ECI2 drives melanoma progression via arachidonic acid metabolism and NETs-induced TLR3/COX2/PGE2 positive feedback loop

ECI2 drives melanoma progression via arachidonic acid metabolism and NETs-induced TLR3/COX2/PGE2 positive feedback loop | 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 Article ECI2 drives melanoma progression via arachidonic acid metabolism and NETs-induced TLR3/COX2/PGE2 positive feedback loop Jianqiang Wu, Peijun Wen, Zhaohan Liu, Lixia Chen, Yunyong Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7944532/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Melanoma is a highly malignant, metastatic skin tumor with complex progression mechanisms. This study reveals that the metabolic enzyme ECI2 is significantly overexpressed in melanoma tissues and is closely associated with poor patient prognosis. Functional experiments confirm that ECI2 significantly promotes the proliferation, migration, and invasive capabilities of melanoma cells both in vitro and in vivo. Mechanistic investigations reveal that ECI2 reshapes arachidonic acid metabolism in tumor cells, leading to the accumulation of the metabolite PGE2. These metabolites activate neutrophils, inducing the formation of neutrophil extracellular trap networks (NETs) via reactive oxygen species (ROS)-dependent pathways. Crucially, this study innovatively demonstrates that NETs are not the final effector. NETs activate the TLR3/COX-2/PGE2 axis in melanoma cells, establishing a metabolic-immune positive feedback loop. This circuitry persistently drives malignant tumor behavior, profoundly reshaping the tumor microenvironment. Our work not only proposes ECI2 as a key oncogene and potential therapeutic target but, more importantly, reveals a novel mechanism whereby tumor cells establish a positive feedback dialogue between metabolic reprogramming and innate immune effector NETs. This provides a novel theoretical framework for understanding melanoma progression and developing combined therapeutic strategies. Subject terms : ECI2, arachidonic acid metabolism, neutrophil extracellular traps, melanoma Biological sciences/Cancer/Cancer metabolism Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Skin cancer/Melanoma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Melanoma originates from skin melanocytes and is the most invasive and lethal form of skin malignancy. Although it accounts for less than 5% of skin cancer incidence, it causes the vast majority of skin cancer-related deaths 1 . Advanced metastasis and acquired drug resistance remain significant clinical challenges, with a substantial proportion of patients failing to achieve long-term benefit from current cutting-edge therapies 2 . Therefore, elucidating the core molecular mechanisms underlying melanoma progression and drug resistance, and identifying novel prognostic biomarkers and therapeutic targets, holds immense clinical significance. The malignant evolution of tumors is far from the result of autonomous behavior by cancer cells alone; rather, it is a complex process jointly driven by the tumor microenvironment (TME) in which they reside 3 . The TME comprises diverse cellular components (including immune cells, fibroblasts, endothelial cells) and non-cellular components (such as cytokines, metabolites), collectively shaping a local niche that suppresses immunity while promoting growth and metastasis 3 , 4 . Among the numerous participants in the TME, neutrophils—key effector cells of the innate immune system—have garnered significant attention in recent years 5 . Traditionally, neutrophils are regarded as the first line of defense against pathogen invasion. However, in the tumor context, they can act as pro-tumor “accomplices” by releasing NETs 6 , 7 . NETs are reticular structures composed of a scaffold of decondensed chromatin DNA, embedded with histones and granular proteins such as myeloperoxidase (MPO) and neutrophil elastase (NE) 8 . NETs were initially discovered for trapping and killing pathogens 8 . Current research indicates that in tumors, NETs physically capture circulating tumor cells (CTCs) to promote their engraftment, disrupt the vascular endothelial barrier, directly activate tumor cell proliferation signals, establish an immunosuppressive microenvironment, and activate dormant cancer cells 7 , thereby powerfully driving tumor metastasis 5 , 6 , 9 – 12 . NETs also influence arachidonic acid metabolism 13 . However, the upstream signals driving NET formation within the tumor microenvironment, particularly key inducible factors originating from tumor cells themselves, remain incompletely understood. On the other hand, tumor metabolic reprogramming, a hallmark of cancer, produces not only the cornerstone for sustaining rapid cancer cell proliferation but also crucial messengers regulating the TME 6 , 14 – 16 . Lipid metabolic reprogramming is a key component of this process 17 , 18 . Arachidonic acid (AA), a vital polyunsaturated fatty acid, constitutes a major component of cell membrane phospholipids 19 . Under the action of phospholipase A₂, AA is released from membrane phospholipids and can be metabolized through three primary enzymatic pathways: The cyclooxygenasepathway produces prostaglandins (e.g., PGE2) and thromboxanes; the lipoxygenasepathway generates leukotrienes; and the cytochrome P450 pathway yields eicosapentaenoic acid and eicosatrienoic acids 20 . These AA metabolites, collectively termed eicosanoids, are potent bioactive lipid mediators playing central roles in regulating inflammation, immunity, and tumor progression 19 , 21 . For instance, PGE₂ has been demonstrated to promote tumor cell proliferation, invasion, and angiogenesis while suppressing dendritic cell maturation and T-cell function 22 – 24 . Studies indicate that tumor-derived arachidonic acid can reprogram neutrophils to influence breast cancer resistance 19 . Previous research suggests that Toll-like receptor 3 (TLR3) modulates arachidonic acid metabolism by regulating COX2 13,25 . Although the significance of the AA metabolic pathway in tumors is recognized, its upstream, more fundamental regulatory factors and how it precisely couples with specific immune responses in the TME (such as NET formation) remain an area requiring further exploration. Enoyl-CoA delta isomerase 2 (ECI2), a key cofactor in mitochondrial and peroxisomal fatty acid β-oxidation 26 , catalyzes the isomerization of 3-enoyl-CoA to 2-enoyl-CoA, particularly during unsaturated fatty acid degradation 14 . ECI2 has long been regarded as a fundamental “housekeeping” metabolic enzyme. However, recent scattered evidence suggests it may be differentially expressed in specific cancer types and potentially involved in tumorigenesis 27 . For instance, studies report low ECI2 expression in colorectal cancer, where it influences neutrophil extracellular trap formation 14 . Nevertheless, the expression status and clinical significance of ECI2 in melanoma, particularly whether and how it transcends its classical metabolic function to actively regulate malignant tumor biology and TME remodeling, remain unknown. Based on this background, we propose the scientific hypothesis that ECI2 is abnormally overexpressed in melanoma and drives disease progression through a novel mechanism: establishing a positive feedback loop involving tumor cell-specific AA metabolism and NET formation in the microenvironment. To validate this hypothesis, we conducted a series of studies. We first confirmed ECI2 expression in melanoma tissues and its prognostic value, then investigated its impact on malignant phenotypes of melanoma cells through in vitro and in vivo functional experiments. Subsequently, we delved into its underlying mechanisms, discovering that ECI2 serves as a key driver of AA metabolism and induces NET formation via ROS derived from AA metabolism. Crucially, we revealed that NETs are not passive endpoints but actively enhance AA metabolism in melanoma cells through the TLR3/COX2/PGE2 signaling axis, thereby forming a self-amplifying oncogenic loop. These findings not only reveal an unprecedented oncogenic function of ECI2 in melanoma but also innovatively link AA metabolism, the TLR3/COX2/PGE2 signaling pathway, and neutrophil immune responses. This provides a novel perspective on understanding melanoma progression mechanisms and lays a solid theoretical foundation for developing novel combination therapeutic strategies. Result ECI2 is highly expressed in melanoma and indicates poor prognosis To investigate the clinical relevance of ECI2 in cutaneous melanoma patients, analysis of SKCM data from the TCGA database revealed higher mRNA expression of ECI2 in melanoma compared to normal tissue (Fig. 1A). Immunohistochemical detection of ECI2 expression in normal and tumor tissues from melanoma patients demonstrated elevated expression in melanoma tissue relative to normal skin tissue (Fig. 1B-C). Analysis of TCGA-SKCM data revealed that high ECI2 expression was significantly associated with both Overall Survival (OS) and Disease Specific Survival (DSS) events in melanoma patients (Fig. 1D-E). Furthermore, survival analysis of TCGA-SKCM data using the GEPIA database revealed that melanoma patients with upregulated ECI2 expression exhibited lower OS and disease-free survival (DFS) compared to those with low ECI2 expression (Fig. 1F-G). These findings indicate that ECI2 is highly expressed in melanoma and suggests poor prognosis. ECI2 Promotes Proliferation and Invasive Capacity of Melanoma Cells To investigate the effects of ECI2 on cutaneous melanoma cells, two stable melanoma cell lines overexpressing ECI2 (A375/ECI2 and A2058/ECI2) and two stably knocking down ECI2 (A375/shECI2 and A2058/shECI2) were established. Cells transfected with empty lentiviral vectors served as negative controls. RT-qPCR and WB experiments confirmed successful establishment of stable ECI2-overexpressing and ECI2-knockdown melanoma cell lines (Supplementary Fig. 1A-D). CCK8 cell proliferation assays and colony formation assays demonstrated that ECI2 overexpression promoted the proliferation capacity of A375 and A2058 cells (Fig. 2A-D), whereas ECI2 knockdown had the opposite effect (Supplementary Fig. 1E-G). Furthermore, ECI2 overexpression significantly enhanced melanoma cell migration and invasion detected in wound healing and Transwell assays (Fig. 2E-F), whereas ECI2 knockdown exhibited the opposite effect (Supplementary Fig. 1H-J). Further establishment of a mouse subcutaneous tumor model revealed that ECI2 upregulation significantly promoted melanoma cell growth in vivo, manifested by increased Ki-67 proliferation index, larger tumor volume, and heavier tumor weight (Fig. 2G-J). These in vivo and in vitro experiments collectively indicate that ECI2 overexpression enhances the proliferation, migration, and invasion capabilities of melanoma cells. ECI2 promotes arachidonic acid metabolism in melanoma cells. As a lipid metabolism-related enzyme, we performed KEGG enrichment analysis on TCGA-SKCM data to explore ECI2's molecular mechanisms in melanoma progression. Results indicated ECI2 influences arachidonic acid metabolism (Fig. 3A). To validate these bioinformatics findings, we measured relative AA levels in melanoma cells overexpressing or knocking down ECI2 via ELISA. Results showed elevated AA levels in supernatants from ECI2-overexpressing melanoma cells (Fig. 3B), while ECI2 knockdown produced the opposite effect (Fig. 3C). ELISA measurements of AA content in tumor lysates revealed increased AA levels in mouse tumor tissues from ECI2-overexpressing mice (Fig. 3D). To further elucidate how ECI2 modulates AA metabolism, Western blot analysis revealed that ECI2 promotes COX2 expression. ELISA experiments demonstrated that ECI2 enhances levels of the AA metabolite PGE2 (Fig. 3F-G). Similarly, measurement of PGE2 in tumor lysates indicated that ECI2 promotes PGE2 levels in mouse tumor tissues (Fig. 3H). To assess AA's impact on melanoma cell function, CCK8 proliferation and Transwell invasion assays revealed that AA supplementation enhanced proliferation and invasion in ECI2-silenced melanoma cells (Fig. 3I-K). These findings indicate that ECI2 promotes melanoma cell proliferation and invasion by enhancing arachidonic acid metabolism. ECI2 Promotes NET Formation in the Melanoma Microenvironment To further investigate the molecular mechanisms by which ECI2 promotes melanoma progression, we