Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas

Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas | 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 Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas Jingjing Miao, Boyu Chen, Qingyuan Li, Zhongming Lu, Rui Wang, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5324948/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted 5 You are reading this latest preprint version Abstract Lymphatic metastasis is a well-known factor for head and neck squamous cell carcinoma (HNSCC) that initiates distant metastasis, which caused major death in most patients with cancer. Metabolic reprogramming to support metastasis is regarded as a prominent hallmark of cancers. However, how metabolic disorders drive in HNSCC remains unclear. We firstly established a new classification of HNSCC patients based on metabolism gene expression profiles and identified that an enriched carbohydrate metabolism subgroup that was significantly associated with a high risk of lymphatic metastasis and worse clinical outcome. Moreover, we found that highly activated pyruvate metabolism, a central node in carbohydrate metabolism, endowed tumors with EPHB2 upregulation and promoted lymphatic metastasis independently of VEGF-C/VEGFR3 signaling pathway. Mechanically, high levels of nuclear acetyl-CoA from pyruvate metabolism promoted histone acetylation, which in turn transcriptionally upregulated EPHB2 expression in tumor cells. EPHB2 bound with EFNB1 in lymphatic endothelial cells to alleviate YAP/TAZ-mediated PROX1 transcriptional inhibition, which eventually promoted tumor lymphangiogenesis. Importantly, combined treatment with EFNB1-Fc and VEGFR3 inhibitor synergistic abrogated lymphangiogenesis in vitro and in vivo . These findings uncover the mechanism by which pyruvate metabolism is linked to lymphatic metastasis of tumor and provides a promising therapeutic strategy for the prevention of HNSCC metastasis. head and neck squamous cell carcinoma lymphatic metastasis pyruvate metabolism EPHB2 lymphangiogenesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Approximately 20–30% of patients with head and neck squamous cell carcinoma (HNSCC) suffer from local and regional lymphatic metastasis, which can lead to distant metastasis and cause poor clinical outcomes 1 – 3 . Lymphangiogenesis is considered to be the predominant mechanism for the expansion of tumor-associated lymphatic network, which mediated by lymphatic endothelial cells (LECs) with expressing characteristic molecular markers, including lymphatic vessel endothelial receptor 1 (LYVE-1), prospero homeobox protein 1 (PROX1) and podoplanin (PDPN) 4 . Given that approximately 95% of tumor-associated vessels infiltrated by tumor cells are lymphatic vessels, which play a major role in tumor metastasis, targeting lymphangiogenesis has been considered as a potent and direct strategy for controlling metastatic diseases. VEGF-C, one of the vascular endothelial growth factors (VEGF) family, has long been recognized as a major molecular driver of lymphangiogenesis by activation of VEGFR3 expressed on LECs 5 , 6 . The VEGF-C/VEGFR3 interaction promotes proliferation, migration and tube formation of LECs to induce tumor lymphangiogenesis via maintenance of the MAPK or PI3K/Akt signaling activity 5 , 6 . Based on its essential role in lymphangiogenesis, numerous drugs targeting VEGFC/VEGFR-3 pathway have been established and applied to clinical practice or under the clinical testing and presented partial efficacy against lymphatic metastasis in various cancers 7 , especially in renal cell carcinoma 8 and colorectal cancer 9 . However, numerous clinical trials demonstrated that the magnitude of the clinical benefit of targeting VEGF-C/VEGFR-3 pathway varies among different patient populations 10 – 12 . Moreover, some patients with lymphatic metastatic cancers exhibit low expression of VEGF-C, suggesting the occurrence of VEGF-C-independent lymphangiogenesis. Therefore, further research is needed to identify the comprehensive mechanism of lymphangiogenesis in HNSCC. Metabolic reprogramming is one of the well-established hallmarks of cancer and is responsible for the metastatic spread of tumors 13 . In order to adapt the changes in energy requirements at each step during the metastatic cascade, tumor cells selectively and dynamically modify their metabolic programs 14 , 15 . Recently, more and more studies showed that tumor cells have heterogeneous metabolic preferences and dependencies. Within the same tumor, tumor cells with higher carbohydrate metabolism, which provided fuel of energy production, presented a more aggressive and malignant phenotype via increase of oxidative phosphorylation 16 , 17 . Compared with non-metastatic cancer, the serum pyruvate concentration was higher in patients with metastatic breast cancer 18 and highly invasive ovarian cancer 19 . Pyruvate uptake inducing the production of a-ketoglutarate activated collagen hydroxylation by increasing the activity of the enzyme collagen prolyl-4-hydroxylase (P4HA), which remodeling the extracellular matrix in the lung metastatic niche for breast cancer 20 . Multiple studies have provided evidence that metabolic changes in cancer cells are not just consequential bystander effects because metabolites, but also could directly modulate activity of signaling pathways and global gene expression programmes 21 . Hence, deciphering how metabolic disorders drive lymphatic metastasis may provide potential therapeutic vulnerabilities in HNSCC. Here, we firstly classified HNSCC patients into four subtypes based on unsupervised clustering of metabolism gene expression profiles using TCGA public data and evaluated in GEO cohort. We found that an enriched carbohydrate metabolism subtype, cluster D, was significantly associated with lymphatic metastasis and worse clinical outcome. Meanwhile, we also investigated the underlying mechanism of carbohydrate metabolism triggering lymphatic metastasis in laryngeal cancer. Importantly, blocking EPHB2 with recombinant EFNB1-Fc synergised with VEGFR3 inhibitor treatment to potently suppress lymphatic metastasis in laryngeal cancer. Our findings bring new insight into the links between pyruvate metabolism and lymphatic metastasis, and explore strategies to optimize the treatment for lymphatic metastasis in HNSCC. Methods and materials Patients and datasets This study collected 725 HNSCC patients from two databases: TCGA (n = 532) and GEO (n = 193). For the TCGA cohort, the RNA-seq data and corresponding clinical information were downloaded from the TCGA database ( http://cancergenome.nih.gov/ ). The molecular and immune features were also retrieved 22 . For the GEO cohort, the RNA-seq accompanied with clinical information were obtained from the GEO database (GSE142083, GSE127165, GSE112026 and GSE74927). Identification and validation of metabolic subtypes The 1064 metabolism-related genes from MSigDB were selected for consensus clustering (R package “consensusClusterplus”) to identify robust clusters in the training cohort from TCGA and the genes were detailed in Supplementary Table. S1 . We utilized two methods of elbow method and gap statics to identify the optimized K categories, meanwhile considering the associations between metabolic clusters and survival outcomes. The cumulative distribution function (CDF) and consensus heatmap were adopted to assess the optimal K. To validate the metabolic subtypes in GEO cohort, we first used the R package “limma” to remove batch effects on the 4 GEO-derived HNSCC datasets. Subsequently, we trained a classifier based on the subtypes in TCGA cohort via “Random Forest” algorithm with R package “randomForest”. Then, the classifier was used to predict metabolic subtype for patients in the GEO cohort and each sample was assigned to a metabolic subtype. The in-group-proportion (IGP) statistic with R package “clusterRepro” was performed to assess the reproducibility and similarity of the acquired metabolic subtypes between the training and validation cohorts. Gene set variation analysis (GSVA) GSVA was utilized to evaluate the most significantly enriched molecular pathways of the stemness subtypes by using the “GSVA” package in R 23 . Differential analysis of the enrichment scores of molecular pathways between the different groups was performed by the limma package in R 24 . the signatures with an absolute log2 fold change (FC) > 0.2 (adjusted P < 0.05) were defined as differentially expressed signatures. Connective Mapping analysis The CMap database ( https://clue.io/ ) was used to explore potential compounds targeting the molecular pathways and key genes associated with the metabolic subtype of HNSCC patients 25 . The DEGs between the different metabolic subtypes were employed to query the CMap database, and the most significantly highly expressed genes of each metabolic subtype were considered as key regulatory gene. RNA isolation and qRT-PCR RNA from the indicated cells were extracted using the Trizol reagent (15596026, Invitrogen). cDNA synthesis was carried out using M-MLV Reverse Transcriptase and then the cDNA was used as the template for amplification on a CFX384 Real-Time System (Bio-Rad Laboratories, Singapore). The data were analyzed using the ΔΔCt method. The results from each experiment were normalized to the expression of ACTB. The sequences of the primers used for all qRT-PCR assays are listed in Supplementary Table. S2. Western blot analysis Western blot analyses were performed according to a standard protocol using primary antibodies, including anti-EPHB2 (Cell Signaling Technology, 14389), anti-H3 (Cell Signaling Technology, 9715), anti-H3ac (pan-acetyl) (Invitrogen, PA5-114693), anti-H4 ( Cell Signaling Technology, 2592), anti-H4ac (pan-acetyl) (Invitrogen, PA5-32029), anti-EFBN1 (Santa Cruz Biotechnology, sc-515264), p-YAP (Cell Signaling Technology, 4911), YAP (Cell Signaling Technology, 4912), p-TAZ (Cell Signaling Technology, 59971), TAZ (abcam, ab119373), PROX1 (Proteintech, 67438-1-Ig), TEAD (Cell Signaling Technology, 13295) and anti-a-Tubulin (Cell Signaling Technology, 2144) antibodies. a-Tubulin were used as loading controls. Acetyl-CoA measurement To measure the nucleus acetyl-CoA concentrations, 50 ml of exponentially growing cells was collected in SB buffer (1 M sorbitol, 20 mM Tris (pH 7.4) and 10 mg ml − 1 zymolase 20T) and lysed with EBX (20 mM Tris (pH 7.4), 100 mM NaCl, 0.5% Triton X-100, 15 mM 2-mercaptoethanol and protease inhibitors). One aliquot of the lysate was collected to measure the total acetyl-CoA and the remaining lysate was layered over NIB (20 Mm Tris (pH 7.4), 100 mM NaCl, 1.2 M sucrose, 15 mM 2-mercaptoethanol and protease inhibitors). After centrifugation, the pellet was lysed with 1% Triton X-100, centrifuged and collected as the nuclear fraction. The Acetyl-CoA quantity was measured using an Acetyl-CoA kit (Suzhou Comin Biotechnology) according to the manufacturer’s instructions. The assay was performed in 96-well clear-bottom plates, and the fluorescence was quantified using the SpectraMax M2 (Molecular Devices). The acetyl-CoA