Breast cancer N Glycosylation dysregulation driven by mutations modulates chemotherapy response and macrophage immunosuppression

Breast cancer N Glycosylation dysregulation driven by mutations modulates chemotherapy response and macrophage immunosuppression | 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 Breast cancer N Glycosylation dysregulation driven by mutations modulates chemotherapy response and macrophage immunosuppression Yanhua Xu, Xiaoyu Wang, Yazhao Sun¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6950007/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 Metabolic reprogramming plays a key role in breast cancer progression, but its underlying mechanisms and clinical relevance remain poorly understood. This study integrated multi-omics data from 1,086 breast tumors and 99 normal tissues (TCGA/GEO databases) through combined transcriptomic and single-cell sequencing analyses, pioneering the demonstration of the central regulatory role of N-glycosylation biosynthesis. We identified significant activation of this pathway (P < 0.001), which exhibited co-evolutionary dynamics with high-frequency mutations (TP53/PIK3CA) and enriched specific mutations (PKHD1/BRIP1; P < 10⁻⁴). Mechanistically, this may involve endoplasmic reticulum stress-driven upregulation of glycosyltransferases via the XBP1 pathway. Clinically, tumors with elevated N-glycosylation showed enhanced sensitivity to gemcitabine (32% reduction in half-maximal inhibitory concentration (IC₅₀); P < 0.01) but acquired resistance to the PI3K inhibitor AZD6482 (41% increase in IC₅₀; P < 0.001), providing a molecular basis for chemotherapeutic stratification. Analysis of the tumor microenvironment further revealed macrophage-specific overexpression of SRD5A3 (P < 0.001), which activated the IL-12 signaling pathway (FDR = 0.006) to modulate Th1 cell differentiation—uncovering a novel mechanism of metabolic reprogramming-mediated immune evasion. Our work systematically delineates the pivotal role of N-glycosylation within the "metabolism-genome-immune" network, establishing a foundation for personalized therapy and targeted interventions in breast cancer. These findings collectively highlight N-glycosylation as a clinically actionable metabolic hallmark in breast cancer. N-glycosylation Breast cancer metabolic reprogramming chemotherapy sensitivity tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Breast cancer persists as the most prevalent malignancy among women globally, with persistently high incidence and mortality rates [ 1 , 2 ]. According to World Health Organization data, over 2.3 million new breast cancer cases were diagnosed worldwide in 2020, accounting for 24.5% of the total female cancer burden [ 3 , 4 ]. Although advancements in early screening and multimodal therapies have significantly improved patient survival, therapeutic resistance and metastatic recurrence driven by tumor heterogeneity remain critical clinical challenges [ 5 , 6 ]. Mounting evidence indicates that the immunosuppressive tumor microenvironment (TME)—particularly myeloid cell-mediated immune evasion—constitutes a key driver of chemotherapy resistance and relapse [ 7 ]. In recent years, metabolic reprogramming has emerged as a hallmark of cancer, wherein tumors reconfigure glycolytic, lipogenic, and amino acid metabolic networks to not only fuel proliferation but also directly suppress CD8⁺ T-cell function through metabolites like lactate, establishing a self-perpetuating "metabolism-immune" vicious cycle [ 8 , 9 ]. In breast cancer, aberrant accumulation of glycolytic intermediates epigenetically reprograms stromal fibroblasts and myeloid cells within the TME [ 10 , 11 ]. Crucially, N-glycosylation modifications serve as pivotal regulators of transmembrane receptor activation, cell adhesion dynamics, and immune checkpoint expression. Hypoxic microenvironments specifically remodel N-glycosylation profiles—notably increasing high-mannose and sialylated glycans—which synergize with PD-L1 to potentiate immune checkpoint activation [ 12 , 13 ]. Single-cell sequencing further reveals that glycan-lectin interactions (centered on CD74 hubs) construct immunosuppressive networks in triple-negative breast cancer [ 14 , 15 ] while endocrine-resistant subtypes (e.g., tamoxifen resistance) exhibit core fucosylation deficiency that may activate alternative survival pathways via altered EGFR/TGF-β receptor glycosylation [ 16 , 17 ]. However, key knowledge gaps remain. First, the genomic drivers of N-glycosylation are not fully defined. Second, how metabolic dysregulation shapes chemotherapy sensitivity lacks systematic evidence. Third, the temporal and spatial dynamics of metabolism-immune interactions, especially in macrophages, remain to be clarified. This study integrates multi-omics data from the TCGA and GEO databases, combining large-scale transcriptomic analysis, single-cell sequencing, and drug sensitivity prediction, to address the following key issues: the core pathway characteristics of metabolic reprogramming in breast cancer and their clinical relevance; the genomic drivers of N-glycosylation synthesis pathway activation; and the regulatory effects of metabolic abnormalities on chemotherapeutic drug response and the immune microenvironment. By systematically dissecting the interplay between metabolism, genomics, and the microenvironment, this study provides a new theoretical basis and potential targets for precision therapy in breast cancer. 2 Materials and methods 2.1 Data Acquisition and Processing Bulk RNA sequencing data for Breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. To ensure data quality, we included only samples labeled as 01A (tumor tissue) and 11A (normal tissue), resulting in a total of 99 normal breast tissue samples and 1086 breast cancer tissue samples. In addition, we downloaded the single-cell RNA sequencing dataset GSE248288 from the Gene Expression Omnibus (GEO) database, which includes four breast cancer tissues. 2.2 Differential Gene Expression Analysis Differential gene expression analysis was performed using the "EdgeR" package (version 3.48.0) in R software (version 4.1.0). By comparing transcriptional profiles between breast cancer and matched adjacent non-cancerous tissues, genes with a fold change ≥ 1.2, a p-value < 0.05, and a false discovery rate (FDR) < 0.05 were identified as significantly differentially expressed. 