{"paper_id":"32b97af9-eabc-4530-948d-b20a38408d29","body_text":"Systemic purinergic dysregulation in melanoma revealed by soluble P2X4 receptor fragments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Systemic purinergic dysregulation in melanoma revealed by soluble P2X4 receptor fragments Roland Martin Teras¹, Jyri Teras², Igor Kuprijanov, Caroline Khaddaj, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8396511/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Melanoma progression involves coordinated immune suppression, altered receptor-mediated signalling, and tumour-driven metabolic reprogramming. To evaluate these systemic alterations, we integrated three datasets: flow-cytometric profiling of immune cell subsets and P2X4 expression in peripheral blood leukocytes from melanoma patients and healthy controls; molecular detection of P2X4 in plasma, leukocytes, and urine using Western blotting and immunoprecipitation; and NMR-based metabolomic profiling of serum and saliva. Melanoma patients exhibited reduced CD4⁺ T-helper cells, altered Tc/Treg balance, and eosinophil heterogeneity with elevated P2X4 expression. Truncated P2X4 receptor fragments were detected in plasma and urine of some melanoma patients but not in controls. Metabolomic analyses revealed tumour-associated metabolic shifts, including elevated branched-chain amino acids in both serum and saliva and many alterations associated with dysbiosis were detected in melanoma patients’ saliva. These findings highlight the convergence of immune dysregulation, purinergic P2X4 signalling, and systemic metabolic remodelling in melanoma. The presence of soluble P2X4 fragments, together with metabolomic fingerprints, supports their potential as minimally invasive biomarkers for disease monitoring. Cancer metabolism Immune dysregulation Melanoma P2X4 receptor Purinergic signalling Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Melanoma is an aggressive malignancy characterized by rapid metastatic spread, profound resistance to immune surveillance, and frequent therapeutic failure. Although immune checkpoint inhibitors have transformed clinical outcomes, reliable biomarkers for early detection, prognosis, and treatment monitoring remain limited. Increasing evidence shows that melanoma progression is shaped by complex interactions among tumour-driven metabolic remodelling, the immune system, and the tumour microenvironment (TME). The TME critically influences melanoma growth and immune evasion by shaping leukocyte recruitment, angiogenesis, and lymphatic dissemination [1]. Because melanoma commonly metastasizes through lymphatic pathways [2], the immunological state of sentinel lymph nodes (SLNs) provides essential insight into disease progression. Eosinophils—particularly those with inflammatory phenotypes—are enriched in metastatic SLNs and exhibit increased P2X4 receptor expression under hypoxic conditions [3]. These cells actively contribute to antitumor immunity by recruiting cytotoxic T cells and modulating vascular and macrophage function [4; 5], suggesting that their altered abundance and receptor profile may have prognostic value. Melanoma employs multiple immune-escape mechanisms, including suppressed cytotoxic T-cell activity, expanded regulatory T-cell populations, and impaired NK and dendritic cell interactions [6]. These immunological changes coincide with systemic metabolic alterations detectable in blood and saliva, reflecting the tumour’s reprogramming of host energy pathways to support survival and proliferation. Among key molecular pathways connecting immunity and metabolism, purinergic signalling has emerged a critical regulator of inflammation, immune cell activation, angiogenesis, and autophagy. The P2X4 receptor, an ATP-gated ion channel, is stored in lysosomes and contributes to Ca²⁺ transport and autophagic flux [7; 8]. In colorectal cancer, P2X4 activity promotes M1-like TAM polarization via cGAS–STING signalling and correlates with favourable immune infiltration and prognosis [9]. Across diverse human cancers, P2X4 overexpression is consistently associated with poor clinical outcomes [10]. Although P2X4 is primarily membrane-localized, low concentrations of extracellular receptor—including truncated forms—have been detected and may arise from vesicular release or epithelial injury [11]. Based on this evidence, we hypothesized that immune dysregulation, altered P2X4 signalling, and systemic metabolic reprogramming converge in melanoma and that these changes can be captured through minimally invasive sampling. To test this hypothesis, we integrated three complementary datasets: (1) flow-cytometric profiling of immune cell subsets and P2X4 expression in peripheral leukocytes; (2) molecular detection of P2X4 in plasma, leukocytes, and urine using Western blotting and RT-qPCR; and (3) NMR-based metabolomic analysis of serum and saliva. This multi-cohort framework enables a comprehensive assessment of immune, purinergic, and metabolic alterations in melanoma and provides insight into biomarker candidates that may complement existing diagnostic approaches (Fig 1). Biological samples obtained from melanoma patients, including lymph node tissue, saliva, plasma, and urine, were analysed to assess cellular and extracellular components of P2X4-mediated purinergic signalling. Leukocyte-associated P2X4 expression was evaluated by flow cytometry using P2X4-specific antibodies. Soluble P2X4 species present in patient-derived fluids were isolated by immunoprecipitation saliva and blood were subjected to downstream metabolite analysis. Together, these approaches demonstrate the presence of both cell-associated and extracellular P2X4 across multiple biological compartments relevant to purinergic signalling in melanoma. Materials and Methods Study cohorts: T hree partially overlapping patient cohorts were analysed. Cohort 1: Five melanoma patients and five healthy controls were included for detailed immune profiling (Tc, Th, B, NK, eosinophils, Tregs, and P2X4 expression). Cohort 2: Eight melanoma patients and eight controls (C1-3; C5-9) were analysed for a broader range of immune subsets and P2X4 expression. A subset of these patients was further examined for P2X4 protein (Western blot, immunoprecipitation from plasma and leukocytes) and mRNA expression (RT-qPCR in leukocytes). Urine samples were analysed for P2X4 fragments. Cohort 3: Five melanoma patients and five healthy controls were included for NMR-based metabolomic profiling of serum (or plasma for M4 and C4) and saliva, metabolite quantification, and statistical analyses. A summary of metadata about the three cohorts is shown in Table 1. Table 1. Characteristics of study participants, three partially overlapping patient cohorts with healthy controls Cohort 1 Cohort 2 Cohort 3 Imm cells Metastatic LN/ ctr LN Imm cells Mel patient /healthy ctr blood Metabolome Mel patient / ctr blood, saliva, urine Sex (F/M) Year of birth Melanoma primary size (mm) / Stage Melanoma Patients (M) 1 1 F 1961 40 2 2 M 1950 12 3 3 M 1967 1,4 4 4 M 1963 3,8 5 5 F 1981 (no data)* 6 F 1937 9 7 M 1976 13 8 F 1964 7 M1 F 1963 IV M2 M 1952 IIIC M3 F 1949 IV M4 M 1940 IV M5 M 1936 IV Alzheimer patient Neuro N1 M 1953 Healthy controls (CTR; C) 1 1 C5 F 1958 2 2 C1 F 1957 3 3 C3 M 1950 4 5 F 1987 5 6 F 2003 7 C2 M 1969 8 C4 M 1981 9 F 1989 * No data available, as the melanoma was treated surgically in Ukraine. Control no 4 was excluded because it was later determined that he was not clinically healthy. All melanoma patients were recruited at the North Estonia Medical Centre (PERH) during standard surgical excision and sentinel node biopsy. Healthy controls were recruited from volunteer cohorts. Informed consent was obtained from all participants in accordance with the Tallinn Medical Research Ethics Committee (license no. 1193). Clinical trial number: not applicable. All procedures complied with the Declaration of Helsinki and institutional data protection guidelines. Sample collection Urine, saliva, and blood samples were collected from patients and controls. Saliva and blood were collected in the morning under fasting conditions. Patient blood samples were collected at PERH; control samples were collected at Synlab (Tallinn, Estonia). All samples were transported to Tallinn University of Technology at 4 °C and processed immediately upon arrival. Saliva, urine, and blood handling procedures followed standardized institutional protocols to minimize pre-analytical variation. Lymph node sample preparation and staining . Fresh lymph nodes were buffered in phosphate-buffered saline (PBS) and transported on ice to the Immunology Laboratory within 2–3 h of surgical removal. Nodes were mechanically dissociated through a sterile filter, washed twice with PBS, and kept on ice. Cells were stained with the following antibodies:CD4-FITC (BioLegend, USA); CD3-PE (BD Biosciences, USA); CD8-PE (BioLegend, USA); CD56-PE (BD Biosciences, USA); CD3-FITC (BD Biosciences, USA); CD45-FITC (BioLegend, USA); Siglec-8-PE (BioLegend, USA); anti human P2X4-FITC (TUT Laboratory of Immunology). Samples were analysed on a flow cytometer for immune subset characterization. Appropriate isotype controls and fluorescence compensation were used. Data acquisition parameters (voltages, gating strategy, and event counts) followed standard laboratory practices. HEK293 cell culture and transfection . HEK293 cells (ATCC) were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Invitrogen) supplemented with 10% fetal bovine serum (Sigma-Aldrich) and penicillin/streptomycin. Mouse P2X4-mCherry plasmids (EX-Mm24590-M56; GeneCopoeia) and an additional mouse P2X4 expression construct (TUT) were used. Cells were transfected using polyethyleneimine (PEI; 10 µg DNA and 20 µl PEI at 1 mg/mL) and incubated for 24 h. Transfection efficiency was monitored by mCherry fluorescence, and only cultures with adequate reporter expression were used for downstream assays. Immunoprecipitation . HEK293 cells were lysed using non-denaturing lysis buffer (ProteoJET™ Mammalian Cell Lysis Buffer, Fermentas). SureBeads™ Protein G Magnetic Beads (Bio-Rad) were incubated with monoclonal anti-human P2X4 (clone mAb27, IgG2b/κ) for 30 min at room temperature. Antibody-coated beads were incubated overnight at 4 °C with 100 µl melanoma or healthy control plasma, HEK293 supernatant or cell lysate. After washing with non-denaturing lysis buffer, immune complexes were eluted by boiling in Laemmli buffer with 2-mercaptoethanol. All steps were performed using low-adhesion tubes to minimize protein loss during washing and transfer. ATP-dependent secretion assay. Transfected or non-transfected HEK293 cells were washed and incubated in serum-free DMEM for 12 h. ATP (50 µM final) was added to each 10 cm dish, and supernatants were collected after 30 min and 4 h. Supernatants were cleared by brief centrifugation prior to filtration to remove cell debris, ensuring consistent protein recovery. Samples were filtered using Amicon Ultra-15 concentrators (Merck Millipore). Western blot . Samples (immunoprecipitates, lysates, culture medium, or human biofluids) were separated on 12% SDS–polyacrylamide gels and transferred to nitrocellulose membranes using the Trans-Blot Turbo system (Bio-Rad). Membranes were blocked with PBS containing 5% non-fat milk powder. Primary antibody for incubation was rabbit anti-P2X4 (Alomone Labs; 1:300 in PBS + 5% fat milk powder, overnight, 4 °C). Secondary antibody: HRP-conjugated pig anti-rabbit IgG (DAKO; 1:2000) in PBS. Signals were developed using SuperSignal West Dura and SuperSignal West Atto substrates (Thermo Scientific). Exposure times were optimized to maintain signals within the linear detection range. Membranes were counterstained with Coomassie Brilliant Blue R-250. NMR sample preparation : Whole-mouth saliva samples were centrifuged at 3000 RCF for 5 min at 4 °C, blood samples at 1300 RCF for 10 minutes, resulting supernatant and serum were frozen, later thawed on ice and filtered through 3 kDa Amicon filters (previously washed with warm water 5 times, 4 of which 10 min centrifuge, last time 15 min) centrifuged at 12,000 RCF for 35 min at 4 °C. 540 µl of filtered saliva sample was mixed with 60 µl of 1.5 M K-phosphate buffer in Chenomx Internal Standard IS-2 (Chenomx Inc., Edmonton, Alberta, Canada) containing 50 mM imidazole. 