Transcriptional Heterogeneity of Tumor-Associated High Endothelial Venules Defines Inflammatory and Stress-Metabolic States with Distinct Prognostic Associations

Transcriptional Heterogeneity of Tumor-Associated High Endothelial Venules Defines Inflammatory and Stress-Metabolic States with Distinct Prognostic Associations | 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 Transcriptional Heterogeneity of Tumor-Associated High Endothelial Venules Defines Inflammatory and Stress-Metabolic States with Distinct Prognostic Associations Hwal-Seok Choi, Yongjun Lee, Jin-Woo Choi, So-Jeong Kim, Hyunji Kim, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8783132/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Tumor-associated high endothelial venules (TA-HEVs) mediate lymphocyte trafficking into tumors and modulate the tumor microenvironment, with reported effects on clinical outcomes. However, reports have described discordant associations across cancers and microenvironmental contexts. Studies on state-specific, pan-cancer analyses of TA-HEV function remain limited. We integrated publicly available single-cell RNA sequencing datasets from 11 cancer types. Functional features of TA-HEVs were inferred by pathway enrichment and single-cell gene-set scoring for pathway gene sets. State-specific programs were applied to The Cancer Genome Atlas dataset to assess their clinical impact. We constructed a comprehensive atlas of tumor-associated endothelial cells and identified TA-HEV subclusters. Five TA-HEV subclusters were grouped into two functional states: inflammatory and stress-metabolic. The inflammatory TA-HEVs were enriched for innate immune stimulation, cytokine/chemokine signaling, and MHC class II antigen presentation, whereas the stress-metabolic TA-HEVs were characterized by the unfolded protein response, heat shock pathways, oxidative phosphorylation, and ATP biosynthesis. Across cancers, the stress-metabolic TA-HEV state was generally associated with worse prognosis, while the inflammatory TA-HEV state showed context-dependent associations. Together, these findings define TA-HEVs as a heterogeneous endothelial population comprising distinct functional states with divergent clinical associations, providing a pan-cancer framework for interpreting TA-HEV signals in tumor biology. High endothelial venules Single-cell RNA sequencing Transcriptomics Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The tumor microenvironment plays a pivotal role in driving cancer progression, shaping therapeutic responses, and determining patient prognosis 1 . The tumor vasculature not only provides oxygen and nutrients but also serves as a critical gateway for regulating the entry of immune cells into tumor tissue 2 . In particular, high endothelial venules (HEVs) are specialized post-capillary venules that mediate lymphocyte trafficking into secondary lymphoid organs, and they have been observed across a variety of human cancers 3 . As evidence accumulated that HEVs within tumors may modulate the microenvironment through lymphocyte recruitment, studies of tumor-associated HEVs (TA-HEVs) have surged 4 – 7 . Early histological studies revealed that TA-HEVs are associated with favorable clinical outcomes across several cancer types, including breast cancer, gastric cancer, and head and neck cancers 2 , 8 , 9 . However, others have reported neutral or even adverse associations depending on tumor type, anatomic localization, and microenvironmental context 8 , 10 . These discordant associations raise the possibility that TA-HEVs exist in distinct functional states shaped by local cues. Most prior studies have evaluated TA-HEVs within a single tumor type, with integrative efforts mostly limited to cross-study or meta-analytic approaches 10 . In addition, a systematic, state-resolved characterization of TA-HEVs across cancers has been lacking. In the present study, we integrated publicly available single-cell RNA sequencing (scRNA-seq) datasets from 11 cancer types to construct a comprehensive atlas of tumor-associated endothelial cells (TA-ECs) and to identify TA-HEV populations, thereby enabling a state-resolved view of their transcriptional and functional heterogeneity. Results Transcriptional classification of tumor-associated endothelial cells To reliably identify TA-HEVs across heterogeneous scRNA-seq datasets (Fig. 1 A), we profiled 47,748 TA-ECs from 11 solid tumor types using canonical markers (PECAM1, VWF, CDH5, PLVAP). Unsupervised clustering revealed six EC subtypes (Fig. 1 B)—venous, arterial, capillary, tip-like, lymphatic, and proliferative—defined by key differentially expressed genes (Supplementary Table S1 ): ACKR1 and SELP (venous), SEMA3G and GJA4 (arterial), CD36 and CA4 (capillary), ESM1 and TP53I11 (tip-like), CCL21 and PDPN (lymphatic), and MKI67 and ASPM (proliferative) (Fig. 1 C-E). Venous ECs were the most abundant (50.7%), while proliferative ECs were rare (2.5%) (Fig. S1 ). At the tumor-type level, venous ECs were the dominant population in most cancers, whereas lymphatic and proliferative ECs were enriched in specific tumor contexts (Fig. 1 F). These results indicated that TA-ECs can be clearly classified into six transcriptionally distinct subtypes, providing a transcriptional framework for the identification of the TA-HEV populations. Identification of TA-HEV subclusters within venous ECs Next, we focused on venous ECs from the initial analysis to identify the TA-HEV EC population. Secondary unsupervised clustering identified 20 transcriptionally distinct subclusters (Fig. 2 A-B and Supplementary Table S2 ). UMAP visualization showed that these subclusters were evenly distributed across tumor types without apparent dominance by a specific tumor type (Fig. S2 ). Based on HEV marker expression and HEV signature gene scores, five subclusters (0, 1, 5, 6, 12) were defined as -> classified as TA-HEVs, four (7, 9, 13, 14) as immature venous ECs, and the rest as mature venous ECs (Fig. 2 C-D and Fig. S3 ). Pathway analysis based on differentially expressed genes (DEGs) of each venous subgroup showed that TA-HEVs were enriched in immune-related pathways, including leukocyte cell-cell adhesion (Fig. 2 E and Supplementary Table S3 ). Immature ECs were enriched in developmental and extracellular matrix organization, and mature ECs were enriched in cytoskeletal regulation. These were consistent with their annotated biological identities. Pseudotime analysis revealed a trajectory from immature to mature ECs, with TA-HEVs occupying a distinct region along the venous endothelial trajectory (Fig. 2 F), consistent with their annotation as a specialized venous endothelial state. Functional heterogeneity of TA-HEVs reveals distinct inflammatory and stress-metabolic subtypes Pathway analysis based on DEGs of the five TA-HEV subclusters defined above revealed two functional groups: inflammatory and stress-metabolic (non-inflammatory) (Fig. 3 A and Supplementary Table S4 ). Among inflammatory TA-HEVs, subcluster 0 was enriched in pathways related to innate immune stimulation and leukocyte recruitment and expressed high IL6 (Fig. 3 B and Fig. S4 ), suggesting early immune activation. Subcluster 12 was enriched in leukocyte migration and cytokine signaling, consistent with a role in immune activation and functional modulation (Fig. 3 C). Subcluster 1 was enriched in MHC-II–mediated antigen presentation, supported by higher gene set scores for MHC-II ( p < 0.001, t -test; Fig. 3 D). Non-inflammatory TA-HEVs exhibited stress and metabolic signatures. Subcluster 5 showed higher gene set scores for endoplasmic reticulum (ER) and proteotoxic stress pathways, including heat stress and unfolded protein responses ( p < 0.001 for both, t -test; Fig. 3 E), and subcluster 6 showed higher gene set scores for oxidative phosphorylation