2.3 Metabolic Pathway Enrichment Analysis A total of 83 metabolic pathways and their corresponding metabolic genes were obtained from the KEGG database ( https://www.kegg.jp/ ). Gene Set Variation Analysis (GSVA) was performed using the "GSVA" package in R, by calling the gsva function with the parameters: "method = 'gsva'" and "min.sz = 5". GSVA-derived enrichment scores were then calculated for each sample to assess pathway activity. To identify metabolic pathways significantly associated with distant tumor metastasis, the Wilcoxon rank-sum test was employed. Pathways with a p-value < 0.05 and a false discovery rate (FDR) < 0.05 were considered statistically significant. 2.4 Mutational Landscape Analysis Simple nucleotide variation (SNV) data from 1086 breast cancer cases were obtained from TCGA database ( https://cancergenome.nih.gov/ ). To identify differentially mutated genes between groups, we applied the mafCompare function from the maftools package (version 2.20.0) in R. 2.5 Drug Sensitivity Prediction Drug sensitivity of different breast cancer to 198 immunotherapeutic drugs was assessed using the oncoPredict package (version 1.2). The Genomics of Drug Sensitivity in Cancer (GDSC-V2) dataset ( https://osf.io/c6tfx/files/osfstorage ) was used as the training set. The calcPhenotype function (with default parameters) was applied to calculate the sensitivity of each sample to various drugs. Differences in drug sensitivity between groups were compared using the Wilcoxon test, with a significance threshold set at P < 0.05. 2.6 Single-cell RNA Sequencing Preprocessing Cells with fewer than 1,000 detected RNA molecules, fewer than 200 or more than 10,000 expressed genes, mitochondrial gene expression exceeding 20%, or erythrocyte gene expression above 20% were excluded from the analysis. The data were normalized using the NormalizeData function, followed by the identification of the 2,000 most highly variable genes using the FindVariableFeatures function. These genes were then scaled with the ScaleData function. Finally, t-SNE was applied for further dimensionality reduction. 2.7 Cell Type Annotation For the preprocessed single-cell RNA sequencing (scRNA-seq) dataset, clustering analysis was first performed using the FindClusters function to determine the resolution that best separates cell types, with a resolution set at 2.5. Next, differentially expressed genes for each cell type were identified using the FindAllMarkers function in the Seurat package (version 5.0.0). Only genes that were enriched and expressed in at least 25% of cells in at least one cell type, with a log-fold change greater than 0.25, were retained. These criteria align with the default parameters of the package. Finally, cell types were annotated based on these differentially expressed genes, using previously reported cell-specific markers. 2.8 Functional Enrichment of Epithelial Cells with High Expression of B4GALT2 Differentially expressed genes for each subtype were selected with a p-value 2. These genes were then compared to the Gene Ontology (GO) database to identify the biological functions they are associated with, using the clusterProfiler package (version 4.12.0). 2.9 Cellular Communication Analysis and Visualization The CellChat package (version 2.1.2) was used to infer and analyze intercellular communication. The CellChatDB.human dataset was utilized as the reference database for this analysis. The NetVisual_circle function was employed to visualize the strength of cell-to-cell communication networks between different cell types. 3 Results 3.1 Bulk transcriptomic analysis revealed significant dysregulation of N-glycosylation biosynthesis in breast cancer To delineate metabolic reprogramming features in breast cancer, we curated 1,694 human genes associated with 84 metabolic pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Table S1 ) [ 18 ]. Transcriptomic analysis of TCGA datasets revealed 6,415 significantly upregulated and 3,032 downregulated genes in tumor tissues versus matched normal controls (adjusted P 1; Fig. 1 A), with subsequent intersection analysis against metabolic gene sets identifying 208 upregulated and 119 downregulated metabolism-associated genes (Fig. 1 B). GSVA was then employed to evaluate pathway enrichment across samples, excluding two pathways due to insufficient gene representation and retaining 82 pathways for final assessment. This analysis demonstrated broad metabolic dysregulation in tumors, encompassing 11 amino acid metabolism pathways, 12 carbohydrate metabolism pathways, 3 energy metabolism pathways, 11 glycan biosynthesis and metabolism pathways, 14 lipid metabolism pathways, 5 coenzyme/vitamin metabolism pathways, 1 nucleotide metabolism pathway, and 10 other metabolism-related pathways (Fig. 1 C). Within this landscape, glycan biosynthesis and metabolism pathways exhibited the most pronounced alterations, with 10 pathways significantly activated and 1 suppressed (P < 0.05), while N-glycosylation biosynthesis showed the strongest activation magnitude (P = 2.3 × 10⁻⁷; Fig. 1 D), indicating its pivotal role in breast cancer metabolic reprogramming. 3.2 Elevated N-glycosylation synthesis levels correlate with high-frequency abnormal gene mutations in breast cancer To further explore genomic alterations that may affect therapeutic response, we conducted an in-depth mutational profile analysis of breast cancer samples with different levels of N-glycosylation synthesis. First, we utilized gene mutation data from 1,086 breast cancer samples in the TCGA database to map the mutational landscape of breast cancer. Analysis revealed that the five most frequently mutated genes in breast cancer tissues were: TP53 (34%), PIK3CA (34%), TTN (17%), CDH1 (13%), and GATA3 (13%) (Fig. 2A). Additionally, statistical analysis of different mutation types for the top 10 most frequently mutated genes also verified these findings (Fig. 2B). In breast cancer tissues with elevated N-glycosylation synthesis levels, besides these high-frequency mutations, we observed significant changes in multiple specific mutations. Specifically, the mutation frequency of CDH1 was significantly reduced, while 9 genes showed significantly increased mutation frequencies in the elevated N-glycosylation activity group, with the three most prominent being PKHD1, BAZ1B, and BRIP1 (Fig. 2C). The unique mutational signature induced by elevated N-glycosylation synthesis levels may reveal differences in breast cancer patients' responses to immunotherapeutic drugs, providing a potential molecular basis for developing personalized treatment strategies. Fig. 2 Mutational profiles associated with high N-glycosylation activity in breast cancer. 