350 µl filtered serum or plasma sample was mixed with 280 µl 100 mM Na-phosphate buffer containing 50 mM imidazole and 70 ul IS-2. Final ratio for all samples: 90% H₂O / 10% D₂O. All samples were vortexed for 2 min and centrifuged at 14,000 RCF for 2 min at 4 °C. NMR acquisition and spectral processing : ¹H NMR spectra were acquired on a Bruker Avance III 700 MHz spectrometer using the noesypr1d pulse sequence (mixing time 0.1 s; 12 ppm spectral width; at least 256 scans).Free induction decay files were processed using Chenomx NMR Suite v10. Automatic phase correction was followed by manual adjustment. Shim correction and 0.5 Hz line broadening were applied, pH was calibrated by imidazole signal at 8 ppm. Metabolite quantification . Metabolites were quantified using Chenomx NMR Suite. The first spectrum was profiled manually. Profile from the first spectrum was imported to subsequent spectra and adjusted manually. DSS-d6 served as the internal standard. Confocal microscopy. Kidney biopsy samples were cryo-embedded, snap-frozen, and stored at −25 °C. Sections (5 µm) were stained with anti-human P2X4 mAb27-FITC (1:800), Hoechst 33342, and control antibodies. Imaging was performed with Zeiss Axioskop 2 or LSM 780 confocal microscopes (63× objective). Images were analysed in ImageJ. Microscope laser intensity, detector gain, and pinhole settings were kept constant across samples to ensure comparable signal acquisition . Statistical analysis. Flow cytometry data were exported and analysed graphically and statistically using Microsoft Excel. Normality was assessed using the Shapiro–Wilk test. Comparisons were analysed using unpaired t -tests (normally distributed data) or Mann–Whitney U tests (non-normal data). Statistical differences were defined as significant if p < 0.05 and very significant if p < 0.01. MetaboAnalyst v6.0 (Xia Lab @ McGill university, Montreal, QC, Canada; [12] was used to perform Principal Component Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) with metabolite data. For serum metabolite concentrations the normalization procedure involved data scaling as auto scaling (mean-centred and divided by the standard deviation of each variable) for saliva metabolite concentrations Probabilistic Quotient Normalization (PQN) was also applied. Results 1. Immune cell alterations in lymph node and blood To characterize immune alterations associated with melanoma metastasis, lymph nodes from eight patients with histologically confirmed micrometastatic melanoma were analysed by flow cytometry. Leukocyte size, granularity, and lineage markers were used to assess immune subset composition in metastatic versus non-metastatic lymph nodes. Lymph nodes. Metastatic (sentinel) lymph nodes (LNmeta) consistently yielded approximately twice as many total cells as non-metastatic nodes (LN), based on visual assessment of cell pellets and subsequent cell counting (observational finding not shown in Fig. 2). This likely reflects inflammatory infiltration or tumour-associated immune remodelling. Non-metastatic LN contained significantly higher frequencies of CD4⁺ T-helper (Th) cells compared to LNmeta (Fig. 2a). P2X4⁺ eosinophils were significantly more abundant in LNmeta compared with LN counterparts (Fig. 2g), indicating eosinophil activation or receptor upregulation within the metastatic microenvironment. In LNmeta, we also observed regulatory T cells (Treg) enrichment: 7.27 ± 2.80% Treg cells, whereas paired control LN from the same patients harbored 3.33 ± 0.97% (Fig. 2i), reflecting an approximately 2.2-fold increase (p = 0.031). These findings are consistent with previous reports of preferential accumulation of regulatory T cells in tumour-involved lymph nodes in melanoma. Peripheral blood. Analysis of blood samples from melanoma patients (cohorts 1 and 2) revealed additional immune alterations (Fig. 2 b, d, f, h, j): CD4⁺ T-helper cells were consistently reduced in melanoma patients relative to healthy controls. CD8⁺ cytotoxic T cells exhibited patient-to-patient variability with an overall trend toward reduction. Natural killer (NK) cells were highly variable, ranging from severely reduced (~2%) to markedly elevated (16–18%). Eosinophils were elevated in the blood of melanoma patients (Fig. 2h). Flow-cytometric analysis of regulatory T (Treg; labelled as Tr in the figure) cells revealed an increased frequency in the peripheral blood of melanoma patients compared with healthy controls. In blood, Tregs represented 5.49 ± 2.42% of CD4⁺ T cells in melanoma patients (n = 8) and 3.01 ± 1.13% in controls (n = 8), corresponding to an approximately 1.8-fold increase (Welch’s t-test, p = 0.026). 2. P2X4 protein expression in urine and plasma To investigate and quantify P2X4 protein and gene expression in human biofluids, Western blotting and RT-qPCR analyses were performed. Western blot analyses detected both full-length (~50–55 kDa) and truncated (~25 kDa) forms of the P2X4 receptor in urine and immunoprecipitated human plasma samples. The predicted molecular weight of the P2X4 subunit is ~43 kDa (UniProt Q99571), but experimentally observed bands appeared heavier (up to ~60 kDa), which is consistent with post-translational modifications such as glycosylation. Multiple bands of differing sizes were detected (Fig. 3), likely reflecting glycosylation, degradation, or oligomerization of the receptor [13], although, technical artifacts cannot be entirely excluded [14]. In urine samples from melanoma patients M4 and M5, strong P2X4 signals were detected at the expected molecular weights, accompanied by additional higher-molecular-weight forms consistent with N-glycosylated variants. In contrast, urine from patient N1 (neuroinflammatory disease) showed only a weak, modified P2X4 band (Fig. 3a). The strong P2X4 signals in M4 and M5, absent in all healthy controls, suggest possible pathological relevance—potentially reflecting epithelial damage in the kidney or bladder caused by metastatic disease. These findings support the potential utility of urinary P2X4 as a disease-associated marker. The presence of P2X4 in melanoma patient plasma was further validated by immunoprecipitation from 100 μl of plasma (Fig. 3b). Clear protein signals were detected in melanoma patient samples M4 and M5, whereas no signal was observed in healthy controls, consistent with RT-qPCR results (Fig. S1). Sample M3 was excluded due to insufficient plasma volume. These results indicate that melanoma patients secrete both truncated and post-translationally modified forms of P2X4 into plasma and urine—an effect not observed in control samples. Together, these data suggest that circulating P2X4 fragments may serve as a promising non-invasive biomarker. 3. ATP stimulation induces the release of P2X4 into the culture medium of transfected HEK293 cells To examine whether purinergic stimulation can promote the extracellular appearance of P2X4, HEK293 cells transiently expressing murine P2X4 were exposed to ATP and analysed for P2X4-immunoreactive species in conditioned media. In the absence of ATP stimulation, no P2X4 signal was detected in the culture medium of transfected cells, despite robust intracellular expression confirmed in cell lysates (Fig. 4). Following ATP treatment, P2X4-immunoreactive species became detectable in the conditioned medium of mP2X4-transfected cells at both 30 min and 4 h. In contrast, no P2X4 signal was observed in conditioned media from ATP-treated untransfected HEK293 cells, arguing against nonspecific ATP-induced cell lysis as the source of extracellular P2X4. These findings demonstrate that ATP stimulation is sufficient to induce the extracellular appearance of P2X4-immunoreactive species in P2X4-expressing cells. Although the molecular form of the released species cannot be resolved from this experiment alone, the ATP-dependent effect provides mechanistic suppot for the presence of non-cell-associated P2X4 detected in plasma and urine samples from melanoma patients. RT-qPCR analysis further showed that P2X4 mRNA was upregulated in leukocytes from some melanoma patients compared with healthy controls (Fig. S1), indicating both transcriptional and post-translational alterations of P2X4 in melanoma and supporting the detectability of circulating receptor fragments in biofluids. 4. Metabolomic profiling To investigate systemic metabolic alterations in melanoma, we performed NMR-based metabolomic profiling of serum from 4 melanoma patients and 4 healthy controls, plasma from one melanoma patient and one control subject, and saliva from all of the same 10 participants. A total of 57 metabolites were quantified in serum, 55 in plasma and 60 in saliva samples. The missing values (not quantified) percentage in serum and plasma combined was much lower compared to saliva, 3.21% versus 17.8%. In serum, melanoma patients exhibited reduced concentrations of pyroglutamate, glycine, acetone, 3-methyl-2-oxovalerate, 2-hydroxybutyrate, 2-aminobutyrate, and increased concentrations of glucose, arabinose, mannose, branched-chain amino acids (isoleucine, valine and leucine), aromatic amino acids (phenylalanine and tyrosine), urea, pyruvate, and taurine, see the complete list in Table S1. Glycerol was quantified in all samples; however, it was excluded from downstream analyses because it likely originated from the filtration process. 3-hydroxybutyrate and acetoacetate were elevated manyfold in one control subject (C3), creatinine and dimethyl sulfone were also markedly higher in that sample compared to all others, making it difficult to assess the significance of those metabolites in further analysis due to the small remaining sample size (3 controls) after removing the outlier. Fumarate was detected only in melanoma patients, serum samples of M1 and M5 and plasma sample from M4. However, Principal Component Analysis (PCA) did not show clear separation between melanoma and control samples. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) scores plot suggested distinct grouping (see Fig. S2 and S3 for VIP scores plot). Even though clear univariate differences (like for glucose) were present, the data still have weak multivariate structure, leading to negative Q² (Fig. S4). The biologically meaningful differences that the data contains are not strong or consistent enough across metabolites to support a reliable OPLS-DA predictive model with our current sample size. These findings should be interpreted as hypothesis-generating. Metabolic alterations were also evident in saliva, the samples from melanoma patients were generally more concentrated compared to the controls (see the complete list of metabolites quantified in Table S2). After normalization, several metabolites, including cadaverine, phenylalanine, leucine, succinate, putrescine, lysine, and valine were still elevated consistently in melanoma samples. Overall, ethanol, isoleucine, proline, glycine, alanine, trimethylamine, sarcosine, 5-aminopentanoate, choline, and acetate also displayed higher concentrations in melanoma. There was less 4-hydroxyphenylacetate in all melanoma samples than controls, but it was not detected in one control subject (C2). Dimethyl sulfone, methanol, xanthine, malonate, and 2-hydroxyisovalerate were also generally less abundant in melanoma samples. As in the serum analysis, the saliva PCA did not show clear separation between melanoma and control samples and OPLS-DA segregation between groups on the scores plot was very clear, but the model had a negative Q² (Fig. S5–S7). Aspartate was detected only in melanoma samples, it was not quantified in M2 due to high noise level in the spectrum, however the signals do appear to be there at 2.8 ppm at a comparable level to those in other melanoma samples. Threonine and tryptophan were detected only in M3 and M4. Discussion Immune dysregulation. In agreement with the review by Tucci et al. (2019), our analysis of peripheral blood immune subsets in melanoma patients revealed a pronounced imbalance between effector and regulatory populations. Several patients displayed markedly reduced percentages of CD8⁺ cytotoxic T cells compared with healthy controls, consistent with the impaired cytotoxicity described as a hallmark of melanoma immune evasion. Natural killer (NK) cell frequencies displayed considerable heterogeneity, with some patients showing elevated levels and others demonstrating marked reductions. This pattern aligns with previous reports highlighting the susceptibility of the NK–dendritic cell axis to tumour-derived suppressive mediators, including prostaglandin E₂ and adenosine. Eosinophils—less frequently discussed in the context of melanoma—also exhibited dysregulation in our cohort, suggesting that additional immune compartments may participate in melanoma-associated immune remodelling. Our flow cytometric data showed that Treg cells were increased in both peripheral blood and tumor-draining lymph nodes of melanoma patients. In peripheral blood, the frequency of Treg cells was elevated from a mean of 3.01% in healthy controls to 5.49% in melanoma patients (≈1.8-fold increase), consistent with earlier reports of expanded circulating CD4⁺CD25^high/FOXP3⁺ Tregs in melanoma and other solid tumours [15;16]. In lymphoid tissue, we observed an approximately 2.2-fold enrichment of CD4⁺CD25⁺ cells in metastatic sentinel lymph nodes compared with anatomically distant, tumour-free lymph nodes from the same patients. This magnitude of enrichment closely mirrors the ~2-fold increase in CD4⁺CD25^high Tregs reported by Viguier et al. in metastatic melanoma lymph nodes relative to tumour-free nodes and autologous peripheral blood [17]. Patients M1, M3-5 in cohort 1 had melanoma stage IV (metastasis in LN, tissues and organs) while M2 had stage III (metastasis in LN). Together with prior studies showing that FOXP3⁺ Treg density in metastatic sentinel nodes is associated with poor clinical outcome [18], our data support the concept that Treg accumulation in tumour-draining lymph nodes is a conserved feature of melanoma-associated immune suppression. RT-qPCR analysis showed that P2X4 mRNA was upregulated in leukocytes from two analysed melanoma patients compared with healthy controls (Fig. S1), raising the possibility that this receptor influences systemic immune composition and may represent a previously underappreciated immunoregulatory checkpoint in melanoma. Relative P2X4 mRNA expression levels in PBMCs were 1.40-fold for patient M3 and 5.42-fold for patient M5, compared with a mean of 0.23 in healthy controls. This upregulation is consistent with reports in other pathological conditions, including cancer [19; 20; 21]. Because P2X4 expression can be influenced by a variety of factors—including comorbidities, smoking, alcohol consumption, and physical activity [19; 21; 22; 23], the observed differences cannot be attributed solely to melanoma. Given the small sample size of our cohort, no definitive conclusions can be drawn. Nevertheless, these findings highlight P2X4 as a candidate worth further mechanistic and translational investigation. P2X4 receptor signalling and secretion. P2X4 has previously been implicated in inflammation, immune regulation, and angiogenesis. Our findings extend this understanding by demonstrating that truncated and modified P2X4 fragments are detectable in the plasma and urine of melanoma patients but absent in healthy controls. This observation suggests that melanoma-associated processes may promote receptor shedding, proteolytic cleavage, or dysregulated turnover, resulting in the generation of soluble P2X4 fragments. Western blot analysis confirmed the presence of distinct P2X4 proteins in the urine of several patients. The absence of urinary P2X4 in other melanoma patients could reflect milder disease, differential tumour burden, comorbid conditions, or individual biological variability. Nonetheless, because none of the control samples exhibited detectable P2X4, the presence of urinary P2X4 appears to represent a disease-associated feature. Thus, P2X4 excretion may serve as a potential marker for identifying pathological conditions, including melanoma. The functional implications of soluble P2X4 are significant. Soluble receptor fragments could act as decoy receptors for extracellular ATP, thereby modulating purinergic signalling within the tumour microenvironment. Alternatively, their presence in biofluids may reflect increased receptor turnover or heightened purinergic activity associated with tumour progression. Notably, in this study, Western blot detection was performed on undiluted urine samples without prior concentration or filtration, yet clear P2X4 signals were still observed in some cases—indicating biologically meaningful levels in certain patients. Although Fig. 4 does not formally define the molecular nature of the extracellular P2X4 species, the ATP-dependent release observed in vitro parallels the detection of non-cell-associated P2X4 immunoreactivity in melanoma plasma samples. Together, these findings support the existence of a soluble P2X4 pool that may arise through regulated release and/or proteolytic processing under inflammatory conditions. The detection of P2X4 in urine offers an appealing non-invasive biomarker avenue for melanoma detection or monitoring. Future studies should evaluate whether urinary P2X4 correlates with disease stage, tumour burden, renal involvement, or therapy-related effects, and whether its presence carries prognostic or predictive value. P2X4 is known to be expressed in secretory epithelial tissues, including the kidneys and prostate [24]. Under normal conditions, membrane-associated P2X4 contributes to ATP-mediated ion transport, inflammatory regulation, and epithelial homeostasis [25]. Its detection in extracellular fluids such as plasma and urine may therefore indicate epithelial damage, inflammation, or active release via extracellular vesicles. P2X4 localization within the cytoplasm of renal tubular epithelial cells in biopsy samples from patients with glomerulosclerosis and membranous nephropathy (Fig. S8) suggests a potential role for this receptor in renal inflammation and pathology. Notably, strong nuclear localization of P2X4 was observed in one patient with glomerulosclerosis—a pattern that may reflect pathological shifts associated with apoptosis, stress responses, or cytokine regulation [26]. Through epithelial injury in the kidney, P2X4 may be released into the urinary space. The presence of P2X4 fragments in circulation highlights their potential as non-invasive biomarkers of disease activity or tissue injury. Furthermore, P2X4 mRNA upregulation in leukocytes from some melanoma patients supports the idea that this receptor is subject to active transcriptional regulation in the context of tumour-associated inflammation. Together, these molecular findings are consistent with—and expand—the growing body of evidence linking purinergic signalling to tumour immunology. Full uncropped gels and blots images of western blots are shown in Fig S9. Systemic metabolic reprogramming. Metabolomic profiling of srum and saliva revealed broad tumour-associated metabolic alterations, although no changes were statistically significant, which could be expected for such a small set of subjects. Elevated levels of branched-chain amino acids (BCAA) in both serum and saliva indicate increased proteolysis, often noted in cancer patients, mirroring systemic metabolic state such as insulin resistance and increased protein catabolism. High BCAA levels can enhance Treg persistence via mTOR shifts [27] which corresponds to elevated Tregs. In saliva, aspartate was detected only in melanoma samples, however, the trend was not reflected in serum and plasma samples, where aspartate was universally present at similar rates (µM average ± SD [range] for melanoma 16.6 ± 7.3 [9.6 – 25.8] vs controls 18.0 ± 10.0 [7.2 – 28.8]), suggesting either increased local release – higher epithelial turnover, subtle mucosal inflammation – or altered local microbial metabolism that produces/accumulates aspartate. Since serum is tightly regulated, subtle aspartate changes may be buffered by liver metabolism, kidney clearance, systemic amino acid homeostasis. On the contrary, saliva is more sensitive to local release and is more easily perturbed by low-abundance metabolites. Therefore, saliva could be more sensitive early detector of tumour-related amino acid changes. Aspartate has been detected in the saliva of younger healthy subjects (age 24–42), but at lower concentrations on average 33.30 ± 19.39 uM [28] than the melanoma patients in this study exemplified 42.31± 40.16 [17.22 – 102.00]. To our knowledge, this is the first documented case linking salivary aspartate to a disease. Lower levels of 4-hydroxyphenylacetate, a microbial degradation product of tyrosine, and higher concentrations of ethanol, acetate, succinate, polyamines and basic amino acids (cadaverine, putrescine, lysine, 5-aminopentanoate) in saliva samples of cancer patients can also be associated with microbiome imbalance, possibly in response to systemic inflammation. These changes could reflect increased oral