Single-cell profiling reveals systemic immune reprogramming in muscle-invasive urothelial carcinoma

Single-cell profiling reveals systemic immune reprogramming in muscle-invasive urothelial carcinoma | 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 Single-cell profiling reveals systemic immune reprogramming in muscle-invasive urothelial carcinoma Jiazi Cha, Yufan Yang, Jinshan Yang, Jiahao Guo, Hao Xie, Xinxin Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8525731/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 Urothelial carcinoma (UC) is classified into non-muscle-invasive (NMI) and muscle-invasive (MI) subtypes, with the latter associated with poor prognosis. Although tumor-infiltrating immune responses have been extensively studied, how the peripheral immune system is reprogrammed during disease progression remains largely unexplored. Understanding systemic immune alterations may reveal mechanisms underlying disease progression. Unlike most previous studies focusing solely on bladder UC, our cohort also included upper tract UC cases, providing a broader view of systemic immune alterations in urothelial carcinoma. Methods We performed single-cell RNA sequencing of peripheral blood mononuclear cells from 20 UC patients (5 MI and 15 NMI), integrating publicly available healthy control and MI datasets. A total of 144,019 high-quality cells were analyzed to characterize immune cell composition, functional states, and intercellular communication. Results Seven major immune cell populations were identified, with T and NK cells predominating. MI samples exhibited upregulation of genes related to chromatin remodeling, mitochondrial metabolism, and protein translation, alongside downregulation of immune signaling pathways, indicating metabolic stress and immune suppression. Pseudotime analysis revealed an MI-specific CD4⁺T-cell differentiation trajectory enriched in genes such as SLC25A6 and H3F3B and regulated by YBX1. B cells and monocytes showed functional reprogramming, with MI B cells metabolically active and NMI B cells immune-active. MI classical monocytes exhibited pro-tumor phenotypes and suppressed CD8⁺T-cell function via TGF-β signaling, with downstream JUN and SLC7A5 correlating with poor survival. Intercellular communication among T cells, B cells, and monocytes was enhanced in MI. Conclusions These findings indicate that peripheral immune cells in MI UC undergo stage-specific functional reprogramming, combining immune suppression, metabolic adaptation, and enhanced intercellular crosstalk. This highlights potential targets for immunomodulatory therapy and provides new insights into systemic immune alterations underlying urothelial carcinoma progression. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Urothelial carcinoma (UC) of the bladder is one of the most common malignancies of the urinary tract, accounting for more than 90% of bladder cancers worldwide( 1 , 2 ). UC can also arise in the upper urinary tract, including the renal pelvis and ureter, collectively referred to as upper tract urothelial carcinoma (UTUC)( 2 – 5 ), which shares similar histopathological features but distinct clinical and molecular characteristics compared to bladder UC( 6 – 8 ). Clinically, UC is categorized into non-muscle-invasive (NMI) and muscle-invasive (MI) subtypes, which differ markedly in biological behavior, therapeutic response, and prognosis( 9 – 11 ). While most NMI tumors are confined to the mucosa and can be managed by transurethral resection and intravesical therapy( 12 , 13 ), approximately 10–30% of cases progress to the MI stage, characterized by high recurrence rates, metastasis, and poor clinical outcomes( 14 ). Understanding the cellular and molecular mechanisms driving this transition remains a major challenge in UC research. Accumulating evidence has highlighted the critical role of the immune system in UC pathogenesis and progression( 15 , 16 ). Both tumor-infiltrating and peripheral immune cells contribute to tumor evolution through complex regulatory networks involving immune activation, suppression, and metabolic adaptation( 17 – 19 ). Among these, T cells, B cells, and monocytes play key roles in antitumor immunity and immune evasion( 20 – 24 ). However, the detailed transcriptional programs and intercellular communication patterns underlying these immune alterations remain incompletely understood( 25 , 26 ).While tumor-infiltrating immune cells have been extensively studied, peripheral blood mononuclear cells (PBMCs) offer a minimally invasive window into systemic immune changes in UC and may help identify biomarkers of disease progression and therapeutic response. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled high-resolution characterization of immune cell heterogeneity and functional states, providing unprecedented insights into tumor-associated immune dynamics( 27 – 31 ). Previous scRNA-seq studies have primarily focused on tumor-infiltrating lymphocytes within the bladder microenvironment, whereas the peripheral immune landscape—reflecting systemic immune responses and potential biomarkers of disease progression—has received less attention( 32 , 33 ). Characterizing the peripheral immune system at single-cell resolution may therefore provide a more comprehensive understanding of host-tumor interactions and identify novel immunotherapeutic targets( 34 , 35 ). In this study, we performed scRNA-seq on PBMCs from patients with MIUC and NMIUC, including both bladder and upper tract cases, and integrated publicly available datasets from healthy controls and additional MI cases. By profiling over 140,000 high-quality immune cells, we systematically delineated the immune cell composition, transcriptional heterogeneity, and intercellular communication networks associated with disease stage. Our analysis revealed profound immune-metabolic reprogramming in MIUC, characterized by enhanced mitochondrial metabolism, chromatin remodeling, and suppression of immune signaling pathways. We further identified TGF-β–mediated monocyte–T-cell crosstalk as a potential driver of systemic immune suppression in MIUC. These findings provide a comprehensive single-cell atlas of peripheral immune alterations in UC and uncover potential molecular targets for immunomodulatory therapy. Methods Isolation of PBMC Peripheral blood samples were collected from patients prior to surgery using 10-ml ethylenediaminetetraacetic acid (EDTA) tubes. Upon receipt, whole blood was centrifuged at 700×g for 10 min. Plasma was removed and stored at − 80°C. PBMCs were isolated via density gradient centrifugation using Ficoll400 cell separation solution with a density of 1.077 g/ml (17-1440-03; GE Healthcare, USA). Red blood cells were removed using ACK lysis buffer (A10492-01; Gibco, USA). Cell viability was assessed by Trypan blue staining, and viable cells were immediately processed for scRNA-seq. scRNA-seq Droplet-based scRNA-seq was performed on the MGI-DNBSEQ-T7RS platforms, following manufacturer instructions. PBMC were resuspended in PBS containing 0.04% bovine serum albumin (BSA) (9048-46-8; Fisher Bioreagents, USA) and loaded onto the MGI platform. Libraries were prepared and sequenced on an DNBSEQ-T7RS flow cell. To minimize batch effects, matched specimens from the same patient were processed in parallel during library preparation and sequenced on the same flow cell. Publicly available data Additional scRNA-seq data were obtained from the Gene Expression Omnibus (GEO) (accessions: GSE149689, GSE157278, GSE267718). Bulk RNA-seq data for validation were sourced from The Cancer Genome Atlas Program (TCGA). ScRNA-seq data preprocessing and quality control Raw FASTQ files were processed using DNBC4Tools (v2.1.3). Reads were aligned to the Homo sapiens GRCh38.114 reference genome, and gene expression counts were generated based on cell barcodes and unique molecular identifiers (UMIs). Subsequent quality control and filtering were performed in Seurat (v5.3.0), retaining genes expressed in ≥ 3 cells and cells with ≥ 200 detected genes. Data were normalized using the LogNormalize method, and confounding factors—such as mitochondrial gene expression and total UMI counts—were regressed out. Doublet removal Potential doublets were identified and removed using DoubletFinder (v.2.0.3)( 36 ). The assumed doublet formation rate was set to 0.8% per 1,000 cells. Cells were classified as doublet-low confidence, doublet-high confidence, or singlet. Only singlets were retained for downstream analysis. Dimensionality reduction and clustering Filtered data normalized and scaled using Normalize Data and Scale Data functions of Seurat (v5.3.0). The top 2,000 variable genes were selected for principal component analysis (PCA) using the FindVariable Features function. The top 30 principal components were used for clustering via the FindClusters function. Harmony ( 37 ) was used to remove batch effect between samples. Lastly, a two-dimensional visualization was generated using the uniform manifold approximation and projection (UMAP) algorithm. Differential expression analysis Differentially expressed genes (DEGs) were identified using the Seurat FindMarkers function in Seurat with a Wilcoxon likelihood-ratio test. Genes present in over 10% of cells within a cluster and exhibiting an average log fold change (LogFC) value exceeding 0.25 were classified as DEGs. Pathway enrichment analysis To explore the potential functions of DEGs, we performed Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis using the clusterProfiler (v4.14.6)( 38 ). GSEA was conducted using the Reactome pathway gene set (c2.cp.reactome.v2024.1.Hs.symbols.gmt), without pre-filtering by logFC, allowing the normalized enrichment score (NES) to reflect the overall directional trends. For GO enrichment analysis, DEGs were used as input, and pathways with an adjusted p-value (p_adj) less than 0.05 were considered significantly enriched. Pseudotime analysis Pseudotime analysis was performed using Monocle2 (v2.34.0)( 39 ), which unsupervisedly sorted single cells to reconstruct cell fate trajectories and evaluate pseudo time-dependent gene expression. Dimension reduction was carried out using DDRTree (v0.1.5). Cell trajectories were visualized based on subtypes and states, and the Basic Differential Analysis algorithm identified DEGs associated with pseudotime progression. Branch expression analysis modeling (BEAM) analysis was further used to identify genes regulated in a branching-dependent manner. Transcription factor (TFs) regulatory networks Transcription factor regulatory analysis was performed using pySCENIC (v0.12.1)( 40 ) to identify TFs and their target genes. Regulon Specificity Score (RSS) was calculated based on cell group information to screen for group-specific TFs. The top 10 TFs in the MI group were selected, and regulatory interactions with a weight greater than 10 were used to construct the TF-target regulatory networks. Cell-cell communication analysis Cell-cell interactions were inferred with CellChat (v.1.6.1) ( 41 ) and NicheNet (v.2.2.1)( 42 ) in R based on established ligand-receptor pairs provided by each tool. The top 20 active ligands inferred by NicheNet were used prioritize high-confidence interactions . Pseudobulk differential expression analysis Based on the Seurat object, the MI and NMI groups were selected for differential expression analysis. A pseudobulk approach was applied by aggregating single-cell expression matrices per patient using the PatientID metadata( 43 , 44 ). The sum method was used to calculate the total gene counts within each sample, generating a pseudobulk expression matrix. Differential expression between the MI and NMI groups was assessed with DESeq2. Gene signature module scoring Module scores were calculated to evaluate the expression activity of predefined gene signatures in single cells using the AddModuleScore function in Seurat (v.5.3.1.9999). Each score represents the overall expression level of a given gene set within individual cells. Survival analysis mRNA expression and clinical data for TCGA-BLCA were obtained from the TCGA database. Only tumor samples with matched expression and clinical information were included. Patients were stratified into high- (≥ 75th percentile) and low-expression (≤ 25th percentile) groups based on target gene expression. Overall survival (OS, in months) was analyzed using Kaplan-Meier curves, and group differences were assessed with the log-rank test. Analyses were performed using R packages survival (v.3.8.3) and survminer (v.0.5.1). Statistical analysis All statistical analyses were performed in R (v.3.8.3). Differential gene expression was assessed using Wilcoxon tests or pseudobulk DESeq2, and pathway enrichment was conducted using GSEA and GO analysis (adjusted p < 0.05). Correlations between cell type proportions were calculated using Pearson