and ATP biosynthesis ( p < 0.001 for both, t -test; Fig. 3 F). Thus, inflammatory TA-HEVs (0, 1, 12) are associated with immune-interactive transcriptional programs, while stress–metabolic TA-HEVs (5, 6) engage in stress adaptation and energy metabolism, demonstrating functional heterogeneity within TA-HEVs. Prognostic associations of TA-HEV states across cancer types To evaluate the potential clinical relevance of TA-HEV functional heterogeneity, we generated subcluster-specific gene signatures and applied them to The Cancer Genome Atlas (TCGA) bulk RNA-seq data. Cox regression analysis revealed cancer type-specific prognostic differences among TA-HEV subclusters (Fig. 4 A, B). The inflammatory TA-HEV score displayed cancer type–dependent prognostic patterns: it was significantly associated with favorable survival in skin cutaneous melanoma (SKCM) (Hazard Ratio; HR = 0.81, 95% CI 0.70–0.94, p = 0.004) and breast invasive carcinoma (BRCA) (HR = 0.82, 95% CI 0.68–0.96, p = 0.017), but significantly correlated with worse outcomes in stomach adenocarcinoma (STAD) (HR = 1.20, 95% CI 1.02–1.41, p = 0.027), and pancreatic adenocarcinoma (PAAD) (HR = 1.26, 95% CI 1.04–1.53, p = 0.019). In contrast, the stress-metabolic HEV score showed statistically significant adverse effects in STAD (HR = 1.19, 95% CI 1.02–1.41, p = 0.028) and kidney renal cell carcinoma (KIRC) (HR = 1.30, 95% CI 1.11–1.53, p = 0.001). Although several cancer types did not reach nominal significance, effect estimates were directionally adverse in most cohorts, indicating a consistent trend toward poorer survival associated with the stress-metabolic state (detailed HRs, 95% CIs and p-values in Supplementary Table S5 ). Stratification of patients by high or low inflammatory and stress-metabolic TA-HEV scores revealed distinct survival patterns (Fig. 4 C). In SKCM, patients with high inflammatory and low stress-metabolic scores exhibited significantly improved survival, whereas those with high stress-metabolic and low inflammatory scores showed poor outcomes. The high inflammatory TA-HEV group also exhibited an overall increase in estimated immune cell fractions, with particularly prominent enrichment of CD8⁺ T cells (Fig. 4 D). In contrast, prognosis in lung squamous cell carcinoma (LUSC) was primarily driven by the stress-metabolic TA-HEV score, and a high inflammatory TA-HEV score was not accompanied by a clear increase in estimated immune cell fractions (Fig. 4 E, F). Collectively, these results define TA-HEVs as a transcriptionally and functionally distinct endothelial population comprising inflammatory and stress–metabolic states with opposing biological and prognostic implications. Discussion TA-HEVs are often described as beneficial gateways for lymphocyte entry into tumors. However, comprehensive analyses of their functional roles across cancers remain limited. Here, by integrating single-cell and bulk transcriptomic data from multiple cancer types, we delineate two distinct TA-HEV programs–an inflammatory state and a stress-metabolic state–that exhibit opposing, tumor-context-dependent associations with patient outcomes. To identify TA-HEVs, we constrained subclustering to the tumor-associated venous endothelial lineage. According to the classical definition, HEVs are specialized post-capillary venules. Thus, our analytic strategy is consistent with the developmental and anatomical identity of TA-HEVs 11 , 12 . Beyond canonical HEV marker expression, we also applied complementary criteria, including signature scores, pathway enrichment, and a pseudotime analysis. This biologically coherent and dataset-agnostic framework suggests that the observed TA-HEV states reflect genuine biological change rather than artifacts. Accordingly, the cancer-specific differences in TA-HEV prevalence and state usage can be interpreted as a valid, tumor-context-dependent heterogeneity. The inflammatory TA-HEV program encompasses innate immune stimulation, cytokine/chemokine signaling, and MHC-II antigen presentation. In SKCM, the inflammatory TA-HEV state coincides with higher immune cell infiltration, most prominently CD8⁺ T-cells, and is associated with improved survival. These observations are consistent with reports that TA-HEVs are associated with enhanced lymphocyte recruitment, correlating with clinical benefit 13 . In BRCA, STAD, and LUSC, a high inflammatory TA-HEV score was not accompanied by a clear increase in estimated immune cell fractions and was associated with adverse prognosis. These findings suggest that similar inflammatory transcriptional programs may not translate into effective immune infiltration within immunosuppressive microenvironments 14 . The stress-metabolic TA-HEV program characterized by the unfolded protein response (UPR), heat shock pathways, oxidative phosphorylation, and ATP biosynthesis is associated with poor outcomes across multiple cancers. This observation aligns with literature implicating endothelial metabolic reprogramming and ER stress/UPR pathways in shaping immunosuppressive vascular niches and therapy resistance 15 . Overall, TA-HEV function is not uniform but varies by state and tumor context. Our results also underscore the clinical significance of TA-HEV state subtype. Collapsing TA-HEV subclusters into state-specific TA-HEV scores provides a practical, cohort-scale means of risk stratification in cancer patients. In immunogenic tumors such as SKCM, inflammatory-high/stress-metabolic-low patients showed more favorable survivals, whereas a higher stress-metabolic TA-HEV score consistently marked high-risk patients across tumor contexts. These findings further suggest that vascular-targeted or HEV-modulating strategies should be context-tailored. These observations suggest that TA-HEV states may inform context-dependent vascular or endothelial-focused strategies, particularly in settings where stress-metabolic programs are associated with adverse immune landscapes and tumor-promoting microenvironments. Although scRNA-seq data limit sample numbers, we mitigated this by pan-cancer integration and by validating single-cell findings with bulk RNA-seq deconvolution. Deconvolution cannot reproduce single-cell resolution, but it offers cohort-scale readout. To our knowledge, this is the first pan-cancer definition of TA-HEV states with prognostic relevance. TA-HEV states differ transcriptionally and functionally, vary by tumor context, and are associated with opposing survival outcomes. These results propose TA-HEV states as potential biomarkers and therapeutic targets and highlight their context-dependent dual roles. Methods Single-cell RNA-seq data collection and preprocessing scRNA-seq data from 317 primary tumor tissue samples (307 patients) across 11 solid cancer types were analyzed, totaling ~1.3 million cells after quality control. The cohorts included: stomach adenocarcinoma (31 patients, total 140,217 cells); breast invasive carcinoma (52 patients, total 347,945 cells); colorectal cancer (29 patients, total 73,777 cells); esophageal carcinoma (60 patients, total 192,078 cells); thyroid carcinoma (7 patients, total 65,580 cells); non-small cell lung cancer (50 patients, total 105,435 cells); melanoma (10 patients, total 132,464 cells); cutaneous squamous cell carcinoma (10 patients, total 26,299 cells); pancreatic adenocarcinoma (16 patients, total 42,272 cells); head and neck squamous cell carcinoma (21 patients, total 100,499 cells); and renal cell carcinoma (8 patients, total 100,916 cells). Only datasets without prior cell-type enrichment were included, while peripheral blood, metastatic, matched normal, and healthy donor samples were excluded. Detailed information for each dataset is provided in Supplementary Table S6. Raw gene count matrices were processed using the Seurat R package 16 . Cells with 20% mitochondrial