analyzed the TIMER database and found a significant positive correlation between ECI2 expression and neutrophil infiltration (Fig. 4A). Further analysis of tumor tissues from a subcutaneous tumor model in mice revealed significantly increased neutrophil infiltration in ECI2-overexpressing tumors compared to controls (Fig. 4B-C). In vitro functional experiments with co-cultured melanoma cells and human neutrophils demonstrated that ECI2 overexpression significantly enhanced melanoma cell proliferation and invasion capacity in the presence of neutrophils (Fig. 4D, 4F), whereas ECI2 knockdown produced the opposite effect (Fig. 4E, 4G). Recent studies have revealed that ECI2 influences NET formation in colorectal cancer. We sought to determine whether ECI2 influences NET formation in melanoma. Detection of NET markers citrullinated H3 (cit-H3) and myeloperoxidase (MPO) in mouse tumor tissues revealed that ECI2 significantly promoted NET formation (Fig. 5A). Sytox immunofluorescence and MPO-DNA ELISA assays revealed increased NET formation in neutrophils cultured with supernatant from ECI2-overexpressing melanoma cells (Fig. 5B-E), whereas supernatant from ECI2-silenced melanoma cells exhibited the opposite effect (Supplementary Fig. 2). Analysis of human melanoma tissue revealed significantly increased NET formation in ECI2-overexpressing tumors (Fig. 5F). Correlation analysis between ECI2 expression and NET formation in human melanoma tissue demonstrated a significant positive correlation (Fig. 5G). Collectively, these findings indicate that ECI2 promotes NET formation within the melanoma microenvironment. ECI2 Promotes ROS-Mediated NET Formation in Neutrophils via Tumor-Derived Arachidonate Given that tumor-derived arachidonate metabolism can reprogram neutrophils 19 , we investigated whether ECI2-regulated arachidonate metabolism modulates NET formation in melanoma. We performed Sytox immunofluorescence and MPO-DNA ELISA assays. Results revealed that NET formation in neutrophils cultured with supernatant from ECI2-knockdown melanoma cells was enhanced by phorbol 12-myristate 13-acetate (PMA, a potent NET inducer) and also by arachidonic acid (Fig. 6A-D). Similarly, increased NET formation in neutrophils cultured with supernatant from ECI2-overexpressing melanoma cells was inhibited by DNase I (a NET-DNA-degrading nuclease) and by the arachidonic acid metabolism inhibitor aspirin (Supplementary Fig. 3A-D). Given that ROS is a known key factor in NET formation, we performed an ELISA assay to investigate the molecular mechanism by which ECI2 promotes NET formation in the melanoma microenvironment. Results showed that treating neutrophils with supernatant from ECI2-knockdown melanoma cells reduced ROS levels in the supernatant, while further addition of AA increased ROS levels (Fig. 6E). Similarly, ROS levels increased in supernatants from ECI2-overexpressing melanoma cells after neutrophil culture, but decreased upon aspirin addition (Fig. 6F). To further validate ROS necessity in ECI2-mediated NET formation regulation within the melanoma microenvironment, When DPI blocked neutrophil NADPH oxidase activity, the ability of neutrophils to produce NETs was significantly impaired (Supplementary Fig. 3A-D). To examine the combined roles of arachidonic acid metabolism and NET formation in melanoma cells, ECI2-knockdown melanoma cells were co-cultured with neutrophils and subjected to different treatments, followed by assessment of melanoma cell proliferation and invasion. Results showed that ECI2-mediated suppression of melanoma cell proliferation and invasion was reversed by AA, while DNase I reversed AA's effects (Fig. 6G-H). Similarly, aspirin reversed both proliferation and invasion promoted by ECI2 overexpression in melanoma cells, while PMA reversed aspirin's effects (Supplementary Fig. 3E-F). These findings suggest that ECI2 promotes ROS-mediated NET formation in neutrophils via tumor-derived arachidonic acid, thereby enhancing melanoma cell proliferation and invasion. The TLR3/COX2/PGE2 axis in melanoma cells regulated by ECI2 via NETs promotes arachidonic acid metabolism Research indicates that NETs can stimulate Toll-like receptor 3 to induce cyclooxygenase-2 activation and prostaglandin E2 production 13 . We sought to determine whether ECI2-regulated NET formation could influence arachidonic acid metabolism via the TLR3/COX2/PGE2 axis. First, we performed RT-qPCR, WB, PGE2 ELISA, and AA ELISA assays. Results showed that co-culturing ECI2-silenced melanoma cells with neutrophils suppressed TLR3 mRNA levels, COX2 protein expression, PGE2, and AA levels in melanoma cells, while PMA reversed the effects of ECI2 silencing (Fig. 7A-D). Similarly, co-culturing ECI2-overexpressing melanoma cells with neutrophils elevated melanoma cell TLR3 mRNA levels, COX2 protein content, and PGE2/AA levels, while DNase I reversed the effects of ECI2 overexpression (Supplementary Fig. 4A-D). Further co-culture of ECI2-silenced melanoma cells with neutrophils, followed by various treatments, assessed melanoma cell proliferation and invasion. Results showed that the inhibitory effect of ECI2 silencing on melanoma cell proliferation and invasion was reversed by PMA or TLR3 overexpression in melanoma cells, and the PMA effect was subsequently reversed by aspirin (Fig. 7E-F). Similarly, the promotion of melanoma cell proliferation and invasion by ECI2 overexpression was reversed by DNase I or TLR3 silencing in melanoma cells, and the effect of DNase I was subsequently reversed by AA (Supplementary Fig. 4E-F). Using an in vivo mouse subcutaneous tumor model, aspirin and DNase I were found to reverse the tumorigenic promotion by ECI2 overexpression. Combined treatment with aspirin and DNase I resulted in smaller tumor volumes and lower body weights compared to either treatment alone (Fig. 7G-J). Discussion In this study, we identified ECI2 as a key driver of melanoma progression. ECI2 expression was significantly elevated in melanoma tissues, and TCGA analysis revealed that high ECI2 levels correlated with advanced disease stage and reduced patient survival, suggesting ECI2 may serve as a poor prognostic marker for melanoma. Previous literature has reported ECI2 as a tumor suppressor in colorectal cancer, where its low expression correlates with poorer prognosis 14 . In contrast, our findings reveal an opposite pattern in melanoma, where high ECI2 expression is associated with poor prognosis, indicating that ECI2 function exhibits tumor type specificity. This discovery expands our understanding of ECI2's role in tumorigenesis and progression. Furthermore, studies indicate that ECI2 is a target gene of the androgen receptor in prostate cancer, promoting tumor cell survival 27 , supporting the possibility of ECI2's pro-tumorigenic role in certain cancers. Thus, our study is the first to reveal the abnormal overexpression of ECI2 in melanoma and its clinical significance, providing a new perspective for melanoma prognosis assessment and molecular subtyping. Functional experiments further confirmed ECI2's pro-tumorigenic effects in melanoma. By genetically manipulating ECI2 levels in commonly used human melanoma cell lines (e.g., A375, A2058), we found that ECI2 overexpression significantly enhanced cell proliferation and clonogenic efficiency while promoting migration and invasion; conversely, ECI2 knockdown suppressed these malignant phenotypes. In vivo experiments in a mouse subcutaneous tumor model demonstrated that tumors formed by ECI2-overexpressing melanoma cells grew more rapidly, consistent with in vitro findings. These data indicate that ECI2 drives tumor growth and invasive potential within melanoma cells, supporting its role as an oncogene. Previous studies on ECI2 have primarily focused on its metabolic functions 14 , with no prior reports on its functional role in melanoma. Our work fills this gap. This discovery functionally validates the association between ECI2 overexpression and poor prognosis, highlighting the innovation of our study: revealing that ECI2 is not merely a prognostic marker but a driving force in melanoma progression. As an enzyme involved in lipid metabolism, ECI2 plays a role in tumor promotion. To elucidate the mechanism underlying this effect, we focused on lipid metabolic pathways and discovered that ECI2 significantly enhances arachidonic acid metabolism in melanoma cells while activating the downstream COX-2/PGE2 pathway. In ECI2-upregulated cells, levels of arachidonic acid metabolites increased, accompanied by substantial production of COX-2 and its catalyzed product PGE2. As a polyunsaturated fatty acid, arachidonic acid serves as a precursor for multiple bioactive lipids, including prostaglandins and leukotrienes known to regulate tumor-associated inflammation 19 , 20 , 28 , 29 . The COX-2/PGE2 axis is a key driver of cancer progression and immune evasion 30 – 32 . COX-2 is frequently overexpressed in invasive tumors, including melanoma, and elevated COX-2 levels correlate with poor clinical outcomes 33 . As the primary COX-2 product, PGE₂ activates pro-tumor pathways (e.g., PKA/CREB, β-catenin, and PI3K/AKT signaling) by acting on receptors (EP1–EP4) while simultaneously suppressing anti-tumor immune responses 30 , 34 . In melanoma, COX-2-derived PGE₂ is a known driver of the immunosuppressive microenvironment 33 . By modulating T cell activity, PGE₂ enables tumor cells to evade immune clearance 30 , 32 . PGE₂ is a recognized pro-tumor mediator that promotes tumor progression through multiple mechanisms, including suppression of anti-tumor immunity and enhancement of tumor cell proliferation and survival 30 , 31 . Our findings indicate that ECI2-mediated arachidonate metabolism reprogramming induces increased PGE₂ release by melanoma cells, thereby creating an immunosuppressive microenvironment conducive to tumor growth. One plausible explanation is that ECI2 accelerates AA β-oxidation, driving oxidative metabolism of reactive oxygen species (H₂O₂) and intermediate metabolites, thereby activating signaling pathways (e.g., NF-κB) and upregulating COX-2 and other AA metabolic enzymes. The net outcome is increased COX-2-dependent conversion of AA to PGE₂. Regardless, we reveal a direct functional link between the metabolic enzyme ECI2 and the immune mediator PGE₂, deepening our understanding of the “metabolism-inflammation” axis in melanoma. Notably, previous studies in mouse melanoma models have demonstrated that tumor-cell-derived PGE₂ is a critical factor enabling immune evasion and sustained tumor growth 31 . Additionally, Yu et al. recently reported that triple-negative breast cancer cells synthesize and secrete arachidonic acid-enriched lipids, thereby reprogramming tumor-infiltrating neutrophils to an immunosuppressive phenotype, leading to therapeutic resistance 19 . While these studies highlight the role of arachidonic acid metabolites in tumor immune evasion, our research further traces upstream in melanoma, demonstrating for the first time the regulatory function of the key upstream enzyme ECI2 on the COX-2/PGE2 pathway. This mechanistic discovery expands our understanding of how metabolic reprogramming in melanoma drives inflammation and immune suppression. Interestingly, we found that alongside altering tumor metabolism, ECI2 also promotes melanoma progression by influencing the tumor microenvironment. Specifically, melanomas overexpressing ECI2 induce increased neutrophil-derived NET formation within the tumor microenvironment. NETs are structures composed of reticular DNA and granular proteins released by neutrophils 8 , recently found to promote tumor growth and metastasis 9 , 12 , 14 , 35 . We detected increased levels of typical NETs markers in tumor tissues from the ECI2 overexpression group, such as citrullinated histone H3 and myeloperoxidase-positive reticular structures, indicating that more neutrophils underwent NETosis (the process of NET formation). The presence of NETs is believed to promote the adhesion of circulating tumor cells and distant metastasis 10 , 36 , 37 . For instance, neutrophil-released DNA nets can trap circulating tumor cells, thereby increasing the probability of metastasis site formation 10 . Furthermore, proteases and other bioactive molecules released by NETs can remodel the tumor microenvironment, activate dormant cancer cells, and accelerate disease progression 36 . Thus, our findings suggest that