodelling of LAMs in the tumour microenvironment after HGG recurrence. LAMs could serve as a potential prognostic predictor for patients with glioma, and LAMs could play a role by participating in the immune response and glioma-associated immune checkpoints in order to help maintain the malignant phenotype of the neuroglial tumor cells.\u003c/p\u003e","manuscriptTitle":"Lipid-associated macrophage remodeling is present in the tumor microenvironment after high-grade glioma recurrence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:53:12","doi":"10.21203/rs.3.rs-7925396/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28d6d542-35a3-4c08-9095-2fe06649becc","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-27T04:13:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:53:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7925396","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7925396","identity":"rs-7925396","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}