levels were normalized to the corresponding protein concentrations. For the total and nuclear fractions, the quantity of acetyl-CoA levels was normalized to the corresponding protein concentrations. Histone H3 were analysed by western blots to ensure that there was no cross-contamination between compartments during subcellular fractionation. Tube formation of hLECs The hLEC tube formation assay was performed by first pipetting 200 µl 50% Matrigel (BD Biosciences, Bedford, MA) into a 24-well plate, which was then polymerized for 30 min at 37°C. LECs (2 × 10 4 ) in 200 µl of CM were added to each well and incubated at 37°C, 5% CO2 for 12 h. Images were taken using a bright-field microscope (Eclipse 80i, Nikon, Tokyo, Japan) at ×100 magnification. The capillary tubes were quantified by measuring the total length of completed tubule structures. Tumor organoid culture Freshly excised laryngeal tumor tissue of patients with lymphatic metastasis were harvested and dissociated enzymatically for 30–40 min in digestion media containing 1 mg/ml collagenase I (Sigma-Aldrich), 0.5 mg/ml Dispase (Roche), 50 µg/ml dNase (Sigma-Aldrich) in RPMI medium containing 2% FCS. Supernatant was washed and filtered through a 100 µM cell strainer. Cells were then embedded in basement membrane extract (#E0282, Sigma Aldrich) and cultured in patient-derived tumor organoid culture medium (Accurate international biotech). All tissues were collected from patients with laryngeal cancer who have undergone surgical treatment in SYSUCC. The use of human specimens was carried out in accordance with the Helsinki declaration and approved by the ethics committee of SYSUCC. Xenograft tumor models and treatments BALB/c-nude mice (Male, 4–5 weeks old) and NOD-SCID (Male, 4–5 weeks old) obtained from Gempharmatech Co., Ltd. were used for examination of the LN metastasis of TU212 tumors and the growth of patient-derived tumor organoid, respectively. Mice were housed in barrier facilities on a 12 hours light/dark cycle. The inguinal LN metastasis model was performed as previously reported 26 . Recombinant human EFNB1-Fc was purchased from R&D systems (7654-EB) and SAR131675 was purchased from MCE (HY-15458). EFNB1-Fc (2.5 mg/kg, twice per week) 27 or SAR131675 (100mg/kg, once per day) 28 were administered 10 days after tumor inoculation. For the inguinal LN metastasis model, after a month of inoculation, mice were euthanized. Inguinal LNs were first measured to calculate volumes. LNs were used for quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of hCK and mACTB to determine the proportion of LN-spread TU212 cells. For the subcutaneous tumors, tumor volumes were measured weekly. Mice were euthanized and tumors were collected after 7 weeks of inoculation, and IHC staining of lymphatic endothelial cell markers LYVE-1 to indicated the intra-tumoral lymphangiogenesis. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University and performed following the Declaration of Helsinki. Statistical analysis All statistical analyses were conducted using R software, GraphPad Prism 6.0 (GraphPad Inc., San Diego, CA, USA), or SPSS 16.0 (IBM, Chicago, IL, USA). Kaplan–Meier analysis with log-rank test was performed to assess survival difference between subtypes. Chisquare test was carried out to determine the difference of clinical and molecular features between subtypes. For comparisons of three groups, one-way analysis and ANOVA test of variance were used. Univariate and multivariate Cox regression analyses were performed to determine factors with prognostic value. P < 0.05 was considered statistically significant. Results Consensus clustering identifies four subtypes in HNSCC based on the metabolic features The workflow is shown in Fig. 1 A. A total of 725 patients were enrolled in our study: 532 from TCGA were assigned to the discovery cohort, 193 from the GEO database were assigned to the validation cohort. To explore a novel molecular classification of HNSCC patients, we performed unsupervised consensus clustering in the TCGA cohort based on the expression patterns of 1064 metabolism-relevant genes from MSigDB ( Supplementary Table. S1 ). To select the optimal and stable cluster number, cumulative distribution function (CDF) curve was calculated to determine the optimal k value, and k = 4 was eventually chosen as an optimal number of clusters after comprehensive consideration (Fig. 1 B and Supplementary Fig. 1A-F , four subclasses were designated Cluster A, Cluster B, Cluster C, and Cluster D). Following this, tSNE (t-Distributed Stochastic Neighbor Embedding) analysis also demonstrated that HNSCC patients under the 4 classifications had significantly different expression profiles (Fig. 1 C). To further validate the assignments of subtypes, we first corrected the batch effects across datasets from GEO database and examined the expression patterns via principal component analysis ( Supplementary Fig. 1G-H ). Subsequently, leveraging TCGA cohort-based grouping data, we established a random forest model to classify patients from GEO into the same four metabolic subgroups (Fig. 1 D). Of note, a significant prognostic difference in recurrence-free survival (RFS) and overall survival (OS) was observed among subtypes, especially with shorter RFS for Cluster D than others and better RFS for Cluster A than others (Fig. 1 E-F). To further explored the different metabolic characteristics in distinct subclasses, GSVA was conducted to quantify the enrichment levels of multiple metabolism processes which were divided into five main categories (Lipid metabolism, Carbohydrate metabolism, Amino acid metabolism, Nucleotide metabolism, and Antioxidant metabolism) and the enrichment patterns were demonstrated as a heatmap in Fig. 1 G. We found that higher levels of metabolism processes in the Cluster D subtype were mainly carbohydrate, amino acid, nucleotide and antioxidant, which indicated that tumors of this subtype might have enhanced energy metabolism as well as proliferation and viability under oxidative stress. Cluster B displayed higher levels of metabolism processes that were related to lipid, nucleotide and antioxidant, whereas amino acid and carbohydrate metabolism were relatively enriched in Cluster C. Cluster A subtype, by contrast, showed few enrichments of metabolism signatures, which implied low metabolic activities. Cluster D with upregulated carbohydrate metabolism signatures was associated with lymphatic metastasis in HNSCC To assess the clinical relevance of the metabolic subtypes identified above, we next determine correlations with clinicopathologic features. In the TCGA cohort, Cluster D was significantly associated with histopathology grade, advanced clinical stage, HPV infection, smoking, male, and older patients ( Supplementary Fig. 2A ). Similarly, the correlation of Cluster D subtype with advanced clinical stage, smoking habit and older patients were also observed in GEO cohort ( Supplementary Fig. 2A ). In particular, we found that the Cluster D subtype was significantly associated with advanced N-stage in both cohorts, which is generally associated with a poorer prognosis in head and neck cancer patients (Fig. 2 A-B). In addition, the GSVA score of lymphangiogenesis-related gene sets showed that Cluster D possessed molecular features which highly related to lymphatic metastasis, while Cluster A was relatively deficient in these features (Fig. 2 C-D). EPHB2 was the key driven gene of the Cluster D subtype in HNSCC Focusing on the subcluster with the most potential for lymphatic metastasis, we further conducted the connective mapping to identify the key gene for the subcluster in HNSCC (Fig. 2 E). The key differentially expressed genes (DEGs) were exhibited in Supplementary Fig. 2A B-C , and EPHB2 was the top upregulated-gene of DEGs, which was specifically higher expressed in Cluster D than others (Fig. 2 F). Notably, a significantly high expression of EPHB2 was also observed on the HNSCC patients in Cluster D (Fig. 2 G). Moreover, correlation analysis indicated that the high EPHB2 expression was significantly associated with lymphatic metastasis of HNSCC (Fig. 2 H). The GSVA score of lymphangiogenesis-related gene sets also showed that high EPHB2 expression closely related to lymphatic metastasis (Fig. 2 I). In addition, the specific metabolism signatures of high-expressed EPHB2 group were similar to the metabolic patterns of previously reported in Cluster D subtype, involving gene signatures of carbohydrate, amino acid and nucleotide metabolism (Fig. 2 J). These findings indicated that EPHB2 was defined as the key gene of the Cluster D subtype and may played a predominant role in lymphatic metastasis of HNSCC patients. Enhanced carbohydrate metabolism upregulated EPHB2 in HNSCC Previous studies have been reported that carbohydrate metabolism feeds into the regulation of gene expression via metabolic enzymes and metabolites, of which pyruvate metabolism was regarded as a central part. Therefore, we detected whether pyruvate contributed to EPHB2 upregulation. Indeed, exogenously adding pyruvate upregulated the levels of EPHB2 expression in laryngeal cancer cells (Fig. 3 A). In addition, TM-1, a blocker of pyruvate dehydrogenase kinase (PDHK), treatment abolished pyruvate-induced increase of EPHB2 expression (Fig. 3 A), suggesting that pyruvate metabolism played an essential role in EPHB2 upregulation, which may result from the accumulation of acetyl-CoA, the major metabolite of pyruvate. Acetyl-CoA was a substrate for acetylation reactions and directly affects the function of all acetyltransferases 29 – 31 . Acetyl-CoA produced by pyruvate is localized inside mitochondria and needs to be exported for nuclear histone acetylation as citrate or acetate, which was converted back to acetyl-CoA in the cytosol by ATP citrate lyase (ACLY) or acetyl-CoA synthase (ACSS2) (Fig. 3 B). Given the upregulated pyruvate metabolism signatures in EPHB2-high cells and cluster D subtype, we found that pyruvate treatment increased the acetyl-CoA levels in nucleus, and eventually enhanced histone H3 and H4 acetylation, which resulted in the augment of EPHB2 transcription levels. Whereas, depletion of ACLY and ACSS2 abolished pyruvate-induced EPHB2 transcription activation by decreasing the levels of nuclear acetyl-CoA content and histone acetylation (Fig. 3 C-F). Similarly to pyruvate treatment, glucose significantly augmented nuclear acetyl-CoA content, and enriched p300, H3K27ac and RNA polymerase II (pol II) on the EPHB2 promoter, which substantially activate EPHB2 transcription (Fig. 3 G-I). Whereas, 2-Deoxy-D-glucose (2-DG), as a competitive inhibitor of glycolysis to reducing pyruvate production, suppressed glycolysis-induced EPHB2 transcriptional activation (Fig. 3 G-I). These results demonstrated that highly activated carbohydrate metabolism with increase of acetyl-CoA production transcriptionally upregulated EPHB2 expression via promoting histone acetylation in HNSCC. EPHB2 promoted lymphatic metastasis To investigate whether EPHB2 was involved in lymphatic metastasis, we conducted a series of experiments in vitro and in vivo . The conditioned