melanoma cells “recruit” and activate neutrophils to generate NETs via ECI2, with this tumor-neutrophil interaction further driving malignant tumor evolution. In the existing literature, reports on how tumor cells regulate NET formation in melanoma remain scarce. Our study reveals, from a metabolic perspective, that ECI2 within tumor cells can serve as a key regulator influencing the inflammatory state of the tumor microenvironment, highlighting the novelty of our work. To elucidate the mechanism by which ECI2 promotes NET formation, we focused on the link between arachidonic acid metabolism and neutrophil activation. Results indicate that ECI2-upregulated melanoma cells may stimulate neutrophil ROS production by secreting arachidonic acid or its metabolites, thereby inducing NETosis. It is well established that NET formation is highly dependent on ROS signaling generated by NADPH oxidase 14 , 38 – 40 . Our co-culture experiments support this: when neutrophil NADPH oxidase activity was blocked or arachidonic acid released by tumor cells was neutralized, the ability of neutrophils to produce NETs was significantly impaired. These findings indicate that arachidonic acid from melanoma cells drives NET formation by triggering neutrophil ROS production. This mechanism echoes Yu et al.'s discovery in human breast cancer that tumor-derived lipids significantly influence neutrophil phenotype and function 19 . However, our study advances this understanding by explicitly identifying a specific metabolic pathway—the arachidonic acid-ROS axis—as crucial for regulating NETosis. This not only deepens our comprehension of how tumor metabolites act as signaling molecules to influence innate immune responses but also suggests that targeting this pathway may reduce tumor-associated NET formation, thereby inhibiting tumor progression. Notably, we uncovered a positive feedback loop between arachidonic acid metabolism and immunity mediated by ECI2: tumor cells promote NET formation, which in turn enhances melanoma cell metabolic activity via TLR3 signaling, further driving tumor progression. Mechanistically, in the NET-rich tumor microenvironment, TLR3 on melanoma cells becomes activated, subsequently upregulating COX-2 expression and increasing production of pro-tumor mediators like PGE2. This constitutes the TLR3/COX-2/PGE2 axis through which NETs act on melanoma cells. TLR3 typically recognizes molecular patterns such as double-stranded RNA 41 , 42 . We hypothesize that abundant NETs may carry released nucleic acids or activate the TLR3 pathway through other mechanisms, thereby promoting AA metabolism in melanoma cells and establishing a positive feedback loop between tumors and neutrophils. These finding parallels recent reports that NETs induce metabolic reprogramming in HSCs via the TLR3/COX-2 pathway, contributing to MASH liver fibrosis progression 13 . Our study is the first to demonstrate that the TLR3/COX-2/PGE2 pathway in melanoma cells within the tumor microenvironment is regulated by tumor-associated NETs and contributes to tumor promotion. This positive feedback mechanism highlights the complex bidirectional regulatory relationship between tumor metabolism and innate immunity, offering a novel perspective on the link between inflammatory amplification and tumor progression in the melanoma microenvironment. Conclusion In summary, this study reveals ECI2's pivotal role in melanoma progression: by promoting arachidonic acid metabolism and PGE2 production, ECI2 not only directly drives tumor cell proliferation and invasion but also establishes a metabolic-immune positive feedback loop. This loop amplifies tumor-promoting behavior by inducing NET formation and activating the TLR3/COX-2/PGE2 axis in tumor cells. This finding highlights the central role of tumor cell lipid metabolic reprogramming in tumor-immune cell interactions, enriching our understanding of the mechanisms underlying malignant melanoma progression (Supplementary Fig. 5). Clinically, inhibiting ECI2 and its associated pathways (such as COX-2/PGE2 and NETosis) holds promise as a novel intervention strategy. Previous studies have demonstrated that blocking the COX-2/PGE2 pathway can reverse immune suppression and enhance immunotherapy efficacy 31 . Therefore, targeted drug intervention at key nodes of the ECI2-mediated arachidonate metabolism-NETs positive feedback loop may offer novel therapeutic benefits for melanoma patients. The innovation of this study lies in integrating tumor metabolism and immunology perspectives to reveal a novel molecular mechanism underlying melanoma progression. Materials and Methods Ethical Statement This study complies with all relevant ethical guidelines of Sun Yat-sen University. All animal experimentation protocols were approved by the Animal Ethics Committee of Sun Yat-sen University and conducted in accordance with the Guidelines for Laboratory Animal Use. Collection and use of clinical data were approved by the Institutional Review Board of the First Affiliated Hospital of Sun Yat-sen University. Cell Culture Two human cutaneous melanoma cell lines, A375 and A2058, were obtained from the Chinese Academy of Sciences Cell Bank (Shanghai). All cells were identified by short tandem repeat (STR) profiling and had been passaged for less than 6 months after thawing. A375 and A2058 cell lines were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) supplemented with 10% fetal bovine serum (Hyclone, Logan, USA) and 100 μU/mL penicillin/streptomycin (Gibco) at 37°C in a 5% CO₂ incubator. Different cell treatment conditions were: AA (100 μM/mL, Solarbio), aspirin (2 mmol/L, MCE), DNase I (0.25 U/mL, Roche), PMA (25 ng/mL, Sigma), and DPI (10 μM, Selleck). Tissue Preparation Paraffin-embedded tissue specimens were collected from 32 patients with primary melanoma treated at the First Affiliated Hospital of Sun Yat-sen University between 2000 and 2025. None of these patients received chemotherapy or radiotherapy prior to surgery. Disease staging was determined using the PTNM classification system based on tumor size, lymph node metastasis, and distant metastasis. Immunohistochemistry Paraffin-embedded sections were heated, dewaxed, rehydrated, and placed in sodium citrate buffer for antigen retrieval. Slides were then immersed in 3% hydrogen peroxide and sealed with sheep 10% FBS/PBS. After three rinses, sections were incubated with the primary antibody (anti-ECI2, 1:400 dilution, Proteintech) at 4°C. Sections were washed three times with PBS, then incubated with the secondary antibody (anti-rabbit IgG, 1:2000 dilution, Proteintech) at 37°C for 40 minutes. After staining with 3,4-dihydroxybenzidine (DAB), sections were counterstained with hematoxylin, dehydrated, mounted, and examined. IHC scoring based on staining intensity and percentage of positive tumor cells was performed by two independent pathologists blinded to clinical data. Staining intensity was graded as 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). The extent score was assigned based on the percentage of positively stained area within the entire tumor region or entire section: 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), 4 (76-100%). The sum of the intensity and extent scores served as the final ECI2 score (0–14). A final staining score greater than 7 was considered high expression, while a score less than or equal to 7 was classified as low ECI2 expression. RNA Extraction and qPCR Trizol reagent (Invitrogen, Carlsbad, CA) was used to extract total RNA from cells. The PrimeScript RT Kit (Promega, USA) was employed for cDNA synthesis. qPCR was performed using SYBR PreMix ex Taq Benchmark (Takala, China) on an ABI7500 real-time PCR system (Applied Biosystems, USA). Gene expression was detected using the comparative 2^(-ΔΔCT) method. Primers for amplifying ECI2 were 5'-GCCGTTTTACTGAGGGAATTTGT-3' (forward) and 5'-CTGGACCATTGACCACTGCAA-3' (reverse). The primer sequences for amplifying TLR3 were 5'-GAAGCAGGCGTCCTTGGACTT-3' (forward) and 5'-TGTGCTGAATTCCGAGATCCA-3' (reverse). GAPDH served as the endogenous control. Western Blot Analysis (WB) Cells were lysed using RIPA lysis buffer supplemented with protease inhibitor cocktail and PMSF. Lysates were centrifuged at 12,000 g for 30 minutes, and protein concentration was measured using the BCA assay. Proteins were separated by SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes. After blocking in TBST buffer containing 5% skim milk, membranes were incubated overnight at 4°C with designated primary antibodies. The next day, after washing, membranes were incubated with appropriate secondary antibodies at room temperature for 1 hour. Signals were subsequently detected using Enhanced Chemiluminescence (Pierce, Rockford, IL, USA). Primary antibodies included: anti-ECI2 (1:1000 dilution, Proteintech); β-tubulin (1:5000 dilution, M20045, Abmart); COX2 (1:1000 dilution, Proteintech). CCK8 Cell Proliferation Assay Seed 1×10³ cells into a 96-well plate and incubate for 24 hours. Add 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfonyl)-2H-tetrazolium salt (CCK-8, USA) solution to each well and incubate for 2 hours. Read using a microplate reader (Bio-Rad, USA). Repeat the experiment three times. Colony Formation Assay Cells were seeded into 6-well plates (400 cells per well) and cultured for 2 weeks. Cells were fixed with 4% paraformaldehyde for 30 minutes and stained with 1% Giemsa stain for 15 minutes. The number of colonies exceeding 50 cells was counted. Three independent experiments were performed. Cell Wound Healing Assay 1.2 × 10⁶ cells were seeded into 6-well tissue culture plates and incubated for 24 hours. A scratch wound was created using a 10 μl pipette tip. Plates were washed three times and cultured in serum-free DMEM. Wound closure was observed at 0 h and 48 h. Images were captured to assess cell migration levels. Cell migration capacity was quantified by measuring the distance between the leading edges of cells in three randomly selected microscopic fields (×200 magnification) at each time point. Transwell Invasion Assay The upper chamber of Transwell chambers was pre-coated with Matrigel. 2×10⁵ cells suspended in serum-free medium were placed in the upper chamber of 8μm pore Transwells (BD Biosciences, USA), while the lower chamber was filled with 10% FBS as a chemotactic stimulus. Cells were cultured at 37°C for 2 days. Cells successfully migrating through the 8μm pores were stained with 0.5% crystal violet for 15 minutes. Cell counts were performed by randomly selecting five fields of view (×200 magnification) under a microscope. AA Content, PGE2 Content, and ROS Detection AA content and PGE2 were measured using corresponding ELISA kits (Shanghai Xinfan Biotechnology Co., Ltd.) according to the manufacturer's instructions. ROS was measured using an ELISA kit (Yeasen) according to the manufacturer's instructions. MPO-DNA Enzyme-Linked Immunosorbent Assay (ELISA) The MPO-DNA complex was identified using a capture ELISA. A 96-well microtiter plate was coated with anti-MPO monoclonal antibody (Proteintech, 22225-1-AP) as the capture antibody (75 μl per well) overnight at 4°C. After blocking with 1% BSA (125 μl per well), add 40 μl of sample and peroxidase-labeled anti-DNA monoclonal antibody (Roche, 11774425001), incubating at room temperature for 2 hours. Add peroxidase substrate (ABTS) (Roche, 11774425001). After incubating at 37°C in the dark for 40 minutes, measure the optical density at 405 nm using a microplate reader. Neutrophil Isolation Neutrophils were isolated from human peripheral blood using dextran-Ficoll-Paque Premium (GE Healthcare) via density gradient centrifugation. Immunofluorescence staining for neutrophil markers confirmed >95% purity of isolated neutrophils. Unless otherwise specified, neutrophils were cultured in RPMI 1640 medium supplemented with 20% FBS. Sytox Green Dye Staining For visualization, neutrophils cultured in conditioned medium (CM) from different melanoma cell lines were seeded into 96-well plates for incubation. The impermeable DNA dye SytoxGreen (Thermo Fisher Scientific, 1:10,000) and the permeable DNA dye Hoechst 33342 (Thermo Fisher Scientific, 1:1000) were added to the incubation system. At the end of incubation, plates were transferred directly to a fluorescence microscope to observe NET formation. Each experiment used neutrophils from different donors, with three independent replicates performed