proteolysis or microbial shifts toward decarboxylating taxa. They are also compatible with tumour-driven systemic amino acid release that reaches saliva. Serum levels of 2-hydroxybutyrate, 2-aminobutyrate consistently trend downward in melanoma patients. These metabolic patterns alongside glutamate/glutamine shifts indicate redox stress, one of the earliest metabolic symptoms of cancer. Together with elevated glucose this points towards a tumour-associated insulin resistance-like metabolic phenotype, and this pattern is consistent with early-stage or non-cachectic cancer metabolism. The changes in aromatic amino acid levels, phenylalanine and tyrosine in melanoma samples are linked with inflammation and immune modulation, respectively. Furthermore, fumarate, a known oncometabolite was found in three melanoma patients and none of the control subjects. Put together, this is a remarkably coherent pattern for such a small dataset, while statistical power is low, the biological signals align strongly with known cancer metabolomic signatures. Summary Integration of immune, molecular, and metabolic alterations Together, our findings reveal three converging layers of systemic dysregulation in melanoma: Immune dysregulation: Reduced Th cells and elevated Tregs support the immune escape model described by Tucci et al. NK cell variability and eosinophil heterogeneity further underscore tumour-driven remodelling of circulating leukocytes. P2X4 signalling: Detection of truncated P2X4 fragments in plasma and urine represents a novel observation suggesting receptor shedding or proteolytic processing. Upregulation of P2X4 mRNA in leukocytes supports an active role for purinergic signalling at the tumour–immune interface. Metabolic reprogramming: NMR metabolomics revealed systemic metabolic shifts, particularly in amino acid pathways, reflecting tumour metabolic demands and organism-wide stress responses. This integrative view highlights the multifaceted systemic footprint of melanoma. Notably, the identification of soluble P2X4 receptor fragments together with serum/saliva metabolic fingerprints points toward promising non-invasive biomarker strategies that complement traditional immune profiling Limitation of the study The small sample size limits definitive conclusions. Larger, longitudinal studies are needed to validate the clinical utility of soluble P2X4 fragments and metabolic signatures as biomarkers of disease activity, progression, or treatment response. Future perspectives Future work should elucidate the molecular mechanisms governing P2X4 receptor processing and secretion in melanoma and explore how purinergic signalling interacts with tumour metabolism and immune suppression. Ultimately, integrating immune, purinergic, and metabolic profiling may yield a comprehensive framework for personalized disease monitoring and biomarker-guided therapeutic strategies in melanoma Conclusions This study provides an integrated view of systemic alterations in melanoma by combining immune profiling, purinergic receptor analysis, and metabolomic characterization. We show that melanoma is associated with marked immune dysregulation, including reduced cytotoxic T-cell frequencies, elevated regulatory T cells, and heterogeneous NK and eosinophil responses. In parallel, we identify truncated and modified P2X4 receptor fragments in the plasma and urine of melanoma patients—an observation absent in healthy controls—together with elevated P2X4 mRNA expression in leukocytes. These findings highlight P2X4 as a potential contributor to tumour–immune interactions and as a promising non-invasive biomarker candidate . Metabolomic profiling of serum and saliva further revealed robust metabolic reprogramming associated with cancer. The analysis of both biofluids underscores the potential of metabolic signatures for disease detection and monitoring. Together, these immune, molecular, and metabolic alterations paint a cohesive picture of the systemic footprint of melanoma. Importantly, the detection of soluble P2X4 fragments and salivary/serum metabolite changes points to new opportunities for non-invasive biomarker development . While larger cohorts are needed to validate these findings, this work establishes a foundation for future studies integrating purinergic signalling, immune modulation, and metabolic profiling to support personalized monitoring and therapeutic strategies in melanoma Declarations Funding This work was supported by Personal research grants (PRG)1832 (Principal Investigator: Dr Ago Samoson, Tallinn University of Technology). Additional support for S.R.B. and A.R were provided by COST Action CA21130 (European Cooperation in Science and Technology). Acknowledgments All authors would like to thank the staff of the North Estonia Medical Centre, especially Svetlana Bogoljubova , who collected patient samples, and Birgit Truumees , who performed the immunofluorescence staining and imaging of kidney epithelial cells. Competing Interests The authors declare that they have no financial or non-financial interests that are directly or indirectly related to the work submitted for publication. Data Availability Statement The data presented in this study are available on request from the corresponding author. Informed Consent Statement All samples were collected after written informed consent, in accordance with the Declaration of Helsinki (1975) and following approval from the relevant ethics committees. Author contribution. RMT and SRB conceived and designed the study. SRB, LK, AK, IK, CK and LT performed the experiments. RMT, SRB, and LK wrote the manuscript. JT contributed to data curation, supervision, and manuscript review and editing. IK, CK, AK, LT, and AR performed primary data analysis and contributed to figure preparation. AS and SRB supervised the study and acquired funding. References Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017 Feb 28;387:61-68. doi: 10.1016/j.canlet.2016.01.043. Epub 2016 Feb 1. PMID: 26845449. Alitalo A, Detmar M. Interaction of tumor cells and lymphatic vessels in cancer progression. Oncogene. 2012 Oct 18;31(42):4499-508. doi: 10.1038/onc.2011.602. Epub 2011 Dec 19. PMID: 22179834. Paalme, V., Rump, A., Mädo, K., Teras, M., Truumees, B., Aitai, H., Ratas, K., Bourge, M., Chiang, C.-S., Ghalali, A., Tordjmann, T., Teras, J., Boudinot, P., Kanellopoulos, J. M., & Rüütel Boudinot, S. (2019). Human Peripheral Blood Eosinophils Express High Levels of the Purinergic Receptor P2X4. 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Supplementary Files 251224S1S9.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviews received at journal 08 Feb, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers invited by journal 02 Jan, 2026 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 18 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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P2X4-expressing immune cell subsets were analysed by flow cytometry.\\u003cbr\\u003e\\n \\u003cstrong\\u003e(a, c, e, g, i)\\u003c/strong\\u003e Immune cell subset frequencies in non-metastatic lymph nodes (LN) versus metastatic lymph nodes (LNmeta) from melanoma patients (n = 8). Y-axes indicate the percentage of each lineage among total viable lymph node cells. Panels show (a) CD4⁺ T helper (Th) cells, (c) CD8⁺ cytotoxic T (Tc) cells, (e) NK cells, (g) eosinophils, and (i) regulatory T (Treg) cells.\\u003cbr\\u003e\\n \\u003cstrong\\u003e(b, d, f, h, j)\\u003c/strong\\u003e Corresponding immune cell subset frequencies in peripheral blood from melanoma patients (Mel) and healthy controls (Ctr) (n = 8). Y-axes indicate the percentage of each lineage among total leukocytes following erythrocyte lysis. Panels show (b) Th cells, (d) Tc cells, (f) NK cells, (h) eosinophils, and (j) Treg cells.\\u003cbr\\u003e\\nP2X4⁺ eosinophils were significantly increased in LNmeta and in the peripheral blood of melanoma patients compared with controls. Box plots show the median (horizontal line), mean (×), interquartile range (box), and range (whiskers); individual points represent values from individual donors. Statistical significance is indicated as \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05 (*) and \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01 (**).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8396511/v1/3a8bea535d05c99deee3781d.jpeg\"},{\"id\":99506582,\"identity\":\"9642649e-b0f3-418d-8659-28c60be7e6d7\",\"added_by\":\"auto\",\"created_at\":\"2026-01-05 08:42:28\",\"extension\":\"jpeg\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":66290,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDetection of soluble P2X4 species in urine and plasma samples\\u003c/strong\\u003e\\u003cbr\\u003e\\n \\u003cstrong\\u003e(a)\\u003c/strong\\u003e Representative Western blot analysis of urine samples (10 μl per lane) probed with a P2X4-specific antibody. Lanes from left to right correspond to urine from five melanoma patients (M1–M5), one patient with a neuroinflammatory disease (N1), a molecular weight marker, and three healthy control samples (C1–C3).\\u003cbr\\u003e\\n \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Immunoprecipitation of P2X4 from plasma samples (100 μl input) followed by Western blot detection. Lanes from left to right show plasma from melanoma patients (M1, M2, M4, M5), two healthy controls (C1, C2), a prestained protein ladder (Thermo Scientific), and three additional healthy control samples (C3, C7, C8). Sample M3 was excluded due to insufficient plasma volume. Blots were performed under reducing conditions. Both full-length and lower-molecular-weight P2X4-immunoreactive species were detected.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8396511/v1/a1ef8ecbefc3bdc660f4036a.jpeg\"},{\"id\":99790614,\"identity\":\"9768bc71-71cf-4342-baa3-7d0a841b13ac\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 12:58:25\",\"extension\":\"jpeg\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":29002,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eATP stimulation induces extracellular appearance of P2X4-immunoreactive species in mP2X4-transfected HEK293 cells\\u003c/strong\\u003e\\u003cbr\\u003e\\nRepresentative Western blot analysis of P2X4-immunoreactive species detected in cell lysates and conditioned media from HEK293 cells transfected with murine P2X4 (mP2X4). Cells were incubated in serum-free medium and stimulated with \\u0026nbsp;final concentration of 50 mM ATP for 30 min or 4 h, after which conditioned media were collected and concentrated for analysis. Lanes 1 (30 min) and 6 (4 h) show conditioned media from untransfected HEK293 cells treated with ATP (negative controls). Lanes 2 (30 min) and 7 (4 h) show conditioned media from mP2X4-transfected cells without ATP stimulation. Lanes 3 (30 min) and 8 (4 h) show conditioned media from mP2X4-transfected cells following ATP stimulation. Lanes 4 and 5 contain lysates from mP2X4-transfected cells and serve as positive controls for intracellular P2X4 expression. Samples were analysed under reducing conditions. ATP stimulation resulted in the appearance of P2X4-immunoreactive species in the conditioned medium of transfected cells, whereas no signal was detected in media from untransfected controls.