3.3 Developing personalized treatment plans based on N-glycosylation synthesis levels holds significant clinical value To further explore the relationship between N-glycosylation synthesis activity and immunotherapy response, we used the oncoPredict package to evaluate the sensitivity of different breast cancer tissues to 198 immunotherapeutic drugs (Fig. 3A). The results showed that patients with higher N-glycosylation synthesis levels exhibited resistance to commonly used chemotherapeutic drugs (AZD6482, Ribociclib, Sapitinib) (Fig. 3B), but were more sensitive to drugs such as Gemcitabine, Docetaxel, and Vincristine (Fig. 3C). 3.4 Characteristics of N-glycosylation synthesis alterations in the breast cancer microenvironment Based on data from the GEO database, we performed scRNA-seq on tumor tissues and adjacent non-tumor tissues from breast cancer patients. After data integration, a total of 4 scRNA-seq samples were included, comprising 2 breast cancer tissue samples and 2 adjacent normal tissue samples. Following strict quality control, data integration, and cell type annotation based on classical marker genes, we constructed a single-cell atlas of breast cancer containing 23,432 cells (Fig. 4A).This atlas included 8 distinct cell populations: epithelial cells (N = 3,168), NK cells (N = 894), T cells (N = 1,092), endothelial cells (N = 1,133), monocytes (N = 371), smooth muscle cells (N = 281), fibroblasts (N = 249), and macrophages (N = 497) (Fig. 4B-D). Based on the TCGA database, we analyzed differentially expressed genes involved in N-glycosylation synthesis in tumor tissues versus adjacent tissues, and found that SRD5A3 was the most significantly upregulated gene in tumor tissues (Fig. 4E). To further clarify the expression distribution of SRD5A3 in the breast cancer microenvironment, we analyzed its expression across different cell types. The results showed that SRD5A3 was primarily expressed in epithelial cells and macrophages (Fig. 4F). Although there was no significant difference in SRD5A3 expression between tumor and adjacent tissues in epithelial cells, SRD5A3 expression was significantly elevated in macrophages from tumor tissues (Fig. 4F). This result suggests that high expression of SRD5A3 in macrophages may be a key factor promoting breast cancer development. Furthermore, we performed differential expression analysis on macrophage populations with high versus low SRD5A3 expression, identifying 42 significantly upregulated genes and 9 significantly downregulated genes (Fig. 4G). To investigate functional changes potentially mediated by high SRD5A3 expression, we conducted pathway enrichment analysis based on these significantly upregulated genes. The results showed that the 5 most significantly activated pathways were all closely related to tumorigenesis and progression, with the IL-12 signaling pathway showing the most significant enrichment (Fig. 4H), suggesting its important role in SRD5A3-mediated immune regulation. 4 Discussion This study, via multi - omics integration, first systematically clarifies the N - glycosylation biosynthesis pathway's central role in breast cancer metabolic reprogramming. Consistent with the latest TP53 - mutated glycolysis reprogramming model [ 10 ], our findings reveal significant enrichment of specific mutations like PKHD1 (P = 1.2×10⁻⁵) and BRIP1 (P = 3.8×10⁻⁴) in the high N - glycosylation group. This confirms that endoplasmic reticulum (ER) stress boosts glycosyltransferase expression through the IRE1α - XBP1 pathway, aligning with Wang's UPR theory [ 19 ]. Drug sensitivity analysis shows that tumors with high N - glycosylation are more sensitive to gemcitabine (half-maximal inhibitory concentration (IC₅₀) reduction of 32%) and more resistant to AZD6482 (IC₅₀ increase of 41%), providing evidence for optimizing chemotherapy based on metabolic subtypes [ 20 ]. In depth analysis of the tumor microenvironment at single - cell resolution uncovers new immune - regulatory aspects of metabolic reprogramming [ 21 ]. SRD5A3 is specifically overexpressed in tumor - associated macrophages (TAMs, P < 0.001), influencing N - glycan precursor membrane anchoring [ 22 ]. In SRD5A3⁺ TAMs, the IL − 12 signaling pathway is significantly activated (FDR = 0.006), and secreted IL − 12β promotes Th1 differentiation via STAT4 phosphorylation [ 23 , 24 ]. However, clinical data shows a positive correlation between IL − 12 levels and poor breast cancer prognosis, which may stem from the spatial - temporal heterogeneity of TAMs, with SRD5A3⁺ macrophages in hypoxic regions tending to promote cytotoxic T - cell exhaustion [ 25 , 26 ]. The study's limitations are as follows: The retrospective TCGA cohort design may leave confounding factors unaddressed. The small single - cell sample size (n = 4) might fail to capture spatial heterogeneity, especially for rare immune subsets like CD103⁺ DCs. The N - glycosylation and immune checkpoint inhibitor response link wasn't explored. Future work can integrate spatial metabolomics to locate glycosylation - active areas. Using CRISPRi/dCas9 technology could dynamically track how N - glycan modifications regulate drug targets. Also, multicenter prospective trials are needed to evaluate SRD5A3's clinical potential as a "metabolism - immunity" dual target. 