per assay. Immunofluorescence for NET Formation For paraffin-embedded tissue samples, after dewaxing, antigen retrieval was performed using a citric acid solution in a microwave oven. Overnight incubation at 4°C was conducted in a mixture of two primary antibodies. The primary antibodies used included anti-rabbit cit-H3 (Abcam) and anti-mouse MPO (Proteintech). The following day, after washing with cold PBS, samples were incubated for 1 hour at room temperature in the dark with a mixture of two different species-specific secondary antibodies. The secondary antibodies used included Alexa Fluor 488-labeled anti-rabbit antibody and Alexa Fluor 594-labeled anti-mouse antibody. Cells were counterstained with 4′,6-diamino-2-phenylpyridine (DAPI) (Sigma-Aldrich) for nuclear visualization. Slides were sealed with a coverslip mounting medium containing an antifluorescence quencher. Each sample was examined and photographed under a fluorescence microscope. NET formation was quantified by calculating the percentage of citrullinated histone H3 (cit-H3)-positive cells within the field of view. Mouse Subcutaneous Tumor Formation Model BALB/C-nu/nu nude mice (3–4 weeks old) were purchased from Guangdong Yao Kang and housed at 12–18°C with 22–50% humidity under a 60-hour light-dark cycle. All quantitative analyses were performed in triplicate for statistical evaluation. A375 cells (4 × 10⁶) from each group (NC/ECI2) were injected subcutaneously into mice. Tumor volume (length × width² × 0.5) was measured every 4 days. To investigate the effect of DNase I, mice received daily subcutaneous injections of DNase I (2.5 mg/kg, Roche) following A375 ECI2 cell injection, with PBS administered to controls. For the impact of aspirin, mice received oral aspirin (40 mg/kg) every 3 days after A375 ECI2 cell injection. At the conclusion of the animal experiments, all mice were euthanized by carbon dioxide inhalation. Tumor size was visually assessed and photographed during necropsy. Tumor tissue was excised, fixed in 10% neutral formaldehyde, paraffin-embedded, dehydrated, sectioned into serial histological sections, and stained with hematoxylin and eosin (HE). HE sections were examined under a light microscope. Immunofluorescence was used to detect neutrophil infiltration (anti-Ly-6G antibody, Abcam) and NET formation (MPO, cit-H3) in tumor tissues. Statistical Analysis and Reproducibility Error bars represent mean ± standard deviation. Statistical analysis was performed using Prism9 (GraphPad Software), ImageJ software, or SPSS Statistics 22 (IBM Corp.). For comparisons between two groups, P values were calculated using a two-tailed Student's t-test. For comparisons involving more than two groups, P values were calculated using analysis of variance (ANOVA). Correlation analysis was performed using Correlation of Ozone correlations. Survival curves were plotted using the Kaplan-Meier method. P < 0.05 was considered statistically significant (*, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns indicates no significant difference). Each experiment was independently replicated at least three times. Declarations Acknowledgments Funding This work was supported by the China Postdoctoral Science Foundation (Grant No. 2025M771958). Author Contributions PJW and ZHL performed the experiments, analyzed the data, and drafted the manuscript. LXC and ZHL collected CRC tissue and performed IHC. WYZ and LXC assisted with the animal experiments. YYL, HOY and SNL contributed to the data analysis. JQW designed the experiments and revised the manuscript. Ethical Approval and Informed Consent All experiments involving patients were approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University and complied with the Declaration of Helsinki. Informed consent was not required as the data will be analyzed anonymously. 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Additional Declarations (Not answered) Supplementary Files Supplementarymaterial.docx Supplementary material SupplementalMaterialFulllengthwesternblots.docx Supplemental Material-Full length western blots Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7944532","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544417948,"identity":"bda8697a-69fb-40ce-98c6-2881527f2e45","order_by":0,"name":"Jianqiang 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06:27:22","extension":"png","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":177558,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/89c5e24921aa7c960fbd8b92.png"},{"id":96787665,"identity":"35a4e840-34fb-4207-ad85-74088979cafe","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"png","order_by":28,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":66833,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/0b063ef0bb5a97bb773849fd.png"},{"id":96787668,"identity":"e35d8ce7-0a56-45b9-aa35-03acec11a993","added_by":"auto","created_at":"2025-11-26 06:27:22","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129910,"visible":true,"origin":"","legend":"","description":"","filename":"CDDIS2567270structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/b37a078b4eca8747db110a15.xml"},{"id":96915287,"identity":"d52d72a9-d0a7-4a90-a5c1-bd783ad0bd84","added_by":"auto","created_at":"2025-11-27 14:07:04","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":147567,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/b7e17011bfdfe9d0e5a726f4.html"},{"id":96787646,"identity":"c4bbf62f-aafc-4188-afa3-bcf3c960c9e2","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":504167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 is highly expressed in melanoma and indicates poor prognosis.\u003c/strong\u003e (A) Analysis of ECI2 expression in melanoma and normal tissues from the TCGA-SKCM project. (B-C) Immunohistochemical detection of ECI2 expression in paired normal skin and melanoma tissues (\u003cem\u003en\u003c/em\u003e = 32). (C) Quantitative analysis of ECI2 immunohistochemistry results. (D-E) Differences in ECI2 levels among melanoma patients experiencing OS events (D) and DSS events (E) in the TCGA-SKCM project. (F-G) Survival analysis of overall survival (F) and disease-specific survival (G) in melanoma patients with low versus high ECI2 expression using the GEPIA database.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/5511fb55655f98f0236350ae.jpeg"},{"id":96787645,"identity":"24400c10-a6ea-4295-9b8b-d8d776bbd678","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":672859,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 promotes proliferation and invasiveness in melanoma cells.\u003c/strong\u003e (A–B) CCK8 assay demonstrating proliferation changes in A375 (A) and A2058 (B) melanoma cells following ECI2 overexpression. (C–D) Representative images of colony formation by ECI2-overexpressing melanoma cells. D shows quantitative analysis. (E) Representative images of wound healing assays demonstrating migration ability of ECI2-overexpressing melanoma cells (scale bar 100 μm). Quantitative analysis shown in the right panel. (F) Representative images of Transwell invasion assays demonstrating invasion of ECI2-overexpressing melanoma cells (scale bar 50 μm). Quantitative analysis shown in the right panel. (G-J) Establishment of a subcutaneous tumor model in mice overexpressing ECI2 (20 days post-injection, \u003cem\u003en\u003c/em\u003e = 4). (G) Photograph of the tumor at the end of the experiment. (H) Representative images of HE staining and Ki-67 immunohistochemistry of the tumor (scale bar 50 μm). (I-J) Tumor volume (I) and weight (J) at the end of the experiment.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/4727340842f5c7bffcaf4102.jpeg"},{"id":96787653,"identity":"2973b92e-1ceb-48f1-9de2-a447a4154002","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":455210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 promotes arachidonate metabolism in melanoma cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) KEGG enrichment analysis of TCGA-SKCM data showing pathways enriched by ECI2 in melanoma. (B–C) ELISA assay measuring relative AA levels in melanoma cells overexpressing (B) or knocking down (C) ECI2. (D) ELISA assay measuring AA content in tumor lysates from ECI2-overexpressing and control groups. (E) WB detection of COX2 levels in melanoma cells overexpressing or knocking down ECI2. (F-G) ELISA assay detecting relative PGE2 levels in melanoma cells overexpressing (F) or knocking down (G) ECI2. (H) ELISA assay measuring PGE2 content in tumor lysates. (I-J) CCK8 proliferation assay assessing proliferation capacity of ECI2-silenced melanoma cells upon AA addition. (K) Transwell invasion assay evaluating invasion capacity of ECI2-silenced melanoma cells upon AA addition.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/45c064d758a776abd6a674d9.jpeg"},{"id":96917630,"identity":"1c4002a1-e8d2-4f31-8201-8ee841c31af9","added_by":"auto","created_at":"2025-11-27 14:10:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":573390,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 promotes neutrophil infiltration in the melanoma microenvironment. \u003c/strong\u003e(A) TIMER database analysis of ECI2 correlation with immune cell infiltration in melanoma. (B–C) Immunofluorescence analysis of neutrophil infiltration in subcutaneous tumors formed by ECI2-overexpressing melanoma cell lines in mice (scale bar 20 μm, \u003cem\u003en\u003c/em\u003e = 4). Mice neutrophils labeled with Ly-6G. (C) Quantitative analysis of neutrophils. (D–G) In vitro functional assays of co-culturing melanoma cells overexpressing (D, F) or knocking down (E, G) ECI2 with neutrophils. (D–E) CCK8 assay for melanoma cell proliferation. (F–G) Transwell invasion assay for melanoma cell invasive capacity (scale bar 50 μm, \u003cem\u003en\u003c/em\u003e = 3). Quantitative results shown in the right panel.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/1174bd182e4c8ef9ad74c0c0.jpeg"},{"id":96787662,"identity":"258e973d-c69f-473e-8d6f-a20d9222a7e3","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":498777,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 promotes NET formation in the melanoma microenvironment.\u003c/strong\u003e (A) Immunofluorescence analysis of NETs in subcutaneous tumors formed by ECI2-overexpressing melanoma cell lines in mice (scale bar 20 μm, \u003cem\u003en\u003c/em\u003e = 4). Right panel shows quantitative analysis. (B–E) NET formation by neutrophils incubated with conditioned medium from ECI2-overexpressing melanoma cells, visualized by Sytox immunofluorescence (B, D) and MPO-DNA ELISA (C, E) (scale bar 20 μm, \u003cem\u003en\u003c/em\u003e = 3). (F) Immunofluorescence analysis of NET formation in human melanoma tissues with low (left panel) and high (right panel) ECI2 expression (scale bar 20 μm, \u003cem\u003en\u003c/em\u003e= 32). Quantitative analysis is shown in the right panel. (G) Correlation analysis of ECI2 expression and NET formation in human melanoma tissues (\u003cem\u003en\u003c/em\u003e= 32).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/d650ca8e6c19940effba71e4.jpeg"},{"id":96787674,"identity":"11c9eec4-9033-4538-8b4a-71a966f29e25","added_by":"auto","created_at":"2025-11-26 06:27:22","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":532745,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 promotes ROS-mediated NET formation via arachidonic acid.\u003c/strong\u003e (A–D) Changes in NET formation in neutrophils incubated successively with PMA and AA in ECI2-knockdown melanoma cell supernatants, detected by Sytox immunofluorescence (A, C) and MPO-DNA ELISA (B, D) (scale bar 20 μm). (E) ELISA assay detecting ROS changes in neutrophils incubated with ECI2-knockdown melanoma cell supernatant supplemented with AA. (F) ELISA assay detecting ROS changes in neutrophils incubated with ECI2-overexpressing melanoma cell supernatant supplemented with aspirin. (G-H) CCK8 proliferation assay (G) and Transwell invasion assay (H) assessing changes in melanoma cell proliferation and invasion capacity after co-culture with neutrophils (with or without AA and DNase I) in ECI2-silenced melanoma cells (scale bar 50 μm).\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/410b263867804e00c34070f0.jpeg"},{"id":96787663,"identity":"eca1bc1c-e5ef-4060-8e94-7369ccdfdfb8","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":692268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eECI2 promotes arachidonic acid metabolism via the TLR3/COX2/PGE2 axis regulated by NETs.