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8396511/v1/2983aec1e158146bbf2ace09.jpeg\"},{\"id\":99803324,\"identity\":\"2c8ce233-a8e8-483a-bdf9-20e71a1bb647\",\"added_by\":\"auto\",\"created_at\":\"2026-01-08 14:10:05\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1507080,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8396511/v1/60170574-2c8d-4edb-a155-72bc62a98ec6.pdf\"},{\"id\":99506586,\"identity\":\"a8d40a1c-6416-4ebc-9a5f-9bb49c69feb9\",\"added_by\":\"auto\",\"created_at\":\"2026-01-05 08:42:28\",\"extension\":\"docx\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2540718,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"251224S1S9.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8396511/v1/819db997bde22e363e257b68.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Systemic purinergic dysregulation in melanoma revealed by soluble P2X4 receptor fragments\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eMelanoma is an aggressive malignancy characterized by rapid metastatic spread, profound resistance to immune surveillance, and frequent therapeutic failure. Although immune checkpoint inhibitors have transformed clinical outcomes, reliable biomarkers for early detection, prognosis, and treatment monitoring remain limited. Increasing evidence shows that melanoma progression is shaped by complex interactions\\u0026nbsp;among\\u0026nbsp;tumour-driven metabolic remodelling, the immune system, and the tumour microenvironment (TME).\\u003c/p\\u003e\\n\\u003cp\\u003eThe TME critically influences melanoma growth and immune evasion by shaping leukocyte recruitment, angiogenesis, and lymphatic dissemination [1].\\u0026nbsp;Because melanoma commonly metastasizes through lymphatic pathways [2], the immunological state of sentinel lymph nodes (SLNs) provides essential insight into disease progression. Eosinophils\\u0026mdash;particularly those with inflammatory phenotypes\\u0026mdash;are enriched in metastatic SLNs and exhibit increased P2X4 receptor expression under hypoxic conditions [3].\\u0026nbsp;These cells actively contribute to antitumor immunity by recruiting cytotoxic T cells and modulating vascular and macrophage function [4; 5],\\u0026nbsp;suggesting that their altered abundance and receptor profile may have prognostic value.\\u003c/p\\u003e\\n\\u003cp\\u003eMelanoma employs multiple immune-escape mechanisms, including suppressed cytotoxic T-cell activity, expanded regulatory T-cell populations, and impaired NK and dendritic cell interactions\\u0026nbsp;[6].\\u0026nbsp;These immunological changes coincide with systemic metabolic alterations detectable in blood and saliva, reflecting the tumour\\u0026rsquo;s reprogramming of host energy pathways to support survival and proliferation.\\u003c/p\\u003e\\n\\u003cp\\u003eAmong key molecular pathways connecting immunity and metabolism, purinergic signalling has emerged a critical regulator of inflammation, immune cell activation, angiogenesis, and autophagy. The P2X4 receptor, an ATP-gated ion channel, is stored in lysosomes and contributes to Ca\\u0026sup2;⁺ transport and autophagic flux\\u0026nbsp;[7; 8]. In colorectal cancer, P2X4 activity promotes M1-like TAM polarization via cGAS\\u0026ndash;STING signalling and correlates with favourable immune infiltration and prognosis\\u0026nbsp;[9].\\u0026nbsp;Across diverse human cancers, P2X4 overexpression is consistently associated with poor clinical outcomes [10]. Although P2X4 is primarily membrane-localized, low concentrations of extracellular receptor\\u0026mdash;including truncated forms\\u0026mdash;have been detected and may arise from vesicular release or epithelial injury\\u0026nbsp;[11].\\u003c/p\\u003e\\n\\u003cp\\u003eBased on this evidence, we hypothesized that immune dysregulation, altered P2X4 signalling, and systemic metabolic reprogramming converge in melanoma and that these changes can be captured through minimally invasive sampling. To test this hypothesis, we integrated three complementary datasets: (1) flow-cytometric profiling of immune cell subsets and P2X4 expression in peripheral leukocytes; (2) molecular detection of P2X4 in plasma, leukocytes, and urine using Western blotting and RT-qPCR; and (3) NMR-based metabolomic analysis of serum and saliva. This multi-cohort framework enables a comprehensive assessment of immune, purinergic, and metabolic alterations in melanoma and provides insight into biomarker candidates that may complement existing diagnostic approaches (Fig 1).\\u003c/p\\u003e\\n\\u003cp\\u003eBiological samples obtained from melanoma patients, including lymph node tissue, saliva, plasma, and urine, were analysed to assess cellular and extracellular components of P2X4-mediated purinergic signalling. Leukocyte-associated P2X4 expression was evaluated by flow cytometry using P2X4-specific antibodies. Soluble P2X4 species present in patient-derived fluids were isolated by immunoprecipitation saliva and blood were subjected to downstream metabolite analysis. Together, these approaches demonstrate the presence of both cell-associated and extracellular P2X4 across multiple biological compartments relevant to purinergic signalling in melanoma.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eStudy cohorts:\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003eT\\u003c/strong\\u003ehree partially overlapping patient cohorts were analysed.\\u003cstrong\\u003eCohort 1:\\u003c/strong\\u003e Five melanoma patients and five healthy controls were included for detailed immune profiling (Tc, Th, B, NK, eosinophils, Tregs, and P2X4 expression). \\u003cstrong\\u003eCohort 2:\\u003c/strong\\u003e Eight melanoma patients and eight controls (C1-3; C5-9) were analysed for a broader range of immune subsets and P2X4 expression. A subset of these patients was further examined for P2X4 protein (Western blot, immunoprecipitation from plasma and leukocytes) and mRNA expression (RT-qPCR in leukocytes). Urine samples were analysed for P2X4 fragments. \\u003cstrong\\u003eCohort 3:\\u003c/strong\\u003e Five melanoma patients and five healthy controls were included for NMR-based metabolomic profiling of serum (or plasma for M4 and C4) and saliva, metabolite quantification, and statistical analyses. A summary of metadata about the three cohorts is shown in Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003eTable 1. Characteristics of study participants, three partially overlapping patient cohorts with healthy controls\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCohort 1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCohort 2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eCohort 3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eImm cells\\u003c/p\\u003e\\n \\u003cp\\u003eMetastatic LN/ ctr LN\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eImm cells\\u003c/p\\u003e\\n \\u003cp\\u003eMel patient\\u003c/p\\u003e\\n \\u003cp\\u003e/healthy ctr\\u003c/p\\u003e\\n \\u003cp\\u003eblood\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMetabolome\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003eMel patient\\u003c/p\\u003e\\n \\u003cp\\u003e/ ctr blood, saliva, urine\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eSex (F/M)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eYear of birth\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMelanoma primary size\\u003c/p\\u003e\\n \\u003cp\\u003e(mm) /\\u003c/p\\u003e\\n \\u003cp\\u003eStage\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eMelanoma\\u003c/p\\u003e\\n \\u003cp\\u003ePatients (M)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1961\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1950\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e12\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1967\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1,4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3,8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1981\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e(no data)*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1937\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1976\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1964\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1952\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIIIC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1949\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1940\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1936\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eIV\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eAlzheimer\\u003c/p\\u003e\\n \\u003cp\\u003epatient\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eNeuro\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eN1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1953\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eHealthy controls (CTR; C)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1958\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1957\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1950\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1987\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e6\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e2003\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1969\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eC4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1981\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003eF\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003cp\\u003e1989\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\"\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cul\\u003e\\n \\u003cli\\u003e* No data available, as the melanoma was treated surgically in Ukraine.\\u003c/li\\u003e\\n \\u003cli\\u003eControl no 4 was excluded because it was later determined that he was not clinically healthy.\\u003c/li\\u003e\\n\\u003c/ul\\u003e\\n\\u003cp\\u003eAll melanoma patients were recruited at the \\u003cstrong\\u003eNorth Estonia Medical Centre\\u003c/strong\\u003e (PERH) during standard surgical excision and sentinel node biopsy.\\u0026nbsp;Healthy controls were recruited from volunteer cohorts. Informed consent was obtained from all participants in accordance with the \\u003cstrong\\u003eTallinn Medical Research Ethics Committee\\u003c/strong\\u003e (license no. 1193). Clinical trial number: not applicable.\\u0026nbsp;All procedures complied with the Declaration of Helsinki and institutional data protection guidelines.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eSample collection\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUrine, saliva, and blood samples were collected from patients and controls. Saliva and blood were collected in the morning under fasting conditions. Patient blood samples were collected at PERH; control samples were collected at Synlab (Tallinn, Estonia). All samples were transported to Tallinn University of Technology at 4 °C and processed immediately upon arrival. Saliva, urine, and blood handling procedures followed standardized institutional protocols to minimize pre-analytical variation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eLymph node sample preparation and staining\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e.