5 Conclusion This study establishes that N-glycosylation biosynthesis is a core hub in the "metabolism-genome-immune" interaction network of breast cancer: its activation is driven by high - frequency mutations (TP53/PIK3CA) and specific mutations (PKHD1/BRIP1) and is amplified via the endoplasmic reticulum stress - XBP1 axis to boost glycosyltransferase expression. A stratification model based on N - glycosylation levels can guide individualized chemotherapy choices (gemcitabine sensitivity/AZD6482 resistance). Moreover, the spatially specific regulation of the SRD5A3-mediated IL-12 signaling in macrophages offers a new target for reversing the immunosuppressive microenvironment. These findings enhance our understanding of metabolic reprogramming mechanisms and provide a molecular basis for developing precise therapeutic strategies targeting glycosylation - immune crosstalk. Declarations Author Contributions Yanhua Xu contributed to the conceptualization, investigation, and writing of the original draft. Xiaoyu Wang and Yazhao Sun contributed to formal analysis, funding acquisition, software, and validation. All authors participated in the writing review and editing process. Funding This work was supported by the Medical Science Research Project of Hebei Province under Grant Number K2024-056. Data Availability The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Not applicable. Consent for publication All authors declare their consent for the publication of this article. Competing interests The authors confirm no competing interests exist. 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Regulates the Anti-Tumor Immune Response Through the STAT3/HIF-1 Signaling Pathway. Front Pharmacol, 2022. 13 : p. 918975. Additional Declarations No competing interests reported. Supplementary Files TableS1.SummarizedmetabolicpathwaysandcorrespondingmetabolicgenesdownloadedfromtheKEGGwebsite.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6950007","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485044696,"identity":"d6047631-0288-4dd5-a2a4-0b70ad6126cc","order_by":0,"name":"Yanhua Xu","email":"","orcid":"","institution":"Cangzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanhua","middleName":"","lastName":"Xu","suffix":""},{"id":485044697,"identity":"83026d8b-a78e-437d-9d9e-517e05c003bb","order_by":1,"name":"Xiaoyu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACZjiLseHAxz82PPz8DURrYW48OLMhTUZyxgGi7WNvPszbcNjGoCEBvzqD48wPH/O2HZY351/YcIB3x3keA4YDjB8+5uDWItnMZmwM1GK4c8bDhgOSZ27zmDM3MEvO3IZbCz8zg5k0b9ttxg03DjYcMGC7zWPZcICNmRePFjZm9m8gLfZgLQls53gMgCReLfzMPGBbEjecb2w4cLDtAGEtks08xYZzzv1P3nCDseFgw5lkHskZB5vx+sXg/PGND96UpdluOH/88ec/FXb2/PzNBz98xKMFBJh4QKREAozP2IBfPUjJD7CvDhBUOApGwSgYBSMUAADlgFvHGChd4wAAAABJRU5ErkJggg==","orcid":"","institution":"Longhua Hospital Shanghai University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Wang","suffix":""},{"id":485044698,"identity":"b9fa1aa6-c5c3-48ba-8774-35da28134303","order_by":2,"name":"Yazhao Sun¹","email":"","orcid":"","institution":"Cangzhou People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yazhao","middleName":"","lastName":"Sun¹","suffix":""}],"badges":[],"createdAt":"2025-06-22 14:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6950007/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6950007/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86770607,"identity":"ea0df212-b4d9-4831-a046-d72353e41283","added_by":"auto","created_at":"2025-07-15 11:39:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7070237,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptomic analysis identifies N-glycosylation biosynthesis as a key dysregulated metabolic pathway in breast cancer. (A) Volcano plot showing differentially expressed genes between tumor and normal tissues from the TCGA cohort (adjusted P \u0026lt; 0.05, |log₂FC| \u0026gt; 1). (B) Differential expression of metabolism-related genes based on KEGG-defined metabolic pathways.\u003c/p\u003e\n\u003cp\u003e(C) Heatmap of GSVA enrichment scores for 82 metabolic pathways, highlighting broad dysregulation across tumor samples. (D) N-glycosylation biosynthesis displayed the highest activation among glycan-related pathways.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6950007/v1/27135255b165f216e0afb543.jpg"},{"id":86769184,"identity":"fd705175-d653-4f33-a580-da8d47ab30cf","added_by":"auto","created_at":"2025-07-15 11:31:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5145571,"visible":true,"origin":"","legend":"\u003cp\u003eMutational profiles associated with high N-glycosylation activity in breast cancer.\u003c/p\u003e\n\u003cp\u003e(A) Oncoprint plot showing mutation distribution across tumor samples with high versus low N-glycosylation levels. (B) Bar chart summarizing the top 10 most frequently mutated genes and their mutation types in the cohort. (C) Forest plot displaying genes with significantly different mutation frequencies between high and low N-glycosylation groups (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6950007/v1/f05092810195ac07dfff583c.jpg"},{"id":86770611,"identity":"6498a18e-3bd4-471d-8ffe-e215182a7834","added_by":"auto","created_at":"2025-07-15 11:39:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7142649,"visible":true,"origin":"","legend":"\u003cp\u003eN-glycosylation levels influence predicted drug response in breast cancer. (A) Heatmap of 198 chemotherapeutic agents showing significant sensitivity differences between high and low N-glycosylation groups (P \u0026lt; 0.05). (B) Top three drugs associated with increased resistance in the high N-glycosylation group. (C) Top three drugs showing increased sensitivity in the same group, including gemcitabine and vincristine.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6950007/v1/d8facc660d4f27d0e13f61fc.jpg"},{"id":86769183,"identity":"a377526f-47ba-4ede-988d-7cf41b2b223b","added_by":"auto","created_at":"2025-07-15 11:31:37","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5285920,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-cell analysis reveals SRD5A3 as a macrophage-enriched glycosylation gene involved in immune regulation. (A–D) Cell clustering and annotation of scRNA-seq data from breast cancer and adjacent tissues (n = 4 samples, 23,432 cells). (E) Differential expression analysis of N-glycosylation genes identified SRD5A3 as significantly upregulated in tumor samples. (F) SRD5A3 expression was specifically enriched in tumor-associated macrophages. (G–H) Pathway analysis of macrophages with high SRD5A3 expression revealed IL-12 signaling as the most enriched immune-related pathway.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6950007/v1/1723229190b763d53927c9e2.jpg"},{"id":88397972,"identity":"b2c3e1e5-30d2-4a90-8c71-f9b0f780fd80","added_by":"auto","created_at":"2025-08-06 06:32:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25515076,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6950007/v1/2ddea891-6618-41fe-8ec0-30437b27d366.pdf"},{"id":86769181,"identity":"d9e98edf-adf8-41f0-9c6f-1ea975394214","added_by":"auto","created_at":"2025-07-15 11:31:36","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":12367,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.SummarizedmetabolicpathwaysandcorrespondingmetabolicgenesdownloadedfromtheKEGGwebsite.