reads, or identified as doublets by DoubletFinder were excluded 17 . Gene counts were normalized with NormalizeData() (scale factor = 10,000), highly variable genes (HVGs) were identified using FindVariableFeatures() , and data were scaled with ScaleData() . Dimensionality reduction was performed by principal component analysis (PCA) with RunPCA() . A shared nearest-neighbor graph was constructed with FindNeighbors() , followed by clustering using FindClusters() with a resolution parameter of 0.8. Uniform manifold approximation and projection (UMAP) was performed with RunUMAP() , with dataset-specific parameter settings for the number of principal components (dims = 10–20). Unless otherwise specified, default parameters of the Seurat functions were applied. Data integration and unsupervised clustering ECs were characterized from the total cells of each dataset based on the expression of pan-EC markers (PECAM1, VWF, CDH5, and PLVAP). Count matrices of the identified ECs were then integrated and processed as described above, including normalization, identification of HVGs, scaling, and dimensionality reduction by PCA. Batch correction was performed using the Harmony R package with dims.use = 20 parameter 18 . For visualization, RunUMAP() and FindNeighbors() were applied with reduction = "harmony" and dims = 20. Unsupervised clustering was performed using FindClusters() with a resolution of 0.8. Major EC types were annotated according to well-established markers, and clusters expressing signatures of non-endothelial cell types were excluded, after which the above steps were repeated. The venous endothelial subgroup was further reclustered at multiple resolutions, and cluster purity was evaluated using the ratio of global unshifted entropy (ROGUE) value 19 . The resolution yielding the highest ROGUE score (resolution = 1.35) was selected and applied in FindClusters() . TA-HEV subclusters were identified based on the enrichment of a previously reported 34-gene HEV signature 20,21 . The HEV signature score was calculated using AddModuleScore() in Seurat. Differential expression, trajectory, and functional analysis Cluster-specific DEGs were identified with Seurat FindAllMarkers() (adjusted p < 0.05). Gene Ontology (GO) enrichment was performed with clusterProfiler 22 . Differentiation states were inferred by CytoTRACE2 23 , and pseudotime trajectories constructed using Monocle3 24 . Functional module scores, including MHC class II (MHC-II, KEGG NETWORK: N00590), Antigen processing (GO:0019886), Response to heat stress (R-HSA-3371556), Response to unfolded protein (GO:0034620), Oxidative phosphorylation (HALLMARK_OXIDATIVE_PHOSPHORYLATION), and ATP metabolic process (GO:0046034) were calculated with Seurat AddModuleScore(). The gene lists used for signature score calculation, including HEV signature genes, were provided in Supplementary Table S7. Bulk RNA-seq data validation Bulk RNA-seq data from TCGA pan-cancer cohort were analyzed by single-sample gene set enrichment analysis (ssGSEA) using the top 20 upregulated DEGs from each TA-HEV subcluster. Inflammatory (subclusters 0, 1, 12) and stress-metabolic (subclusters 5, 6) TA-HEV scores were derived as the averages of ssGSEA scores from their respective subclusters. For survival analysis, these scores were standardized by Z-score transformation within each cancer type and analyzed by univariate Cox proportional hazards regression for overall survival (OS), censored at 10 years. Kaplan–Meier (KM) curves were generated after stratifying patients into four groups by the median values of both scores, and survival differences were tested using the log-rank method. Data visualization and statistical analysis Gene expression heatmaps, dot plots, and box plots were generated using the SeuratExtend package, whereas UMAP plots were produced with Seurat. GO heatmaps were created with ComplexHeatmap, and bar plots as well as survival analysis plots were generated using ggplot2. Statistical analyses were performed using R 4.2.2. Statistical tests included Kruskal–Wallis, Wilcoxon, t -test, and log-rank tests, with p < 0.05 considered significant. HRs and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models. Immune cell composition in TCGA samples was inferred using CIBERSORTx with the LM22 signature matrix, yielding estimated immune cell fractions for each sample 25 . All software and tools used in this study are summarized in Supplementary Table S8. Declarations Ethics declarations Not applicable Consent to publish Not applicable Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data availability Publicly available scRNA-seq datasets used in this study are available from the Gene Expression Omnibus (GEO). Accessions for datasets used in this study: STAD: GSE183904, GSE176078, BRCA: GSE176078, GSE161529, CRC: GSE132465, GSE188711, ESCA: GSE160269, THCA: GSE184362, NSCLC: GSE148071, GSE131907, MELA: GSE277165, cSCC: GSE144236, PAAD: GSE155698, HNSC: GSE164690, GSE172577, RCC: GSE178481. Bulk RNA-seq data from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas cohort were obtained from the Genomic Data Commons(gdc.cancer.gov/about-data/publications/pancanatlas), including the following TCGA cohorts: TCGA-BRCA, TCGA-COAD, TCGA-ESCA, TCGA-HNSC, TCGA-KIRC, TCGA-LUAD, TCGA-LUSC, TCGA-PAAD, TCGA-SKCM, TCGA-STAD, and TCGA-THCA. Research funding This study was supported by multiple grants including: National Research Foundation grants funded by the Korea government (No. RS-2021-NR061894 to JR, M-2022-C0460-00037 to JR, RS-2023-00275161 to H-KP, and RS-2020-NR048734 to C-SP), Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (HR21C1003 to JR), grants from the Asan Institute for Life Sciences (grant no. 2022IL0002, 2022IP0090, 2022IF0010, and 2024IL0001 to C-SP). Author contributions H-SC : Conception, data interpretation, and manuscript writing. YL : Data acquisition, analysis, interpretation, and manuscript writing. J-WC : Data analysis, interpretation, and manuscript writing. S-JK : Manuscript review. HK : Manuscript review. H-KP : Funding acquisition and manuscript review. JR : Funding acquisition, conception, data interpretation, and manuscript writing. C-SP : Funding acquisition, conception, data interpretation, and manuscript writing. References Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat Med 19 , 1423-1437 (2013). https://doi.org/10.1038/nm.3394 Martinet, L. et al. Human solid tumors contain high endothelial venules: association with T- and B-lymphocyte infiltration and favorable prognosis in breast cancer. Cancer Res 71 , 5678-5687 (2011). https://doi.org/10.1158/0008-5472.Can-11-0431 Girard, J. P. & Springer, T. A. High endothelial venules (HEVs): specialized endothelium for lymphocyte migration. 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Supplementary Files SupplementalTableS3.xlsx SupplementalTableS6.xlsx SupplementalTableS5.xlsx SupplementalTableS1.xlsx SupplementalTableS2.xlsx SupplementalTableS4.xlsx SupplementalTableS7.xlsx SupplementalTableS8.xlsx SupplementaryFigures.