correlation and tested for significance with Student’s t-test. Cell-cell communication was inferred using CellChat and NicheNet , with gene module scores computed using the Seurat AddModuleScore function. Survival analyses were performed using Kaplan-Meier curves and log-rank tests, with p < 0.05 considered significant. Results Single-cell transcriptional landscape of peripheral immune cells in UC To characterize the systemic immune landscape of UC, we performed scRNA-seq on PBMCs from 20 UC patients using the MGI-DNBSEQ-T7RS platform (Table S1 )( 45 ). The cohort included five patients with MIUC and fifteen with NMIUC. In contrast to previous studies focused primarily on bladder UC, our study also included patients with UTUC, providing a broader representation of disease-associated immune alterations( 46 ). In addition, we integrated publicly available datasets from the GEO database, including nine healthy control (HC) samples from GSE149689 and GSE157278, as well as three MIUC samples from GSE267718( 47 – 52 ). After integrating all datasets, a total of 32 samples were obtained, comprising 9 HC, 15 NMI, and 8 MI samples (Fig. 1 A). Following stringent quality control, 144,019 high-quality cells were retained for downstream analysis, including 37,006 (25.67%) from HC, 67,349 (46.76%) from NMI, and 39,664 (27.54%) from MI samples. We identified 7 major cell clusters using graph-based clustering of UMAP (Fig. 1 B), including T cells (expressing CD3D,CD3E ), NK cells ( NKG7,GNLY ), classical monocytes ( CD14, VCAN, LYZ, FCN1 ), non-classical monocytes ( FCGR3A, IFITM3 ), B cells ( CD79A, CD79B, IGHM , IGHM ), conventional dendritic cells (cDC cells; CD1C, FCER1A, CD1E ), platelets (PPBP, PF4, TUBB1 ), and cycling cells ( STMN1, TOP2A, MKI67 ) (Fig. 1 C, D; Table S3). The proportion of cell populations in PBMCs varies among individuals. T cells and NK cells account for 44–94%, B account for 1–19% ,class monocytes account for 1–34%, no-class monocytes account for 0–10%,and dendritic cells account for only 0–2% (Fig. 1 F). Consistent with known PBMC composition, the scRNA-seq data of this study showed that the isolated PBMCs consisted largely of T and NK cells (77.17%), followed by monocytes (13.12%) and B cells (7.14%) (Fig. 1 F; Table S2 ). Although the overall distribution of immune subsets was largely comparable among the three groups, cycling cells, cDCs, and non-classical monocytes were significantly enriched in HC group (p < 0.05) (Fig. 1 E). No novel or missing cell subsets were found in the MI group. Characterization of T and NK cell heterogeneity and transcriptomic changes in UC Considering the central role of T and NK cells in the antitumor immunity, their compositional and molecular alterations throughout the disease progression may be closely linked to the pathogenesis of UC. Thus, we further investigated their dynamic changes at a finer resolution by re-clustering T and NK cells. This analysis yielded 20 distinct subpopulations (Fig. 2 A-C). NK cells, characterized by high expression of GNLY, NKG7 , and KLRF1 , were further divided into five subclusters. CD4⁺T cells were further subdivided into seven transcriptionally distinct subclusters: four naïve subsets (CD4_Naive_RORC, CD4_Naive_GPR183, CD4_Naive_CCR7, and CD4_Naive_LEF1) highly expressing LEF1 , CCR7 , and SELL ; a regulatory T cell (Treg) cluster (CD4_Treg_FOXP3) marked by FOXP3 , IL2RA , and TNFRSF4 ; a memory T cell (Tm) cluster (CD4_Tm_IL7R) expressing IL7R , LTB , and KLF2 ; and a stress-responsive T cell subset (CD4_STR_HSP1A1) with elevated HSPA1A , FOS , and JUN . CD8⁺ T cells were classified into five transcriptionally distinct subclusters: one naïve subset (CD8_Naive) expressing CCR7, SELL, IL7R , and TCF7 ; three effector subsets (CD8_Teff_MSC-AS1, CD8_Teff_MIR142HG, and CD8_Teff_ZNF683) with elevated expression of cytotoxic molecules GZMB, GZMA , and GZMH ; a stress-responsive subset (CD8_STR_IFNG) displaying high expression of stress-induced genes; a memory-like cluster (CD8_Tem_GZMK) expressing GZMK ; and a mucosal-associated invariant T (MAIT) cell population (CD8_MAIT) defined by elevated expression of SLC4A10, KLRB1 , and ZBTB16 . In addition, we detected another T cell cluster with undefined characteristics, which we designated as T_unknow. The proportions of these cell populations showed no obvious differences between MI and NMI samples (Fig. 2 D). Comparative transcriptomic analysis revealed that in MIUC genes related to histone-coding (e.g., H3F3B, H2AFZ, HIST1H2AC ), mitochondrial function (e.g., SLC25A6, ATP5MD ), and protein translation (e.g., LARS, YARS ) are significantly upregulated, suggesting enhanced chromatin remodeling and metabolic and biosynthetic activity supporting tumor invasiveness and migratory potential (Fig. 2 G; Table S4)( 53 ). In addition, the single-cell RNA-seq data were subjected to a “pseudobulk” processing approach, followed by differential expression analysis using DESeq2. The results showed strong concordance between the DESeq2-based differential expression results and those obtained from the FindMarkers analysis, further supporting the robustness of the identified DEGs (Fig. 2 H). Pathway analysis showed indicated that NMI tumors showed upregulation of immune-related pathways, including B cell and T cell activation, Fc-gamma receptor signaling, and cell adhesion, indicating enhanced immune activity. In contrast, MI tumors upregulated pathways related to protein homeostasis, RNA splicing, mitochondrial function, and apoptosis, suggesting higher protein metabolic stress and mitochondrial activity (Fig. 2 E). Reactome pathway analysis revealed downregulation of Fc receptor-mediated immune signaling in MI tumors, further indicating a reduced immune response (Fig. 2 F)( 54 ). Overall, NMI tumors exhibited an immune-active phenotype, whereas MI tumors displayed metabolic reprogramming and potential immune suppression. Functional module scoring analysis in CD4⁺T cells and CD8⁺T cells further supported these trends( 55 ). In CD4⁺T cells, MI samples exhibited higher scores for anti-apoptosis, glycolysis, oxidative phosphorylation (OXPHOS), and stress response modules, whereas NMI samples showed higher scores for chemokine/chemokine receptor, cytokine/cytokine receptor, cytotoxicity, and activation/effector function modules. In CD8 T cells, MI samples demonstrated elevated scores for anti-apoptosis, pro-apoptosis, fatty acid metabolism, glycolysis, oxidative phosphorylation, and stress response modules, whereas NMI samples displayed higher scores for chemokine/chemokine receptor, cytotoxicity, and activation/effector function modules (Fig. 3 A). Together, these results indicate that the functional states of T cells mirror the broader metabolic and immune characteristics of the T/NK cell population. Single-cell pseudotime analysis implicates CD4⁺ T cells in UC progression We analyzed the cell trajectory of representative CD4⁺T cells from UC and identified five cell states (State 1–State 5). A major branching trajectory was observed, representing the differentiation of naïve T cells into Treg cells and Tm cells. We found that both MI and NMI groups originated from naïve T cells. At the end of the differentiation trajectory, State 1 was exclusively present in MI group, whereas States 2–4 showed no substantial differences between the two groups (Fig. 3 D, E). This finding suggests a distinct evolutionary trajectory characterized by the specific emergence or enrichment of State 1 cells associated with muscle invasion. We further explored the DEGs associated with MI differentiation trajectories and categorized them into five modules. DEGs in Module 1 and 4 were primarily expressed in State 1 CD4⁺T cells (Fig. 3 F, G). Notably, genes in Module 1 overlapped significantly with the upregulated DEGs associated with MI, including ATP5MD, ATP5MPL, SLC25A6, H2AFZ, H3F3A , and H3F3B . These genes were markedly upregulated in the MI-specific State 1, suggesting that they may play a critical role in the mechanisms underlying muscle invasion in UC. SCENIC analysis identified distinct TF regulatory networks across UC cells. TFs and their regulatory networks showed significant differences between MI and NMI. Using MI and NMI as grouping criteria, we identified the top 10 TFs with the highest AUC scores in each group (Fig. 3 H). Subsequently, a regulatory network was constructed in the MI group between these top 10 TFs and their downstream target genes. The results showed that YBX1 emerged as a key regulator targeting multiple downstream genes (Fig. 3 I), including SLC25A6 —a gene also significantly upregulated in the MI-specific State1 CD4 + T cells determined by Monocle2 and in MI vs. NMI comparisons. Finally, the differential expression of SLC25A6 between MI and NMI samples was validated using the BLCA dataset from the TCGA database. The results revealed an increasing trend of SLC25A6 expression in the MI group but with no statistical significance (Fig. 3 J). Collectively, these findings suggest that SLC25A6 may be closely associated with muscle invasion in UC. Functional reprogramming of B cells in MIUC Based on variations in the expression density of membrane surface molecules, B cells were segregated into seven distinct subsets (Fig. 4 A). Each subset exhibited a unique expression pattern and biological activity, suggesting the intricate involvement of B cells in immune regulation. Four clusters represented naïve B cells (B_Naive_IGLC5, B_Naive_LAMC1, B_Naive_IGHD, B_Naive_SLCO2B1), characterized by the expression of canonical markers CD79A, IL4R, IGHD, IGHM , and TCL1A . A platelet-admixed naïve B cell cluster (B_Naive_Platelets) co-expressed platelet markers ( PPBP, PF4, GP9 ). Two clusters corresponded to memory B cells (B_Memory_FCGR2C and B_Memory_TNFRSF13B), marked by the expression of TGHA1, IGHG1, CD27 , and TNFRSF18B (Fig. 4 B). Among these seven B cell clusters, only B_Memory_TNFRSF13B showed a significantly higher abundance in NMI compared to MI samples (p < 0.05), whereas the proportions of other clusters did not differ significantly between the two groups (Fig. 4 C, D). DEG analysis in B cells revealed that MI-upregulated genes were predominantly enriched in chromatin remodeling, cytoskeletal reorganization, energy metabolism, and immune regulation, exhibiting a substantial overlap with those upregulated in T/NK cells (Fig. 4 F, Table S4). We visualized the representative pathways upregulated in B cells from NMI and MI (Fig. 4 G). In NMI, upregulated pathways mainly involved T cell activation, lymphocyte proliferation, antigen receptor-mediated signaling, and immune cell adhesion, indicating a close interaction of B cells with an active immune microenvironment. Supplementary analysis of REACTOME pathways revealed enrichment in REACTOME_FORMATION_OF_THE_BETA_CATENIN_TCF_TRANSACTIVATING_COMPLEX and REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES (Fig. 4 E), suggesting that NMI B cells not only exhibit functional immune activity but also enhance proliferation and functional competence via Wnt/Beta-catenin signaling and second messenger pathways, supporting a functionally active B cell state in NMI. In contrast, in MI, upregulated pathways were enriched in RNA processing and splicing, mitochondrial function, protein translation, and telomere maintenance, suggesting a shift toward metabolic and survival programs at the expense of immune activity (Fig. 4 G). Functional and immunological differences of monocytes in UC Peripheral blood monocytes were identified and classified into classical (CD14⁺) and non-classical monocytes (FCGR3A⁺)( 56 ). Classical monocytes were further subclustered into seven transcriptionally distinct subsets, whereas non-classical monocytes into two subpopulations (Figs. 4 H, I). An additional cluster co-expressing PPBP was annotated as platelet-contaminated classical monocytes. In MI, class_Mono_1 and class_Mono_2 were significantly enriched, whereas class_Mono_4 was predominantly enriched in NMI (p < 0.05) (Fig. 5 B), indicating that distinct monocyte subsets may exert different functions in tumor progression and immune regulation. Notably, the MI-enriched monocyte subsets (class_Mono_1 and class_Mono_2) exhibited higher scores of M2 polarization, angiogenesis, and phagocytosis (Fig. 5 A), suggesting their potential involvement in tumor-associated immunosuppression, microenvironment remodeling, and enhanced invasive capacity ( 57 ). Compared with NMI, genes upregulated in MI were primarily involved in chromatin remodeling, mitochondrial metabolism, protein degradation, signal transduction, and non-coding RNA regulation, and showed strong concordance with DEGs in T and B cells (Fig. 5 D; Table S4), suggesting potential coordinated functional reprogramming between monocytes and lymphocytes in shaping the immune microenvironment associated with muscle invasion. In