medium (CM) from EPHB2-overexpressing laryngeal cancer cells significantly increased, while which from silencing EPHB2 cells suppressed lymphangiogenesis in vitro , as indicated by the migration and tube formation of lymphatic endothelial cells (LECs) (Fig. 4 A-C). We further injected with control, EPHB2-overexpressing or EPHB2-silengcing Tu-212 cells at the footpad to investigated the role of EPHB2 in lymphatic metastasis in vivo . After 45 days of inoculation, we sacrificed the mice, extracted, and analyzed the primary footpad tumors and popliteal lymph nodes. Results showed that EPHB2 promoted, while depletion of EPHB2 inhibited, LN metastasis as indicated by the volumes of LNs and the number of metastatic Tu-212 cells in LNs (Fig. 4 D-E). Additionally, the relative mRNA ratio of human CK to mouse ACTB indicated a higher proportion of colonized tumor cells in LNs in the EPHB2-ovexpressing group, which was significantly decreased in the EPHB2-silencing group (Fig. 4 F-G). These results suggested that EPHB2 was contributed to lymphatic metastasis in laryngeal cancer. EPHB2 inhibited HIPPO signaling activation to triggering lymphangiogenesis via EFNB1-induced YAP/TAZ cytoplasmic retention Interestingly, VGX-100, a highly specific human monoclonal antibody for VEGF-C, or SAR131675, a selective VEGFR3 inhibitor, treatments only slightly impaired EPHB2-induced lymphangiogenesis, suggesting that EPHB2 may promote lymphangiogenesis in a VEGF-C independent manner (Fig. 5 A). Recently, several studies have identified Hippo-YAP/TAZ signaling components as novel players in lymphatic vascular development by regulating LECs specification, differentiation, and sprouting during early lymphatic development and maintaining lymphatic integrity during adulthood, which modulate lymphatic plasticity throughout life via regulating prospero homeobox 1 (PROX1) activity 32 . Next, we detected the HIPPO pathway and found that EPHB2 treatment dramatically decreased the transcriptional activity of YAP/TAZ in LECs (Fig. 5 B). EPHB2 was the member of Eph receptors whose extracellular signal transduction relied on the binding of their cognate membrane-tethered ligands, known as ephrin B ligands 33 . As shown in Fig. 5 C, silencing of EFNB1 resulted in the most significant reduction of YAP/TAZ transcriptional activity in LECs with EPHB2 treatment. Furthermore, western blotting analysis showed that EPHB2 enhance YAP and TAZ phosphorylation, which lose the function of nuclear translocation and binding with downstream transcription factor TEAD, eventually reduced PROX1 expression (Fig. 5 D). However, depletion EFNB1 of LECs inhibited EPHB2-induced YAP phosphorylation and then improved PROX1 expression (Fig. 5 D). immunofluorescence (IF) staining revealed that EPHB2 enhanced YAP/TAZ cytoplasmic localization, while YAP/TAZ showed more nuclear localization in EFNB1-silencing LECs (Fig. 5 E). Moreover, Co-IP assays indicated that EPHB2 promoted the interaction between EFNB1 and YAP/TAZ, while abolished the interaction between YAP/TAZ and its co-factor TEAD (Fig. 5 F-G). Take together, these results demonstrated that EPHB2 induced EFNB1 capturing YAP/TAZ in cytoplasm, resulted in alleviating HIPPO activation-induced PROX1 transcriptional repression, finally triggered lymphangiogenesis. Combined with EFNB1-Fc promoted VEGFR3 inhibitor efficiency in laryngeal cancer with lymphatic metastasis Finally, we assessed the therapeutic effect of blocking EPHB2 by EFNB1-Fc on laryngeal cancer with lymphatic metastasis. As shown in Fig. 6 A-B, EFNB1-Fc reduced the migration and tube formation of LECs in CM derived from EPHB2-overexpressing laryngeal cancer cells. Further, we injected 2 organoids from laryngeal cancer tissues with lymphatic metastasis to NSG mice. The administration of EFNB1-Fc slightly abolished tumor volumes, while dramatically decreased intra-tumoral lymphangiogenesis in vivo as indicated by immunofluorescence (IF) and immunohistochemical (IHC) staining of LEC markers LYVE-1 (Fig. 6 C-D). The therapeutic effect of combined EFNB1-Fc and SAR131675 on lymphatic metastasis in laryngeal cancer was further validated using in vivo mouse models. Mice were randomly divided into four groups, and equally injected Tu-212 cells at the footpad. After 10 days of tumor inoculation, we then then treated them with either PBS, EFNB1-Fc, SAR131675 or EFNB1-Fc + SAR131675. After a month of treatment, mice were euthanized and the metastasis in LNs was analyzed. Remarkably, the coadministration of EFNB1-Fc and SAR131675 dramatically shrank the volumes of LNs and the number of metastatic cancer cells in LNs compared with EFNB1-Fc or SAR131675 monotherapy (Fig. 6 E-G). Therefore, our results demonstrated that, in laryngeal cancer, highly activated pyruvate metabolism increase acetyl-CoA production, and then transcriptionally upregulated EPHB2 levels, which inhibited YAP/TAZ access to the nucleus and resulted in augment of PROX1 expression, eventually induced lymphangiogenesis (Fig. 6 H). In addition, combined treatment with EFNB1-Fc and SAR131675 exerted synergistic effects on blocking lymphatic metastasis, suggesting that targeting EPHB2 might be a potential strategy to patients who do not respond to VEGFR3 inhibitor. Discussion In this study, we firstly established a new classification of HNSCC patients based on metabolism gene expression profiles related to lymph node metastasis. Four metabolic subtypes were identified, and their clinical characteristics, metabolic signatures were also explored. Especially Cluster D, which has a higher level of carbohydrate metabolism, was highly related to lymphatic metastasis and significantly associated with a worse RFS outcome. We found that EPHB2 was the key gene of the cluster D subtype, then demonstrated that highly activated carbohydrate metabolism with increase of acetyl-CoA production could transcriptionally upregulated EPHB2 expression via promoting histone acetylation in cluster D subtype. Additionally, we found that EPHB2 was relevant to resistance of multiple VEGFR3 inhibitors, indicating that it promoted lymphangiogenesis in a VEGF-C independent manner. Furthermore, EPHB2 induced EFNB1 to capture YAP/TAZ in cytoplasm, resulted in inhibiting HIPPO activation-induced PROX1 transcriptional repression, finally triggered lymphangiogenesis. Our findings extended the molecular subtyping of HNSCC patients and showed that combination of EFNB1-Fc and VEGFR3 inhibitor exerted synergistic effects on blocking lymphatic metastasis, suggesting that targeting EPHB2 might be a potential strategy to patients who do not respond to VEGFR3 inhibitor. Recently, metabolite profiling has become the informative approach to elucidate tumor heterogeneity. Previously published study recruited a cohort of 9125 TCGA samples across 33 cancer types and characterised tumour subtypes based on the expression of seven metabolic pathways 34 . In pancreatic ductal adenocarcinoma, Daemen et al. conducted broad metabolite profiling and identified three subtypes that showed distinct metabolite profiles associated with glycolysis, lipogenesis and redox pathways 35 . And some researchers identified and validated three highly distinct metabolic subtypes in the lower-grade glioma patients, then developed a metabolic signature with better performance of prognosis prediction 36 . In this study, we classified the HNSCC patients into four subtypes based on the metabolism patterns, and found cluster D has the worst RFS and Cluster A has the best RFS. Then we identified the Cluster D possessed molecular features which highly related to lymphatic metastasis, while Cluster A was relatively deficient in these features. Meanwhile, metabolic reprogramming and epigenetic modifications are the hallmarks of cancer cells 37 , 38 . Previous studies showed that epigenetic modifications were closely related to cancer cell metabolism regulations, such as histone methylation, acetylation, DNA methylation and RNA N6-methyladenosine (m6A) methylation. Cancer metabolic reprogramming involves mainly a shift from oxidative phosphorylation to aerobic glycolysis, providing essential raw materials and energy support for tumor growth, participating in the tumor immune response. Glycolysis is the main source of energy for tumor cell proliferation, and as the key node in metabolism and the main producer of energy, acetyl-CoA plays an important role in the invasion and migration of cancer. Previous research has shown that an increase in the acetyl-CoA synthesis rate promotes posttranslational histone acetylation and accelerates cell division 39 , 40 , for example, the production of ACLY-dependent acetyl-CoA was proved playing an vital role in early stage pancreatic cancer development, by targeting the acetyl-CoA-dependent pathway and using combined bromodomain and extraterminal domain expression inhibition and statin therapy, the proliferation of cancer cells and tumor growth can be inhibited 41 . Another study indicated that maintaining ACLY levels and regulating acetyl-CoA levels promoted the proliferation, metastasis, and even drug resistance in nasopharyngeal carcinoma cells 42 . In this study, we also found that in laryngeal carcinoma, pyruvate treatment could increase the acetyl-CoA levels in nucleus and H3 acetylation, then increased the EPHB2 transcription levels, while depletion of ACLY and ACSS2 abolished pyruvate-induced EPHB2 transcription activation by decreasing the levels of nuclear acetyl-CoA content and H3 acetylation, demonstrating that highly activated carbohydrate metabolism with increase of acetyl-CoA production could transcriptionally upregulate the EPHB2 expression via promoting histone acetylation. In previously published studies related to lymphangiogenesis, VEGF-C and VEGF-D played significant roles by activating its receptor VEGFR-3 on LECs to activate a protein kinase C/ERK signaling cascade which ultimately triggers the phosphorylation of AKT and the proliferation and migration of these LECs, thus promoting lymphangiogenesis 43 . Additionally, there are many enzymes, bioactive lipids, chemokines, adhesion molecules, and noncoding RNAs that also participate in lymphangiogenesis by functioning in either a VEGF-C/D-dependent or -independent manner. Recently, several studies have identified Hippo-YAP/TAZ signaling components as novel players in lymphatic vascular development by regulating LECs specification, differentiation, sprouting during early lymphatic development and maintaining lymphatic integrity during adulthood, which modulate lymphatic plasticity throughout life via regulating PROX1 activity 32 . In Hippo signaling, nuclear YAP and TAZ regulate a wide variety of target genes, whereas cytoplasmic YAP and TAZ are targeted for degradation. Notably, YAP/TAZ depletion or hyperactivation in LECs during embryonic development results in structurally aberrant and poorly functional lymphatics and lethality, highlighting its importance in early development of the lymphatic system 32 , 44 . In this study, we found that EPHB2 promoted lymphangiogenesis in a VEGF-C independent manner, and giving EPHB2 treatment could dramatically repress the