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 11 Mar, 2026 Reviewers agreed at journal 23 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor invited by journal 11 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8783132","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593172572,"identity":"a799fbbb-d2cd-4dfa-99be-ded2e327b2f4","order_by":0,"name":"Hwal-Seok Choi","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"Hwal-Seok","middleName":"","lastName":"Choi","suffix":""},{"id":593172573,"identity":"511c08fe-c60b-4003-9dc1-1980ef8c4a59","order_by":1,"name":"Yongjun Lee","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"Yongjun","middleName":"","lastName":"Lee","suffix":""},{"id":593172574,"identity":"ee7f8e47-571f-4154-b136-92c796480c08","order_by":2,"name":"Jin-Woo Choi","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"Jin-Woo","middleName":"","lastName":"Choi","suffix":""},{"id":593172589,"identity":"c76918e7-401c-47b9-84a0-a6464df5d58b","order_by":3,"name":"So-Jeong Kim","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"So-Jeong","middleName":"","lastName":"Kim","suffix":""},{"id":593172596,"identity":"13aad8bf-10ba-4f29-9215-09bd44e79fa3","order_by":4,"name":"Hyunji Kim","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"Hyunji","middleName":"","lastName":"Kim","suffix":""},{"id":593172604,"identity":"1ccf898e-b7c4-495a-9173-92c87b5c2484","order_by":5,"name":"Hyo-Kyung Pak","email":"","orcid":"","institution":"University of Ulsan","correspondingAuthor":false,"prefix":"","firstName":"Hyo-Kyung","middleName":"","lastName":"Pak","suffix":""},{"id":593172606,"identity":"5649407f-4469-4c70-b02b-9c5464e0cdc9","order_by":6,"name":"Jin Roh","email":"","orcid":"","institution":"Ajou University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Roh","suffix":""},{"id":593172612,"identity":"172f9668-2c2e-42ea-9c16-b6923ea009f5","order_by":7,"name":"Chan-Sik Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFACxmYQacAPF2BnYCZOi2QDTICZoBaIAgODA8Rq4e8/3GxcUHPP2Pj46cQPDH9s5AwOMzAbV+DRInEjsTl5xrFiM7MzuZslGNvSjEFaEs/g0WIgwdh8mIctwcbsQO42BsaGw4kbgFoONuDTwn8QqOVfgo1x/9ttDAx//hOhhQHoMN62BDMDCaAtDGwHwFoS8WkB+cV4Zl+CscSNt5slEtuSjSUPMzYb4tPC33/8sXTBtwTD/v7cjR8+/LGT4zvefFgSnxYQQERDAphkJKSBgXBkj4JRMApGwQgHAFa+SiUcNP8XAAAAAElFTkSuQmCC","orcid":"","institution":"University of Ulsan","correspondingAuthor":true,"prefix":"","firstName":"Chan-Sik","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2026-02-04 07:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8783132/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8783132/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104808112,"identity":"b18b584a-1ef0-40e4-9ddb-4d2e1740df66","added_by":"auto","created_at":"2026-03-17 12:16:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2094679,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell transcriptomic clustering of endothelial cells (ECs) across multiple cancer types\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eOverview of the study cohort showing the number of tumor samples, total single cells, and ECs included in the analysis across 11 cancer types. (Created with \u003ca href=\"http://biorender.com/\"\u003eBioRender.com\u003c/a\u003e) \u003cstrong\u003e(B)\u003c/strong\u003e ECs were identified from the total single-cell population based on the expression of established EC markers (PECAM1, VWF, CDH5, PLVAP) and subjected to unsupervised clustering. \u003cstrong\u003e(C)\u003c/strong\u003eEach cluster was annotated as venous, arterial, capillary, tip-like, lymphatic, or proliferating ECs according to the expression patterns of top differentially expressed genes. Representative marker gene expression is shown in UMAP plots. \u003cstrong\u003e(D)\u003c/strong\u003eThe expression of cluster-specific marker genes aligns with annotated cluster characteristics, confirming the accuracy and reliability of the clustering strategy. \u003cstrong\u003e(E)\u003c/strong\u003e Bubble plot showing the scaled expression and detection frequency of canonical marker genes across endothelial clusters. \u003cstrong\u003e(F)\u003c/strong\u003e The proportional distribution of each EC cluster across cancer types demonstrates that clusters are represented across diverse tumors without bias toward a specific tumor\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/b482b5e2532fb79fbd130c01.png"},{"id":103071642,"identity":"ecf36059-6c6d-4c50-b25b-122dba3c11f2","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4032105,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of the tumor-associated high endothelial venule (TA-HEV) subcluster within the venous EC cluster\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e To define the TA-HEV population, venous ECs identified in the initial clustering step were subjected to an additional round of unsupervised clustering, and the optimal resolution parameter was determined. \u003cstrong\u003e\u0026nbsp;(B)\u003c/strong\u003e Heatmap showing the top differentially expressed genes across the 20 venous EC subclusters. \u003cstrong\u003e(C)\u003c/strong\u003eHEV gene expression patterns and \u003cstrong\u003e(D)\u003c/strong\u003e HEV signature scores across subclusters were analyzed to functionally classify venous ECs and to define the TA-HEV population. \u003cstrong\u003e(E)\u003c/strong\u003e Based on HEV gene expression and signature scores, venous ECs were divided into three compartments: TA-HEVs, immature venous ECs, and mature venous ECs. Pathway enrichment analysis demonstrated that each compartment exhibited distinct functional signatures, confirming the reliability of these annotations. \u003cstrong\u003e(F)\u003c/strong\u003e Pseudotime trajectory analysis revealed a continuum from immature to mature venous ECs, with TA-HEVs occupying a distinct region along the venous endothelial trajectory, consistent with their specialized endothelial identity.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/18b8f44ab2208cf5537fef89.png"},{"id":103071641,"identity":"ae7faf9d-40ef-4d84-97e8-40ea8bdc00ad","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1789009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional heterogeneity of TA-HEV subclusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003ePathway enrichment analysis of the five TA-HEV subclusters. Subclusters 0, 1, and 12 were enriched for immune-related pathways, subcluster 5 for stress-related pathways, and subcluster 6 for metabolic processes. \u003cstrong\u003e(B)\u003c/strong\u003eDot plots showing interleukin expression across TA-HEV subclusters, highlighting that immune-related subclusters exhibit elevated cytokine transcription. \u003cstrong\u003e(C)\u003c/strong\u003e Dot plots showing differential expression of chemokines, adhesion molecules, and selectins across subclusters, demonstrating cluster-specific regulation of leukocyte recruitment machinery. \u003cstrong\u003e(D)\u003c/strong\u003e Box plots of MHC class II and antigen-processing signature scores, showing that subcluster 1 is specialized for antigen presentation. \u003cstrong\u003e(E)\u003c/strong\u003e Box plots demonstrating selective enrichment of stress-related gene signatures in subcluster 5. \u003cstrong\u003e(F) \u003c/strong\u003eBox plots demonstrating enrichment of metabolic gene signatures in subcluster 6. Statistical significance was assessed using unpaired Student’s t-test (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/e74a6f62af0fcf40af419589.png"},{"id":103071635,"identity":"2b284248-5580-461c-9211-2ec449ad7f15","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1210528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical and immune-associated features of TA-HEV inflammatory and stress-metabolic states\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003eForest plots of Cox proportional hazards models using TA-HEV state scores in TCGA cohorts. \u003cstrong\u003e(A)\u003c/strong\u003e Inflammatory TA-HEV score. \u003cstrong\u003e(B)\u003c/strong\u003e Stress-metabolic TA-HEV score. Points show hazard ratios (HR) with 95% confidence interval (CI); colors denote direction (blue, HR \u0026lt; 1; red, HR \u0026gt; 1). \u003cstrong\u003e(C)\u003c/strong\u003eKaplan–Meier curves after stratifying patients into four groups by median splits of inflammatory and stress-metabolic scores (low/low, low/high, high/low, high/high) in SKCM; \u003cem\u003ep\u003c/em\u003e values by log-rank test. \u003cstrong\u003e(D)\u003c/strong\u003e Box plots of selected immune fractions (e.g., M1/M2 macrophages, CD8⁺ T cells) across the four groups in SKCM. Group