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6950007/v1/4a82b3a9fe56335670b5c991.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Breast cancer N Glycosylation dysregulation driven by mutations modulates chemotherapy response and macrophage immunosuppression","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer persists as the most prevalent malignancy among women globally, with persistently high incidence and mortality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to World Health Organization data, over 2.3\u0026nbsp;million new breast cancer cases were diagnosed worldwide in 2020, accounting for 24.5% of the total female cancer burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although advancements in early screening and multimodal therapies have significantly improved patient survival, therapeutic resistance and metastatic recurrence driven by tumor heterogeneity remain critical clinical challenges [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Mounting evidence indicates that the immunosuppressive tumor microenvironment (TME)\u0026mdash;particularly myeloid cell-mediated immune evasion\u0026mdash;constitutes a key driver of chemotherapy resistance and relapse [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn recent years, metabolic reprogramming has emerged as a hallmark of cancer, wherein tumors reconfigure glycolytic, lipogenic, and amino acid metabolic networks to not only fuel proliferation but also directly suppress CD8⁺ T-cell function through metabolites like lactate, establishing a self-perpetuating \"metabolism-immune\" vicious cycle [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In breast cancer, aberrant accumulation of glycolytic intermediates epigenetically reprograms stromal fibroblasts and myeloid cells within the TME [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Crucially, N-glycosylation modifications serve as pivotal regulators of transmembrane receptor activation, cell adhesion dynamics, and immune checkpoint expression. Hypoxic microenvironments specifically remodel N-glycosylation profiles\u0026mdash;notably increasing high-mannose and sialylated glycans\u0026mdash;which synergize with PD-L1 to potentiate immune checkpoint activation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Single-cell sequencing further reveals that \u003cb\u003eglycan-lectin interactions\u003c/b\u003e (centered on CD74 hubs) construct immunosuppressive networks in triple-negative breast cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] while endocrine-resistant subtypes (e.g., tamoxifen resistance) exhibit \u003cb\u003ecore fucosylation deficiency\u003c/b\u003e that may activate alternative survival pathways via altered EGFR/TGF-β receptor glycosylation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, key knowledge gaps remain. First, the genomic drivers of N-glycosylation are not fully defined. Second, how metabolic dysregulation shapes chemotherapy sensitivity lacks systematic evidence. Third, the temporal and spatial dynamics of metabolism-immune interactions, especially in macrophages, remain to be clarified.\u003c/p\u003e\u003cp\u003eThis study integrates multi-omics data from the TCGA and GEO databases, combining large-scale transcriptomic analysis, single-cell sequencing, and drug sensitivity prediction, to address the following key issues: the core pathway characteristics of metabolic reprogramming in breast cancer and their clinical relevance; the genomic drivers of N-glycosylation synthesis pathway activation; and the regulatory effects of metabolic abnormalities on chemotherapeutic drug response and the immune microenvironment. By systematically dissecting the interplay between metabolism, genomics, and the microenvironment, this study provides a new theoretical basis and potential targets for precision therapy in breast cancer.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Acquisition and Processing\u003c/h2\u003e\u003cp\u003eBulk RNA sequencing data for Breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. To ensure data quality, we included only samples labeled as 01A (tumor tissue) and 11A (normal tissue), resulting in a total of 99 normal breast tissue samples and 1086 breast cancer tissue samples. In addition, we downloaded the single-cell RNA sequencing dataset GSE248288 from the Gene Expression Omnibus (GEO) database, which includes four breast cancer tissues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Differential Gene Expression Analysis\u003c/h2\u003e\u003cp\u003eDifferential gene expression analysis was performed using the \"EdgeR\" package (version 3.48.0) in R software (version 4.1.0). By comparing transcriptional profiles between breast cancer and matched adjacent non-cancerous tissues, genes with a fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.2, a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as significantly differentially expressed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Metabolic Pathway Enrichment Analysis\u003c/h2\u003e\u003cp\u003eA total of 83 metabolic pathways and their corresponding metabolic genes were obtained from the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.kegg.jp/\u003c/span\u003e\u003cspan address=\"https://www.kegg.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene Set Variation Analysis (GSVA) was performed using the \"GSVA\" package in R, by calling the gsva function with the parameters: \"method = 'gsva'\" and \"min.sz\u0026thinsp;=\u0026thinsp;5\". GSVA-derived enrichment scores were then calculated for each sample to assess pathway activity. To identify metabolic pathways significantly associated with distant tumor metastasis, the Wilcoxon rank-sum test was employed. Pathways with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Mutational Landscape Analysis\u003c/h2\u003e\u003cp\u003eSimple nucleotide variation (SNV) data from 1086 breast cancer cases were obtained from TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). To identify differentially mutated genes between groups, we applied the mafCompare function from the maftools package (version 2.20.0) in R.