transcriptional activity of YAP/TAZ in LECs. Then we proved that EPHB2 induced EFNB1 expression by capturing YAP/TAZ in cytoplasm, alleviating HIPPO activation-induced PROX1 transcriptional repression, finally triggered lymphangiogenesis in laryngeal carcinoma cells. Conclusion In summary, this study firstly reclassified HNSCC from the metabolic perspective and proposed four subtypes with distinct prognosis and metabolic phenotype. Among these four subtypes, Cluster D was highly related to lymphatic metastasis and significantly associated with a worse RFS outcome, which also has a higher level of carbohydrate metabolism. Then we demonstrated that for this subtype of laryngeal cancer, highly activated pyruvate metabolism increased acetyl-CoA production, and then transcriptionally upregulated EPHB2 levels, which promoted YAP/TAZ cytoplasmic localization and resulted in augment of PROX1 expression, eventually induced lymphangiogenesis. Our findings extended the molecular subtyping of HNSCC patients and showed that combination of EFNB1-Fc and VEGFR3 inhibitor exerted synergistic effects on blocking lymphatic metastasis, suggesting that targeting EPHB2 might be a potential strategy to patients who do not respond to VEGFR3 inhibitor. Declarations Acknowledgements This work was funded by the Guangzhou Municipal Science and Technology Project (No. 202002020024), the Natural Science Foundation of Guangdong Province (No. 2024A1515013244), the National Natural Science Foundation of China (No. 82073330) and National Natural Science Foundation of China for Young Scholars (No. 82202946). Author contributions statement Conception and design: S.C., S.Z., and H.Z. Methodology and software: J.M., B.C., Q.L., and Z,L. Data curation, validation: J.M., B.C., Q.L., Z,L., R.W., C.W., X.J., D.S., Y.L., D.S., Y.O., and X.C. Writing–original draft: J.M., B.C., Q.L., and Z,L. Project administration, writing–review and editing: S.C., S.Z., H.Z., and M.Z. Study supervision: S.C., S.Z., and H.Z. Competing interests The authors declare no competing interests. Ethics statement The patient gave informed consent for the collection of clinical information, tissue collection, and research testing under the Internal Review and Ethics Board of Sun Yat-sen University Cancer Center (Approval No. GZR2024-132). All animal experiments were approved by the Ethics Committee of Sun Yat-sen University Cancer Center (L102012024008W). References Beckham TH, et al. Long-term survival in patients with metastatic head and neck squamous cell carcinoma treated with metastasis-directed therapy. Br J Cancer. 2019;121:897–903. 10.1038/s41416-019-0601-8 . Chow LQM. Head and Neck Cancer. N Engl J Med. 2020;382:60–72. 10.1056/NEJMra1715715 . Mehlen P, Puisieux A. Metastasis: a question of life or death. Nat Rev Cancer. 2006;6:449–58. 10.1038/nrc1886 . Li YL, Hung WC. Reprogramming of sentinel lymph node microenvironment during tumor metastasis. J Biomed Sci. 2022;29:84. 10.1186/s12929-022-00868-1 . Al-Rawi MA, Mansel RE, Jiang WG. 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Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2025 Read the published version in Journal of Translational Medicine → Version 1 posted Editorial decision: Major revision 02 Dec, 2024 Reviewers agreed at journal 14 Nov, 2024 Reviewers invited by journal 14 Nov, 2024 Editor assigned by journal 04 Nov, 2024 First submitted to journal 24 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5324948","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378351714,"identity":"320ca090-58cc-4045-9ace-12f48dc79b85","order_by":0,"name":"Jingjing Miao","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Miao","suffix":""},{"id":378351715,"identity":"f4cf784a-cf40-49fd-b5ce-4bf759085253","order_by":1,"name":"Boyu Chen","email":"","orcid":"","institution":"Sun Yat-sen University First Affiliated 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University Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"shuwei","middleName":"","lastName":"chen","suffix":""}],"badges":[],"createdAt":"2024-10-24 10:18:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5324948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5324948/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-025-06305-9","type":"published","date":"2025-03-12T15:58:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":71741328,"identity":"87f045eb-0fb3-451d-b865-f47d179c0cb8","added_by":"auto","created_at":"2024-12-18 08:10:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":13485442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification distinct metabolic subtypes in HNSCC through metabolism gene profiling. \u003c/strong\u003eA. a Flow chart shows the computational method to classify tumor samples into metabolic subtypes. TCGA cohort was used as a discovery set, GEO cohort was collected as validation sets. B. Heatmaps of four clusters defined in TCGA cohort. The cumulative distribution function (CDF) curve was calculated to determine the optimal k value, and k = 4 was eventually chosen as an optimal number of clusters after comprehensive consideration. C-D. Principal component analysis (PCA) supported the stratification into four HNSCC subclasses in TCGA cohort (C) and GEO cohort (D). E. Recurrence-free survival (RFS) of four subclasses (Cluster A, Cluster B, Cluster C and Cluster D) in TCGA cohort. F. Overall survival (OS) of four subclasses (Cluster A, Cluster B, Cluster C and Cluster D) in TCGA cohort. G. Heatmaps of differential enrichments of metabolism-related signatures in the TCGA cohort. Lipid, carbohydrate, amino acid, nucleotide and vitamin metabolism signatures were presented.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/ace048d318209434a93f6857.png"},{"id":71741324,"identity":"d48ab413-3487-4251-98c8-7ddac18b3197","added_by":"auto","created_at":"2024-12-18 08:10:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6296151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConnective mapping found that EPHB2 was the key gene of cluster D and associated with lymphatic metastasis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. Correlation of the HNSCC subclasses with N-stage in the TCGA cohort (A) and GEO cohort (B). C-D. Boxplot of the signature score for lymphangiogenesis-related signatures distinguished by different subclasses in the TCGA cohort (C) and GEO cohort (D). E.\u003cstrong\u003e \u003c/strong\u003eA Flow chart shows the connective mapping method to find the key gene of different metabolic subtypes. F. The relative expression level of EPHB2 in different metabolic subtypes. G. Correlation analyses between Cluster D and EPHB2 expression in HNSCC patient specimens. H. Correlation analyses between lymphatic metastasis and EPHB2 expression in HNSCC patient specimens. I. Boxplot of the signature score for lymphangiogenesis-related signatures distinguished by different EPHB2 expression levels. J. Heatmaps of differential enrichments of metabolism-related signatures in high- or low-EPHB2 groups.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/9953e7007cdfa5c3b62495d3.png"},{"id":71741325,"identity":"aa575927-56f3-4cdc-b0d5-b6dd97e8f877","added_by":"auto","created_at":"2024-12-18 08:10:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5237773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnhanced carbohydrate metabolism upregulated EPHB2 in HNSCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. qRT-PCR (upper) and western blot analysis (lower) of EPHB2 under different concentration of pyruvate with or without TM-1. a-Tubulin was used as a loading control. B. The source of nuclear acetyl-CoA. C. Nuclear acetyl-CoA levels in the indicated groups. D. The levels of histone H3 acetylation in lysine 9, 14 and 27. E. The luciferase activities of EPHB2 reporter in the indicated groups. F. qRT-PCR (upper) and western blot analysis (lower) of EPHB2 in the indicated groups. G. Nuclear acetyl-CoA levels under pyruvate, glucose and 2-DG treatment. H. ChIP assays examining the enrichment of P300, H327ac and Pol II on the EPHB2 promoter under pyruvate, glucose and 2-DG treatment. I. qRT-PCR (upper) and western blot analysis (lower) of EPHB2 under pyruvate, glucose and 2-DG treatment.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/7485887ef8cf92485daea480.png"},{"id":71741789,"identity":"1a49bd75-cd61-4b1a-8360-18518a1d5096","added_by":"auto","created_at":"2024-12-18 08:18:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15054986,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEPHB2 promoted lymphatic metastasis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Western blot analysis of EPHB2 in whole cell lysate (WCL) and supernatant (Sup) in control, EPHB2-silenced, and EPHB2-overexpressing TU212 and TU686 cells. B. LECs were incubated with the indicated CM and then subjected to transwell migration assays. Scale bars: 100 µm. C. Tube-formation assays of LECs incubated with the indicated CM. Scale bars: 200 µm. D. Image of the inguinal lymph nodes (LNs) from each group. E. Quantification of LNs volumes in each group. F. qRT-PCR analysis of hCK relative to mACTB in the LNs from each group. The ratio indicated the proportion of metastatic cells. G. The analysis of metastasis LNs ration in each group.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/8b528b22c18fb62620f42094.png"},{"id":71741326,"identity":"b3494122-e499-4dd9-ae08-473537e2b84a","added_by":"auto","created_at":"2024-12-18 08:10:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9409108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEPHB2 inhibited HIPPO signaling activation to triggering lymphangiogenesis via EFNB1-induced YAP/TAZ cytoplasmic localization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. LECs were incubated with the indicated CM and then subjected to transwell migration assays (left) and tube-formation assays (right) under treated with VGX-100 (30 μg/mL) or SAR131675 (40nM). Representative images of three biological replicates were shown. B. Luciferase reporter assays of Hippo pathway in TU212 cells with or without EPHB2 addition. C. Luciferase reporter assays of Hippo pathway in TU212 cells with silencing EFNB1, EFNB2, and EFNB3, respectively. D. Western blotting of the expression of critical genes of Hippo signaling pathway and PROX1 under treatment of EPHB2 in control and EFNB1-silenced laryngeal cancer cells. a-Tubulin was used as a loading control. E. The immunofluorescence (IF) staining of YAP was performed in indicated groups. F. Immunoprecipitation (IP) assays of EFNB1, EPHB2, YAP and TAZ in TU212 cells. G. Immunoprecipitation (IP) assays of EFNB1, EPHB2, YAP, TAZ and TEAD in TU212 cells.