differences by Kruskal–Wallis test with post-hoc pairwise comparisons. \u003cstrong\u003e(E)\u003c/strong\u003e Kaplan–Meier curves after stratifying patients into four groups by median splits of inflammatory and stress-metabolic scores in LUSC; \u003cem\u003ep\u003c/em\u003e values by log-rank test. \u003cstrong\u003e(F)\u003c/strong\u003e Box plots of selected immune fractions across the four groups in LUSC. Group differences by Kruskal–Wallis test with post-hoc pairwise comparisons. Statistical significance (*\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001). Abbreviations: BRCA, Breast invasive carcinoma; SKCM, Skin cutaneous melanoma; KIRC, Kidney renal clear cell carcinoma; LUAD, Lung adenocarcinoma; HNSC, Head and neck squamous cell carcinoma; COAD, Colon adenocarcinoma; THCA, Thyroid carcinoma; ESCA, Esophageal carcinoma; LUSC, Lung squamous cell carcinoma; STAD, Stomach adenocarcinoma; PAAD, Pancreatic adenocarcinoma\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/f9372a6e96df95d48c1b4397.png"},{"id":104809257,"identity":"f287b660-f57a-40c5-85ef-0d3fa55e2a84","added_by":"auto","created_at":"2026-03-17 12:49:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9451049,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/a46db7b0-37d0-41b2-95b6-0242cfe215e2.pdf"},{"id":103071632,"identity":"1563b506-771b-4ad5-acaf-5a903cc75464","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":73412,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/106660640affcd43934105e1.xlsx"},{"id":103071634,"identity":"cac772ff-4bd6-46f1-9eed-cc9c7ec0bf31","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":80443,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/b58a0c37743b57ca69bcbe9f.xlsx"},{"id":103071633,"identity":"6135901b-1c82-4bba-a4a2-c32e20e5035f","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18803,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/9694e65ebf6ec1a056610604.xlsx"},{"id":103503890,"identity":"9be1afcb-d710-4a3f-9a3e-6763c88ff50b","added_by":"auto","created_at":"2026-02-26 13:04:05","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":321548,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/531dcf165a30ddf6b2da8559.xlsx"},{"id":103071636,"identity":"61319061-45f6-453c-a633-366c11826055","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":542044,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/5d315fd0cfb671a87ac8dea7.xlsx"},{"id":103071639,"identity":"1322efe1-122e-4252-bd07-515e389e6a5b","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":171757,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/994b4238339abcb12f051f8f.xlsx"},{"id":103071640,"identity":"8f2a5f22-966b-46dc-9d24-6118d3945248","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16036,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/ebdb1ef27e6b491cdc09cd23.xlsx"},{"id":103504520,"identity":"8335728b-f689-47e4-ab60-cb9267639f3c","added_by":"auto","created_at":"2026-02-26 13:20:22","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":53399,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/5b9d1b47d606cfa74208d00e.xlsx"},{"id":103071644,"identity":"36dfe930-c8ac-44ff-83b7-d4d213e13eee","added_by":"auto","created_at":"2026-02-20 12:34:29","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":375065,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8783132/v1/647249298d850a500d4a4807.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptional Heterogeneity of Tumor-Associated High Endothelial Venules Defines Inflammatory and Stress-Metabolic States with Distinct Prognostic Associations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe tumor microenvironment plays a pivotal role in driving cancer progression, shaping therapeutic responses, and determining patient prognosis \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The tumor vasculature not only provides oxygen and nutrients but also serves as a critical gateway for regulating the entry of immune cells into tumor tissue \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In particular, high endothelial venules (HEVs) are specialized post-capillary venules that mediate lymphocyte trafficking into secondary lymphoid organs, and they have been observed across a variety of human cancers \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. As evidence accumulated that HEVs within tumors may modulate the microenvironment through lymphocyte recruitment, studies of tumor-associated HEVs (TA-HEVs) have surged \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEarly histological studies revealed that TA-HEVs are associated with favorable clinical outcomes across several cancer types, including breast cancer, gastric cancer, and head and neck cancers \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, others have reported neutral or even adverse associations depending on tumor type, anatomic localization, and microenvironmental context \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These discordant associations raise the possibility that TA-HEVs exist in distinct functional states shaped by local cues. Most prior studies have evaluated TA-HEVs within a single tumor type, with integrative efforts mostly limited to cross-study or meta-analytic approaches \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In addition, a systematic, state-resolved characterization of TA-HEVs across cancers has been lacking.\u003c/p\u003e \u003cp\u003eIn the present study, we integrated publicly available single-cell RNA sequencing (scRNA-seq) datasets from 11 cancer types to construct a comprehensive atlas of tumor-associated endothelial cells (TA-ECs) and to identify TA-HEV populations, thereby enabling a state-resolved view of their transcriptional and functional heterogeneity.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional classification of tumor-associated endothelial cells\u003c/h2\u003e \u003cp\u003eTo reliably identify TA-HEVs across heterogeneous scRNA-seq datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), we profiled 47,748 TA-ECs from 11 solid tumor types using canonical markers (PECAM1, VWF, CDH5, PLVAP). Unsupervised clustering revealed six EC subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB)\u0026mdash;venous, arterial, capillary, tip-like, lymphatic, and proliferative\u0026mdash;defined by key differentially expressed genes (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e): ACKR1 and SELP (venous), SEMA3G and GJA4 (arterial), CD36 and CA4 (capillary), ESM1 and TP53I11 (tip-like), CCL21 and PDPN (lymphatic), and MKI67 and ASPM (proliferative) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-E). Venous ECs were the most abundant (50.7%), while proliferative ECs were rare (2.5%) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). At the tumor-type level, venous ECs were the dominant population in most cancers, whereas lymphatic and proliferative ECs were enriched in specific tumor contexts (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). These results indicated that TA-ECs can be clearly classified into six transcriptionally distinct subtypes, providing a transcriptional framework for the identification of the TA-HEV populations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of TA-HEV subclusters within venous ECs\u003c/h3\u003e\n\u003cp\u003eNext, we focused on venous ECs from the initial analysis to identify the TA-HEV EC population. Secondary unsupervised clustering identified 20 transcriptionally distinct subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B and Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). UMAP visualization showed that these subclusters were evenly distributed across tumor types without apparent dominance by a specific tumor type (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Based on HEV marker expression and HEV signature gene scores, five subclusters (0, 1, 5, 6, 12) were defined as -\u0026gt; classified as TA-HEVs, four (7, 9, 13, 14) as immature venous ECs, and the rest as mature venous ECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D and Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Pathway analysis based on differentially expressed genes (DEGs) of each venous subgroup showed that TA-HEVs were enriched in immune-related pathways, including leukocyte cell-cell adhesion (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Immature ECs were enriched in developmental and extracellular matrix organization, and mature ECs were enriched in cytoskeletal regulation. These were consistent with their annotated biological identities. Pseudotime analysis revealed a trajectory from immature to mature ECs, with TA-HEVs occupying a distinct region along the venous endothelial trajectory (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eF), consistent with their annotation as a specialized venous endothelial state.