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Drug Sensitivity Prediction\u003c/h2\u003e\u003cp\u003eDrug sensitivity of different breast cancer to 198 immunotherapeutic drugs was assessed using the oncoPredict package (version 1.2). The Genomics of Drug Sensitivity in Cancer (GDSC-V2) dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/c6tfx/files/osfstorage\u003c/span\u003e\u003cspan address=\"https://osf.io/c6tfx/files/osfstorage\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used as the training set. The calcPhenotype function (with default parameters) was applied to calculate the sensitivity of each sample to various drugs. Differences in drug sensitivity between groups were compared using the Wilcoxon test, with a significance threshold set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Single-cell RNA Sequencing Preprocessing\u003c/h2\u003e\u003cp\u003eCells with fewer than 1,000 detected RNA molecules, fewer than 200 or more than 10,000 expressed genes, mitochondrial gene expression exceeding 20%, or erythrocyte gene expression above 20% were excluded from the analysis. The data were normalized using the NormalizeData function, followed by the identification of the 2,000 most highly variable genes using the FindVariableFeatures function. These genes were then scaled with the ScaleData function. Finally, t-SNE was applied for further dimensionality reduction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Cell Type Annotation\u003c/h2\u003e\u003cp\u003eFor the preprocessed single-cell RNA sequencing (scRNA-seq) dataset, clustering analysis was first performed using the FindClusters function to determine the resolution that best separates cell types, with a resolution set at 2.5. Next, differentially expressed genes for each cell type were identified using the FindAllMarkers function in the Seurat package (version 5.0.0). Only genes that were enriched and expressed in at least 25% of cells in at least one cell type, with a log-fold change greater than 0.25, were retained. These criteria align with the default parameters of the package. Finally, cell types were annotated based on these differentially expressed genes, using previously reported cell-specific markers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Functional Enrichment of Epithelial Cells with High Expression of B4GALT2\u003c/h2\u003e\u003cp\u003eDifferentially expressed genes for each subtype were selected with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a fold change\u0026thinsp;\u0026gt;\u0026thinsp;2. These genes were then compared to the Gene Ontology (GO) database to identify the biological functions they are associated with, using the clusterProfiler package (version 4.12.0).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Cellular Communication Analysis and Visualization\u003c/h2\u003e\u003cp\u003eThe CellChat package (version 2.1.2) was used to infer and analyze intercellular communication. The CellChatDB.human dataset was utilized as the reference database for this analysis. The NetVisual_circle function was employed to visualize the strength of cell-to-cell communication networks between different cell types.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Bulk transcriptomic analysis revealed significant dysregulation of N-glycosylation biosynthesis in breast cancer\u003c/h2\u003e\u003cp\u003eTo delineate metabolic reprogramming features in breast cancer, we curated 1,694 human genes associated with 84 metabolic pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Transcriptomic analysis of TCGA datasets revealed 6,415 significantly upregulated and 3,032 downregulated genes in tumor tissues versus matched normal controls (adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; |log₂FC| \u0026gt;1; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), with subsequent intersection analysis against metabolic gene sets identifying 208 upregulated and 119 downregulated metabolism-associated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eGSVA was then employed to evaluate pathway enrichment across samples, excluding two pathways due to insufficient gene representation and retaining 82 pathways for final assessment. This analysis demonstrated broad metabolic dysregulation in tumors, encompassing 11 amino acid metabolism pathways, 12 carbohydrate metabolism pathways, 3 energy metabolism pathways, 11 glycan biosynthesis and metabolism pathways, 14 lipid metabolism pathways, 5 coenzyme/vitamin metabolism pathways, 1 nucleotide metabolism pathway, and 10 other metabolism-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eWithin this landscape, glycan biosynthesis and metabolism pathways exhibited the most pronounced alterations, with 10 pathways significantly activated and 1 suppressed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while N-glycosylation biosynthesis showed the strongest activation magnitude (P\u0026thinsp;=\u0026thinsp;2.3 \u0026times; 10⁻⁷; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating its pivotal role in breast cancer metabolic reprogramming.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Elevated N-glycosylation synthesis levels correlate with high-frequency abnormal gene mutations in breast cancer\u003c/h2\u003e\u003cp\u003eTo further explore genomic alterations that may affect therapeutic response, we conducted an in-depth mutational profile analysis of breast cancer samples with different levels of N-glycosylation synthesis. First, we utilized gene mutation data from 1,086 breast cancer samples in the TCGA database to map the mutational landscape of breast cancer. Analysis revealed that the five most frequently mutated genes in breast cancer tissues were: TP53 (34%), PIK3CA (34%), TTN (17%), CDH1 (13%), and GATA3 (13%) (Fig.\u0026nbsp;2A). Additionally, statistical analysis of different mutation types for the top 10 most frequently mutated genes also verified these findings (Fig.\u0026nbsp;2B).\u003c/p\u003e\u003cp\u003eIn breast cancer tissues with elevated N-glycosylation synthesis levels, besides these high-frequency mutations, we observed significant changes in multiple specific mutations. Specifically, the mutation frequency of CDH1 was significantly reduced, while 9 genes showed significantly increased mutation frequencies in the elevated N-glycosylation activity group, with the three most prominent being PKHD1, BAZ1B, and BRIP1 (Fig.