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/34dc64e1815420f4ed097c21.png"},{"id":71741331,"identity":"0df7b937-22bb-4f4d-b895-9a0120525d63","added_by":"auto","created_at":"2024-12-18 08:10:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24112149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined with EFNB1-Fc promoted VEGFR3 inhibitor efficiency in laryngeal cancer with lymphatic metastasis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. LECs were incubated with the indicated CM with or without EFNB1-Fc treatment and then subjected to transwell migration assays. Scale bars: 100 µm. B. Tube-formation assays of LECs incubated with the indicated CM with or without EFNB1-Fc treatment. Scale bars: 200 µm. C. The tumor volume of 2 patients-derived organoids (PDO) treated with or without EFNB1-Fc. D. The IHC staining of lymphatic endothelial cell markers LYVE-1 was performed in tumors from 2 PDO. E. Image (left) and quantification (right) of LNs volumes in each group. F. qRT-PCR analysis of hCK relative to mACTB in the LNs from each group. The ratio indicated the proportion of metastatic cells. G. The analysis of metastasis LNs ration in each group. H. Study model: pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in HNSCC.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/b07eac1940a4c718b23e9383.png"},{"id":78690838,"identity":"2e022cc4-26b4-40a2-85f7-234dd9de0896","added_by":"auto","created_at":"2025-03-17 16:15:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":68352700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/d7700e25-fd50-4e60-8a08-db178c4d7b9b.pdf"},{"id":71743252,"identity":"e45d7e88-edda-4a6e-8432-854ad39026b4","added_by":"auto","created_at":"2024-12-18 08:26:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7615213,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-5324948/v1/ece7408e57f5f1b4efd36272.docx"}],"financialInterests":"","formattedTitle":"Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately 20\u0026ndash;30% of patients with head and neck squamous cell carcinoma (HNSCC) suffer from local and regional lymphatic metastasis, which can lead to distant metastasis and cause poor clinical outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Lymphangiogenesis is considered to be the predominant mechanism for the expansion of tumor-associated lymphatic network, which mediated by lymphatic endothelial cells (LECs) with expressing characteristic molecular markers, including lymphatic vessel endothelial receptor 1 (LYVE-1), prospero homeobox protein 1 (PROX1) and podoplanin (PDPN)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Given that approximately 95% of tumor-associated vessels infiltrated by tumor cells are lymphatic vessels, which play a major role in tumor metastasis, targeting lymphangiogenesis has been considered as a potent and direct strategy for controlling metastatic diseases.\u003c/p\u003e \u003cp\u003eVEGF-C, one of the vascular endothelial growth factors (VEGF) family, has long been recognized as a major molecular driver of lymphangiogenesis by activation of VEGFR3 expressed on LECs\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The VEGF-C/VEGFR3 interaction promotes proliferation, migration and tube formation of LECs to induce tumor lymphangiogenesis via maintenance of the MAPK or PI3K/Akt signaling activity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Based on its essential role in lymphangiogenesis, numerous drugs targeting VEGFC/VEGFR-3 pathway have been established and applied to clinical practice or under the clinical testing and presented partial efficacy against lymphatic metastasis in various cancers\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, especially in renal cell carcinoma\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e and colorectal cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, numerous clinical trials demonstrated that the magnitude of the clinical benefit of targeting VEGF-C/VEGFR-3 pathway varies among different patient populations\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Moreover, some patients with lymphatic metastatic cancers exhibit low expression of VEGF-C, suggesting the occurrence of VEGF-C-independent lymphangiogenesis. Therefore, further research is needed to identify the comprehensive mechanism of lymphangiogenesis in HNSCC.\u003c/p\u003e \u003cp\u003eMetabolic reprogramming is one of the well-established hallmarks of cancer and is responsible for the metastatic spread of tumors\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In order to adapt the changes in energy requirements at each step during the metastatic cascade, tumor cells selectively and dynamically modify their metabolic programs\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Recently, more and more studies showed that tumor cells have heterogeneous metabolic preferences and dependencies. Within the same tumor, tumor cells with higher carbohydrate metabolism, which provided fuel of energy production, presented a more aggressive and malignant phenotype via increase of oxidative phosphorylation\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Compared with non-metastatic cancer, the serum pyruvate concentration was higher in patients with metastatic breast cancer\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e and highly invasive ovarian cancer\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Pyruvate uptake inducing the production of a-ketoglutarate activated collagen hydroxylation by increasing the activity of the enzyme collagen prolyl-4-hydroxylase (P4HA), which remodeling the extracellular matrix in the lung metastatic niche for breast cancer\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Multiple studies have provided evidence that metabolic changes in cancer cells are not just consequential bystander effects because metabolites, but also could directly modulate activity of signaling pathways and global gene expression programmes\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Hence, deciphering how metabolic disorders drive lymphatic metastasis may provide potential therapeutic vulnerabilities in HNSCC.\u003c/p\u003e \u003cp\u003eHere, we firstly classified HNSCC patients into four subtypes based on unsupervised clustering of metabolism gene expression profiles using TCGA public data and evaluated in GEO cohort. We found that an enriched carbohydrate metabolism subtype, cluster D, was significantly associated with lymphatic metastasis and worse clinical outcome. Meanwhile, we also investigated the underlying mechanism of carbohydrate metabolism triggering lymphatic metastasis in laryngeal cancer. Importantly, blocking EPHB2 with recombinant EFNB1-Fc synergised with VEGFR3 inhibitor treatment to potently suppress lymphatic metastasis in laryngeal cancer. Our findings bring new insight into the links between pyruvate metabolism and lymphatic metastasis, and explore strategies to optimize the treatment for lymphatic metastasis in HNSCC.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and datasets\u003c/h2\u003e \u003cp\u003eThis study collected 725 HNSCC patients from two databases: TCGA (n\u0026thinsp;=\u0026thinsp;532) and GEO (n\u0026thinsp;=\u0026thinsp;193). For the TCGA cohort, the RNA-seq data and corresponding clinical information were downloaded from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"http://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The molecular and immune features were also retrieved\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For the GEO cohort, the RNA-seq accompanied with clinical information were obtained from the GEO database (GSE142083, GSE127165, GSE112026 and GSE74927).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification and validation of metabolic subtypes\u003c/h3\u003e\n\u003cp\u003eThe 1064 metabolism-related genes from MSigDB were selected for consensus clustering (R package \u0026ldquo;consensusClusterplus\u0026rdquo;) to identify robust clusters in the training cohort from TCGA and the genes were detailed in \u003cb\u003eSupplementary Table. S1\u003c/b\u003e. We utilized two methods of elbow method and gap statics to identify the optimized K categories, meanwhile considering the associations between metabolic clusters and survival outcomes. The cumulative distribution function (CDF) and consensus heatmap were adopted to assess the optimal K.\u003c/p\u003e \u003cp\u003eTo validate the metabolic subtypes in GEO cohort, we first used the R package \u0026ldquo;limma\u0026rdquo; to remove batch effects on the 4 GEO-derived HNSCC datasets. Subsequently, we trained a classifier based on the subtypes in TCGA cohort via \u0026ldquo;Random Forest\u0026rdquo; algorithm with R package \u0026ldquo;randomForest\u0026rdquo;. Then, the classifier was used to predict metabolic subtype for patients in the GEO cohort and each sample was assigned to a metabolic subtype. The in-group-proportion (IGP) statistic with R package \u0026ldquo;clusterRepro\u0026rdquo; was performed to assess the reproducibility and similarity of the acquired metabolic subtypes between the training and validation cohorts.\u003c/p\u003e\n\u003ch3\u003eGene set variation analysis (GSVA)\u003c/h3\u003e\n\u003cp\u003eGSVA was utilized to evaluate the most significantly enriched molecular pathways of the stemness subtypes by using the \u0026ldquo;GSVA\u0026rdquo; package in R\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Differential analysis of the enrichment scores of molecular pathways between the different groups was performed by the limma package in R\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. the signatures with an absolute log2 fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;0.2 (adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were defined as differentially expressed signatures.\u003c/p\u003e\n\u003ch3\u003eConnective Mapping analysis\u003c/h3\u003e\n\u003cp\u003eThe CMap database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://clue.io/\u003c/span\u003e\u003cspan address=\"https://clue.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to explore potential compounds targeting the molecular pathways and key genes associated with the metabolic subtype of HNSCC patients\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The DEGs between the different metabolic subtypes were employed to query the CMap database, and the most significantly highly expressed genes of each metabolic subtype were considered as key regulatory gene.\u003c/p\u003e\n\u003ch3\u003eRNA isolation and qRT-PCR\u003c/h3\u003e\n\u003cp\u003eRNA from the indicated cells were extracted using the Trizol reagent (15596026, Invitrogen). cDNA synthesis was carried out using M-MLV Reverse Transcriptase and then the cDNA was used as the template for amplification on a CFX384 Real-Time System (Bio-Rad Laboratories, Singapore). The data were analyzed using the ΔΔCt method. The results from each experiment were normalized to the expression of ACTB. The sequences of the primers used for all qRT-PCR assays are listed in \u003cb\u003eSupplementary Table. S2.\u003c/b\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot analysis\u003c/h2\u003e \u003cp\u003eWestern blot analyses were performed according to a standard protocol using primary antibodies, including anti-EPHB2 (Cell Signaling Technology, 14389), anti-H3 (Cell Signaling Technology, 9715), anti-H3ac (pan-acetyl) (Invitrogen, PA5-114693), anti-H4 ( Cell Signaling Technology, 2592), anti-H4ac (pan-acetyl) (Invitrogen, PA5-32029), anti-EFBN1 (Santa Cruz Biotechnology, sc-515264), p-YAP (Cell Signaling Technology, 4911), YAP (Cell Signaling Technology, 4912), p-TAZ (Cell Signaling Technology, 59971), TAZ (abcam, ab119373), PROX1 (Proteintech, 67438-1-Ig), TEAD (Cell Signaling Technology, 13295) and anti-a-Tubulin (Cell Signaling Technology, 2144) antibodies. a-Tubulin were used as loading controls.