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eFunctional heterogeneity of TA-HEVs reveals distinct inflammatory and stress-metabolic subtypes\u003c/h3\u003e\n\u003cp\u003ePathway analysis based on DEGs of the five TA-HEV subclusters defined above revealed two functional groups: inflammatory and stress-metabolic (non-inflammatory) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and Supplementary Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). Among inflammatory TA-HEVs, subcluster 0 was enriched in pathways related to innate immune stimulation and leukocyte recruitment and expressed high IL6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e), suggesting early immune activation. Subcluster 12 was enriched in leukocyte migration and cytokine signaling, consistent with a role in immune activation and functional modulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Subcluster 1 was enriched in MHC-II\u0026ndash;mediated antigen presentation, supported by higher gene set scores for MHC-II (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003et\u003c/em\u003e-test; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Non-inflammatory TA-HEVs exhibited stress and metabolic signatures. Subcluster 5 showed higher gene set scores for endoplasmic reticulum (ER) and proteotoxic stress pathways, including heat stress and unfolded protein responses (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003e0.001\u003c/em\u003e for both, \u003cem\u003et\u003c/em\u003e-test; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), and subcluster 6 showed higher gene set scores for oxidative phosphorylation and ATP biosynthesis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both, \u003cem\u003et\u003c/em\u003e-test; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). Thus, inflammatory TA-HEVs (0, 1, 12) are associated with immune-interactive transcriptional programs, while stress\u0026ndash;metabolic TA-HEVs (5, 6) engage in stress adaptation and energy metabolism, demonstrating functional heterogeneity within TA-HEVs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003ePrognostic associations of TA-HEV states across cancer types\u003c/h3\u003e\n\u003cp\u003eTo evaluate the potential clinical relevance of TA-HEV functional heterogeneity, we generated subcluster-specific gene signatures and applied them to The Cancer Genome Atlas (TCGA) bulk RNA-seq data. Cox regression analysis revealed cancer type-specific prognostic differences among TA-HEV subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The inflammatory TA-HEV score displayed cancer type\u0026ndash;dependent prognostic patterns: it was significantly associated with favorable survival in skin cutaneous melanoma (SKCM) (Hazard Ratio; HR\u0026thinsp;=\u0026thinsp;0.81, 95% CI 0.70\u0026ndash;0.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and breast invasive carcinoma (BRCA) (HR\u0026thinsp;=\u0026thinsp;0.82, 95% CI 0.68\u0026ndash;0.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), but significantly correlated with worse outcomes in stomach adenocarcinoma (STAD) (HR\u0026thinsp;=\u0026thinsp;1.20, 95% CI 1.02\u0026ndash;1.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), and pancreatic adenocarcinoma (PAAD) (HR\u0026thinsp;=\u0026thinsp;1.26, 95% CI 1.04\u0026ndash;1.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019). In contrast, the stress-metabolic HEV score showed statistically significant adverse effects in STAD (HR\u0026thinsp;=\u0026thinsp;1.19, 95% CI 1.02\u0026ndash;1.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028) and kidney renal cell carcinoma (KIRC) (HR\u0026thinsp;=\u0026thinsp;1.30, 95% CI 1.11\u0026ndash;1.53, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Although several cancer types did not reach nominal significance, effect estimates were directionally adverse in most cohorts, indicating a consistent trend toward poorer survival associated with the stress-metabolic state (detailed HRs, 95% CIs and p-values in Supplementary Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eStratification of patients by high or low inflammatory and stress-metabolic TA-HEV scores revealed distinct survival patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In SKCM, patients with high inflammatory and low stress-metabolic scores exhibited significantly improved survival, whereas those with high stress-metabolic and low inflammatory scores showed poor outcomes. The high inflammatory TA-HEV group also exhibited an overall increase in estimated immune cell fractions, with particularly prominent enrichment of CD8⁺ T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In contrast, prognosis in lung squamous cell carcinoma (LUSC) was primarily driven by the stress-metabolic TA-HEV score, and a high inflammatory TA-HEV score was not accompanied by a clear increase in estimated immune cell fractions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003eCollectively, these results define TA-HEVs as a transcriptionally and functionally distinct endothelial population comprising inflammatory and stress\u0026ndash;metabolic states with opposing biological and prognostic implications.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTA-HEVs are often described as beneficial gateways for lymphocyte entry into tumors. However, comprehensive analyses of their functional roles across cancers remain limited. Here, by integrating single-cell and bulk transcriptomic data from multiple cancer types, we delineate two distinct TA-HEV programs–an inflammatory state and a stress-metabolic state–that exhibit opposing, tumor-context-dependent associations with patient outcomes.\u003c/p\u003e \u003cp\u003eTo identify TA-HEVs, we constrained subclustering to the tumor-associated venous endothelial lineage. According to the classical definition, HEVs are specialized post-capillary venules. Thus, our analytic strategy is consistent with the developmental and anatomical identity of TA-HEVs \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Beyond canonical HEV marker expression, we also applied complementary criteria, including signature scores, pathway enrichment, and a pseudotime analysis. This biologically coherent and dataset-agnostic framework suggests that the observed TA-HEV states reflect genuine biological change rather than artifacts. Accordingly, the cancer-specific differences in TA-HEV prevalence and state usage can be interpreted as a valid, tumor-context-dependent heterogeneity.