\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003eFresh lymph nodes were buffered in phosphate-buffered saline (PBS) and transported on ice to the Immunology Laboratory within 2–3 h of surgical removal. Nodes were mechanically dissociated through a sterile filter, washed twice with PBS, and kept on ice. Cells were stained with the following antibodies:CD4-FITC (BioLegend, USA); CD3-PE (BD Biosciences, USA); CD8-PE (BioLegend, USA); CD56-PE (BD Biosciences, USA); CD3-FITC (BD Biosciences, USA); CD45-FITC (BioLegend, USA); Siglec-8-PE (BioLegend, USA); anti human P2X4-FITC (TUT Laboratory of Immunology). Samples were analysed on a flow cytometer for immune subset characterization. Appropriate isotype controls and fluorescence compensation were used. Data acquisition parameters (voltages, gating strategy, and event counts) followed standard laboratory practices.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eHEK293 cell culture and transfection\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e.\\u003c/strong\\u003e HEK293 cells (ATCC) were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Invitrogen) supplemented with 10% fetal bovine serum (Sigma-Aldrich) and penicillin/streptomycin. Mouse P2X4-mCherry plasmids (EX-Mm24590-M56; GeneCopoeia) and an additional mouse P2X4 expression construct (TUT) were used. Cells were transfected using polyethyleneimine (PEI; 10 µg DNA and 20 µl PEI at 1 mg/mL) and incubated for 24 h. Transfection efficiency was monitored by mCherry fluorescence, and only cultures with adequate reporter expression were used for downstream assays.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eImmunoprecipitation\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e.\\u003c/em\\u003e\\u003c/strong\\u003e HEK293 cells were lysed using non-denaturing lysis buffer (ProteoJET™ Mammalian Cell Lysis Buffer, Fermentas). SureBeads™ Protein G Magnetic Beads (Bio-Rad) were incubated with monoclonal anti-human P2X4 (clone mAb27, IgG2b/κ) for 30 min at room temperature. Antibody-coated beads were incubated overnight at 4 °C with 100 µl melanoma or healthy control plasma, HEK293 supernatant or cell lysate. After washing with \\u0026nbsp;non-denaturing lysis buffer, immune complexes were eluted by boiling in Laemmli buffer with 2-mercaptoethanol. All steps were performed using low-adhesion tubes to minimize protein loss during washing and transfer.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eATP-dependent secretion assay.\\u003c/em\\u003e\\u003c/strong\\u003eTransfected or non-transfected HEK293 cells were washed and incubated in serum-free DMEM for 12 h. ATP (50 µM final) was added to each 10 cm dish, and supernatants were collected after 30 min and 4 h. Supernatants were cleared by brief centrifugation prior to filtration to remove cell debris, ensuring consistent protein recovery. Samples were filtered using Amicon Ultra-15 concentrators (Merck Millipore).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eWestern blot\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e.\\u003c/em\\u003e\\u003c/strong\\u003eSamples (immunoprecipitates, lysates, culture medium, or human biofluids) were separated on 12% SDS–polyacrylamide gels and transferred to nitrocellulose membranes using the Trans-Blot Turbo system (Bio-Rad). Membranes were blocked with PBS containing 5% non-fat milk powder. Primary antibody for incubation was rabbit anti-P2X4 (Alomone Labs; 1:300 in PBS + 5% fat milk powder, overnight, 4 °C). Secondary antibody: HRP-conjugated pig anti-rabbit IgG (DAKO; 1:2000) in PBS. Signals were developed using SuperSignal West Dura and SuperSignal West Atto substrates (Thermo Scientific). Exposure times were optimized to maintain signals within the linear detection range. Membranes were counterstained with Coomassie Brilliant Blue R-250.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNMR sample preparation\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e:\\u003c/em\\u003e\\u003c/strong\\u003e Whole-mouth saliva samples were centrifuged at 3000 RCF for 5 min at 4 °C, blood samples at 1300 RCF for 10 minutes, resulting supernatant and serum were frozen, later thawed on ice and filtered through 3 kDa Amicon filters (previously washed with warm water 5 times, 4 of which 10 min centrifuge, last time 15 min) centrifuged at 12,000 RCF for 35 min at 4 °C. 540 µl of filtered saliva sample was mixed with 60 µl of 1.5 M K-phosphate buffer in Chenomx Internal Standard IS-2 (Chenomx Inc., Edmonton, Alberta, Canada) containing 50 mM imidazole. 350 µl filtered serum or plasma sample was mixed with 280 µl 100 mM Na-phosphate buffer containing 50 mM imidazole and 70 ul IS-2. Final ratio for all samples: 90% H₂O / 10% D₂O. All samples were vortexed for 2 min and centrifuged at 14,000 RCF for 2 min at 4 °C.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eNMR acquisition and spectral processing\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e:\\u003c/em\\u003e\\u003c/strong\\u003e ¹H NMR spectra were acquired on a Bruker Avance III 700 MHz spectrometer using the \\u003cem\\u003enoesypr1d\\u003c/em\\u003e pulse sequence (mixing time 0.1 s; 12 ppm spectral width; at least 256 scans).Free induction decay files were processed using Chenomx NMR Suite v10. Automatic phase correction was followed by manual adjustment. Shim correction and 0.5 Hz line broadening were applied, pH was calibrated by imidazole signal at 8 ppm.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eMetabolite quantification\\u003c/em\\u003e\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cem\\u003e.\\u003c/em\\u003e\\u003c/strong\\u003eMetabolites were quantified using Chenomx NMR Suite. The first spectrum was profiled manually. Profile from the first spectrum was imported to subsequent spectra and adjusted manually. DSS-d6 served as the internal standard.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eConfocal microscopy.\\u0026nbsp;\\u003c/em\\u003e\\u003c/strong\\u003eKidney biopsy samples were cryo-embedded, snap-frozen, and stored at −25 °C. Sections (5 µm) were stained with anti-human P2X4 mAb27-FITC (1:800), Hoechst 33342, and control antibodies. Imaging was performed with Zeiss Axioskop 2 or LSM 780 confocal microscopes (63× objective). Images were analysed in ImageJ. Microscope laser intensity, detector gain, and pinhole settings were kept constant across samples to ensure comparable signal acquisition\\u003csup\\u003e.\\u003c/sup\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eStatistical analysis.\\u003c/em\\u003e\\u003c/strong\\u003eFlow cytometry data were exported and analysed graphically and statistically using Microsoft Excel. Normality was assessed using the Shapiro–Wilk test. Comparisons were analysed using unpaired \\u003cem\\u003et\\u003c/em\\u003e-tests (normally distributed data) or Mann–Whitney \\u003cem\\u003eU\\u003c/em\\u003e tests (non-normal data). Statistical differences were defined as significant if \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05 and very significant if \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.01. MetaboAnalyst v6.0 (Xia Lab @ McGill university, Montreal, QC, Canada; [12] was used to perform Principal Component Analysis (PCA) and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) with metabolite data. For serum metabolite concentrations the normalization procedure involved data scaling as auto scaling (mean-centred and divided by the standard deviation of each variable) for saliva metabolite concentrations Probabilistic Quotient Normalization (PQN) was also applied.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e1. Immune cell alterations in lymph node and blood\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo characterize immune alterations associated with melanoma metastasis, lymph nodes from eight patients with histologically confirmed micrometastatic melanoma were analysed by flow cytometry. Leukocyte size, granularity, and lineage markers were used to assess immune subset composition in metastatic versus non-metastatic lymph nodes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLymph nodes.\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eMetastatic (sentinel) lymph nodes (LNmeta) consistently yielded approximately twice as many total cells as non-metastatic nodes (LN), based on visual assessment of cell pellets and subsequent cell counting (observational finding not shown in Fig. 2). This likely reflects inflammatory infiltration or tumour-associated immune remodelling.\\u003c/p\\u003e\\n\\u003cp\\u003eNon-metastatic LN contained significantly higher frequencies of CD4⁺ T-helper (Th) cells compared to LNmeta (Fig. 2a).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eP2X4⁺ eosinophils were significantly more abundant in LNmeta compared with LN counterparts (Fig. 2g), indicating eosinophil activation or receptor upregulation within the metastatic microenvironment.\\u003c/p\\u003e\\n\\u003cp\\u003eIn LNmeta, we also observed regulatory T cells (Treg) enrichment: 7.27 \\u0026plusmn; 2.80% Treg cells, whereas paired control LN from the same patients harbored 3.33 \\u0026plusmn; 0.97% (Fig. 2i),\\u0026nbsp;reflecting an approximately 2.2-fold increase (p = 0.031). These findings are consistent with previous reports of preferential accumulation of regulatory T cells in tumour-involved lymph nodes in melanoma.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePeripheral blood.\\u003c/strong\\u003e Analysis of blood samples from melanoma patients (cohorts 1 and 2) revealed additional immune alterations (Fig. 2\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eb, d, f, h, j): \\u003cstrong\\u003eCD4⁺ T-helper cells were\\u0026nbsp;\\u003c/strong\\u003econsistently reduced in melanoma patients relative to healthy controls. \\u003cstrong\\u003eCD8⁺ cytotoxic T cells\\u003c/strong\\u003e exhibited patient-to-patient variability with an overall trend toward reduction. \\u003cstrong\\u003eNatural killer (NK) cells were\\u003c/strong\\u003e highly variable, ranging from severely reduced (~2%) to markedly elevated (16\\u0026ndash;18%). Eosinophils were elevated in the blood of melanoma patients (Fig. 2h). Flow-cytometric analysis of regulatory T (Treg; labelled as Tr in the figure) cells revealed an increased frequency in the peripheral blood of melanoma patients compared with healthy controls. In blood, Tregs represented 5.49 \\u0026plusmn; 2.42% of CD4⁺ T cells in melanoma patients (n = 8) and 3.01 \\u0026plusmn; 1.13% in controls (n = 8), corresponding to an approximately 1.8-fold increase (Welch\\u0026rsquo;s t-test, p = 0.026).