UC, monocytes exhibit distinct functional reprogramming between tumor stages. In NMI, upregulated genes were mainly enriched in pathways related to protein translation, ribosomal biogenesis, actin cytoskeleton regulation, and T cell activation, suggesting a metabolically active and immune-supportive monocyte phenotype that may facilitate adaptive immune responses. In contrast, monocytes from MI showed enrichment in antiviral defense, type I interferon production, pattern recognition receptor and NF-κB signaling, as well as lysosomal organization and RNA splicing pathways. These features indicate a shift toward a “virus-like” innate immune activation and chronic inflammatory state (Fig. 5 C). Collectively, NMI-associated monocytes appear to promote immune activation, whereas MI-associated monocytes display sustained inflammation and immune tolerance, reflecting a functional transition from an immunostimulatory phenotype to a pro-inflammatory yet immunosuppressive state during tumor progression. TGF-β-mediated monocyte-CD8⁺ T cell crosstalk drives immune remodeling in MIUC To further investigate the potential role of the MI-enriched monocyte subsets class_Mono_1 and class_Mono_2 in disease progression, we extracted cells from MI samples and performed correlation analysis across cellular subpopulations (Fig. 5 E). The results revealed that class_Mono_1 and class_Mono_2 exhibited a significant negative correlation with the effector memory CD8⁺T cell subset CD8_Tem_GZMK (p < 0.05) (Fig. 6 A), suggesting potential suppression of CD8⁺ T cell effector function. To further elucidate the potential intercellular communication mechanisms, we conducted CellChat analysis to identify ligand–receptor interactions between monocytes and CD8_Tem_GZMK cells (Fig. 6 B). We found that monocytes highly expressed TGF-β family ligands ( TGFB1, TGFB2 , and TGFB3 ), which could interact with corresponding receptors TGFBR1, TGFBR2 , and ACVR1 on CD8_Tem_GZMK cells, indicating that TGF-β signaling may mediate the crosstalk between these populations. Subsequently, we performed NicheNet analysis by designating class_Mono_1 and class_Mono_2 as sender cells and CD8_Tem_GZMK as receiver cells, with genes upregulated in CD8_Tem_GZMK cells from MI samples defined as target genes (Fig. 6 C). TGFB1 was predicted to act through its receptor TGFBR3, regulating downstream target genes such as CITED2, JUN , and SLC7A5 . Further survival analysis based on the TCGA cohort revealed that high expression of JUN and SLC7A5 was significantly associated with reduced patient survival, suggesting that these genes play important roles in tumor progression and immune regulation (Fig. 6 D). In the TCGA-BLCA dataset, both JUN and SLC7A5 showed higher expression in MI samples compared with NMI samples, although only JUN reached statistical significance (p < 0.05) (Fig. 6 E). Consistently, at the single-cell level, JUN expression in CD8⁺T cells was markedly increased in MI samples relative to NMI samples (Fig. 6 F). These observations support the involvement of these genes in immune alterations driven by MI. Taken together, these results suggest that MI-enriched monocytes (class_Mono_1 and class_Mono_2) may suppress effector CD8⁺T cell activation through the TGF-β–TGFBR3 signaling axis, regulating downstream target genes ( CITED2, JUN, SLC7A5 ). The association of elevated JUN and SLC7A5 expression with poor patient survival, combined with their upregulation in CD8⁺ T cells from MI samples, highlights their roles in MI-driven immune dysregulation, with JUN appearing particularly prominent. Finally, we applied CellChat analysis to compare intercellular communication among all cell subsets between MI and NMI tissues. The results revealed that both the overall communication strength and the number of interactions were markedly increased in the MI group compared with the NMI group (Fig. 6 G), indicating a more active immune crosstalk under MI conditions. Notably, B cells exhibited the most pronounced increase in both the number and strength of outgoing signaling interactions (Fig. 6 H), suggesting that they may play a pivotal role in reshaping the immune microenvironment during MI. Further ligand-receptor pair analysis demonstrated enhanced T cell- and monocyte-B cell interactions in the MI group, primarily involving antigen presentation (HLA class II molecules-CD4) and immune-regulatory signaling pathways (PTPRC–CD22, SELPLG–SELL) (Figure. S1). The enhancement of these interactions suggests that, under MI conditions, T cells and monocytes may promote B-cell activation and function by facilitating antigen presentation and adhesion signaling, thereby contributing to the amplification of the immune response. Discussion In this study, we generated a comprehensive single-cell atlas of peripheral blood immune cells in patients with MI and NMI UC. By integrating our scRNA-seq data with publicly available datasets, we profiled over 140,000 immune cells, yielding high-resolution insights into immune composition, functional states, and intercellular communication in UC. In contrast to most previous studies confined to bladder UC, our cohort also incorporated UTUC cases, thereby capturing a broader spectrum of disease manifestations and enhancing the generalizability of our findings( 46 ). Our results reveal substantial stage-specific immune reprogramming, characterized by metabolic adaptation, chromatin remodeling, and immune suppression during MI progression. T and NK cells, the major components of peripheral immune cells, exhibit marked transcriptional reprogramming in MIUC( 58 ). Compared with NMI patients, MI T cells show significant downregulation of the FcγR signaling pathway. Fcγ receptors on immune cells mediate immune complex recognition and regulate antibody-dependent cellular cytotoxicity, phagocytosis, and cytokine secretion( 59 ). In UC, FcγR activation is generally associated with the immune activity of T/B cells and monocytes, and its downregulation suggests suppressed antibody-dependent immune responses, potentially contributing to immune evasion and tumor progression( 60 ). Pseudotime analysis revealed an MI-specific CD4⁺ T-cell differentiation trajectory accompanied by upregulation of the transcription factor YBX1. YBX1, a key transcriptional regulator, modulates metabolic genes including SLC25A6 , enhancing mitochondrial ADP/ATP exchange and energy metabolism, thereby supporting T-cell survival and stress adaptation. Notably, this metabolic adaptation coexists with diminished immune effector function, resulting in a “metabolically active but immunosuppressed” state, which may underlie systemic immune suppression in MIUC. Supporting this, a study in pancreatic cancer cells demonstrated that PTPMT1 maintains mitochondrial homeostasis via interactions with SLC25A6 and NDUFS2 , with PTPMT1 inhibition causing mitochondrial damage, loss of membrane potential, and tumor cell death. In our MIUC peripheral blood single-cell transcriptome data, SLC25A6 was significantly upregulated, suggesting that immune cells may engage similar mitochondrial regulatory mechanisms to adapt to the tumor-associated metabolic and stress environment( 61 ). We thus hypothesize that MIUC peripheral immune cells enhance SLC25A6 function to sustain mitochondrial energy supply and survival, contributing to systemic immune suppression. Future studies should examine SLC25A6 protein expression, mitochondrial function, energy metabolism, and cell functional status in PBMCs to validate this hypothesis and explore potential immunotherapeutic interventions. B cells and monocytes also undergo substantial functional reprogramming during MIUC progression. MI B cells shift from an immune-active to a metabolically focused, survival-oriented phenotype, characterized by enhanced mitochondrial function, upregulated protein translation, and telomere maintenance pathways. This metabolic prioritization may allow B cells to survive in the tumor-associated systemic environment but likely comes at the cost of immune effector functions. Similarly, classical monocytes in MI acquire pro-tumor characteristics, displaying enhanced M2 polarization, angiogenesis, and phagocytosis, suggesting a role in establishing a tumor-supportive microenvironment and facilitating immune evasion. Notably, MI-enriched monocyte subsets suppress CD8⁺ T cell effector functions via TGF-β signaling, with downstream targets JUN and SLC7A5 , whose high expression correlates with poor patient survival, indicating a potential role in systemic immune suppression and disease progression. TGF-β is known in late-stage tumors to promote epithelial-mesenchymal transition (EMT), enhance tumor migration and invasion, and modulate the immune microenvironment to facilitate immune escape, highlighting its potential as a therapeutic target, particularly in immunotherapy combination strategies( 62 , 63 ). JUN , as an AP-1 transcription factor, promotes cell cycle progression, upregulates key cell cycle genes, and suppresses the p53/p21 axis to enhance proliferation; previous in vitro and in vivo studies support its role as a positive regulator of tumor growth( 64 , 65 ). However, these studies primarily focus on tumor tissues, and its function in PBMCs has not been reported. In our MIUC peripheral blood single-cell transcriptomic data, we observed upregulation of JUN , suggesting that it may contribute to immune suppression in peripheral immune cells and support tumor progression. Enhanced intercellular communication among T cells, B cells, and monocytes in MI further underscores coordinated immune remodeling, likely mediated through ligand-receptor interactions and metabolic crosstalk. Mechanistically, we hypothesize that this functional reprogramming integrates metabolic adaptation with immunosuppressive signaling, enabling immune cells to survive under tumor-induced stress while attenuating antitumor responses. Future studies should validate these pathways at the protein and functional levels, including mitochondrial activity, cytokine secretion, and cytotoxicity, to establish causal links between immune-metabolic reprogramming and systemic immune suppression in MIUC. The limitations of this study include the relatively small sample size of the MI patients (n = 8) compared with the NMI patients(n = 20), which may affect the detection of differences in certain cell subsets. In addition, some analyses did not reach statistical significance (p ≥ 0.05) but showed observable trends, suggesting potential biological relevance. Future studies with larger, independent cohorts are needed to validate these observations and confirm their statistical and biological significance. Conclusions Collectively, this study revealed that peripheral immune cells in MIUC undergo stage-specific functional reprogramming, combining immune suppression, metabolic adaptation, and enhanced intercellular crosstalk, highlighting potential targets for immunomodulatory therapy. Our findings highlight the complex interplay among immune suppression, metabolic adaptation, and intercellular crosstalk in MIUC. By incorporating both bladder UC and UTUC cases, this study provides a more comprehensive view of peripheral immune dysregulation across the UC spectrum. Our single-cell atlas serves as a valuable framework for understanding systemic immune alterations and identifying potential immunomodulatory targets, including TGF-β signaling and metabolic regulators, for therapeutic intervention. Future studies with larger, anatomically stratified cohorts, experimental validations and mechanistic studies are warranted to further understand these immune-metabolic shifts and to validate their clinical relevance across different UC sites. Abbreviations UC Urothelial Carcinoma MI Muscle-Invasive NMI Non-Muscle-Invasive UTUC Upper Tract Urothelial Carcinoma PBMC Peripheral Blood Mononuclear Cells scRNA-seq Single-Cell RNA Sequencing HC Healthy Control DEGs Differentially Expressed Genes GO Gene Ontology GSEA Gene Set Enrichment Analysis UMAP Uniform Manifold Approximation and Projection PCA Principal Component Analysis cDC Conventional Dendritic Cells Treg Regulatory T Cells MAIT Mucosal-Associated Invariant T Cells OXPHOS Oxidative Phosphorylation TF Transcription Factor RSS Regulon Specificity Score BEAM Branch Expression Analysis Modeling SCENIC Single-Cell Regulatory Network Inference and Clustering NicheNet Ligand-Receptor Signaling Inference Tool CellChat Cell–Cell Communication Analysis Tool TCGA The Cancer Genome Atlas DESeq2 Differential Expression Analysis Tool ACK Ammonium-Chloride-Potassium BSA Bovine Serum Albumin UMI Unique Molecular Identifier Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Yantai Yuhuangding Hospital (IRB#: 2025-887). All patients provided written informed consent before enrollment. The patient provided written informed consent per the Declaration of Helsinki principles. Consent for publication All authors have reviewed and approved the final version of the manuscript and consent to its publication in the journal BMC Cancer. Availability of data and materials All raw sequencing data have been deposited in the Genome Sequence Archive (GSA) at NGDC under accession number HRA014953. (https://ngdc.cncb.ac.cn/gsa/) Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Taishan Scholar Program of Shandong Province (Grant No. TSQN202103198), the Shandong Provincial Natural Science Foundation (Grant No. ZR2024MH305), the Program for Scientific and Technological Innovation Development in Yantai City (Grant No. 2024YT06000818), the Medical and Health Technology Program in Shandong province (Grant No. 202402050885) and National Key Laboratory of Proteomics Open Research Fund (Grant No. SKLP-O202209). Authors' contributions J.C., Y.Y., and J.Y. contributed equally to the study, including study design, data analysis and manuscript writing. J.G., H.X., and X.L. assisted with sample collection and data processing. C.L., J.W. and Y.W. supervised the study and provided critical guidance on study design and interpretation. All authors reviewed the manuscript and approved the final version. Acknowledgements Not applicable References Saginala K, Barsouk A, Aluru JS, Rawla P, Padala SA, Barsouk A. 