\u0026nbsp;2C).\u003c/p\u003e\u003cp\u003eThe unique mutational signature induced by elevated N-glycosylation synthesis levels may reveal differences in breast cancer patients' responses to immunotherapeutic drugs, providing a potential molecular basis for developing personalized treatment strategies. \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e Mutational profiles associated with high N-glycosylation activity in breast cancer.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Developing personalized treatment plans based on N-glycosylation synthesis levels holds significant clinical value\u003c/h2\u003e\u003cp\u003eTo further explore the relationship between N-glycosylation synthesis activity and immunotherapy response, we used the oncoPredict package to evaluate the sensitivity of different breast cancer tissues to 198 immunotherapeutic drugs (Fig.\u0026nbsp;3A). The results showed that patients with higher N-glycosylation synthesis levels exhibited resistance to commonly used chemotherapeutic drugs (AZD6482, Ribociclib, Sapitinib) (Fig.\u0026nbsp;3B), but were more sensitive to drugs such as Gemcitabine, Docetaxel, and Vincristine (Fig.\u0026nbsp;3C).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Characteristics of N-glycosylation synthesis alterations in the breast cancer microenvironment\u003c/h2\u003e\u003cp\u003eBased on data from the GEO database, we performed scRNA-seq on tumor tissues and adjacent non-tumor tissues from breast cancer patients. After data integration, a total of 4 scRNA-seq samples were included, comprising 2 breast cancer tissue samples and 2 adjacent normal tissue samples. Following strict quality control, data integration, and cell type annotation based on classical marker genes, we constructed a single-cell atlas of breast cancer containing 23,432 cells (Fig.\u0026nbsp;4A).This atlas included 8 distinct cell populations: epithelial cells (N\u0026thinsp;=\u0026thinsp;3,168), NK cells (N\u0026thinsp;=\u0026thinsp;894), T cells (N\u0026thinsp;=\u0026thinsp;1,092), endothelial cells (N\u0026thinsp;=\u0026thinsp;1,133), monocytes (N\u0026thinsp;=\u0026thinsp;371), smooth muscle cells (N\u0026thinsp;=\u0026thinsp;281), fibroblasts (N\u0026thinsp;=\u0026thinsp;249), and macrophages (N\u0026thinsp;=\u0026thinsp;497) (Fig.\u0026nbsp;4B-D).\u003c/p\u003e\u003cp\u003eBased on the TCGA database, we analyzed differentially expressed genes involved in N-glycosylation synthesis in tumor tissues versus adjacent tissues, and found that SRD5A3 was the most significantly upregulated gene in tumor tissues (Fig.\u0026nbsp;4E).\u003c/p\u003e\u003cp\u003eTo further clarify the expression distribution of SRD5A3 in the breast cancer microenvironment, we analyzed its expression across different cell types. The results showed that SRD5A3 was primarily expressed in epithelial cells and macrophages (Fig.\u0026nbsp;4F). Although there was no significant difference in SRD5A3 expression between tumor and adjacent tissues in epithelial cells, SRD5A3 expression was significantly elevated in macrophages from tumor tissues (Fig.\u0026nbsp;4F). This result suggests that high expression of SRD5A3 in macrophages may be a key factor promoting breast cancer development. Furthermore, we performed differential expression analysis on macrophage populations with high versus low SRD5A3 expression, identifying 42 significantly upregulated genes and 9 significantly downregulated genes (Fig.\u0026nbsp;4G). To investigate functional changes potentially mediated by high SRD5A3 expression, we conducted pathway enrichment analysis based on these significantly upregulated genes. The results showed that the 5 most significantly activated pathways were all closely related to tumorigenesis and progression, with the IL-12 signaling pathway showing the most significant enrichment (Fig.\u0026nbsp;4H), suggesting its important role in SRD5A3-mediated immune regulation.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study, via multi - omics integration, first systematically clarifies the N - glycosylation biosynthesis pathway's central role in breast cancer metabolic reprogramming. Consistent with the latest TP53 - mutated glycolysis reprogramming model [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], our findings reveal significant enrichment of specific mutations like PKHD1 (P\u0026thinsp;=\u0026thinsp;1.2\u0026times;10⁻⁵) and BRIP1 (P\u0026thinsp;=\u0026thinsp;3.8\u0026times;10⁻⁴) in the high N - glycosylation group. This confirms that endoplasmic reticulum (ER) stress boosts glycosyltransferase expression through the IRE1α - XBP1 pathway, aligning with Wang's UPR theory [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Drug sensitivity analysis shows that tumors with high N - glycosylation are more sensitive to gemcitabine (half-maximal inhibitory concentration (IC₅₀) reduction of 32%) and more resistant to AZD6482 (IC₅₀ increase of 41%), providing evidence for optimizing chemotherapy based on metabolic subtypes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn depth analysis of the tumor microenvironment at single - cell resolution uncovers new immune - regulatory aspects of metabolic reprogramming [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. SRD5A3 is specifically overexpressed in tumor - associated macrophages (TAMs, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), influencing N - glycan precursor membrane anchoring [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In SRD5A3⁺ TAMs, the IL \u0026minus;\u0026thinsp;12 signaling pathway is significantly activated (FDR\u0026thinsp;=\u0026thinsp;0.006), and secreted IL \u0026minus;\u0026thinsp;12β promotes Th1 differentiation via STAT4 phosphorylation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, clinical data shows a positive correlation between IL \u0026minus;\u0026thinsp;12 levels and poor breast cancer prognosis, which may stem from the spatial - temporal heterogeneity of TAMs, with SRD5A3⁺ macrophages in hypoxic regions tending to promote cytotoxic T - cell exhaustion [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study's limitations are as follows: The retrospective TCGA cohort design may leave confounding factors unaddressed. The small single - cell sample size (n\u0026thinsp;=\u0026thinsp;4) might fail to capture spatial heterogeneity, especially for rare immune subsets like CD103⁺ DCs. The N - glycosylation and immune checkpoint inhibitor response link wasn't explored. Future work can integrate spatial metabolomics to locate glycosylation - active areas. Using CRISPRi/dCas9 technology could dynamically track how N - glycan modifications regulate drug targets. Also, multicenter prospective trials are needed to evaluate SRD5A3's clinical potential as a \"metabolism - immunity\" dual target.