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u0026nbsp;\\u003cstrong\\u003e2. P2X4 protein expression in urine and plasma\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate and quantify P2X4 protein and gene expression in human biofluids, Western blotting and RT-qPCR analyses were performed. Western blot analyses detected both full-length (~50\\u0026ndash;55 kDa) and truncated (~25 kDa) forms of the P2X4 receptor in urine and immunoprecipitated human plasma samples. The predicted molecular weight of the P2X4 subunit is ~43 kDa (UniProt Q99571), but experimentally observed bands appeared heavier (up to ~60 kDa), which is consistent with post-translational modifications such as glycosylation. Multiple bands of differing sizes were detected (Fig. 3), likely reflecting glycosylation, degradation, or oligomerization of the receptor [13], although, technical artifacts cannot be entirely excluded [14].\\u003c/p\\u003e\\n\\u003cp\\u003eIn urine samples from melanoma patients M4 and M5, strong P2X4 signals were detected at the expected molecular weights, accompanied by additional higher-molecular-weight forms consistent with N-glycosylated variants. In contrast, urine from patient N1 (neuroinflammatory disease) showed only a weak, modified P2X4 band (Fig. 3a). The strong P2X4 signals in M4 and M5, absent in all healthy controls, suggest possible pathological relevance\\u0026mdash;potentially reflecting epithelial damage in the kidney or bladder caused by metastatic disease. These findings support the potential utility of urinary P2X4 as a disease-associated marker.\\u003c/p\\u003e\\n\\u003cp\\u003eThe presence of P2X4 in melanoma patient plasma was further validated by immunoprecipitation from 100 \\u0026mu;l of plasma (Fig. 3b). Clear protein signals were detected in melanoma patient samples M4 and M5, whereas no signal was observed in healthy controls, consistent with RT-qPCR results (Fig. S1). Sample M3 was excluded due to insufficient plasma volume. These results indicate that melanoma patients secrete both truncated and post-translationally modified forms of P2X4 into plasma and urine\\u0026mdash;an effect not observed in control samples. Together, these data suggest that circulating P2X4 fragments may serve as a promising non-invasive biomarker.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3. ATP stimulation induces the release of P2X4 into the culture medium of transfected HEK293 cells\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo examine whether purinergic stimulation can promote the extracellular appearance of P2X4, HEK293 cells transiently expressing murine P2X4 were exposed to ATP and analysed for P2X4-immunoreactive species in conditioned media. In the absence of ATP stimulation, no P2X4 signal was detected in the culture medium of transfected cells, despite robust intracellular expression confirmed in cell lysates (Fig. 4). Following ATP treatment, P2X4-immunoreactive species became detectable in the conditioned medium of mP2X4-transfected cells at both 30 min and 4 h. In contrast, no P2X4 signal was observed in conditioned media from ATP-treated untransfected HEK293 cells, arguing against nonspecific ATP-induced cell lysis as the source of extracellular P2X4.\\u003c/p\\u003e\\n\\u003cp\\u003eThese findings demonstrate that ATP stimulation is sufficient to induce the extracellular appearance of P2X4-immunoreactive species in P2X4-expressing cells. Although the molecular form of the released species cannot be resolved from this experiment alone, the ATP-dependent effect provides mechanistic suppot for the presence of non-cell-associated P2X4 detected in plasma and urine samples from melanoma patients.\\u003c/p\\u003e\\n\\u003cp\\u003eRT-qPCR analysis further showed that P2X4 mRNA was upregulated in leukocytes from some melanoma patients compared with healthy controls (Fig. S1), indicating both transcriptional and post-translational alterations of P2X4 in melanoma and supporting the detectability of circulating receptor fragments in biofluids.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4. Metabolomic profiling\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo investigate systemic metabolic alterations in melanoma, we performed NMR-based metabolomic profiling of serum from 4 melanoma patients and 4 healthy controls, plasma from one melanoma patient and one control subject, and saliva from all of the same 10 participants. A total of 57 metabolites were quantified in serum, 55 in plasma and 60 in saliva samples. The missing values (not quantified) percentage in serum and plasma combined was much lower compared to saliva, 3.21% versus 17.8%.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn serum, melanoma patients exhibited reduced concentrations of pyroglutamate, glycine, acetone, 3-methyl-2-oxovalerate, 2-hydroxybutyrate, 2-aminobutyrate, and increased concentrations of glucose, arabinose, mannose, branched-chain amino acids (isoleucine, valine and leucine), aromatic amino acids (phenylalanine and tyrosine), urea, pyruvate, and taurine, see the complete list in Table S1. Glycerol was quantified in all samples; however, it was excluded from downstream analyses because it likely originated from the filtration process.\\u0026nbsp;3-hydroxybutyrate and acetoacetate were elevated manyfold in one control subject (C3), creatinine and dimethyl sulfone were also markedly higher in that sample compared to all others, making it difficult to assess the significance of those metabolites in further analysis due to the small remaining sample size (3 controls) after removing the outlier. Fumarate was detected only in melanoma patients, serum samples of M1 and M5 and plasma sample from M4.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHowever, Principal Component Analysis (PCA) did not show clear separation between melanoma and control samples. Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) scores plot suggested distinct grouping (see Fig. S2 and S3 for VIP scores plot). Even though clear univariate differences (like for glucose) were present, the data still have weak multivariate structure, leading to negative Q\\u0026sup2; (Fig. S4). The biologically meaningful differences that the data contains are not strong or consistent enough across metabolites to support a reliable OPLS-DA predictive model with our current sample size. These findings should be interpreted as hypothesis-generating.\\u003c/p\\u003e\\n\\u003cp\\u003eMetabolic alterations were also evident in saliva, the samples from melanoma patients were generally more concentrated compared to the controls (see the complete list of metabolites quantified in Table S2).\\u0026nbsp;After normalization, several metabolites, including cadaverine, phenylalanine, leucine, succinate, putrescine, lysine, and valine were still elevated consistently in melanoma samples. Overall, ethanol, isoleucine, proline, glycine, alanine, trimethylamine, sarcosine, 5-aminopentanoate, choline, and acetate also displayed higher concentrations in melanoma.\\u003c/p\\u003e\\n\\u003cp\\u003eThere was less 4-hydroxyphenylacetate in all melanoma samples than controls, but it was not detected in one control subject (C2). Dimethyl sulfone, methanol, xanthine, malonate, and 2-hydroxyisovalerate were also generally less abundant in melanoma samples. As in the serum analysis, the saliva PCA did not show clear separation between melanoma and control samples and OPLS-DA segregation between groups on the scores plot was very clear, but the model had a negative Q\\u0026sup2; (Fig. S5\\u0026ndash;S7). Aspartate was detected only in melanoma samples, it was not quantified in M2 due to high noise level in the spectrum, however the signals do appear to be there at 2.8 ppm at a comparable level to those in other melanoma samples. Threonine and tryptophan were detected only in M3 and M4.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eImmune dysregulation.\\u003c/strong\\u003e In agreement with the review by Tucci et al. (2019), our analysis of peripheral blood immune subsets in melanoma patients revealed a pronounced imbalance between effector and regulatory populations. Several patients displayed markedly reduced percentages of CD8⁺ cytotoxic T cells compared with healthy controls, consistent with the impaired cytotoxicity described as a hallmark of melanoma immune evasion. Natural killer (NK) cell frequencies displayed considerable heterogeneity, with some patients showing elevated levels and others demonstrating marked reductions. This pattern aligns with previous reports highlighting the susceptibility of the NK\\u0026ndash;dendritic cell axis to tumour-derived suppressive mediators, including prostaglandin E₂ and adenosine. Eosinophils\\u0026mdash;less frequently discussed in the context of melanoma\\u0026mdash;also exhibited dysregulation in our cohort, suggesting that additional immune compartments may participate in melanoma-associated immune remodelling.\\u003c/p\\u003e\\n\\u003cp\\u003eOur flow cytometric data showed that Treg cells were increased in both peripheral blood and tumor-draining lymph nodes of melanoma patients. In peripheral blood, the frequency of Treg cells was elevated from a mean of 3.01% in healthy controls to 5.49% in melanoma patients (\\u0026asymp;1.8-fold increase), consistent with earlier reports of expanded circulating CD4⁺CD25^high/FOXP3⁺ Tregs in melanoma and other solid tumours [15;16].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn lymphoid tissue, we observed an approximately 2.2-fold enrichment of CD4⁺CD25⁺ cells in metastatic sentinel lymph nodes compared with anatomically distant, tumour-free lymph nodes from the same patients. This magnitude of enrichment closely mirrors the ~2-fold increase in CD4⁺CD25^high Tregs reported by Viguier et al. in metastatic melanoma lymph nodes relative to tumour-free nodes and autologous peripheral blood [17].\\u0026nbsp;Patients M1, M3-5 in cohort 1 had melanoma stage IV (metastasis in LN, tissues and organs) while M2 had stage III (metastasis in LN).\\u0026nbsp;Together with prior studies showing that FOXP3⁺ Treg density in metastatic sentinel nodes is associated with poor clinical outcome [18],\\u0026nbsp;our data support the concept that Treg accumulation in tumour-draining lymph nodes is a conserved feature of melanoma-associated immune suppression.\\u003c/p\\u003e\\n\\u003cp\\u003eRT-qPCR analysis showed that P2X4 mRNA was upregulated in leukocytes from two analysed melanoma patients compared with healthy controls (Fig. S1), raising the possibility that this receptor influences systemic immune composition and may represent a previously underappreciated immunoregulatory checkpoint in melanoma. Relative P2X4 mRNA expression levels in PBMCs were 1.40-fold for patient M3 and 5.42-fold for patient M5, compared with a mean of 0.23 in healthy controls. This upregulation is consistent with reports in other pathological conditions, including cancer [19; 20; 21].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eBecause P2X4 expression can be influenced by a variety of factors\\u0026mdash;including comorbidities, smoking, alcohol consumption, and physical activity [19; 21; 22; 23],\\u0026nbsp;the observed differences cannot be attributed solely to melanoma. Given the small sample size of our cohort, no definitive conclusions can be drawn. Nevertheless, these findings highlight P2X4 as a candidate worth further mechanistic and translational investigation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eP2X4 receptor signalling and secretion.