\u003c/strong\u003e (A-D) RT-qPCR, WB, PGE2 ELISA, and AA ELISA assays reveal alterations in TLR3 mRNA levels, COX2 protein expression, PGE2, and AA levels in ECI2-silenced melanoma cells after co-culture with neutrophils (with or without PMA). (E-F) CCK8 proliferation assay (E) and Transwell invasion assay (F) (scale bar 50 μm) assessing melanoma cell proliferation and invasion after co-culture with neutrophils (with or without PMA and aspirin) in melanoma cells with silenced ECI2 (or co-overexpressing TLR3). (G-J) Subcutaneous tumor models were established in mice using melanoma cells overexpressing ECI2, treated with aspirin, DNase I, or combined aspirin plus DNase I. (G) Photographs of tumors at the end of the experiment. (H) Representative HE-stained images of tumors (scale bar 50 μm). (I–J) Tumor volume (I) and weight (J) at the end of the experiment.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/67129668ac16d60825cb42e7.jpeg"},{"id":97672068,"identity":"b35a7e3e-625a-42d9-a5e8-74dbc6b55615","added_by":"auto","created_at":"2025-12-08 09:33:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5103633,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/4a00039c-5542-490b-95b2-50565668abb5.pdf"},{"id":96787647,"identity":"8c2ff730-2c8f-44f2-81d6-ca7547ba0cf4","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2001157,"visible":true,"origin":"","legend":"Supplementary material","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/350ae751986fbd652f77edd9.docx"},{"id":96787652,"identity":"e41536a0-baf9-4a27-bfb9-d262dcbe21f0","added_by":"auto","created_at":"2025-11-26 06:27:21","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":151503,"visible":true,"origin":"","legend":"Supplemental Material-Full length western blots","description":"","filename":"SupplementalMaterialFulllengthwesternblots.docx","url":"https://assets-eu.researchsquare.com/files/rs-7944532/v1/4ad8a65411bc38cc17aec1db.docx"}],"financialInterests":"(Not answered)","formattedTitle":"ECI2 drives melanoma progression via arachidonic acid metabolism and NETs-induced TLR3/COX2/PGE2 positive feedback loop","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMelanoma originates from skin melanocytes and is the most invasive and lethal form of skin malignancy. Although it accounts for less than 5% of skin cancer incidence, it causes the vast majority of skin cancer-related deaths\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Advanced metastasis and acquired drug resistance remain significant clinical challenges, with a substantial proportion of patients failing to achieve long-term benefit from current cutting-edge therapies\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Therefore, elucidating the core molecular mechanisms underlying melanoma progression and drug resistance, and identifying novel prognostic biomarkers and therapeutic targets, holds immense clinical significance.\u003c/p\u003e\u003cp\u003eThe malignant evolution of tumors is far from the result of autonomous behavior by cancer cells alone; rather, it is a complex process jointly driven by the tumor microenvironment (TME) in which they reside\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The TME comprises diverse cellular components (including immune cells, fibroblasts, endothelial cells) and non-cellular components (such as cytokines, metabolites), collectively shaping a local niche that suppresses immunity while promoting growth and metastasis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Among the numerous participants in the TME, neutrophils\u0026mdash;key effector cells of the innate immune system\u0026mdash;have garnered significant attention in recent years\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Traditionally, neutrophils are regarded as the first line of defense against pathogen invasion. However, in the tumor context, they can act as pro-tumor \u0026ldquo;accomplices\u0026rdquo; by releasing NETs\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. NETs are reticular structures composed of a scaffold of decondensed chromatin DNA, embedded with histones and granular proteins such as myeloperoxidase (MPO) and neutrophil elastase (NE)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. NETs were initially discovered for trapping and killing pathogens\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Current research indicates that in tumors, NETs physically capture circulating tumor cells (CTCs) to promote their engraftment, disrupt the vascular endothelial barrier, directly activate tumor cell proliferation signals, establish an immunosuppressive microenvironment, and activate dormant cancer cells\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, thereby powerfully driving tumor metastasis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. NETs also influence arachidonic acid metabolism\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, the upstream signals driving NET formation within the tumor microenvironment, particularly key inducible factors originating from tumor cells themselves, remain incompletely understood.\u003c/p\u003e\u003cp\u003eOn the other hand, tumor metabolic reprogramming, a hallmark of cancer, produces not only the cornerstone for sustaining rapid cancer cell proliferation but also crucial messengers regulating the TME\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Lipid metabolic reprogramming is a key component of this process\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Arachidonic acid (AA), a vital polyunsaturated fatty acid, constitutes a major component of cell membrane phospholipids\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Under the action of phospholipase A₂, AA is released from membrane phospholipids and can be metabolized through three primary enzymatic pathways: The cyclooxygenasepathway produces prostaglandins (e.g., PGE2) and thromboxanes; the lipoxygenasepathway generates leukotrienes; and the cytochrome P450 pathway yields eicosapentaenoic acid and eicosatrienoic acids\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These AA metabolites, collectively termed eicosanoids, are potent bioactive lipid mediators playing central roles in regulating inflammation, immunity, and tumor progression\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. For instance, PGE₂ has been demonstrated to promote tumor cell proliferation, invasion, and angiogenesis while suppressing dendritic cell maturation and T-cell function\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Studies indicate that tumor-derived arachidonic acid can reprogram neutrophils to influence breast cancer resistance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Previous research suggests that Toll-like receptor 3 (TLR3) modulates arachidonic acid metabolism by regulating COX2\u003csup\u003e13,25\u003c/sup\u003e. Although the significance of the AA metabolic pathway in tumors is recognized, its upstream, more fundamental regulatory factors and how it precisely couples with specific immune responses in the TME (such as NET formation) remain an area requiring further exploration.\u003c/p\u003e\u003cp\u003eEnoyl-CoA delta isomerase 2 (ECI2), a key cofactor in mitochondrial and peroxisomal fatty acid β-oxidation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, catalyzes the isomerization of 3-enoyl-CoA to 2-enoyl-CoA, particularly during unsaturated fatty acid degradation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. ECI2 has long been regarded as a fundamental \u0026ldquo;housekeeping\u0026rdquo; metabolic enzyme. However, recent scattered evidence suggests it may be differentially expressed in specific cancer types and potentially involved in tumorigenesis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. For instance, studies report low ECI2 expression in colorectal cancer, where it influences neutrophil extracellular trap formation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the expression status and clinical significance of ECI2 in melanoma, particularly whether and how it transcends its classical metabolic function to actively regulate malignant tumor biology and TME remodeling, remain unknown.\u003c/p\u003e\u003cp\u003eBased on this background, we propose the scientific hypothesis that ECI2 is abnormally overexpressed in melanoma and drives disease progression through a novel mechanism: establishing a positive feedback loop involving tumor cell-specific AA metabolism and NET formation in the microenvironment. To validate this hypothesis, we conducted a series of studies. We first confirmed ECI2 expression in melanoma tissues and its prognostic value, then investigated its impact on malignant phenotypes of melanoma cells through in vitro and in vivo functional experiments. Subsequently, we delved into its underlying mechanisms, discovering that ECI2 serves as a key driver of AA metabolism and induces NET formation via ROS derived from AA metabolism. Crucially, we revealed that NETs are not passive endpoints but actively enhance AA metabolism in melanoma cells through the TLR3/COX2/PGE2 signaling axis, thereby forming a self-amplifying oncogenic loop. These findings not only reveal an unprecedented oncogenic function of ECI2 in melanoma but also innovatively link AA metabolism, the TLR3/COX2/PGE2 signaling pathway, and neutrophil immune responses. This provides a novel perspective on understanding melanoma progression mechanisms and lays a solid theoretical foundation for developing novel combination therapeutic strategies.\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003e\u003cstrong\u003eECI2 is highly expressed in melanoma and indicates poor prognosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the clinical relevance of ECI2 in cutaneous melanoma patients, analysis of SKCM data from the TCGA database revealed higher mRNA expression of ECI2 in melanoma compared to normal tissue (Fig. 1A). Immunohistochemical detection of ECI2 expression in normal and tumor tissues from melanoma patients demonstrated elevated expression in melanoma tissue relative to normal skin tissue (Fig. 1B-C). Analysis of TCGA-SKCM data revealed that high ECI2 expression was significantly associated with both Overall Survival (OS) and Disease Specific Survival (DSS) events in melanoma patients (Fig. 1D-E). Furthermore, survival analysis of TCGA-SKCM data using the GEPIA database revealed that melanoma patients with upregulated ECI2 expression exhibited lower OS and disease-free survival (DFS) compared to those with low ECI2 expression (Fig. 1F-G). These findings indicate that ECI2 is highly expressed in melanoma and suggests poor prognosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECI2 Promotes Proliferation and Invasive Capacity of Melanoma Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the effects of ECI2 on cutaneous melanoma cells, two stable melanoma cell lines overexpressing ECI2 (A375/ECI2 and A2058/ECI2) and two stably knocking down ECI2 (A375/shECI2 and A2058/shECI2) were established. Cells transfected with empty lentiviral vectors served as negative controls. RT-qPCR and WB experiments confirmed successful establishment of stable ECI2-overexpressing and ECI2-knockdown melanoma cell lines (Supplementary Fig. 1A-D). CCK8 cell proliferation assays and colony formation assays demonstrated that ECI2 overexpression promoted the proliferation capacity of A375 and A2058 cells (Fig. 2A-D), whereas ECI2 knockdown had the opposite effect (Supplementary Fig. 1E-G). Furthermore, ECI2 overexpression significantly enhanced melanoma cell migration and invasion detected in wound healing and Transwell assays (Fig. 2E-F), whereas ECI2 knockdown exhibited the opposite effect (Supplementary Fig. 1H-J). Further establishment of a mouse subcutaneous tumor model revealed that ECI2 upregulation significantly promoted melanoma cell growth in vivo, manifested by increased Ki-67 proliferation index, larger tumor volume, and heavier tumor weight (Fig. 2G-J). These in vivo and in vitro experiments collectively indicate that ECI2 overexpression enhances the proliferation, migration, and invasion capabilities of melanoma cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECI2 promotes arachidonic acid metabolism in melanoma cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a lipid metabolism-related enzyme, we performed KEGG enrichment analysis on TCGA-SKCM data to explore ECI2\u0026apos;s molecular mechanisms in melanoma progression. Results indicated ECI2 influences arachidonic acid metabolism (Fig. 3A). To validate these bioinformatics findings, we measured relative AA levels in melanoma cells overexpressing or knocking down ECI2 via ELISA. Results showed elevated AA levels in supernatants from ECI2-overexpressing melanoma cells (Fig. 3B), while ECI2 knockdown produced the opposite effect (Fig. 3C). ELISA measurements of AA content in tumor lysates revealed increased AA levels in mouse tumor tissues from ECI2-overexpressing mice (Fig. 3D). To further elucidate how ECI2 modulates AA metabolism, Western blot analysis revealed that ECI2 promotes COX2 expression. ELISA experiments demonstrated that ECI2 enhances levels of the AA metabolite PGE2 (Fig. 3F-G). Similarly, measurement of PGE2 in tumor lysates indicated that ECI2 promotes PGE2 levels in mouse tumor tissues (Fig. 3H). To assess AA\u0026apos;s impact on melanoma cell function, CCK8 proliferation and Transwell invasion assays revealed that AA supplementation enhanced proliferation and invasion in ECI2-silenced melanoma cells (Fig. 3I-K). These findings indicate that ECI2 promotes melanoma cell proliferation and invasion by enhancing arachidonic acid metabolism.