\u003c/p\u003e \u003cp\u003eThe inflammatory TA-HEV program encompasses innate immune stimulation, cytokine/chemokine signaling, and MHC-II antigen presentation. In SKCM, the inflammatory TA-HEV state coincides with higher immune cell infiltration, most prominently CD8⁺ T-cells, and is associated with improved survival. These observations are consistent with reports that TA-HEVs are associated with enhanced lymphocyte recruitment, correlating with clinical benefit \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In BRCA, STAD, and LUSC, a high inflammatory TA-HEV score was not accompanied by a clear increase in estimated immune cell fractions and was associated with adverse prognosis. These findings suggest that similar inflammatory transcriptional programs may not translate into effective immune infiltration within immunosuppressive microenvironments\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The stress-metabolic TA-HEV program characterized by the unfolded protein response (UPR), heat shock pathways, oxidative phosphorylation, and ATP biosynthesis is associated with poor outcomes across multiple cancers. This observation aligns with literature implicating endothelial metabolic reprogramming and ER stress/UPR pathways in shaping immunosuppressive vascular niches and therapy resistance \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Overall, TA-HEV function is not uniform but varies by state and tumor context.\u003c/p\u003e \u003cp\u003eOur results also underscore the clinical significance of TA-HEV state subtype. Collapsing TA-HEV subclusters into state-specific TA-HEV scores provides a practical, cohort-scale means of risk stratification in cancer patients. In immunogenic tumors such as SKCM, inflammatory-high/stress-metabolic-low patients showed more favorable survivals, whereas a higher stress-metabolic TA-HEV score consistently marked high-risk patients across tumor contexts. These findings further suggest that vascular-targeted or HEV-modulating strategies should be context-tailored. These observations suggest that TA-HEV states may inform context-dependent vascular or endothelial-focused strategies, particularly in settings where stress-metabolic programs are associated with adverse immune landscapes and tumor-promoting microenvironments.\u003c/p\u003e \u003cp\u003eAlthough scRNA-seq data limit sample numbers, we mitigated this by pan-cancer integration and by validating single-cell findings with bulk RNA-seq deconvolution. Deconvolution cannot reproduce single-cell resolution, but it offers cohort-scale readout. To our knowledge, this is the first pan-cancer definition of TA-HEV states with prognostic relevance. TA-HEV states differ transcriptionally and functionally, vary by tumor context, and are associated with opposing survival outcomes. These results propose TA-HEV states as potential biomarkers and therapeutic targets and highlight their context-dependent dual roles.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSingle-cell RNA-seq\u003cem\u003e \u003c/em\u003edata collection and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003escRNA-seq data from 317 primary tumor tissue samples (307 patients) across 11 solid cancer types were analyzed, totaling ~1.3 million cells after quality control. The cohorts included: stomach adenocarcinoma (31 patients, total 140,217 cells); breast invasive carcinoma (52 patients, total 347,945 cells); colorectal cancer (29 patients, total 73,777 cells); esophageal carcinoma (60 patients, total 192,078 cells); thyroid carcinoma (7 patients, total 65,580 cells); non-small cell lung cancer (50 patients, total 105,435 cells); melanoma (10 patients, total 132,464 cells); cutaneous squamous cell carcinoma (10 patients, total 26,299 cells); pancreatic adenocarcinoma (16 patients, total 42,272 cells); head and neck squamous cell carcinoma (21 patients, total 100,499 cells); and renal cell carcinoma (8 patients, total 100,916 cells). Only datasets without prior cell-type enrichment were included, while peripheral blood, metastatic, matched normal, and healthy donor samples were excluded. Detailed information for each dataset is provided in Supplementary Table S6.\u003c/p\u003e\n\u003cp\u003eRaw gene count matrices were processed using the Seurat R package \u003csup\u003e16\u003c/sup\u003e. Cells with \u0026lt;200 detected genes, \u0026gt;20% mitochondrial reads, or identified as doublets by DoubletFinder were excluded \u003csup\u003e17\u003c/sup\u003e. Gene counts were normalized with \u003cem\u003eNormalizeData()\u003c/em\u003e (scale factor = 10,000), highly variable genes (HVGs) were identified using \u003cem\u003eFindVariableFeatures()\u003c/em\u003e, and data were scaled with \u003cem\u003eScaleData()\u003c/em\u003e. Dimensionality reduction was performed by principal component analysis (PCA) with\u003cem\u003e RunPCA()\u003c/em\u003e. A shared nearest-neighbor graph was constructed with\u003cem\u003e FindNeighbors()\u003c/em\u003e, followed by clustering using \u003cem\u003eFindClusters()\u003c/em\u003e with a resolution parameter of 0.8. Uniform manifold approximation and projection (UMAP) was performed with \u003cem\u003eRunUMAP()\u003c/em\u003e, with dataset-specific parameter settings for the number of principal components (dims = 10\u0026ndash;20). Unless otherwise specified, default parameters of the Seurat functions were applied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData integration and unsupervised clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eECs were characterized from the total cells of each dataset based on the expression of pan-EC markers (PECAM1, VWF, CDH5, and PLVAP). Count matrices of the identified ECs were then integrated and processed as described above, including normalization, identification of HVGs, scaling, and dimensionality reduction by PCA. Batch correction was performed using the Harmony R package with dims.use = 20 parameter \u003csup\u003e18\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor visualization, \u003cem\u003eRunUMAP()\u003c/em\u003e and\u003cem\u003e FindNeighbors()\u003c/em\u003e were applied with reduction = \u0026quot;harmony\u0026quot; and dims = 20. Unsupervised clustering was performed using\u003cem\u003e FindClusters()\u003c/em\u003e with a resolution of 0.8. Major EC types were annotated according to well-established markers, and clusters expressing signatures of non-endothelial cell types were excluded, after which the above steps were repeated.\u003c/p\u003e\n\u003cp\u003eThe venous endothelial subgroup was further reclustered at multiple resolutions, and cluster purity was evaluated using the ratio of global unshifted entropy (ROGUE) value \u003csup\u003e19\u003c/sup\u003e. The resolution yielding the highest ROGUE score (resolution = 1.35) was selected and applied in \u003cem\u003eFindClusters()\u003c/em\u003e. TA-HEV subclusters were identified based on the enrichment of a previously reported 34-gene HEV signature \u003csup\u003e20,21\u003c/sup\u003e. The HEV signature score was calculated using \u003cem\u003eAddModuleScore()\u003c/em\u003e in Seurat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential expression, trajectory, and functional analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCluster-specific DEGs were identified with Seurat \u003cem\u003eFindAllMarkers()\u003c/em\u003e (adjusted \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Gene Ontology (GO) enrichment was performed with clusterProfiler \u003csup\u003e22\u003c/sup\u003e. Differentiation states were inferred by CytoTRACE2 \u003csup\u003e23\u003c/sup\u003e, and pseudotime trajectories constructed using Monocle3 \u003csup\u003e24\u003c/sup\u003e. Functional module scores, including MHC class II (MHC-II, KEGG NETWORK: N00590), Antigen processing (GO:0019886), Response to heat stress (R-HSA-3371556), Response to unfolded protein (GO:0034620), Oxidative phosphorylation (HALLMARK_OXIDATIVE_PHOSPHORYLATION), and ATP metabolic process (GO:0046034) were calculated with Seurat \u003cem\u003eAddModuleScore(). \u003c/em\u003eThe gene lists used for signature score calculation, including