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13:03:13","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171880,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/2c6d837fb9d1961bbcbbbd4d.html"},{"id":100407027,"identity":"39625e81-7d69-44f6-97a1-f1ea31333584","added_by":"auto","created_at":"2026-01-16 13:03:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":691823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomes of peripheral blood mononuclear cells (PBMCs) from urothelial carcinoma (UC) patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Schematic overview of the single-cell transcriptome profiling of PBMCs from muscle-invasive (MI) UC, non-muscle-invasive (NMI) and healthy control (HC) patients (created with bioRender.com). \u003cstrong\u003eB\u003c/strong\u003e UMAP visualization of immune cells from 32 tumor samples, colored by annotated cell types (left) and sample groups (right). \u003cstrong\u003eC\u003c/strong\u003eDot plot showing the expression of canonical marker genes used for immune cell type identification. \u003cstrong\u003eD\u003c/strong\u003e Feature plot showing the expression pattern of key marker genes across the UMAP embedding. \u003cstrong\u003eE\u003c/strong\u003e Box plots comparing the relative abundance of immune cell subsets across HC, NMI and MI groups. Statistical significance was assessed by Wilcoxon test (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001; n.s., not significant). \u003cstrong\u003eF\u003c/strong\u003e Bar plot showing the composition of immune cell clusters within each individual tumor sample.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/8b5b565d3940ea32991c7055.png"},{"id":100406743,"identity":"adc5c423-840d-40cb-b367-7a891cf5753e","added_by":"auto","created_at":"2026-01-16 13:03:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":810212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic and functional heterogeneity of T and NK subsets.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e UMAP plot illustrating the distribution of T and NK cell subsets, with each color representing a distinct cell subset. \u003cstrong\u003eB\u003c/strong\u003e Feature plot showing the expression of key marker genes for T and NK cell subpopulation. \u003cstrong\u003eC\u003c/strong\u003e Dot plot illustrating the expression of canonical marker genes used for cell type identification, with CD4⁺ T cells-associated genes on the left and CD8⁺ T cell-associated genes on the right. \u003cstrong\u003eD\u003c/strong\u003e Bar plot showing the distribution of immune cell clusters across HC, MI and NMI groups. \u003cstrong\u003eE\u003c/strong\u003e Bar plot showing the top 10 biologically relevant Gene Ontology (GO) Biological Process (BP) terms enriched among upregulated DEGs in the MI and NMI groups, respectively. Terms were selected from the top 30 enriched terms and are ranked by adjusted p-value (p.adjust). \u003cstrong\u003eF\u003c/strong\u003e Gene Set Enrichment Analysis (GSEA) of DEGs in the MI and NMI groups using the Reactome database. \u003cstrong\u003eG\u003c/strong\u003e Volcano plot identifying upregulated genes in MI and NMI. Genes with significant fold changes are annotated. \u003cstrong\u003eH\u003c/strong\u003e Volcano plot of DEGs identified from pseudobulk DESeq2 analysis of single-cell RNA-seq data deconvoluted to TCGA samples. Genes with significant fold changes are annotated.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/373b693eddafa1d3398f955b.png"},{"id":100406739,"identity":"8e3c2f82-bbc6-4096-847f-fb36981b9e07","added_by":"auto","created_at":"2026-01-16 13:03:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":689206,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional states and pseudotime trajectories of T cells.\u003cbr\u003e\nA–B\u003c/strong\u003e Heatmaps showing module scores of functional gene sets in CD4⁺ (\u003cstrong\u003eA\u003c/strong\u003e) and CD8⁺(\u003cstrong\u003eB\u003c/strong\u003e) T cells from MI and NMI samples. \u003cstrong\u003eC\u003c/strong\u003eUMAP projection of CD4⁺T cells colored by distinct cell subtypes. \u003cstrong\u003eD\u003c/strong\u003ePseudotime trajectory of CD4⁺T cells inferred by Monocle2, colored by pseudotime (top left), cell states (top right), and MI/NMI group affiliation (bottom left and right). \u003cstrong\u003eE\u003c/strong\u003e Ridge plot showing the distribution of CD4⁺T cells from MI and NMI groups along the pseudotime axis. \u003cstrong\u003eF\u003c/strong\u003e Heatmap of branch-dependent genes identified by BEAM analysis at branch_point = 1. \u003cstrong\u003eG\u003c/strong\u003eExpression dynamics of BEAM gene cluster 1 along pseudotime, colored by MI and NMI groups. \u003cstrong\u003eH\u003c/strong\u003e Bubble plot showing the top 10 enriched transcription factors (TFs) in MI and NMI samples. \u003cstrong\u003eI\u003c/strong\u003e Regulatory network of key TFs and their downstream target genes in MI samples. \u003cstrong\u003eJ\u003c/strong\u003e Box plot comparing \u003cem\u003eSLC25A6\u003c/em\u003eexpression in MI and NMI samples from TCGA datasets.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/d680aad9396467d516e0dd21.png"},{"id":100406900,"identity":"ae6a4a35-cbf1-46a9-9c8a-6c19a51c79a2","added_by":"auto","created_at":"2026-01-16 13:03:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":902337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhenotypic and functional characterization of B cell subsets.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e UMAP plot illustrating the distribution of B cell subsets, color by cell subset. \u003cstrong\u003eB\u003c/strong\u003eDot plot illustrating the expression of canonical B cell marker genes. \u003cstrong\u003eC\u003c/strong\u003eHeatmap depicting the preferential distribution of B cell subsets across HC, NMI, and MI samples. \u003cstrong\u003eD\u003c/strong\u003e Box plots showing the relative abundance of immune cell subsets among HC, NMI, and MI samples (Wilcoxon test; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). \u003cstrong\u003eE\u003c/strong\u003e Gene Set Enrichment Analysis (GSEA) of DEGs in the MI and NMI groups using the Reactome database. \u003cstrong\u003eF \u003c/strong\u003eVolcano plot identifying upregulated genes in MI and NMI. Genes with significant fold changes are annotated. \u003cstrong\u003eG\u003c/strong\u003e Bar plot showing the top 10 biologically relevant Gene Ontology (GO) Biological Process (BP) terms enriched by upregulated DEGs in the MI and NMI groups, respectively. These terms were selected from the top 30 most enriched terms and are ranked by enrichment significance (p.adjust). \u003cstrong\u003eH\u003c/strong\u003e UMAP plot illustrating the distribution of monocyte cell subsets, colored by cell subset. \u003cstrong\u003eI\u003c/strong\u003e Feature Plot showing the expression of marker genes used to identify cell types and characteristic genes for each immune cell cluster.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/b5f4409de3cd2416ad07762e.png"},{"id":100406741,"identity":"1ba437f2-e505-4b60-a95f-1ee8b0e9ad31","added_by":"auto","created_at":"2026-01-16 13:03:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":890150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional characterization and subset distribution of monocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Heatmaps showing module scores of functional gene sets across monocyte cell types. \u003cstrong\u003eB\u003c/strong\u003e Box plots showing the relative abundance of immune cell subsets among HC, NMI, and MI (Wilcoxon test; *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001). \u003cstrong\u003eC\u003c/strong\u003e Bar plot showing the top 10 biologically relevant Gene Ontology (GO) Biological Process (BP) terms enriched by upregulated DEGs in the MI and NMI groups, respectively. These terms were selected from the top 30 most enriched terms and are ranked by p.adjust. \u003cstrong\u003eD\u003c/strong\u003e Volcano plot identifying upregulated genes in MI and NMI. Genes with significant fold changes are annotated. \u003cstrong\u003eE\u003c/strong\u003e Pearson correlations are represented by ellipses (shape and color) representing the strength and direction of correlations in the lower triangle, with significance indicated by p-values (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/c6255538e20b975635f5561e.png"},{"id":100406474,"identity":"9e0f87b3-3bdd-493e-857e-05eaa8eba1f9","added_by":"auto","created_at":"2026-01-16 13:02:33","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":615150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell-cell communication analysis in MI and NMI samples.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eScatter plots showing correlations between the proportions of class_Mono_1/class_Mono_2 monocyte subsets and CD8_Tem_GZMK cells in MI samples. Linear regression lines are shown. \u003cstrong\u003eB \u003c/strong\u003eBubble plot generated by CellChat showing differences in cell-cell interactions between NMI and MI samples. Bubble size represents the interaction strength, and color indicates the communication probability. \u003cstrong\u003eC\u003c/strong\u003e NicheNet analysis of prioritized ligands in MI samples. Heatmaps show ligand activity (left), ligand-receptor interaction potential (middle), and ligand-target regulatory potential (right). \u003cstrong\u003eD\u003c/strong\u003e Kaplan-Meier survival curves derived from TCGA cohort data. \u003cstrong\u003eE\u003c/strong\u003e Box plot of \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eSLC7A5\u003c/em\u003e expression in TCGA datasets. \u003cstrong\u003eF\u003c/strong\u003e Box plot of \u003cem\u003eJUN\u003c/em\u003e expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells. \u003cstrong\u003eG\u003c/strong\u003e Bar plot showing overall cell-cell interactions. \u003cstrong\u003eH\u003c/strong\u003e Network plot showing different ligand-receptor interactions among cell subpopulations. Circle color indicates cell subpopulation, circle size reflects the number of pairs. Blue lines: stronger in NMI; red lines: stronger in MI; line thickness represents interaction change magnitude.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/10f396319c08c8d50acf1f04.png"},{"id":105033864,"identity":"69190f82-4eb2-448c-850e-7ecffde30854","added_by":"auto","created_at":"2026-03-20 07:21:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5104531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/cbdf80c1-4181-4122-9278-d865b9a60130.pdf"},{"id":100421496,"identity":"12926d02-2d0d-4f1d-8023-f0289f574dc6","added_by":"auto","created_at":"2026-01-16 13:33:08","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11422,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Table S1. Patient information.\u003c/p\u003e","description":"","filename":"TableS1.xls","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/221ba7b52301b47d3068d8c9.xls"},{"id":100421514,"identity":"74808a6d-41b5-4ea8-8d95-f218576aefe4","added_by":"auto","created_at":"2026-01-16 13:33:12","extension":"xls","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15229,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Table S2. Cell numbers and percentages of each major cell type.\u003c/p\u003e","description":"","filename":"TableS2.xls","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/53b387dac4dc551ffc1acc59.xls"},{"id":100406902,"identity":"96b7a837-8aac-4802-a6df-af547862813b","added_by":"auto","created_at":"2026-01-16 13:03:30","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":55212,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 4: Table S3. Top 100 marker genes of major cell type.