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECI2 Promotes NET Formation in the Melanoma Microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the molecular mechanisms by which ECI2 promotes melanoma progression, we analyzed the TIMER database and found a significant positive correlation between ECI2 expression and neutrophil infiltration (Fig. 4A). Further analysis of tumor tissues from a subcutaneous tumor model in mice revealed significantly increased neutrophil infiltration in ECI2-overexpressing tumors compared to controls (Fig. 4B-C). In vitro functional experiments with co-cultured melanoma cells and human neutrophils demonstrated that ECI2 overexpression significantly enhanced melanoma cell proliferation and invasion capacity in the presence of neutrophils (Fig. 4D, 4F), whereas ECI2 knockdown produced the opposite effect (Fig. 4E, 4G). Recent studies have revealed that ECI2 influences NET formation in colorectal cancer. We sought to determine whether ECI2 influences NET formation in melanoma. Detection of NET markers citrullinated H3 (cit-H3) and myeloperoxidase (MPO) in mouse tumor tissues revealed that ECI2 significantly promoted NET formation (Fig. 5A). Sytox immunofluorescence and MPO-DNA ELISA assays revealed increased NET formation in neutrophils cultured with supernatant from ECI2-overexpressing melanoma cells (Fig. 5B-E), whereas supernatant from ECI2-silenced melanoma cells exhibited the opposite effect (Supplementary Fig. 2). Analysis of human melanoma tissue revealed significantly increased NET formation in ECI2-overexpressing tumors (Fig. 5F). Correlation analysis between ECI2 expression and NET formation in human melanoma tissue demonstrated a significant positive correlation (Fig. 5G). Collectively, these findings indicate that ECI2 promotes NET formation within the melanoma microenvironment. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eECI2 Promotes ROS-Mediated NET Formation in Neutrophils via Tumor-Derived Arachidonate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that tumor-derived arachidonate metabolism can reprogram neutrophils\u003csup\u003e19\u003c/sup\u003e, we investigated whether ECI2-regulated arachidonate metabolism modulates NET formation in melanoma. We performed Sytox immunofluorescence and MPO-DNA ELISA assays. Results revealed that NET formation in neutrophils cultured with supernatant from ECI2-knockdown melanoma cells was enhanced by phorbol 12-myristate 13-acetate (PMA, a potent NET inducer) and also by arachidonic acid (Fig. 6A-D). Similarly, increased NET formation in neutrophils cultured with supernatant from ECI2-overexpressing melanoma cells was inhibited by DNase I (a NET-DNA-degrading nuclease) and by the arachidonic acid metabolism inhibitor aspirin (Supplementary Fig. 3A-D). Given that ROS is a known key factor in NET formation, we performed an ELISA assay to investigate the molecular mechanism by which ECI2 promotes NET formation in the melanoma microenvironment. Results showed that treating neutrophils with supernatant from ECI2-knockdown melanoma cells reduced ROS levels in the supernatant, while further addition of AA increased ROS levels (Fig. 6E). Similarly, ROS levels increased in supernatants from ECI2-overexpressing melanoma cells after neutrophil culture, but decreased upon aspirin addition (Fig. 6F). To further validate ROS necessity in ECI2-mediated NET formation regulation within the melanoma microenvironment, When DPI blocked neutrophil NADPH oxidase activity, the ability of neutrophils to produce NETs was significantly impaired (Supplementary Fig. 3A-D). To examine the combined roles of arachidonic acid metabolism and NET formation in melanoma cells, ECI2-knockdown melanoma cells were co-cultured with neutrophils and subjected to different treatments, followed by assessment of melanoma cell proliferation and invasion. Results showed that ECI2-mediated suppression of melanoma cell proliferation and invasion was reversed by AA, while DNase I reversed AA\u0026apos;s effects (Fig. 6G-H). Similarly, aspirin reversed both proliferation and invasion promoted by ECI2 overexpression in melanoma cells, while PMA reversed aspirin\u0026apos;s effects (Supplementary Fig. 3E-F). These findings suggest that ECI2 promotes ROS-mediated NET formation in neutrophils via tumor-derived arachidonic acid, thereby enhancing melanoma cell proliferation and invasion.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe TLR3/COX2/PGE2 axis in melanoma cells regulated by ECI2 via NETs promotes arachidonic acid metabolism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch indicates that NETs can stimulate Toll-like receptor 3 to induce cyclooxygenase-2 activation and prostaglandin E2 production\u003csup\u003e13\u003c/sup\u003e. We sought to determine whether ECI2-regulated NET formation could influence arachidonic acid metabolism via the TLR3/COX2/PGE2 axis. First, we performed RT-qPCR, WB, PGE2 ELISA, and AA ELISA assays. Results showed that co-culturing ECI2-silenced melanoma cells with neutrophils suppressed TLR3 mRNA levels, COX2 protein expression, PGE2, and AA levels in melanoma cells, while PMA reversed the effects of ECI2 silencing (Fig. 7A-D). Similarly, co-culturing ECI2-overexpressing melanoma cells with neutrophils elevated melanoma cell TLR3 mRNA levels, COX2 protein content, and PGE2/AA levels, while DNase I reversed the effects of ECI2 overexpression (Supplementary Fig. 4A-D). Further co-culture of ECI2-silenced melanoma cells with neutrophils, followed by various treatments, assessed melanoma cell proliferation and invasion. Results showed that the inhibitory effect of ECI2 silencing on melanoma cell proliferation and invasion was reversed by PMA or TLR3 overexpression in melanoma cells, and the PMA effect was subsequently reversed by aspirin (Fig. 7E-F). Similarly, the promotion of melanoma cell proliferation and invasion by ECI2 overexpression was reversed by DNase I or TLR3 silencing in melanoma cells, and the effect of DNase I was subsequently reversed by AA (Supplementary Fig. 4E-F). Using an in vivo mouse subcutaneous tumor model, aspirin and DNase I were found to reverse the tumorigenic promotion by ECI2 overexpression. Combined treatment with aspirin and DNase I resulted in smaller tumor volumes and lower body weights compared to either treatment alone (Fig. 7G-J).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we identified ECI2 as a key driver of melanoma progression. ECI2 expression was significantly elevated in melanoma tissues, and TCGA analysis revealed that high ECI2 levels correlated with advanced disease stage and reduced patient survival, suggesting ECI2 may serve as a poor prognostic marker for melanoma. Previous literature has reported ECI2 as a tumor suppressor in colorectal cancer, where its low expression correlates with poorer prognosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In contrast, our findings reveal an opposite pattern in melanoma, where high ECI2 expression is associated with poor prognosis, indicating that ECI2 function exhibits tumor type specificity. This discovery expands our understanding of ECI2's role in tumorigenesis and progression. Furthermore, studies indicate that ECI2 is a target gene of the androgen receptor in prostate cancer, promoting tumor cell survival\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, supporting the possibility of ECI2's pro-tumorigenic role in certain cancers. Thus, our study is the first to reveal the abnormal overexpression of ECI2 in melanoma and its clinical significance, providing a new perspective for melanoma prognosis assessment and molecular subtyping.\u003c/p\u003e\u003cp\u003eFunctional experiments further confirmed ECI2's pro-tumorigenic effects in melanoma. By genetically manipulating ECI2 levels in commonly used human melanoma cell lines (e.g., A375, A2058), we found that ECI2 overexpression significantly enhanced cell proliferation and clonogenic efficiency while promoting migration and invasion; conversely, ECI2 knockdown suppressed these malignant phenotypes. In vivo experiments in a mouse subcutaneous tumor model demonstrated that tumors formed by ECI2-overexpressing melanoma cells grew more rapidly, consistent with in vitro findings. These data indicate that ECI2 drives tumor growth and invasive potential within melanoma cells, supporting its role as an oncogene. Previous studies on ECI2 have primarily focused on its metabolic functions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, with no prior reports on its functional role in melanoma. Our work fills this gap. This discovery functionally validates the association between ECI2 overexpression and poor prognosis, highlighting the innovation of our study: revealing that ECI2 is not merely a prognostic marker but a driving force in melanoma progression.\u003c/p\u003e\u003cp\u003eAs an enzyme involved in lipid metabolism, ECI2 plays a role in tumor promotion. To elucidate the mechanism underlying this effect, we focused on lipid metabolic pathways and discovered that ECI2 significantly enhances arachidonic acid metabolism in melanoma cells while activating the downstream COX-2/PGE2 pathway. In ECI2-upregulated cells, levels of arachidonic acid metabolites increased, accompanied by substantial production of COX-2 and its catalyzed product PGE2. As a polyunsaturated fatty acid, arachidonic acid serves as a precursor for multiple bioactive lipids, including prostaglandins and leukotrienes known to regulate tumor-associated inflammation\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The COX-2/PGE2 axis is a key driver of cancer progression and immune evasion\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. COX-2 is frequently overexpressed in invasive tumors, including melanoma, and elevated COX-2 levels correlate with poor clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. As the primary COX-2 product, PGE₂ activates pro-tumor pathways (e.g., PKA/CREB, β-catenin, and PI3K/AKT signaling) by acting on receptors (EP1\u0026ndash;EP4) while simultaneously suppressing anti-tumor immune responses\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In melanoma, COX-2-derived PGE₂ is a known driver of the immunosuppressive microenvironment\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. By modulating T cell activity, PGE₂ enables tumor cells to evade immune clearance\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. PGE₂ is a recognized pro-tumor mediator that promotes tumor progression through multiple mechanisms, including suppression of anti-tumor immunity and enhancement of tumor cell proliferation and survival\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that ECI2-mediated arachidonate metabolism reprogramming induces increased PGE₂ release by melanoma cells, thereby creating an immunosuppressive microenvironment conducive to tumor growth. One plausible explanation is that ECI2 accelerates AA β-oxidation, driving oxidative metabolism of reactive oxygen species (H₂O₂) and intermediate metabolites, thereby activating signaling pathways (e.g., NF-κB) and upregulating COX-2 and other AA metabolic enzymes. The net outcome is increased COX-2-dependent conversion of AA to PGE₂. Regardless, we reveal a direct functional link between the metabolic enzyme ECI2 and the immune mediator PGE₂, deepening our understanding of the \u0026ldquo;metabolism-inflammation\u0026rdquo; axis in melanoma. Notably, previous studies in mouse melanoma models have demonstrated that tumor-cell-derived PGE₂ is a critical factor enabling immune evasion and sustained tumor growth\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Additionally, Yu et al. recently reported that triple-negative breast cancer cells synthesize and secrete arachidonic acid-enriched lipids, thereby reprogramming tumor-infiltrating neutrophils to an immunosuppressive phenotype, leading to therapeutic resistance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. While these studies highlight the role of arachidonic acid metabolites in tumor immune evasion, our research further traces upstream in melanoma, demonstrating for the first time the regulatory function of the key upstream enzyme ECI2 on the COX-2/PGE2 pathway. This mechanistic discovery expands our understanding of how metabolic reprogramming in melanoma drives inflammation and immune suppression.