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAcetyl-CoA measurement\u003c/h3\u003e\n\u003cp\u003eTo measure the nucleus acetyl-CoA concentrations, 50 ml of exponentially growing cells was collected in SB buffer (1 M sorbitol, 20 mM Tris (pH 7.4) and 10 mg ml\u0026thinsp;\u0026minus;\u0026thinsp;1 zymolase 20T) and lysed with EBX (20 mM Tris (pH 7.4), 100 mM NaCl, 0.5% Triton X-100, 15 mM 2-mercaptoethanol and protease inhibitors). One aliquot of the lysate was collected to measure the total acetyl-CoA and the remaining lysate was layered over NIB (20 Mm Tris (pH 7.4), 100 mM NaCl, 1.2 M sucrose, 15 mM 2-mercaptoethanol and protease inhibitors). After centrifugation, the pellet was lysed with 1% Triton X-100, centrifuged and collected as the nuclear fraction. The Acetyl-CoA quantity was measured using an Acetyl-CoA kit (Suzhou Comin Biotechnology) according to the manufacturer\u0026rsquo;s instructions. The assay was performed in 96-well clear-bottom plates, and the fluorescence was quantified using the SpectraMax M2 (Molecular Devices). The acetyl-CoA levels were normalized to the corresponding protein concentrations. For the total and nuclear fractions, the quantity of acetyl-CoA levels was normalized to the corresponding protein concentrations. Histone H3 were analysed by western blots to ensure that there was no cross-contamination between compartments during subcellular fractionation.\u003c/p\u003e\n\u003ch3\u003eTube formation of hLECs\u003c/h3\u003e\n\u003cp\u003eThe hLEC tube formation assay was performed by first pipetting 200 \u0026micro;l 50% Matrigel (BD Biosciences, Bedford, MA) into a 24-well plate, which was then polymerized for 30 min at 37\u0026deg;C. LECs (2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e) in 200 \u0026micro;l of CM were added to each well and incubated at 37\u0026deg;C, 5% CO2 for 12 h. Images were taken using a bright-field microscope (Eclipse 80i, Nikon, Tokyo, Japan) at \u0026times;100 magnification. The capillary tubes were quantified by measuring the total length of completed tubule structures.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTumor organoid culture\u003c/h2\u003e \u003cp\u003eFreshly excised laryngeal tumor tissue of patients with lymphatic metastasis were harvested and dissociated enzymatically for 30\u0026ndash;40 min in digestion media containing 1 mg/ml collagenase I (Sigma-Aldrich), 0.5 mg/ml Dispase (Roche), 50 \u0026micro;g/ml dNase (Sigma-Aldrich) in RPMI medium containing 2% FCS. Supernatant was washed and filtered through a 100 \u0026micro;M cell strainer. Cells were then embedded in basement membrane extract (#E0282, Sigma Aldrich) and cultured in patient-derived tumor organoid culture medium (Accurate international biotech). All tissues were collected from patients with laryngeal cancer who have undergone surgical treatment in SYSUCC. The use of human specimens was carried out in accordance with the Helsinki declaration and approved by the ethics committee of SYSUCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eXenograft tumor models and treatments\u003c/h2\u003e \u003cp\u003eBALB/c-nude mice (Male, 4\u0026ndash;5 weeks old) and NOD-SCID (Male, 4\u0026ndash;5 weeks old) obtained from Gempharmatech Co., Ltd. were used for examination of the LN metastasis of TU212 tumors and the growth of patient-derived tumor organoid, respectively. Mice were housed in barrier facilities on a 12 hours light/dark cycle. The inguinal LN metastasis model was performed as previously reported\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Recombinant human EFNB1-Fc was purchased from R\u0026amp;D systems (7654-EB) and SAR131675 was purchased from MCE (HY-15458). EFNB1-Fc (2.5 mg/kg, twice per week)\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e or SAR131675 (100mg/kg, once per day)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e were administered 10 days after tumor inoculation. For the inguinal LN metastasis model, after a month of inoculation, mice were euthanized. Inguinal LNs were first measured to calculate volumes. LNs were used for quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis of hCK and mACTB to determine the proportion of LN-spread TU212 cells. For the subcutaneous tumors, tumor volumes were measured weekly. Mice were euthanized and tumors were collected after 7 weeks of inoculation, and IHC staining of lymphatic endothelial cell markers LYVE-1 to indicated the intra-tumoral lymphangiogenesis. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Sun Yat-sen University and performed following the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using R software, GraphPad Prism 6.0 (GraphPad Inc., San Diego, CA, USA), or SPSS 16.0 (IBM, Chicago, IL, USA). Kaplan\u0026ndash;Meier analysis with log-rank test was performed to assess survival difference between subtypes. Chisquare test was carried out to determine the difference of clinical and molecular features between subtypes. For comparisons of three groups, one-way analysis and ANOVA test of variance were used. Univariate and multivariate Cox regression analyses were performed to determine factors with prognostic value. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConsensus clustering identifies four subtypes in HNSCC based on the metabolic features\u003c/h2\u003e \u003cp\u003eThe workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. A total of 725 patients were enrolled in our study: 532 from TCGA were assigned to the discovery cohort, 193 from the GEO database were assigned to the validation cohort. To explore a novel molecular classification of HNSCC patients, we performed unsupervised consensus clustering in the TCGA cohort based on the expression patterns of 1064 metabolism-relevant genes from MSigDB (\u003cb\u003eSupplementary Table. S1\u003c/b\u003e). To select the optimal and stable cluster number, cumulative distribution function (CDF) curve was calculated to determine the optimal k value, and k\u0026thinsp;=\u0026thinsp;4 was eventually chosen as an optimal number of clusters after comprehensive consideration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cb\u003eSupplementary Fig.\u0026nbsp;1A-F\u003c/b\u003e, four subclasses were designated Cluster A, Cluster B, Cluster C, and Cluster D). Following this, tSNE (t-Distributed Stochastic Neighbor Embedding) analysis also demonstrated that HNSCC patients under the 4 classifications had significantly different expression profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). To further validate the assignments of subtypes, we first corrected the batch effects across datasets from GEO database and examined the expression patterns via principal component analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;1G-H\u003c/b\u003e). Subsequently, leveraging TCGA cohort-based grouping data, we established a random forest model to classify patients from GEO into the same four metabolic subgroups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Of note, a significant prognostic difference in recurrence-free survival (RFS) and overall survival (OS) was observed among subtypes, especially with shorter RFS for Cluster D than others and better RFS for Cluster A than others (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explored the different metabolic characteristics in distinct subclasses, GSVA was conducted to quantify the enrichment levels of multiple metabolism processes which were divided into five main categories (Lipid metabolism, Carbohydrate metabolism, Amino acid metabolism, Nucleotide metabolism, and Antioxidant metabolism) and the enrichment patterns were demonstrated as a heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG. We found that higher levels of metabolism processes in the Cluster D subtype were mainly carbohydrate, amino acid, nucleotide and antioxidant, which indicated that tumors of this subtype might have enhanced energy metabolism as well as proliferation and viability under oxidative stress. Cluster B displayed higher levels of metabolism processes that were related to lipid, nucleotide and antioxidant, whereas amino acid and carbohydrate metabolism were relatively enriched in Cluster C. Cluster A subtype, by contrast, showed few enrichments of metabolism signatures, which implied low metabolic activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCluster D with upregulated carbohydrate metabolism signatures was associated with lymphatic metastasis in HNSCC\u003c/h2\u003e \u003cp\u003eTo assess the clinical relevance of the metabolic subtypes identified above, we next determine correlations with clinicopathologic features. In the TCGA cohort, Cluster D was significantly associated with histopathology grade, advanced clinical stage, HPV infection, smoking, male, and older patients (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). Similarly, the correlation of Cluster D subtype with advanced clinical stage, smoking habit and older patients were also observed in GEO cohort (\u003cb\u003eSupplementary Fig.\u0026nbsp;2A\u003c/b\u003e). In particular, we found that the Cluster D subtype was significantly associated with advanced N-stage in both cohorts, which is generally associated with a poorer prognosis in head and neck cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). In addition, the GSVA score of lymphangiogenesis-related gene sets showed that Cluster D possessed molecular features which highly related to lymphatic metastasis, while Cluster A was relatively deficient in these features (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEPHB2 was the key driven gene of the Cluster D subtype in HNSCC\u003c/h2\u003e \u003cp\u003eFocusing on the subcluster with the most potential for lymphatic metastasis, we further conducted the connective mapping to identify the key gene for the subcluster in HNSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). The key differentially expressed genes (DEGs) were exhibited in \u003cb\u003eSupplementary Fig.