\u003c/p\u003e","description":"","filename":"TableS3.xls","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/facde205d2e2223441695a83.xls"},{"id":100407032,"identity":"a2241c5a-e286-49b1-adb5-3129512385ff","added_by":"auto","created_at":"2026-01-16 13:03:41","extension":"xls","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":56215,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 5: Table S4. Top 100 DEGs in T cells, B cells, and monocytes between MI and NMI.\u003c/p\u003e","description":"","filename":"TableS4.xls","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/f0c3020c472695765df176b5.xls"},{"id":100406989,"identity":"640517bf-747f-4119-b60a-b1000a9d4333","added_by":"auto","created_at":"2026-01-16 13:03:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1818187,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Figure S1. Supplementary Figures: Bubble plot showing differential ligand-receptor interactions between other cell types and B cells in MI and NMI samples. Bubble size represents interaction strength; color indicates differential magnitude.\u003c/p\u003e","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8525731/v1/b699e785b9d3e14502955892.docx"}],"financialInterests":"","formattedTitle":"Single-cell profiling reveals systemic immune reprogramming in muscle-invasive urothelial carcinoma","fulltext":[{"header":"Background","content":"\u003cp\u003eUrothelial carcinoma (UC) of the bladder is one of the most common malignancies of the urinary tract, accounting for more than 90% of bladder cancers worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). UC can also arise in the upper urinary tract, including the renal pelvis and ureter, collectively referred to as upper tract urothelial carcinoma (UTUC)(\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), which shares similar histopathological features but distinct clinical and molecular characteristics compared to bladder UC(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Clinically, UC is categorized into non-muscle-invasive (NMI) and muscle-invasive (MI) subtypes, which differ markedly in biological behavior, therapeutic response, and prognosis(\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). While most NMI tumors are confined to the mucosa and can be managed by transurethral resection and intravesical therapy(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), approximately 10\u0026ndash;30% of cases progress to the MI stage, characterized by high recurrence rates, metastasis, and poor clinical outcomes(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Understanding the cellular and molecular mechanisms driving this transition remains a major challenge in UC research.\u003c/p\u003e \u003cp\u003eAccumulating evidence has highlighted the critical role of the immune system in UC pathogenesis and progression(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Both tumor-infiltrating and peripheral immune cells contribute to tumor evolution through complex regulatory networks involving immune activation, suppression, and metabolic adaptation(\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Among these, T cells, B cells, and monocytes play key roles in antitumor immunity and immune evasion(\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). However, the detailed transcriptional programs and intercellular communication patterns underlying these immune alterations remain incompletely understood(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).While tumor-infiltrating immune cells have been extensively studied, peripheral blood mononuclear cells (PBMCs) offer a minimally invasive window into systemic immune changes in UC and may help identify biomarkers of disease progression and therapeutic response.\u003c/p\u003e \u003cp\u003eRecent advances in single-cell RNA sequencing (scRNA-seq) have enabled high-resolution characterization of immune cell heterogeneity and functional states, providing unprecedented insights into tumor-associated immune dynamics(\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Previous scRNA-seq studies have primarily focused on tumor-infiltrating lymphocytes within the bladder microenvironment, whereas the peripheral immune landscape\u0026mdash;reflecting systemic immune responses and potential biomarkers of disease progression\u0026mdash;has received less attention(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Characterizing the peripheral immune system at single-cell resolution may therefore provide a more comprehensive understanding of host-tumor interactions and identify novel immunotherapeutic targets(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we performed scRNA-seq on PBMCs from patients with MIUC and NMIUC, including both bladder and upper tract cases, and integrated publicly available datasets from healthy controls and additional MI cases. By profiling over 140,000 high-quality immune cells, we systematically delineated the immune cell composition, transcriptional heterogeneity, and intercellular communication networks associated with disease stage. Our analysis revealed profound immune-metabolic reprogramming in MIUC, characterized by enhanced mitochondrial metabolism, chromatin remodeling, and suppression of immune signaling pathways. We further identified TGF-β\u0026ndash;mediated monocyte\u0026ndash;T-cell crosstalk as a potential driver of systemic immune suppression in MIUC. These findings provide a comprehensive single-cell atlas of peripheral immune alterations in UC and uncover potential molecular targets for immunomodulatory therapy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIsolation of PBMC\u003c/h2\u003e \u003cp\u003ePeripheral blood samples were collected from patients prior to surgery using 10-ml ethylenediaminetetraacetic acid (EDTA) tubes. Upon receipt, whole blood was centrifuged at 700\u0026times;g for 10 min. Plasma was removed and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. PBMCs were isolated via density gradient centrifugation using Ficoll400 cell separation solution with a density of 1.077 g/ml (17-1440-03; GE Healthcare, USA). Red blood cells were removed using ACK lysis buffer (A10492-01; Gibco, USA). Cell viability was assessed by Trypan blue staining, and viable cells were immediately processed for scRNA-seq.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003escRNA-seq\u003c/h3\u003e\n\u003cp\u003eDroplet-based scRNA-seq was performed on the MGI-DNBSEQ-T7RS platforms, following manufacturer instructions. PBMC were resuspended in PBS containing 0.04% bovine serum albumin (BSA) (9048-46-8; Fisher Bioreagents, USA) and loaded onto the MGI platform. Libraries were prepared and sequenced on an DNBSEQ-T7RS flow cell. To minimize batch effects, matched specimens from the same patient were processed in parallel during library preparation and sequenced on the same flow cell.\u003c/p\u003e\n\u003ch3\u003ePublicly available data\u003c/h3\u003e\n\u003cp\u003eAdditional scRNA-seq data were obtained from the Gene Expression Omnibus (GEO) (accessions: GSE149689, GSE157278, GSE267718). Bulk RNA-seq data for validation were sourced from The Cancer Genome Atlas Program (TCGA).\u003c/p\u003e\n\u003ch3\u003eScRNA-seq data preprocessing and quality control\u003c/h3\u003e\n\u003cp\u003eRaw FASTQ files were processed using DNBC4Tools (v2.1.3). Reads were aligned to the Homo sapiens GRCh38.114 reference genome, and gene expression counts were generated based on cell barcodes and unique molecular identifiers (UMIs). Subsequent quality control and filtering were performed in Seurat (v5.3.0), retaining genes expressed in \u0026ge;\u0026thinsp;3 cells and cells with \u0026ge;\u0026thinsp;200 detected genes. Data were normalized using the LogNormalize method, and confounding factors\u0026mdash;such as mitochondrial gene expression and total UMI counts\u0026mdash;were regressed out.\u003c/p\u003e\n\u003ch3\u003eDoublet removal\u003c/h3\u003e\n\u003cp\u003ePotential doublets were identified and removed using DoubletFinder (v.2.0.3)(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The assumed doublet formation rate was set to 0.8% per 1,000 cells. Cells were classified as doublet-low confidence, doublet-high confidence, or singlet. Only singlets were retained for downstream analysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDimensionality reduction and clustering\u003c/h2\u003e \u003cp\u003eFiltered data normalized and scaled using \u003cem\u003eNormalize Data\u003c/em\u003e and \u003cem\u003eScale Data\u003c/em\u003e functions of Seurat (v5.3.0). The top 2,000 variable genes were selected for principal component analysis (PCA) using the \u003cem\u003eFindVariable Features\u003c/em\u003e function. The top 30 principal components were used for clustering via the \u003cem\u003eFindClusters\u003c/em\u003e function. \u003cem\u003eHarmony\u003c/em\u003e(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) was used to remove batch effect between samples. Lastly, a two-dimensional visualization was generated using the \u003cem\u003euniform manifold approximation and projection\u003c/em\u003e (UMAP) algorithm.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDifferential expression analysis\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified using the Seurat \u003cem\u003eFindMarkers\u003c/em\u003e function in Seurat with a Wilcoxon likelihood-ratio test. Genes present in over 10% of cells within a cluster and exhibiting an average log fold change (LogFC) value exceeding 0.25 were classified as DEGs.\u003c/p\u003e\n\u003ch3\u003ePathway enrichment analysis\u003c/h3\u003e\n\u003cp\u003eTo explore the potential functions of DEGs, we performed Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis using the clusterProfiler (v4.14.6)(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). GSEA was conducted using the Reactome pathway gene set (c2.cp.reactome.v2024.1.Hs.symbols.gmt), without pre-filtering by logFC, allowing the normalized enrichment score (NES) to reflect the overall directional trends. For GO enrichment analysis, DEGs were used as input, and pathways with an adjusted p-value (p_adj) less than 0.05 were considered significantly enriched.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime analysis\u003c/h2\u003e \u003cp\u003ePseudotime analysis was performed using Monocle2 (v2.34.0)(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), which unsupervisedly sorted single cells to reconstruct cell fate trajectories and evaluate pseudo time-dependent gene expression. Dimension reduction was carried out using DDRTree (v0.1.5). Cell trajectories were visualized based on subtypes and states, and the \u003cem\u003eBasic Differential Analysis\u003c/em\u003e algorithm identified DEGs associated with pseudotime progression. Branch expression analysis modeling (BEAM) analysis was further used to identify genes regulated in a branching-dependent manner.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscription factor (TFs) regulatory networks\u003c/h2\u003e \u003cp\u003eTranscription factor regulatory analysis was performed using pySCENIC (v0.12.1)(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e) to identify TFs and their target genes. Regulon Specificity Score (RSS) was calculated based on cell group information to screen for group-specific TFs. The top 10 TFs in the MI group were selected, and regulatory interactions with a weight greater than 10 were used to construct the TF-target regulatory networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell communication analysis\u003c/h2\u003e \u003cp\u003eCell-cell interactions were inferred with CellChat (v.1.6.1) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) and NicheNet (v.2.2.1)(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) in R based on established ligand-receptor pairs provided by each tool. The top 20 active ligands inferred by NicheNet were used prioritize high-confidence interactions .