HEV signature genes, were provided in Supplementary Table S7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBulk RNA-seq data validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBulk RNA-seq data from TCGA pan-cancer cohort were analyzed by single-sample gene set enrichment analysis (ssGSEA) using the top 20 upregulated DEGs from each TA-HEV subcluster. Inflammatory (subclusters 0, 1, 12) and stress-metabolic (subclusters 5, 6) TA-HEV scores were derived as the averages of ssGSEA scores from their respective subclusters. For survival analysis, these scores were standardized by Z-score transformation within each cancer type and analyzed by univariate Cox proportional hazards regression for overall survival (OS), censored at 10 years. Kaplan\u0026ndash;Meier (KM) curves were generated after stratifying patients into four groups by the median values of both scores, and survival differences were tested using the log-rank method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData visualization and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression heatmaps, dot plots, and box plots were generated using the SeuratExtend package, whereas UMAP plots were produced with Seurat. GO heatmaps were created with ComplexHeatmap, and bar plots as well as survival analysis plots were generated using ggplot2. Statistical analyses were performed using R 4.2.2. Statistical tests included Kruskal\u0026ndash;Wallis, Wilcoxon, \u003cem\u003et\u003c/em\u003e-test, and log-rank tests, with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 considered significant. HRs and 95% confidence intervals (CIs) were estimated using Cox proportional hazards models. Immune cell composition in TCGA samples was inferred using CIBERSORTx with the LM22 signature matrix, yielding estimated immune cell fractions for each sample \u003csup\u003e25\u003c/sup\u003e. All software and tools used in this study are summarized in Supplementary Table S8.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available scRNA-seq datasets used in this study are available from the Gene Expression Omnibus (GEO). Accessions for datasets used in this study: STAD: GSE183904, GSE176078, BRCA: GSE176078, GSE161529, CRC: GSE132465, GSE188711, ESCA: GSE160269, THCA: GSE184362, NSCLC: GSE148071, GSE131907, MELA: GSE277165, cSCC: GSE144236, PAAD: GSE155698, HNSC: GSE164690, GSE172577, RCC: GSE178481. Bulk RNA-seq data from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas cohort were obtained from the Genomic Data Commons(gdc.cancer.gov/about-data/publications/pancanatlas), including the following TCGA cohorts: TCGA-BRCA, TCGA-COAD, TCGA-ESCA, TCGA-HNSC, TCGA-KIRC, TCGA-LUAD, TCGA-LUSC, TCGA-PAAD, TCGA-SKCM, TCGA-STAD, and TCGA-THCA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by multiple grants including: National Research Foundation grants funded by the Korea government (No. RS-2021-NR061894 to JR, M-2022-C0460-00037 to JR, RS-2023-00275161 to H-KP, and RS-2020-NR048734 to C-SP), Korea Health Technology R\u0026amp;D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health \u0026amp; Welfare (HR21C1003 to JR), grants from the Asan Institute for Life Sciences (grant no. 2022IL0002, 2022IP0090, 2022IF0010, and 2024IL0001 to C-SP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH-SC\u003c/strong\u003e: Conception, data interpretation, and manuscript writing. \u003cstrong\u003eYL\u003c/strong\u003e: Data acquisition, analysis, interpretation, and manuscript writing. \u003cstrong\u003eJ-WC\u003c/strong\u003e: Data analysis, interpretation, and manuscript writing. \u003cstrong\u003eS-JK\u003c/strong\u003e: Manuscript review. \u003cstrong\u003eHK\u003c/strong\u003e: Manuscript review. \u003cstrong\u003eH-KP\u003c/strong\u003e: Funding acquisition and manuscript review. \u003cstrong\u003eJR\u003c/strong\u003e: Funding acquisition, conception, data interpretation, and manuscript writing. \u003cstrong\u003eC-SP\u003c/strong\u003e: Funding acquisition, conception, data interpretation, and manuscript writing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eQuail, D. 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M.\u003cem\u003e et al.\u003c/em\u003e Robust enumeration of cell subsets from tissue expression profiles. \u003cem\u003eNat Methods\u003c/em\u003e\u003cstrong\u003e12\u003c/strong\u003e, 453-457 (2015). https://doi.org/10.1038/nmeth.3337\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"High endothelial venules, Single-cell RNA sequencing, Transcriptomics, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8783132/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8783132/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTumor-associated high endothelial venules (TA-HEVs) mediate lymphocyte trafficking into tumors and modulate the tumor microenvironment, with reported effects on clinical outcomes. However, reports have described discordant associations across cancers and microenvironmental contexts. Studies on state-specific, pan-cancer analyses of TA-HEV function remain limited. We integrated publicly available single-cell RNA sequencing datasets from 11 cancer types. Functional features of TA-HEVs were inferred by pathway enrichment and single-cell gene-set scoring for pathway gene sets. State-specific programs were applied to The Cancer Genome Atlas dataset to assess their clinical impact. We constructed a comprehensive atlas of tumor-associated endothelial cells and identified TA-HEV subclusters. Five TA-HEV subclusters were grouped into two functional states: inflammatory and stress-metabolic. The inflammatory TA-HEVs were enriched for innate immune stimulation, cytokine/chemokine signaling, and MHC class II antigen presentation, whereas the stress-metabolic TA-HEVs were characterized by the unfolded protein response, heat shock pathways, oxidative phosphorylation, and ATP biosynthesis. Across cancers, the stress-metabolic TA-HEV state was generally associated with worse prognosis, while the inflammatory TA-HEV state showed context-dependent associations. Together, these findings define TA-HEVs as a heterogeneous endothelial population comprising distinct functional states with divergent clinical associations, providing a pan-cancer framework for interpreting TA-HEV signals in tumor biology.\u003c/p\u003e","manuscriptTitle":"Transcriptional Heterogeneity of Tumor-Associated High Endothelial Venules Defines Inflammatory and Stress-Metabolic States with Distinct Prognostic Associations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 12:34:24","doi":"10.21203/rs.3.rs-8783132/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-26T10:26:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T15:56:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316621688773218600933583274315159111960","date":"2026-03-24T15:47:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T09:12:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141712300064075639211133541967193669452","date":"2026-02-23T10:55:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167123735682482196645274357334590935138","date":"2026-02-20T15:02:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T20:35:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T06:32:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T08:02:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-09T07:50:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-02-09T07:39:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a3ec12a-3952-4a70-9eb8-5e6e4d3637b9","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T08:25:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 12:34:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8783132","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8783132","identity":"rs-8783132","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}