\u003c/p\u003e\u003cp\u003eInterestingly, we found that alongside altering tumor metabolism, ECI2 also promotes melanoma progression by influencing the tumor microenvironment. Specifically, melanomas overexpressing ECI2 induce increased neutrophil-derived NET formation within the tumor microenvironment. NETs are structures composed of reticular DNA and granular proteins released by neutrophils\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, recently found to promote tumor growth and metastasis\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. We detected increased levels of typical NETs markers in tumor tissues from the ECI2 overexpression group, such as citrullinated histone H3 and myeloperoxidase-positive reticular structures, indicating that more neutrophils underwent NETosis (the process of NET formation). The presence of NETs is believed to promote the adhesion of circulating tumor cells and distant metastasis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. For instance, neutrophil-released DNA nets can trap circulating tumor cells, thereby increasing the probability of metastasis site formation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, proteases and other bioactive molecules released by NETs can remodel the tumor microenvironment, activate dormant cancer cells, and accelerate disease progression\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Thus, our findings suggest that melanoma cells \u0026ldquo;recruit\u0026rdquo; and activate neutrophils to generate NETs via ECI2, with this tumor-neutrophil interaction further driving malignant tumor evolution. In the existing literature, reports on how tumor cells regulate NET formation in melanoma remain scarce. Our study reveals, from a metabolic perspective, that ECI2 within tumor cells can serve as a key regulator influencing the inflammatory state of the tumor microenvironment, highlighting the novelty of our work.\u003c/p\u003e\u003cp\u003eTo elucidate the mechanism by which ECI2 promotes NET formation, we focused on the link between arachidonic acid metabolism and neutrophil activation. Results indicate that ECI2-upregulated melanoma cells may stimulate neutrophil ROS production by secreting arachidonic acid or its metabolites, thereby inducing NETosis. It is well established that NET formation is highly dependent on ROS signaling generated by NADPH oxidase\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Our co-culture experiments support this: when neutrophil NADPH oxidase activity was blocked or arachidonic acid released by tumor cells was neutralized, the ability of neutrophils to produce NETs was significantly impaired. These findings indicate that arachidonic acid from melanoma cells drives NET formation by triggering neutrophil ROS production. This mechanism echoes Yu et al.'s discovery in human breast cancer that tumor-derived lipids significantly influence neutrophil phenotype and function\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, our study advances this understanding by explicitly identifying a specific metabolic pathway\u0026mdash;the arachidonic acid-ROS axis\u0026mdash;as crucial for regulating NETosis. This not only deepens our comprehension of how tumor metabolites act as signaling molecules to influence innate immune responses but also suggests that targeting this pathway may reduce tumor-associated NET formation, thereby inhibiting tumor progression.\u003c/p\u003e\u003cp\u003eNotably, we uncovered a positive feedback loop between arachidonic acid metabolism and immunity mediated by ECI2: tumor cells promote NET formation, which in turn enhances melanoma cell metabolic activity via TLR3 signaling, further driving tumor progression. Mechanistically, in the NET-rich tumor microenvironment, TLR3 on melanoma cells becomes activated, subsequently upregulating COX-2 expression and increasing production of pro-tumor mediators like PGE2. This constitutes the TLR3/COX-2/PGE2 axis through which NETs act on melanoma cells. TLR3 typically recognizes molecular patterns such as double-stranded RNA\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. We hypothesize that abundant NETs may carry released nucleic acids or activate the TLR3 pathway through other mechanisms, thereby promoting AA metabolism in melanoma cells and establishing a positive feedback loop between tumors and neutrophils. These finding parallels recent reports that NETs induce metabolic reprogramming in HSCs via the TLR3/COX-2 pathway, contributing to MASH liver fibrosis progression\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Our study is the first to demonstrate that the TLR3/COX-2/PGE2 pathway in melanoma cells within the tumor microenvironment is regulated by tumor-associated NETs and contributes to tumor promotion. This positive feedback mechanism highlights the complex bidirectional regulatory relationship between tumor metabolism and innate immunity, offering a novel perspective on the link between inflammatory amplification and tumor progression in the melanoma microenvironment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study reveals ECI2's pivotal role in melanoma progression: by promoting arachidonic acid metabolism and PGE2 production, ECI2 not only directly drives tumor cell proliferation and invasion but also establishes a metabolic-immune positive feedback loop. This loop amplifies tumor-promoting behavior by inducing NET formation and activating the TLR3/COX-2/PGE2 axis in tumor cells. This finding highlights the central role of tumor cell lipid metabolic reprogramming in tumor-immune cell interactions, enriching our understanding of the mechanisms underlying malignant melanoma progression (Supplementary Fig.\u0026nbsp;5). Clinically, inhibiting ECI2 and its associated pathways (such as COX-2/PGE2 and NETosis) holds promise as a novel intervention strategy. Previous studies have demonstrated that blocking the COX-2/PGE2 pathway can reverse immune suppression and enhance immunotherapy efficacy\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Therefore, targeted drug intervention at key nodes of the ECI2-mediated arachidonate metabolism-NETs positive feedback loop may offer novel therapeutic benefits for melanoma patients. The innovation of this study lies in integrating tumor metabolism and immunology perspectives to reveal a novel molecular mechanism underlying melanoma progression.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complies with all relevant ethical guidelines of Sun Yat-sen University. All animal experimentation protocols were approved by the Animal Ethics Committee of Sun Yat-sen University and conducted in accordance with the Guidelines for Laboratory Animal Use. Collection and use of clinical data were approved by the Institutional Review Board of the First Affiliated Hospital of Sun Yat-sen University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo human cutaneous melanoma cell lines, A375 and A2058, were obtained from the Chinese Academy of Sciences Cell Bank (Shanghai). All cells were identified by short tandem repeat (STR) profiling and had been passaged for less than 6 months after thawing. A375 and A2058 cell lines were cultured in Dulbecco\u0026rsquo;s Modified Eagle\u0026rsquo;s Medium (DMEM) supplemented with 10% fetal bovine serum (Hyclone, Logan, USA) and 100 \u0026mu;U/mL penicillin/streptomycin (Gibco) at 37\u0026deg;C in a 5% CO₂ incubator. Different cell treatment conditions were: AA (100 \u0026mu;M/mL, Solarbio), aspirin (2 mmol/L, MCE), DNase I (0.25 U/mL, Roche), PMA (25 ng/mL, Sigma), and DPI (10 \u0026mu;M, Selleck).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParaffin-embedded tissue specimens were collected from 32 patients with primary melanoma treated at the First Affiliated Hospital of Sun Yat-sen University between 2000 and 2025. None of these patients received chemotherapy or radiotherapy prior to surgery. Disease staging was determined using the PTNM classification system based on tumor size, lymph node metastasis, and distant metastasis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParaffin-embedded sections were heated, dewaxed, rehydrated, and placed in sodium citrate buffer for antigen retrieval. Slides were then immersed in 3% hydrogen peroxide and sealed with sheep 10% FBS/PBS. After three rinses, sections were incubated with the primary antibody (anti-ECI2, 1:400 dilution, Proteintech) at 4\u0026deg;C. Sections were washed three times with PBS, then incubated with the secondary antibody (anti-rabbit IgG, 1:2000 dilution, Proteintech) at 37\u0026deg;C for 40 minutes. After staining with 3,4-dihydroxybenzidine (DAB), sections were counterstained with hematoxylin, dehydrated, mounted, and examined.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIHC scoring based on staining intensity and percentage of positive tumor cells was performed by two independent pathologists blinded to clinical data. Staining intensity was graded as 0 (negative), 1 (weak), 2 (moderate), and 3 (strong). The extent score was assigned based on the percentage of positively stained area within the entire tumor region or entire section: 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), 4 (76-100%). The sum of the intensity and extent scores served as the final ECI2 score (0\u0026ndash;14). A final staining score greater than 7 was considered high expression, while a score less than or equal to 7 was classified as low ECI2 expression.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Extraction and qPCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrizol reagent (Invitrogen, Carlsbad, CA) was used to extract total RNA from cells. The PrimeScript RT Kit (Promega, USA) was employed for cDNA synthesis. qPCR was performed using SYBR PreMix ex Taq Benchmark (Takala, China) on an ABI7500 real-time PCR system (Applied Biosystems, USA). Gene expression was detected using the comparative 2^(-\u0026Delta;\u0026Delta;CT) method. Primers for amplifying ECI2 were 5\u0026apos;-GCCGTTTTACTGAGGGAATTTGT-3\u0026apos; (forward) and 5\u0026apos;-CTGGACCATTGACCACTGCAA-3\u0026apos; (reverse). The primer sequences for amplifying TLR3 were 5\u0026apos;-GAAGCAGGCGTCCTTGGACTT-3\u0026apos; (forward) and 5\u0026apos;-TGTGCTGAATTCCGAGATCCA-3\u0026apos; (reverse). GAPDH served as the endogenous control.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern Blot Analysis (WB)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were lysed using RIPA lysis buffer supplemented with protease inhibitor cocktail and PMSF. Lysates were centrifuged at 12,000 g for 30 minutes, and protein concentration was measured using the BCA assay. Proteins were separated by SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes. After blocking in TBST buffer containing 5% skim milk, membranes were incubated overnight at 4\u0026deg;C with designated primary antibodies. The next day, after washing, membranes were incubated with appropriate secondary antibodies at room temperature for 1 hour. Signals were subsequently detected using Enhanced Chemiluminescence (Pierce, Rockford, IL, USA). Primary antibodies included: anti-ECI2 (1:1000 dilution, Proteintech); \u0026beta;-tubulin (1:5000 dilution, M20045, Abmart); COX2 (1:1000 dilution, Proteintech).