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePseudobulk differential expression analysis\u003c/h2\u003e \u003cp\u003eBased on the Seurat object, the MI and NMI groups were selected for differential expression analysis. A pseudobulk approach was applied by aggregating single-cell expression matrices per patient using the \u003cem\u003ePatientID\u003c/em\u003e metadata(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The sum method was used to calculate the total gene counts within each sample, generating a pseudobulk expression matrix. Differential expression between the MI and NMI groups was assessed with DESeq2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGene signature module scoring\u003c/h2\u003e \u003cp\u003eModule scores were calculated to evaluate the expression activity of predefined gene signatures in single cells using the \u003cem\u003eAddModuleScore\u003c/em\u003e function in Seurat (v.5.3.1.9999). Each score represents the overall expression level of a given gene set within individual cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003emRNA expression and clinical data for TCGA-BLCA were obtained from the TCGA database. Only tumor samples with matched expression and clinical information were included. Patients were stratified into high- (\u0026ge;\u0026thinsp;75th percentile) and low-expression (\u0026le;\u0026thinsp;25th percentile) groups based on target gene expression. Overall survival (OS, in months) was analyzed using Kaplan-Meier curves, and group differences were assessed with the log-rank test. Analyses were performed using R packages \u003cem\u003esurvival\u003c/em\u003e (v.3.8.3) and \u003cem\u003esurvminer\u003c/em\u003e (v.0.5.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in R (v.3.8.3). Differential gene expression was assessed using Wilcoxon tests or pseudobulk DESeq2, and pathway enrichment was conducted using GSEA and GO analysis (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Correlations between cell type proportions were calculated using Pearson correlation and tested for significance with Student\u0026rsquo;s t-test. Cell-cell communication was inferred using \u003cem\u003eCellChat\u003c/em\u003e and \u003cem\u003eNicheNet\u003c/em\u003e, with gene module scores computed using the Seurat \u003cem\u003eAddModuleScore\u003c/em\u003e function. Survival analyses were performed using Kaplan-Meier curves and log-rank tests, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell transcriptional landscape of peripheral immune cells in UC\u003c/h2\u003e \u003cp\u003eTo characterize the systemic immune landscape of UC, we performed scRNA-seq on PBMCs from 20 UC patients using the MGI-DNBSEQ-T7RS platform (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The cohort included five patients with MIUC and fifteen with NMIUC. In contrast to previous studies focused primarily on bladder UC, our study also included patients with UTUC, providing a broader representation of disease-associated immune alterations(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In addition, we integrated publicly available datasets from the GEO database, including nine healthy control (HC) samples from GSE149689 and GSE157278, as well as three MIUC samples from GSE267718(\u003cspan additionalcitationids=\"CR48 CR49 CR50 CR51\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). After integrating all datasets, a total of 32 samples were obtained, comprising 9 HC, 15 NMI, and 8 MI samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Following stringent quality control, 144,019 high-quality cells were retained for downstream analysis, including 37,006 (25.67%) from HC, 67,349 (46.76%) from NMI, and 39,664 (27.54%) from MI samples. We identified 7 major cell clusters using graph-based clustering of UMAP (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), including T cells (expressing \u003cem\u003eCD3D,CD3E\u003c/em\u003e), NK cells (\u003cem\u003eNKG7,GNLY\u003c/em\u003e), classical monocytes (\u003cem\u003eCD14, VCAN, LYZ, FCN1\u003c/em\u003e), non-classical monocytes (\u003cem\u003eFCGR3A, IFITM3\u003c/em\u003e), B cells (\u003cem\u003eCD79A, CD79B, IGHM\u003c/em\u003e, \u003cem\u003eIGHM\u003c/em\u003e), conventional dendritic cells (cDC cells; \u003cem\u003eCD1C, FCER1A, CD1E\u003c/em\u003e), platelets \u003cem\u003e(PPBP, PF4, TUBB1\u003c/em\u003e), and cycling cells (\u003cem\u003eSTMN1, TOP2A, MKI67\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D; Table S3). The proportion of cell populations in PBMCs varies among individuals. T cells and NK cells account for 44\u0026ndash;94%, B account for 1\u0026ndash;19% ,class monocytes account for 1\u0026ndash;34%, no-class monocytes account for 0\u0026ndash;10%,and dendritic cells account for only 0\u0026ndash;2% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Consistent with known PBMC composition, the scRNA-seq data of this study showed that the isolated PBMCs consisted largely of T and NK cells (77.17%), followed by monocytes (13.12%) and B cells (7.14%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Although the overall distribution of immune subsets was largely comparable among the three groups, cycling cells, cDCs, and non-classical monocytes were significantly enriched in HC group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). No novel or missing cell subsets were found in the MI group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of T and NK cell heterogeneity and transcriptomic changes in UC\u003c/h2\u003e \u003cp\u003eConsidering the central role of T and NK cells in the antitumor immunity, their compositional and molecular alterations throughout the disease progression may be closely linked to the pathogenesis of UC. Thus, we further investigated their dynamic changes at a finer resolution by re-clustering T and NK cells. This analysis yielded 20 distinct subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-C). NK cells, characterized by high expression of \u003cem\u003eGNLY, NKG7\u003c/em\u003e, and \u003cem\u003eKLRF1\u003c/em\u003e, were further divided into five subclusters. CD4⁺T cells were further subdivided into seven transcriptionally distinct subclusters: four na\u0026iuml;ve subsets (CD4_Naive_RORC, CD4_Naive_GPR183, CD4_Naive_CCR7, and CD4_Naive_LEF1) highly expressing \u003cem\u003eLEF1\u003c/em\u003e, \u003cem\u003eCCR7\u003c/em\u003e, and \u003cem\u003eSELL\u003c/em\u003e; a regulatory T cell (Treg) cluster (CD4_Treg_FOXP3) marked by \u003cem\u003eFOXP3\u003c/em\u003e, \u003cem\u003eIL2RA\u003c/em\u003e, and \u003cem\u003eTNFRSF4\u003c/em\u003e; a memory T cell (Tm) cluster (CD4_Tm_IL7R) expressing \u003cem\u003eIL7R\u003c/em\u003e, \u003cem\u003eLTB\u003c/em\u003e, and \u003cem\u003eKLF2\u003c/em\u003e; and a stress-responsive T cell subset (CD4_STR_HSP1A1) with elevated \u003cem\u003eHSPA1A\u003c/em\u003e, \u003cem\u003eFOS\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e. CD8⁺ T cells were classified into five transcriptionally distinct subclusters: one na\u0026iuml;ve subset (CD8_Naive) expressing \u003cem\u003eCCR7, SELL, IL7R\u003c/em\u003e, and \u003cem\u003eTCF7\u003c/em\u003e; three effector subsets (CD8_Teff_MSC-AS1, CD8_Teff_MIR142HG, and CD8_Teff_ZNF683) with elevated expression of cytotoxic molecules \u003cem\u003eGZMB, GZMA\u003c/em\u003e, and \u003cem\u003eGZMH\u003c/em\u003e; a stress-responsive subset (CD8_STR_IFNG) displaying high expression of stress-induced genes; a memory-like cluster (CD8_Tem_GZMK) expressing \u003cem\u003eGZMK\u003c/em\u003e; and a mucosal-associated invariant T (MAIT) cell population (CD8_MAIT) defined by elevated expression of \u003cem\u003eSLC4A10, KLRB1\u003c/em\u003e, and \u003cem\u003eZBTB16\u003c/em\u003e. In addition, we detected another T cell cluster with undefined characteristics, which we designated as T_unknow. The proportions of these cell populations showed no obvious differences between MI and NMI samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eComparative transcriptomic analysis revealed that in MIUC genes related to histone-coding (e.g., \u003cem\u003eH3F3B, H2AFZ, HIST1H2AC\u003c/em\u003e), mitochondrial function (e.g., \u003cem\u003eSLC25A6, ATP5MD\u003c/em\u003e), and protein translation (e.g., \u003cem\u003eLARS, YARS\u003c/em\u003e) are significantly upregulated, suggesting enhanced chromatin remodeling and metabolic and biosynthetic activity supporting tumor invasiveness and migratory potential (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG; Table S4)(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). In addition, the single-cell RNA-seq data were subjected to a \u0026ldquo;pseudobulk\u0026rdquo; processing approach, followed by differential expression analysis using DESeq2. The results showed strong concordance between the DESeq2-based differential expression results and those obtained from the FindMarkers analysis, further supporting the robustness of the identified DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Pathway analysis showed indicated that NMI tumors showed upregulation of immune-related pathways, including B cell and T cell activation, Fc-gamma receptor signaling, and cell adhesion, indicating enhanced immune activity. In contrast, MI tumors upregulated pathways related to protein homeostasis, RNA splicing, mitochondrial function, and apoptosis, suggesting higher protein metabolic stress and mitochondrial activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Reactome pathway analysis revealed downregulation of Fc receptor-mediated immune signaling in MI tumors, further indicating a reduced immune response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF)(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Overall, NMI tumors exhibited an immune-active phenotype, whereas MI tumors displayed metabolic reprogramming and potential immune suppression.\u003c/p\u003e \u003cp\u003eFunctional module scoring analysis in CD4⁺T cells and CD8⁺T cells further supported these trends(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In CD4⁺T cells, MI samples exhibited higher scores for anti-apoptosis, glycolysis, oxidative phosphorylation (OXPHOS), and stress response modules, whereas NMI samples showed higher scores for chemokine/chemokine receptor, cytokine/cytokine receptor, cytotoxicity, and activation/effector function modules. In CD8 T cells, MI samples demonstrated elevated scores for anti-apoptosis, pro-apoptosis, fatty acid metabolism, glycolysis, oxidative phosphorylation, and stress response modules, whereas NMI samples displayed higher scores for chemokine/chemokine receptor, cytotoxicity, and activation/effector function modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Together, these results indicate that the functional states of T cells mirror the broader metabolic and immune characteristics of the T/NK cell population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eSingle-cell pseudotime analysis implicates CD4⁺ T cells in UC progression\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe analyzed the cell trajectory of representative CD4⁺T cells from UC and identified five cell states (State 1\u0026ndash;State 5). A major branching trajectory was observed, representing the differentiation of na\u0026iuml;ve T cells into Treg cells and Tm cells. We found that both MI and NMI groups originated from na\u0026iuml;ve T cells. At the end of the differentiation trajectory, State 1 was exclusively present in MI group, whereas States 2\u0026ndash;4 showed no substantial differences between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, E). This finding suggests a distinct evolutionary trajectory characterized by the specific emergence or enrichment of State 1 cells associated with muscle invasion. We further explored the DEGs associated with MI differentiation trajectories and categorized them into five modules. DEGs in Module 1 and 4 were primarily expressed in State 1 CD4⁺T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, G). Notably, genes in Module 1 overlapped significantly with the upregulated DEGs associated with MI, including \u003cem\u003eATP5MD, ATP5MPL, SLC25A6, H2AFZ, H3F3A\u003c/em\u003e, and \u003cem\u003eH3F3B\u003c/em\u003e. These genes were markedly upregulated in the MI-specific State 1, suggesting that they may play a critical role in the mechanisms underlying muscle invasion in UC.