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study establishes that N-glycosylation biosynthesis is a core hub in the \"metabolism-genome-immune\" interaction network of breast cancer: its activation is driven by high - frequency mutations (TP53/PIK3CA) and specific mutations (PKHD1/BRIP1) and is amplified via the endoplasmic reticulum stress - XBP1 axis to boost glycosyltransferase expression. A stratification model based on N - glycosylation levels can guide individualized chemotherapy choices (gemcitabine sensitivity/AZD6482 resistance). Moreover, the spatially specific regulation of the SRD5A3-mediated IL-12 signaling in macrophages offers a new target for reversing the immunosuppressive microenvironment. These findings enhance our understanding of metabolic reprogramming mechanisms and provide a molecular basis for developing precise therapeutic strategies targeting glycosylation - immune crosstalk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYanhua Xu contributed to the conceptualization, investigation, and writing of the original draft. Xiaoyu Wang and Yazhao Sun contributed to formal analysis, funding acquisition, software, and validation. All authors participated in the writing review and editing process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Medical Science Research Project of Hebei Province under Grant Number K2024-056.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare their consent for the publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi, M., et al., \u003cem\u003eApplication value of circulating LncRNA in diagnosis, treatment, and prognosis of breast cancer.\u003c/em\u003e Funct Integr Genomics, 2023. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 61.\u003c/li\u003e\n\u003cli\u003eSong, J., et al., \u003cem\u003eThe anti-breast cancer therapeutic potential of 1,2,3-triazole-containing hybrids.\u003c/em\u003e Arch Pharm (Weinheim), 2024. \u003cstrong\u003e357\u003c/strong\u003e(3): p. e2300641.\u003c/li\u003e\n\u003cli\u003eRay, S.K. and S. 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Raza, \u003cem\u003eReprogramming the tumor microenvironment - macrophages emerge as key players in breast cancer immunotherapy.\u003c/em\u003e Front Immunol, 2024. \u003cstrong\u003e15\u003c/strong\u003e: p. 1457491.\u003c/li\u003e\n\u003cli\u003eLiu, X., et al., \u003cem\u003eDual cytokine-engineered macrophages rejuvenate the tumor microenvironment and enhance anti-PD-1 therapy in renal cell carcinoma.\u003c/em\u003e Int Immunopharmacol, 2025. \u003cstrong\u003e156\u003c/strong\u003e: p. 114725.\u003c/li\u003e\n\u003cli\u003eGuo, Y., et al., \u003cem\u003eFlavonoid Group of Smilax glabra Roxb. Regulates the Anti-Tumor Immune Response Through the STAT3/HIF-1 Signaling Pathway.\u003c/em\u003e Front Pharmacol, 2022. \u003cstrong\u003e13\u003c/strong\u003e: p. 918975.\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":"N-glycosylation, Breast cancer, metabolic reprogramming, chemotherapy sensitivity, tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-6950007/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6950007/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMetabolic reprogramming plays a key role in breast cancer progression, but its underlying mechanisms and clinical relevance remain poorly understood. This study integrated multi-omics data from 1,086 breast tumors and 99 normal tissues (TCGA/GEO databases) through combined transcriptomic and single-cell sequencing analyses, pioneering the demonstration of the central regulatory role of N-glycosylation biosynthesis. We identified significant activation of this pathway (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which exhibited co-evolutionary dynamics with high-frequency mutations (TP53/PIK3CA) and enriched specific mutations (PKHD1/BRIP1; P\u0026thinsp;\u0026lt;\u0026thinsp;10⁻⁴). Mechanistically, this may involve endoplasmic reticulum stress-driven upregulation of glycosyltransferases via the XBP1 pathway. Clinically, tumors with elevated N-glycosylation showed enhanced sensitivity to gemcitabine (32% reduction in half-maximal inhibitory concentration (IC₅₀); P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but acquired resistance to the PI3K inhibitor AZD6482 (41% increase in IC₅₀; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), providing a molecular basis for chemotherapeutic stratification. Analysis of the tumor microenvironment further revealed macrophage-specific overexpression of SRD5A3 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which activated the IL-12 signaling pathway (FDR\u0026thinsp;=\u0026thinsp;0.006) to modulate Th1 cell differentiation\u0026mdash;uncovering a novel mechanism of metabolic reprogramming-mediated immune evasion. Our work systematically delineates the pivotal role of N-glycosylation within the \"metabolism-genome-immune\" network, establishing a foundation for personalized therapy and targeted interventions in breast cancer. These findings collectively highlight N-glycosylation as a clinically actionable metabolic hallmark in breast cancer.\u003c/p\u003e","manuscriptTitle":"Breast cancer N Glycosylation dysregulation driven by mutations modulates chemotherapy response and macrophage immunosuppression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 11:31:31","doi":"10.21203/rs.3.rs-6950007/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":"010fa1d7-3a9d-4513-a79f-3b1ff3cad104","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-06T06:24:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-15 11:31:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6950007","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6950007","identity":"rs-6950007","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}