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCCK8 Cell Proliferation Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeed 1\u0026times;10\u0026sup3; cells into a 96-well plate and incubate for 24 hours. Add 2-(2-methoxy-4-nitrophenyl)-3-(4-nitrophenyl)-5-(2,4-disulfonyl)-2H-tetrazolium salt (CCK-8, USA) solution to each well and incubate for 2 hours. Read using a microplate reader (Bio-Rad, USA). Repeat the experiment three times.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eColony Formation Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells were seeded into 6-well plates (400 cells per well) and cultured for 2 weeks. Cells were fixed with 4% paraformaldehyde for 30 minutes and stained with 1% Giemsa stain for 15 minutes. The number of colonies exceeding 50 cells was counted. Three independent experiments were performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell Wound Healing Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.2 \u0026times; 10⁶ cells were seeded into 6-well tissue culture plates and incubated for 24 hours. A scratch wound was created using a 10 \u0026mu;l pipette tip. Plates were washed three times and cultured in serum-free DMEM. Wound closure was observed at 0 h and 48 h. Images were captured to assess cell migration levels. Cell migration capacity was quantified by measuring the distance between the leading edges of cells in three randomly selected microscopic fields (\u0026times;200 magnification) at each time point.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranswell Invasion Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe upper chamber of Transwell chambers was pre-coated with Matrigel. 2\u0026times;10⁵ cells suspended in serum-free medium were placed in the upper chamber of 8\u0026mu;m pore Transwells (BD Biosciences, USA), while the lower chamber was filled with 10% FBS as a chemotactic stimulus. Cells were cultured at 37\u0026deg;C for 2 days. Cells successfully migrating through the 8\u0026mu;m pores were stained with 0.5% crystal violet for 15 minutes. Cell counts were performed by randomly selecting five fields of view (\u0026times;200 magnification) under a microscope.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAA Content, PGE2 Content, and ROS Detection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAA content and PGE2 were measured using corresponding ELISA kits (Shanghai Xinfan Biotechnology Co., Ltd.) according to the manufacturer\u0026apos;s instructions. ROS was measured using an ELISA kit (Yeasen) according to the manufacturer\u0026apos;s instructions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMPO-DNA Enzyme-Linked Immunosorbent Assay (ELISA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MPO-DNA complex was identified using a capture ELISA. A 96-well microtiter plate was coated with anti-MPO monoclonal antibody (Proteintech, 22225-1-AP) as the capture antibody (75 \u0026mu;l per well) overnight at 4\u0026deg;C. After blocking with 1% BSA (125 \u0026mu;l per well), add 40 \u0026mu;l of sample and peroxidase-labeled anti-DNA monoclonal antibody (Roche, 11774425001), incubating at room temperature for 2 hours. Add peroxidase substrate (ABTS) (Roche, 11774425001). After incubating at 37\u0026deg;C in the dark for 40 minutes, measure the optical density at 405 nm using a microplate reader.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeutrophil Isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeutrophils were isolated from human peripheral blood using dextran-Ficoll-Paque Premium (GE Healthcare) via density gradient centrifugation. Immunofluorescence staining for neutrophil markers confirmed \u0026gt;95% purity of isolated neutrophils. Unless otherwise specified, neutrophils were cultured in RPMI 1640 medium supplemented with 20% FBS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSytox Green Dye Staining\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor visualization, neutrophils cultured in conditioned medium (CM) from different melanoma cell lines were seeded into 96-well plates for incubation. The impermeable DNA dye SytoxGreen (Thermo Fisher Scientific, 1:10,000) and the permeable DNA dye Hoechst 33342 (Thermo Fisher Scientific, 1:1000) were added to the incubation system. At the end of incubation, plates were transferred directly to a fluorescence microscope to observe NET formation. Each experiment used neutrophils from different donors, with three independent replicates performed per assay.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence for NET Formation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor paraffin-embedded tissue samples, after dewaxing, antigen retrieval was performed using a citric acid solution in a microwave oven. Overnight incubation at 4\u0026deg;C was conducted in a mixture of two primary antibodies. The primary antibodies used included anti-rabbit cit-H3 (Abcam) and anti-mouse MPO (Proteintech). The following day, after washing with cold PBS, samples were incubated for 1 hour at room temperature in the dark with a mixture of two different species-specific secondary antibodies. The secondary antibodies used included Alexa Fluor 488-labeled anti-rabbit antibody and Alexa Fluor 594-labeled anti-mouse antibody. Cells were counterstained with 4\u0026prime;,6-diamino-2-phenylpyridine (DAPI) (Sigma-Aldrich) for nuclear visualization. Slides were sealed with a coverslip mounting medium containing an antifluorescence quencher. Each sample was examined and photographed under a fluorescence microscope. NET formation was quantified by calculating the percentage of citrullinated histone H3 (cit-H3)-positive cells within the field of view.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse Subcutaneous Tumor Formation Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBALB/C-nu/nu nude mice (3\u0026ndash;4 weeks old) were purchased from Guangdong Yao Kang and housed at 12\u0026ndash;18\u0026deg;C with 22\u0026ndash;50% humidity under a 60-hour light-dark cycle. All quantitative analyses were performed in triplicate for statistical evaluation. A375 cells (4 \u0026times; 10⁶) from each group (NC/ECI2) were injected subcutaneously into mice. Tumor volume (length \u0026times; width\u0026sup2; \u0026times; 0.5) was measured every 4 days. To investigate the effect of DNase I, mice received daily subcutaneous injections of DNase I (2.5 mg/kg, Roche) following A375 ECI2 cell injection, with PBS administered to controls. For the impact of aspirin, mice received oral aspirin (40 mg/kg) every 3 days after A375 ECI2 cell injection. At the conclusion of the animal experiments, all mice were euthanized by carbon dioxide inhalation. Tumor size was visually assessed and photographed during necropsy. Tumor tissue was excised, fixed in 10% neutral formaldehyde, paraffin-embedded, dehydrated, sectioned into serial histological sections, and stained with hematoxylin and eosin (HE). HE sections were examined under a light microscope. Immunofluorescence was used to detect neutrophil infiltration (anti-Ly-6G antibody, Abcam) and NET formation (MPO, cit-H3) in tumor tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis and Reproducibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eError bars represent mean \u0026plusmn; standard deviation. Statistical analysis was performed using Prism9 (GraphPad Software), ImageJ software, or SPSS Statistics 22 (IBM Corp.). For comparisons between two groups, P values were calculated using a two-tailed Student\u0026apos;s t-test. For comparisons involving more than two groups, P values were calculated using analysis of variance (ANOVA). Correlation analysis was performed using Correlation of Ozone correlations. Survival curves were plotted using the Kaplan-Meier method. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant (*, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; ****, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; ns indicates no significant difference). Each experiment was independently replicated at least three times.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the China Postdoctoral Science Foundation (Grant No. 2025M771958).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePJW and ZHL performed the experiments, analyzed the data, and drafted the manuscript. LXC and ZHL collected CRC tissue and performed IHC. WYZ and LXC assisted with the animal experiments. YYL, HOY and SNL contributed to the data analysis. JQW designed the experiments and revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Informed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments involving patients were approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University and complied with the Declaration of Helsinki. Informed consent was not required as the data will be analyzed anonymously. All animal experiments were conducted under the approval of the Animal Ethics Committee of Sun Yat-sen University, ensuring ethical and humane treatment (approval number 2024002055).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArnold, M.\u003cem\u003e et al.\u003c/em\u003e Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. \u003cem\u003eJAMA dermatology\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e, 495-503, doi:10.1001/jamadermatol.2022.0160 (2022).\u003c/li\u003e\n\u003cli\u003eSchadendorf, D.\u003cem\u003e et al.\u003c/em\u003e Melanoma. \u003cem\u003eLancet (London, England)\u003c/em\u003e \u003cstrong\u003e392\u003c/strong\u003e, 971-984, doi:10.1016/s0140-6736(18)31559-9 (2018).\u003c/li\u003e\n\u003cli\u003eXiao, Y. \u0026amp; Yu, D. 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B.\u003cem\u003e et al.\u003c/em\u003e Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e162\u003c/strong\u003e, 974-986, doi:10.1016/j.cell.2015.07.011 (2015).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7944532/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7944532/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMelanoma is a highly malignant, metastatic skin tumor with complex progression mechanisms. This study reveals that the metabolic enzyme ECI2 is significantly overexpressed in melanoma tissues and is closely associated with poor patient prognosis. Functional experiments confirm that ECI2 significantly promotes the proliferation, migration, and invasive capabilities of melanoma cells both in vitro and in vivo. Mechanistic investigations reveal that ECI2 reshapes arachidonic acid metabolism in tumor cells, leading to the accumulation of the metabolite PGE2. These metabolites activate neutrophils, inducing the formation of neutrophil extracellular trap networks (NETs) via reactive oxygen species (ROS)-dependent pathways. Crucially, this study innovatively demonstrates that NETs are not the final effector. NETs activate the TLR3/COX-2/PGE2 axis in melanoma cells, establishing a metabolic-immune positive feedback loop. This circuitry persistently drives malignant tumor behavior, profoundly reshaping the tumor microenvironment. Our work not only proposes ECI2 as a key oncogene and potential therapeutic target but, more importantly, reveals a novel mechanism whereby tumor cells establish a positive feedback dialogue between metabolic reprogramming and innate immune effector NETs. This provides a novel theoretical framework for understanding melanoma progression and developing combined therapeutic strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubject terms\u003c/b\u003e: ECI2, arachidonic acid metabolism, neutrophil extracellular traps, melanoma\u003c/p\u003e","manuscriptTitle":"ECI2 drives melanoma progression via arachidonic acid metabolism and NETs-induced TLR3/COX2/PGE2 positive feedback loop","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:27:16","doi":"10.21203/rs.3.rs-7944532/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c1ded5c-d827-459c-833a-13e849b65ff0","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57923377,"name":"Biological sciences/Cancer/Cancer metabolism"},{"id":57923378,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":57923379,"name":"Biological sciences/Cancer/Skin cancer/Melanoma"}],"tags":[],"updatedAt":"2026-02-06T11:32:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:27:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7944532","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7944532","identity":"rs-7944532","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}