\u003c/p\u003e \u003cp\u003eSCENIC analysis identified distinct TF regulatory networks across UC cells. TFs and their regulatory networks showed significant differences between MI and NMI. Using MI and NMI as grouping criteria, we identified the top 10 TFs with the highest AUC scores in each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH). Subsequently, a regulatory network was constructed in the MI group between these top 10 TFs and their downstream target genes. The results showed that YBX1 emerged as a key regulator targeting multiple downstream genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI), including \u003cem\u003eSLC25A6\u003c/em\u003e\u0026mdash;a gene also significantly upregulated in the MI-specific State1 CD4\u003csup\u003e+\u003c/sup\u003e T cells determined by Monocle2 and in MI vs. NMI comparisons. Finally, the differential expression of \u003cem\u003eSLC25A6\u003c/em\u003e between MI and NMI samples was validated using the BLCA dataset from the TCGA database. The results revealed an increasing trend of \u003cem\u003eSLC25A6\u003c/em\u003e expression in the MI group but with no statistical significance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). Collectively, these findings suggest that \u003cem\u003eSLC25A6\u003c/em\u003e may be closely associated with muscle invasion in UC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFunctional reprogramming of B cells in MIUC\u003c/h2\u003e \u003cp\u003eBased on variations in the expression density of membrane surface molecules, B cells were segregated into seven distinct subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Each subset exhibited a unique expression pattern and biological activity, suggesting the intricate involvement of B cells in immune regulation. Four clusters represented na\u0026iuml;ve B cells (B_Naive_IGLC5, B_Naive_LAMC1, B_Naive_IGHD, B_Naive_SLCO2B1), characterized by the expression of canonical markers \u003cem\u003eCD79A, IL4R, IGHD, IGHM\u003c/em\u003e, and \u003cem\u003eTCL1A\u003c/em\u003e. A platelet-admixed na\u0026iuml;ve B cell cluster (B_Naive_Platelets) co-expressed platelet markers (\u003cem\u003ePPBP, PF4, GP9\u003c/em\u003e). Two clusters corresponded to memory B cells (B_Memory_FCGR2C and B_Memory_TNFRSF13B), marked by the expression of \u003cem\u003eTGHA1, IGHG1, CD27\u003c/em\u003e, and \u003cem\u003eTNFRSF18B\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Among these seven B cell clusters, only B_Memory_TNFRSF13B showed a significantly higher abundance in NMI compared to MI samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), whereas the proportions of other clusters did not differ significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDEG analysis in B cells revealed that MI-upregulated genes were predominantly enriched in chromatin remodeling, cytoskeletal reorganization, energy metabolism, and immune regulation, exhibiting a substantial overlap with those upregulated in T/NK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF, Table S4). We visualized the representative pathways upregulated in B cells from NMI and MI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). In NMI, upregulated pathways mainly involved T cell activation, lymphocyte proliferation, antigen receptor-mediated signaling, and immune cell adhesion, indicating a close interaction of B cells with an active immune microenvironment. Supplementary analysis of REACTOME pathways revealed enrichment in REACTOME_FORMATION_OF_THE_BETA_CATENIN_TCF_TRANSACTIVATING_COMPLEX and REACTOME_GENERATION_OF_SECOND_MESSENGER_MOLECULES (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), suggesting that NMI B cells not only exhibit functional immune activity but also enhance proliferation and functional competence via Wnt/Beta-catenin signaling and second messenger pathways, supporting a functionally active B cell state in NMI. In contrast, in MI, upregulated pathways were enriched in RNA processing and splicing, mitochondrial function, protein translation, and telomere maintenance, suggesting a shift toward metabolic and survival programs at the expense of immune activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eFunctional and immunological differences of monocytes in UC\u003c/h2\u003e \u003cp\u003ePeripheral blood monocytes were identified and classified into classical (CD14⁺) and non-classical monocytes (FCGR3A⁺)(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Classical monocytes were further subclustered into seven transcriptionally distinct subsets, whereas non-classical monocytes into two subpopulations (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH, I). An additional cluster co-expressing \u003cem\u003ePPBP\u003c/em\u003e was annotated as platelet-contaminated classical monocytes. In MI, class_Mono_1 and class_Mono_2 were significantly enriched, whereas class_Mono_4 was predominantly enriched in NMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), indicating that distinct monocyte subsets may exert different functions in tumor progression and immune regulation. Notably, the MI-enriched monocyte subsets (class_Mono_1 and class_Mono_2) exhibited higher scores of M2 polarization, angiogenesis, and phagocytosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), suggesting their potential involvement in tumor-associated immunosuppression, microenvironment remodeling, and enhanced invasive capacity (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Compared with NMI, genes upregulated in MI were primarily involved in chromatin remodeling, mitochondrial metabolism, protein degradation, signal transduction, and non-coding RNA regulation, and showed strong concordance with DEGs in T and B cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD; Table S4), suggesting potential coordinated functional reprogramming between monocytes and lymphocytes in shaping the immune microenvironment associated with muscle invasion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn UC, monocytes exhibit distinct functional reprogramming between tumor stages. In NMI, upregulated genes were mainly enriched in pathways related to protein translation, ribosomal biogenesis, actin cytoskeleton regulation, and T cell activation, suggesting a metabolically active and immune-supportive monocyte phenotype that may facilitate adaptive immune responses. In contrast, monocytes from MI showed enrichment in antiviral defense, type I interferon production, pattern recognition receptor and NF-κB signaling, as well as lysosomal organization and RNA splicing pathways. These features indicate a shift toward a \u0026ldquo;virus-like\u0026rdquo; innate immune activation and chronic inflammatory state (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Collectively, NMI-associated monocytes appear to promote immune activation, whereas MI-associated monocytes display sustained inflammation and immune tolerance, reflecting a functional transition from an immunostimulatory phenotype to a pro-inflammatory yet immunosuppressive state during tumor progression.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eTGF-β-mediated monocyte-CD8⁺ T cell crosstalk drives immune remodeling in MIUC\u003c/h2\u003e \u003cp\u003eTo further investigate the potential role of the MI-enriched monocyte subsets class_Mono_1 and class_Mono_2 in disease progression, we extracted cells from MI samples and performed correlation analysis across cellular subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). The results revealed that class_Mono_1 and class_Mono_2 exhibited a significant negative correlation with the effector memory CD8⁺T cell subset CD8_Tem_GZMK (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), suggesting potential suppression of CD8⁺ T cell effector function. To further elucidate the potential intercellular communication mechanisms, we conducted CellChat analysis to identify ligand\u0026ndash;receptor interactions between monocytes and CD8_Tem_GZMK cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). We found that monocytes highly expressed TGF-β family ligands (\u003cem\u003eTGFB1, TGFB2\u003c/em\u003e, and \u003cem\u003eTGFB3\u003c/em\u003e), which could interact with corresponding receptors \u003cem\u003eTGFBR1, TGFBR2\u003c/em\u003e, and \u003cem\u003eACVR1\u003c/em\u003e on CD8_Tem_GZMK cells, indicating that TGF-β signaling may mediate the crosstalk between these populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, we performed NicheNet analysis by designating class_Mono_1 and class_Mono_2 as sender cells and CD8_Tem_GZMK as receiver cells, with genes upregulated in CD8_Tem_GZMK cells from MI samples defined as target genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). TGFB1 was predicted to act through its receptor TGFBR3, regulating downstream target genes such as \u003cem\u003eCITED2, JUN\u003c/em\u003e, and \u003cem\u003eSLC7A5\u003c/em\u003e. Further survival analysis based on the TCGA cohort revealed that high expression of \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eSLC7A5\u003c/em\u003e was significantly associated with reduced patient survival, suggesting that these genes play important roles in tumor progression and immune regulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). In the TCGA-BLCA dataset, both \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eSLC7A5\u003c/em\u003e showed higher expression in MI samples compared with NMI samples, although only \u003cem\u003eJUN\u003c/em\u003e reached statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Consistently, at the single-cell level, \u003cem\u003eJUN\u003c/em\u003e expression in CD8⁺T cells was markedly increased in MI samples relative to NMI samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). These observations support the involvement of these genes in immune alterations driven by MI. Taken together, these results suggest that MI-enriched monocytes (class_Mono_1 and class_Mono_2) may suppress effector CD8⁺T cell activation through the TGF-β\u0026ndash;TGFBR3 signaling axis, regulating downstream target genes (\u003cem\u003eCITED2, JUN, SLC7A5\u003c/em\u003e). The association of elevated \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eSLC7A5\u003c/em\u003e expression with poor patient survival, combined with their upregulation in CD8⁺ T cells from MI samples, highlights their roles in MI-driven immune dysregulation, with \u003cem\u003eJUN\u003c/em\u003e appearing particularly prominent.\u003c/p\u003e \u003cp\u003eFinally, we applied CellChat analysis to compare intercellular communication among all cell subsets between MI and NMI tissues. The results revealed that both the overall communication strength and the number of interactions were markedly increased in the MI group compared with the NMI group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), indicating a more active immune crosstalk under MI conditions. Notably, B cells exhibited the most pronounced increase in both the number and strength of outgoing signaling interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH), suggesting that they may play a pivotal role in reshaping the immune microenvironment during MI. Further ligand-receptor pair analysis demonstrated enhanced T cell- and monocyte-B cell interactions in the MI group, primarily involving antigen presentation (HLA class II molecules-CD4) and immune-regulatory signaling pathways (PTPRC\u0026ndash;CD22, SELPLG\u0026ndash;SELL) (Figure. S1). The enhancement of these interactions suggests that, under MI conditions, T cells and monocytes may promote B-cell activation and function by facilitating antigen presentation and adhesion signaling, thereby contributing to the amplification of the immune response.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we generated a comprehensive single-cell atlas of peripheral blood immune cells in patients with MI and NMI UC. By integrating our scRNA-seq data with publicly available datasets, we profiled over 140,000 immune cells, yielding high-resolution insights into immune composition, functional states, and intercellular communication in UC. In contrast to most previous studies confined to bladder UC, our cohort also incorporated UTUC cases, thereby capturing a broader spectrum of disease manifestations and enhancing the generalizability of our findings(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Our results reveal substantial stage-specific immune reprogramming, characterized by metabolic adaptation, chromatin remodeling, and immune suppression during MI progression.\u003c/p\u003e \u003cp\u003eT and NK cells, the major components of peripheral immune cells, exhibit marked transcriptional reprogramming in MIUC(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Compared with NMI patients, MI T cells show significant downregulation of the FcγR signaling pathway. Fcγ receptors on immune cells mediate immune complex recognition and regulate antibody-dependent cellular cytotoxicity, phagocytosis, and cytokine secretion(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). In UC, FcγR activation is generally associated with the immune activity of T/B cells and monocytes, and its downregulation suggests suppressed antibody-dependent immune responses, potentially contributing to immune evasion and tumor progression(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). Pseudotime analysis revealed an MI-specific CD4⁺ T-cell differentiation trajectory accompanied by upregulation of the transcription factor YBX1. YBX1, a key transcriptional regulator, modulates metabolic genes including \u003cem\u003eSLC25A6\u003c/em\u003e, enhancing mitochondrial ADP/ATP exchange and energy metabolism, thereby supporting T-cell survival and stress adaptation. Notably, this metabolic adaptation coexists with diminished immune effector function, resulting in a \u0026ldquo;metabolically active but immunosuppressed\u0026rdquo; state, which may underlie systemic immune suppression in MIUC. Supporting this, a study in pancreatic cancer cells demonstrated that \u003cem\u003ePTPMT1\u003c/em\u003e maintains mitochondrial homeostasis via interactions with \u003cem\u003eSLC25A6\u003c/em\u003e and \u003cem\u003eNDUFS2\u003c/em\u003e, with \u003cem\u003ePTPMT1\u003c/em\u003e inhibition causing mitochondrial damage, loss of membrane potential, and tumor cell death. In our MIUC peripheral blood single-cell transcriptome data, \u003cem\u003eSLC25A6\u003c/em\u003e was significantly upregulated, suggesting that immune cells may engage similar mitochondrial regulatory mechanisms to adapt to the tumor-associated metabolic and stress environment(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). We thus hypothesize that MIUC peripheral immune cells enhance \u003cem\u003eSLC25A6\u003c/em\u003e function to sustain mitochondrial energy supply and survival, contributing to systemic immune suppression. Future studies should examine \u003cem\u003eSLC25A6\u003c/em\u003e protein expression, mitochondrial function, energy metabolism, and cell functional status in PBMCs to validate this hypothesis and explore potential immunotherapeutic interventions.