\u0026nbsp;2A B-C\u003c/b\u003e, and EPHB2 was the top upregulated-gene of DEGs, which was specifically higher expressed in Cluster D than others (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Notably, a significantly high expression of EPHB2 was also observed on the HNSCC patients in Cluster D (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Moreover, correlation analysis indicated that the high EPHB2 expression was significantly associated with lymphatic metastasis of HNSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). The GSVA score of lymphangiogenesis-related gene sets also showed that high EPHB2 expression closely related to lymphatic metastasis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI). In addition, the specific metabolism signatures of high-expressed EPHB2 group were similar to the metabolic patterns of previously reported in Cluster D subtype, involving gene signatures of carbohydrate, amino acid and nucleotide metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). These findings indicated that EPHB2 was defined as the key gene of the Cluster D subtype and may played a predominant role in lymphatic metastasis of HNSCC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEnhanced carbohydrate metabolism upregulated EPHB2 in HNSCC\u003c/h2\u003e \u003cp\u003ePrevious studies have been reported that carbohydrate metabolism feeds into the regulation of gene expression via metabolic enzymes and metabolites, of which pyruvate metabolism was regarded as a central part. Therefore, we detected whether pyruvate contributed to EPHB2 upregulation. Indeed, exogenously adding pyruvate upregulated the levels of EPHB2 expression in laryngeal cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In addition, TM-1, a blocker of pyruvate dehydrogenase kinase (PDHK), treatment abolished pyruvate-induced increase of EPHB2 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), suggesting that pyruvate metabolism played an essential role in EPHB2 upregulation, which may result from the accumulation of acetyl-CoA, the major metabolite of pyruvate. Acetyl-CoA was a substrate for acetylation reactions and directly affects the function of all acetyltransferases\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Acetyl-CoA produced by pyruvate is localized inside mitochondria and needs to be exported for nuclear histone acetylation as citrate or acetate, which was converted back to acetyl-CoA in the cytosol by ATP citrate lyase (ACLY) or acetyl-CoA synthase (ACSS2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Given the upregulated pyruvate metabolism signatures in EPHB2-high cells and cluster D subtype, we found that pyruvate treatment increased the acetyl-CoA levels in nucleus, and eventually enhanced histone H3 and H4 acetylation, which resulted in the augment of EPHB2 transcription levels. Whereas, depletion of ACLY and ACSS2 abolished pyruvate-induced EPHB2 transcription activation by decreasing the levels of nuclear acetyl-CoA content and histone acetylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-F). Similarly to pyruvate treatment, glucose significantly augmented nuclear acetyl-CoA content, and enriched p300, H3K27ac and RNA polymerase II (pol II) on the \u003cem\u003eEPHB2\u003c/em\u003e promoter, which substantially activate EPHB2 transcription (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-I). Whereas, 2-Deoxy-D-glucose (2-DG), as a competitive inhibitor of glycolysis to reducing pyruvate production, suppressed glycolysis-induced EPHB2 transcriptional activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-I). These results demonstrated that highly activated carbohydrate metabolism with increase of acetyl-CoA production transcriptionally upregulated EPHB2 expression via promoting histone acetylation in HNSCC.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eEPHB2 promoted lymphatic metastasis\u003c/h2\u003e \u003cp\u003eTo investigate whether EPHB2 was involved in lymphatic metastasis, we conducted a series of experiments \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. The conditioned medium (CM) from EPHB2-overexpressing laryngeal cancer cells significantly increased, while which from silencing EPHB2 cells suppressed lymphangiogenesis in \u003cem\u003evitro\u003c/em\u003e, as indicated by the migration and tube formation of lymphatic endothelial cells (LECs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). We further injected with control, EPHB2-overexpressing or EPHB2-silengcing Tu-212 cells at the footpad to investigated the role of EPHB2 in lymphatic metastasis \u003cem\u003ein vivo\u003c/em\u003e. After 45 days of inoculation, we sacrificed the mice, extracted, and analyzed the primary footpad tumors and popliteal lymph nodes. Results showed that EPHB2 promoted, while depletion of EPHB2 inhibited, LN metastasis as indicated by the volumes of LNs and the number of metastatic Tu-212 cells in LNs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E). Additionally, the relative mRNA ratio of human CK to mouse ACTB indicated a higher proportion of colonized tumor cells in LNs in the EPHB2-ovexpressing group, which was significantly decreased in the EPHB2-silencing group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-G). These results suggested that EPHB2 was contributed to lymphatic metastasis in laryngeal cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEPHB2 inhibited HIPPO signaling activation to triggering lymphangiogenesis via EFNB1-induced YAP/TAZ cytoplasmic retention\u003c/h2\u003e \u003cp\u003eInterestingly, VGX-100, a highly specific human monoclonal antibody for VEGF-C, or SAR131675, a selective VEGFR3 inhibitor, treatments only slightly impaired EPHB2-induced lymphangiogenesis, suggesting that EPHB2 may promote lymphangiogenesis in a VEGF-C independent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Recently, several studies have identified Hippo-YAP/TAZ signaling components as novel players in lymphatic vascular development by regulating LECs specification, differentiation, and sprouting during early lymphatic development and maintaining lymphatic integrity during adulthood, which modulate lymphatic plasticity throughout life via regulating prospero homeobox 1 (PROX1) activity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Next, we detected the HIPPO pathway and found that EPHB2 treatment dramatically decreased the transcriptional activity of YAP/TAZ in LECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). EPHB2 was the member of Eph receptors whose extracellular signal transduction relied on the binding of their cognate membrane-tethered ligands, known as ephrin B ligands\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, silencing of EFNB1 resulted in the most significant reduction of YAP/TAZ transcriptional activity in LECs with EPHB2 treatment. Furthermore, western blotting analysis showed that EPHB2 enhance YAP and TAZ phosphorylation, which lose the function of nuclear translocation and binding with downstream transcription factor TEAD, eventually reduced PROX1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). However, depletion EFNB1 of LECs inhibited EPHB2-induced YAP phosphorylation and then improved PROX1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). immunofluorescence (IF) staining revealed that EPHB2 enhanced YAP/TAZ cytoplasmic localization, while YAP/TAZ showed more nuclear localization in EFNB1-silencing LECs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Moreover, Co-IP assays indicated that EPHB2 promoted the interaction between EFNB1 and YAP/TAZ, while abolished the interaction between YAP/TAZ and its co-factor TEAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF-G). Take together, these results demonstrated that EPHB2 induced EFNB1 capturing YAP/TAZ in cytoplasm, resulted in alleviating HIPPO activation-induced PROX1 transcriptional repression, finally triggered lymphangiogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCombined with EFNB1-Fc promoted VEGFR3 inhibitor efficiency in laryngeal cancer with lymphatic metastasis\u003c/h2\u003e \u003cp\u003eFinally, we assessed the therapeutic effect of blocking EPHB2 by EFNB1-Fc on laryngeal cancer with lymphatic metastasis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B, EFNB1-Fc reduced the migration and tube formation of LECs in CM derived from EPHB2-overexpressing laryngeal cancer cells. Further, we injected 2 organoids from laryngeal cancer tissues with lymphatic metastasis to NSG mice. The administration of EFNB1-Fc slightly abolished tumor volumes, while dramatically decreased intra-tumoral lymphangiogenesis in vivo as indicated by immunofluorescence (IF) and immunohistochemical (IHC) staining of LEC markers LYVE-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC-D). The therapeutic effect of combined EFNB1-Fc and SAR131675 on lymphatic metastasis in laryngeal cancer was further validated using \u003cem\u003ein vivo\u003c/em\u003e mouse models. Mice were randomly divided into four groups, and equally injected Tu-212 cells at the footpad. After 10 days of tumor inoculation, we then then treated them with either PBS, EFNB1-Fc, SAR131675 or EFNB1-Fc\u0026thinsp;+\u0026thinsp;SAR131675. After a month of treatment, mice were euthanized and the metastasis in LNs was analyzed. Remarkably, the coadministration of EFNB1-Fc and SAR131675 dramatically shrank the volumes of LNs and the number of metastatic cancer cells in LNs compared with EFNB1-Fc or SAR131675 monotherapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE-G).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTherefore, our results demonstrated that, in laryngeal cancer, highly activated pyruvate metabolism increase acetyl-CoA production, and then transcriptionally upregulated EPHB2 levels, which inhibited YAP/TAZ access to the nucleus and resulted in augment of PROX1 expression, eventually induced lymphangiogenesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). In addition, combined treatment with EFNB1-Fc and SAR131675 exerted synergistic effects on blocking lymphatic metastasis, suggesting that targeting EPHB2 might be a potential strategy to patients who do not respond to VEGFR3 inhibitor.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we firstly established a new classification of HNSCC patients based on metabolism gene expression profiles related to lymph node metastasis. Four metabolic subtypes were identified, and their clinical characteristics, metabolic signatures were also explored. Especially Cluster D, which has a higher level of carbohydrate metabolism, was highly related to lymphatic metastasis and significantly associated with a worse RFS outcome. We found that EPHB2 was the key gene of the cluster D subtype, then demonstrated that highly activated carbohydrate metabolism with increase of acetyl-CoA production could transcriptionally upregulated EPHB2 expression via promoting histone acetylation in cluster D subtype. Additionally, we found that EPHB2 was relevant to resistance of multiple VEGFR3 inhibitors, indicating that it promoted lymphangiogenesis in a VEGF-C independent manner. Furthermore, EPHB2 induced EFNB1 to capture YAP/TAZ in cytoplasm, resulted in inhibiting HIPPO activation-induced PROX1 transcriptional repression, finally triggered lymphangiogenesis. Our findings extended the molecular subtyping of HNSCC patients and showed that combination of EFNB1-Fc and VEGFR3 inhibitor exerted synergistic effects on blocking lymphatic metastasis, suggesting that targeting EPHB2 might be a potential strategy to patients who do not respond to VEGFR3 inhibitor.