\u003c/p\u003e \u003cp\u003eB cells and monocytes also undergo substantial functional reprogramming during MIUC progression. MI B cells shift from an immune-active to a metabolically focused, survival-oriented phenotype, characterized by enhanced mitochondrial function, upregulated protein translation, and telomere maintenance pathways. This metabolic prioritization may allow B cells to survive in the tumor-associated systemic environment but likely comes at the cost of immune effector functions. Similarly, classical monocytes in MI acquire pro-tumor characteristics, displaying enhanced M2 polarization, angiogenesis, and phagocytosis, suggesting a role in establishing a tumor-supportive microenvironment and facilitating immune evasion. Notably, MI-enriched monocyte subsets suppress CD8⁺ T cell effector functions via TGF-β signaling, with downstream targets \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eSLC7A5\u003c/em\u003e, whose high expression correlates with poor patient survival, indicating a potential role in systemic immune suppression and disease progression. TGF-β is known in late-stage tumors to promote epithelial-mesenchymal transition (EMT), enhance tumor migration and invasion, and modulate the immune microenvironment to facilitate immune escape, highlighting its potential as a therapeutic target, particularly in immunotherapy combination strategies(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). \u003cem\u003eJUN\u003c/em\u003e, as an AP-1 transcription factor, promotes cell cycle progression, upregulates key cell cycle genes, and suppresses the p53/p21 axis to enhance proliferation; previous in vitro and in vivo studies support its role as a positive regulator of tumor growth(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). However, these studies primarily focus on tumor tissues, and its function in PBMCs has not been reported. In our MIUC peripheral blood single-cell transcriptomic data, we observed upregulation of \u003cem\u003eJUN\u003c/em\u003e, suggesting that it may contribute to immune suppression in peripheral immune cells and support tumor progression. Enhanced intercellular communication among T cells, B cells, and monocytes in MI further underscores coordinated immune remodeling, likely mediated through ligand-receptor interactions and metabolic crosstalk. Mechanistically, we hypothesize that this functional reprogramming integrates metabolic adaptation with immunosuppressive signaling, enabling immune cells to survive under tumor-induced stress while attenuating antitumor responses. Future studies should validate these pathways at the protein and functional levels, including mitochondrial activity, cytokine secretion, and cytotoxicity, to establish causal links between immune-metabolic reprogramming and systemic immune suppression in MIUC.\u003c/p\u003e \u003cp\u003eThe limitations of this study include the relatively small sample size of the MI patients (n\u0026thinsp;=\u0026thinsp;8) compared with the NMI patients(n\u0026thinsp;=\u0026thinsp;20), which may affect the detection of differences in certain cell subsets. In addition, some analyses did not reach statistical significance (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05) but showed observable trends, suggesting potential biological relevance. Future studies with larger, independent cohorts are needed to validate these observations and confirm their statistical and biological significance.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCollectively, this study revealed that peripheral immune cells in MIUC undergo stage-specific functional reprogramming, combining immune suppression, metabolic adaptation, and enhanced intercellular crosstalk, highlighting potential targets for immunomodulatory therapy. Our findings highlight the complex interplay among immune suppression, metabolic adaptation, and intercellular crosstalk in MIUC. By incorporating both bladder UC and UTUC cases, this study provides a more comprehensive view of peripheral immune dysregulation across the UC spectrum. Our single-cell atlas serves as a valuable framework for understanding systemic immune alterations and identifying potential immunomodulatory targets, including TGF-β signaling and metabolic regulators, for therapeutic intervention. Future studies with larger, anatomically stratified cohorts, experimental validations and mechanistic studies are warranted to further understand these immune-metabolic shifts and to validate their clinical relevance across different UC sites.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUrothelial Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMuscle-Invasive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-Muscle-Invasive\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUTUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUpper Tract Urothelial Carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePeripheral Blood Mononuclear Cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003escRNA-seq\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-Cell RNA Sequencing\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy Control\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Ontology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMAP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ecDC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConventional Dendritic Cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTreg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegulatory T Cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMAIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMucosal-Associated Invariant T Cells\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOXPHOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOxidative Phosphorylation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTranscription Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegulon Specificity Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBEAM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBranch Expression Analysis Modeling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCENIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-Cell Regulatory Network Inference and Clustering\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNicheNet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLigand-Receptor Signaling Inference Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCellChat\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCell\u0026ndash;Cell Communication Analysis Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDESeq2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferential Expression Analysis Tool\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAmmonium-Chloride-Potassium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBovine Serum Albumin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnique Molecular Identifier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Yantai Yuhuangding Hospital (IRB#: 2025-887). All patients provided written informed consent before enrollment. The patient provided written informed consent per the Declaration of Helsinki principles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have reviewed and approved the final version of the manuscript and consent to its publication in the journal BMC Cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw sequencing data have been deposited in the Genome Sequence Archive (GSA) at NGDC under accession number HRA014953. (https://ngdc.cncb.ac.cn/gsa/)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Taishan Scholar Program of Shandong Province (Grant No. TSQN202103198), the Shandong Provincial Natural Science Foundation (Grant No. ZR2024MH305), the Program for Scientific and Technological Innovation Development in Yantai City (Grant No. 2024YT06000818), the Medical and Health Technology Program in Shandong province (Grant No. 202402050885) and National Key Laboratory of Proteomics Open Research Fund (Grant No. SKLP-O202209).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.C., Y.Y., and J.Y. contributed equally to the study, including study design, data analysis and manuscript writing. J.G., H.X., and X.L. assisted with sample collection and data processing. C.L., J.W. and Y.W. supervised the study and provided critical guidance on study design and interpretation. All authors reviewed the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaginala K, Barsouk A, Aluru JS, Rawla P, Padala SA, Barsouk A. Epidemiol Bladder Cancer Med Sci (Basel). 2020;8(1).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Oncogene. 2008;27(3):366\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8525731/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8525731/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUrothelial carcinoma (UC) is classified into non-muscle-invasive (NMI) and muscle-invasive (MI) subtypes, with the latter associated with poor prognosis. Although tumor-infiltrating immune responses have been extensively studied, how the peripheral immune system is reprogrammed during disease progression remains largely unexplored. Understanding systemic immune alterations may reveal mechanisms underlying disease progression. Unlike most previous studies focusing solely on bladder UC, our cohort also included upper tract UC cases, providing a broader view of systemic immune alterations in urothelial carcinoma.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed single-cell RNA sequencing of peripheral blood mononuclear cells from 20 UC patients (5 MI and 15 NMI), integrating publicly available healthy control and MI datasets. A total of 144,019 high-quality cells were analyzed to characterize immune cell composition, functional states, and intercellular communication.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSeven major immune cell populations were identified, with T and NK cells predominating. MI samples exhibited upregulation of genes related to chromatin remodeling, mitochondrial metabolism, and protein translation, alongside downregulation of immune signaling pathways, indicating metabolic stress and immune suppression. Pseudotime analysis revealed an MI-specific CD4⁺T-cell differentiation trajectory enriched in genes such as \u003cem\u003eSLC25A6\u003c/em\u003e and \u003cem\u003eH3F3B\u003c/em\u003e and regulated by YBX1. B cells and monocytes showed functional reprogramming, with MI B cells metabolically active and NMI B cells immune-active. MI classical monocytes exhibited pro-tumor phenotypes and suppressed CD8⁺T-cell function via TGF-β signaling, with downstream \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003eSLC7A5\u003c/em\u003e correlating with poor survival. Intercellular communication among T cells, B cells, and monocytes was enhanced in MI.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThese findings indicate that peripheral immune cells in MI UC undergo stage-specific functional reprogramming, combining immune suppression, metabolic adaptation, and enhanced intercellular crosstalk. This highlights potential targets for immunomodulatory therapy and provides new insights into systemic immune alterations underlying urothelial carcinoma progression.\u003c/p\u003e","manuscriptTitle":"Single-cell profiling reveals systemic immune reprogramming in muscle-invasive urothelial carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 10:43:49","doi":"10.21203/rs.3.rs-8525731/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":"24f395af-7e0b-4f31-9b68-c736fa75c179","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-17T21:18:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 10:43:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8525731","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8525731","identity":"rs-8525731","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}