\u003c/p\u003e \u003cp\u003eRecently, metabolite profiling has become the informative approach to elucidate tumor heterogeneity. Previously published study recruited a cohort of 9125 TCGA samples across 33 cancer types and characterised tumour subtypes based on the expression of seven metabolic pathways\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In pancreatic ductal adenocarcinoma, Daemen et al. conducted broad metabolite profiling and identified three subtypes that showed distinct metabolite profiles associated with glycolysis, lipogenesis and redox pathways\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. And some researchers identified and validated three highly distinct metabolic subtypes in the lower-grade glioma patients, then developed a metabolic signature with better performance of prognosis prediction\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. In this study, we classified the HNSCC patients into four subtypes based on the metabolism patterns, and found cluster D has the worst RFS and Cluster A has the best RFS. Then we identified the Cluster D possessed molecular features which highly related to lymphatic metastasis, while Cluster A was relatively deficient in these features.\u003c/p\u003e \u003cp\u003eMeanwhile, metabolic reprogramming and epigenetic modifications are the hallmarks of cancer cells\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Previous studies showed that epigenetic modifications were closely related to cancer cell metabolism regulations, such as histone methylation, acetylation, DNA methylation and RNA N6-methyladenosine (m6A) methylation. Cancer metabolic reprogramming involves mainly a shift from oxidative phosphorylation to aerobic glycolysis, providing essential raw materials and energy support for tumor growth, participating in the tumor immune response. Glycolysis is the main source of energy for tumor cell proliferation, and as the key node in metabolism and the main producer of energy, acetyl-CoA plays an important role in the invasion and migration of cancer. Previous research has shown that an increase in the acetyl-CoA synthesis rate promotes posttranslational histone acetylation and accelerates cell division\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, for example, the production of ACLY-dependent acetyl-CoA was proved playing an vital role in early stage pancreatic cancer development, by targeting the acetyl-CoA-dependent pathway and using combined bromodomain and extraterminal domain expression inhibition and statin therapy, the proliferation of cancer cells and tumor growth can be inhibited\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Another study indicated that maintaining ACLY levels and regulating acetyl-CoA levels promoted the proliferation, metastasis, and even drug resistance in nasopharyngeal carcinoma cells\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In this study, we also found that in laryngeal carcinoma, pyruvate treatment could increase the acetyl-CoA levels in nucleus and H3 acetylation, then increased the EPHB2 transcription levels, while depletion of ACLY and ACSS2 abolished pyruvate-induced EPHB2 transcription activation by decreasing the levels of nuclear acetyl-CoA content and H3 acetylation, demonstrating that highly activated carbohydrate metabolism with increase of acetyl-CoA production could transcriptionally upregulate the EPHB2 expression via promoting histone acetylation.\u003c/p\u003e \u003cp\u003eIn previously published studies related to lymphangiogenesis, VEGF-C and VEGF-D played significant roles by activating its receptor VEGFR-3 on LECs to activate a protein kinase C/ERK signaling cascade which ultimately triggers the phosphorylation of AKT and the proliferation and migration of these LECs, thus promoting lymphangiogenesis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Additionally, there are many enzymes, bioactive lipids, chemokines, adhesion molecules, and noncoding RNAs that also participate in lymphangiogenesis by functioning in either a VEGF-C/D-dependent or -independent manner. Recently, several studies have identified Hippo-YAP/TAZ signaling components as novel players in lymphatic vascular development by regulating LECs specification, differentiation, sprouting during early lymphatic development and maintaining lymphatic integrity during adulthood, which modulate lymphatic plasticity throughout life via regulating PROX1 activity\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In Hippo signaling, nuclear YAP and TAZ regulate a wide variety of target genes, whereas cytoplasmic YAP and TAZ are targeted for degradation. Notably, YAP/TAZ depletion or hyperactivation in LECs during embryonic development results in structurally aberrant and poorly functional lymphatics and lethality, highlighting its importance in early development of the lymphatic system\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In this study, we found that EPHB2 promoted lymphangiogenesis in a VEGF-C independent manner, and giving EPHB2 treatment could dramatically repress the transcriptional activity of YAP/TAZ in LECs. Then we proved that EPHB2 induced EFNB1 expression by capturing YAP/TAZ in cytoplasm, alleviating HIPPO activation-induced PROX1 transcriptional repression, finally triggered lymphangiogenesis in laryngeal carcinoma cells.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study firstly reclassified HNSCC from the metabolic perspective and proposed four subtypes with distinct prognosis and metabolic phenotype. Among these four subtypes, Cluster D was highly related to lymphatic metastasis and significantly associated with a worse RFS outcome, which also has a higher level of carbohydrate metabolism. Then we demonstrated that for this subtype of laryngeal cancer, highly activated pyruvate metabolism increased acetyl-CoA production, and then transcriptionally upregulated EPHB2 levels, which promoted YAP/TAZ cytoplasmic localization and resulted in augment of PROX1 expression, eventually induced lymphangiogenesis. Our findings extended the molecular subtyping of HNSCC patients and showed that combination of EFNB1-Fc and VEGFR3 inhibitor exerted synergistic effects on blocking lymphatic metastasis, suggesting that targeting EPHB2 might be a potential strategy to patients who do not respond to VEGFR3 inhibitor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Guangzhou Municipal Science and Technology Project (No. 202002020024), the Natural Science Foundation of Guangdong Province (No. 2024A1515013244), the National Natural Science Foundation of China (No. 82073330) and National Natural Science Foundation of China for Young Scholars (No. 82202946).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: S.C., S.Z., and H.Z.\u003c/p\u003e\n\u003cp\u003eMethodology and software: J.M., B.C., Q.L., and Z,L.\u003c/p\u003e\n\u003cp\u003eData curation, validation: J.M., B.C., Q.L., Z,L., R.W., C.W., X.J., D.S., Y.L., D.S., Y.O., and X.C.\u003c/p\u003e\n\u003cp\u003eWriting\u0026ndash;original draft: J.M., B.C., Q.L., and Z,L.\u003c/p\u003e\n\u003cp\u003eProject administration, writing\u0026ndash;review and editing: S.C., S.Z., H.Z., and M.Z.\u003c/p\u003e\n\u003cp\u003eStudy supervision: S.C., S.Z., and H.Z.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient gave informed consent for the collection of clinical information, tissue collection, and research testing under the Internal Review and Ethics Board of Sun Yat-sen University Cancer Center (Approval No. GZR2024-132). All animal experiments were approved by the Ethics Committee of Sun Yat-sen University Cancer Center (L102012024008W).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBeckham TH, et al. Long-term survival in patients with metastatic head and neck squamous cell carcinoma treated with metastasis-directed therapy. Br J Cancer. 2019;121:897\u0026ndash;903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41416-019-0601-8\u003c/span\u003e\u003cspan address=\"10.1038/s41416-019-0601-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChow LQM. Head and Neck Cancer. 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Development. 2020;147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1242/dev.195453\u003c/span\u003e\u003cspan address=\"10.1242/dev.195453\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Supplementary Table","content":"\u003cp\u003eSupplementary Tables 1-2 are not available with this version.\u003c/p\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"head and neck squamous cell carcinoma, lymphatic metastasis, pyruvate metabolism, EPHB2, lymphangiogenesis","lastPublishedDoi":"10.21203/rs.3.rs-5324948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5324948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLymphatic metastasis is a well-known factor for head and neck squamous cell carcinoma (HNSCC) that initiates distant metastasis, which caused major death in most patients with cancer. Metabolic reprogramming to support metastasis is regarded as a prominent hallmark of cancers. However, how metabolic disorders drive in HNSCC remains unclear. We firstly established a new classification of HNSCC patients based on metabolism gene expression profiles and identified that an enriched carbohydrate metabolism subgroup that was significantly associated with a high risk of lymphatic metastasis and worse clinical outcome. Moreover, we found that highly activated pyruvate metabolism, a central node in carbohydrate metabolism, endowed tumors with EPHB2 upregulation and promoted lymphatic metastasis independently of VEGF-C/VEGFR3 signaling pathway. Mechanically, high levels of nuclear acetyl-CoA from pyruvate metabolism promoted histone acetylation, which in turn transcriptionally upregulated EPHB2 expression in tumor cells. EPHB2 bound with EFNB1 in lymphatic endothelial cells to alleviate YAP/TAZ-mediated PROX1 transcriptional inhibition, which eventually promoted tumor lymphangiogenesis. Importantly, combined treatment with EFNB1-Fc and VEGFR3 inhibitor synergistic abrogated lymphangiogenesis \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. These findings uncover the mechanism by which pyruvate metabolism is linked to lymphatic metastasis of tumor and provides a promising therapeutic strategy for the prevention of HNSCC metastasis.\u003c/p\u003e","manuscriptTitle":"Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-18 08:10:07","doi":"10.21203/rs.3.rs-5324948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2024-12-02T20:45:37+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-15T01:45:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-15T01:34:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-04T17:05:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2024-10-24T06:17:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af6bb7e3-c6c0-4392-9cfd-2446e7646e0f","owner":[],"postedDate":"December 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-17T16:13:51+00:00","versionOfRecord":{"articleIdentity":"rs-5324948","link":"https://doi.org/10.1186/s12967-025-06305-9","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2025-03-12 15:58:51","publishedOnDateReadable":"March 12th, 2025"},"versionCreatedAt":"2024-12-18 08:10:07","video":"","vorDoi":"10.1186/s12967-025-06305-9","vorDoiUrl":"https://doi.org/10.1186/s12967-025-06305-9","workflowStages":[]},"version":"v1","identity":"rs-5324948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5324948","identity":"rs-5324948","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}