\\u003c/strong\\u003e P2X4 has previously been implicated in inflammation, immune regulation, and angiogenesis. Our findings extend this understanding by demonstrating that truncated and modified P2X4 fragments are detectable in the plasma and urine of melanoma patients but absent in healthy controls. This observation suggests that melanoma-associated processes may promote receptor shedding, proteolytic cleavage, or dysregulated turnover, resulting in the generation of soluble P2X4 fragments.\\u003c/p\\u003e\\n\\u003cp\\u003eWestern blot analysis confirmed the presence of distinct P2X4 proteins in the urine of several patients. The absence of urinary P2X4 in other melanoma patients could reflect milder disease, differential tumour burden, comorbid conditions, or individual biological variability. Nonetheless, because none of the control samples exhibited detectable P2X4, the presence of urinary P2X4 appears to represent a disease-associated feature. Thus, P2X4 excretion may serve as a potential marker for identifying pathological conditions, including melanoma.\\u003c/p\\u003e\\n\\u003cp\\u003eThe functional implications of soluble P2X4 are significant. Soluble receptor fragments could act as decoy receptors for extracellular ATP, thereby modulating purinergic signalling within the tumour microenvironment. Alternatively, their presence in biofluids may reflect increased receptor turnover or heightened purinergic activity associated with tumour progression. Notably, in this study, Western blot detection was performed on undiluted urine samples without prior concentration or filtration, yet clear P2X4 signals were still observed in some cases\\u0026mdash;indicating biologically meaningful levels in certain patients. Although Fig. 4 does not formally define the molecular nature of the extracellular P2X4 species, the ATP-dependent release observed in vitro parallels the detection of non-cell-associated P2X4 immunoreactivity in melanoma plasma samples. Together, these findings support the existence of a soluble P2X4 pool that may arise through regulated release and/or proteolytic processing under inflammatory conditions.\\u003c/p\\u003e\\n\\u003cp\\u003eThe detection of P2X4 in urine offers an appealing non-invasive biomarker avenue for melanoma detection or monitoring. Future studies should evaluate whether urinary P2X4 correlates with disease stage, tumour burden, renal involvement, or therapy-related effects, and whether its presence carries prognostic or predictive value.\\u003c/p\\u003e\\n\\u003cp\\u003eP2X4 is known to be expressed in secretory epithelial tissues, including the kidneys and prostate [24].\\u0026nbsp;Under normal conditions, membrane-associated P2X4 contributes to ATP-mediated ion transport, inflammatory regulation, and epithelial homeostasis [25]. Its detection in extracellular fluids such as plasma and urine may therefore indicate epithelial damage, inflammation, or active release via extracellular vesicles. P2X4 localization within the cytoplasm of renal tubular epithelial cells in biopsy samples from patients with glomerulosclerosis and membranous nephropathy (Fig. S8) suggests a potential role for this receptor in renal inflammation and pathology. Notably, strong nuclear localization of P2X4 was observed in one patient with glomerulosclerosis\\u0026mdash;a pattern that may reflect pathological shifts associated with apoptosis, stress responses, or cytokine regulation [26].\\u0026nbsp;Through epithelial injury in the kidney, P2X4 may be released into the urinary space.\\u003c/p\\u003e\\n\\u003cp\\u003eThe presence of P2X4 fragments in circulation highlights their potential as non-invasive biomarkers of disease activity or tissue injury. Furthermore, P2X4 mRNA upregulation in leukocytes from some melanoma patients supports the idea that this receptor is subject to active transcriptional regulation in the context of tumour-associated inflammation. Together, these molecular findings are consistent with\\u0026mdash;and expand\\u0026mdash;the growing body of evidence linking purinergic signalling to tumour immunology. Full uncropped gels and blots images of western blots are shown in Fig S9.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSystemic metabolic reprogramming.\\u003c/strong\\u003e Metabolomic profiling of srum and saliva revealed broad tumour-associated metabolic alterations, although no changes were statistically significant, which could be expected for such a small set of subjects. Elevated levels of branched-chain amino acids (BCAA) in both serum and saliva indicate increased proteolysis, often noted in cancer patients, mirroring systemic metabolic state such as insulin resistance and increased protein catabolism. High BCAA levels can enhance Treg persistence via mTOR shifts [27] which corresponds to elevated Tregs.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn saliva, aspartate was detected only in melanoma samples, however, the trend was not reflected in serum and plasma samples, where aspartate was universally present at similar rates (\\u0026micro;M average \\u0026plusmn; SD [range] for melanoma 16.6 \\u0026plusmn; 7.3 [9.6 \\u0026ndash; 25.8] vs controls 18.0 \\u0026plusmn; 10.0 [7.2 \\u0026ndash; 28.8]), suggesting either increased local release \\u0026ndash; higher epithelial turnover, subtle mucosal inflammation \\u0026ndash; or altered local microbial metabolism that produces/accumulates aspartate. Since serum is tightly regulated, subtle aspartate changes may be buffered by liver metabolism, kidney clearance, systemic amino acid homeostasis. On the contrary, saliva is more sensitive to local release and is more easily perturbed by low-abundance metabolites. Therefore, saliva could be more sensitive early detector of tumour-related amino acid changes. Aspartate has been detected in the saliva of younger healthy subjects (age 24\\u0026ndash;42), but at lower concentrations on average 33.30\\u0026nbsp;\\u0026plusmn;\\u0026nbsp;19.39 uM\\u0026nbsp;[28]\\u0026nbsp;than the melanoma patients in this study exemplified 42.31\\u0026plusmn; 40.16 [17.22 \\u0026ndash; 102.00]. To our knowledge, this is the first documented case linking salivary aspartate to a disease.\\u003c/p\\u003e\\n\\u003cp\\u003eLower levels of 4-hydroxyphenylacetate, a microbial degradation product of tyrosine, and higher concentrations of ethanol, acetate, succinate, polyamines and basic amino acids (cadaverine, putrescine, lysine, 5-aminopentanoate) in saliva samples of cancer patients can also be associated with microbiome imbalance, possibly in response to systemic inflammation. These changes could reflect increased oral proteolysis or microbial shifts toward decarboxylating taxa. They are also compatible with tumour-driven systemic amino acid release that reaches saliva.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eSerum levels of 2-hydroxybutyrate, 2-aminobutyrate consistently trend downward in melanoma patients. These metabolic patterns alongside glutamate/glutamine shifts indicate redox stress, one of the earliest metabolic symptoms of cancer. Together with elevated glucose this points towards a tumour-associated insulin resistance-like metabolic phenotype, and this pattern is consistent with early-stage or\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003enon-cachectic cancer metabolism. The changes in aromatic amino acid levels, phenylalanine and tyrosine in melanoma samples are linked with inflammation and immune modulation, respectively. Furthermore, fumarate, a known oncometabolite was found in three melanoma patients and none of the control subjects.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003ePut together, this is a remarkably coherent pattern for such a small dataset, while statistical power is low, the biological signals align strongly with known cancer metabolomic signatures.\\u003c/p\\u003e\"},{\"header\":\"Summary\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntegration of immune, molecular, and metabolic alterations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTogether, our findings reveal three converging layers of systemic dysregulation in melanoma: \\u003cstrong\\u003eImmune dysregulation:\\u003c/strong\\u003e Reduced Th cells and elevated Tregs support the immune escape model described by Tucci et al. NK cell variability and eosinophil heterogeneity further underscore tumour-driven remodelling of circulating leukocytes. \\u003cstrong\\u003eP2X4 signalling:\\u003c/strong\\u003e Detection of truncated P2X4 fragments in plasma and urine represents a novel observation suggesting receptor shedding or proteolytic processing. Upregulation of P2X4 mRNA in leukocytes supports an active role for purinergic signalling at the tumour\\u0026ndash;immune interface. \\u003cstrong\\u003eMetabolic reprogramming:\\u003c/strong\\u003e NMR metabolomics revealed systemic metabolic shifts, particularly in amino acid pathways, reflecting tumour metabolic demands and organism-wide stress responses. This integrative view highlights the multifaceted systemic footprint of melanoma. Notably, the identification of soluble P2X4 receptor fragments together with serum/saliva metabolic fingerprints points toward promising \\u003cstrong\\u003enon-invasive biomarker\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003estrategies that complement traditional immune profiling\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLimitation of the study\\u0026nbsp;\\u003c/strong\\u003eThe small sample size limits definitive conclusions. Larger, longitudinal studies are needed to validate the clinical utility of soluble P2X4 fragments and metabolic signatures as biomarkers of disease activity, progression, or treatment response.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFuture perspectives\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eFuture work should elucidate the molecular mechanisms governing P2X4 receptor processing and secretion in melanoma and explore how purinergic signalling interacts with tumour metabolism and immune suppression. Ultimately, integrating immune, purinergic, and metabolic profiling may yield a comprehensive framework for personalized disease monitoring and biomarker-guided therapeutic strategies in melanoma\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u0026nbsp;\\u003c/strong\\u003eThis study provides an integrated view of systemic alterations in melanoma by combining immune profiling, purinergic receptor analysis, and metabolomic characterization. We show that melanoma is associated with marked immune dysregulation, including reduced cytotoxic T-cell frequencies, elevated regulatory T cells, and heterogeneous NK and eosinophil responses. In parallel, we identify \\u003cstrong\\u003etruncated and modified P2X4 receptor fragments\\u003c/strong\\u003e in the plasma and urine of melanoma patients\\u0026mdash;an observation absent in healthy controls\\u0026mdash;together with elevated P2X4 mRNA expression in leukocytes. These findings highlight P2X4 as a potential contributor to tumour\\u0026ndash;immune interactions and as a promising \\u003cstrong\\u003enon-invasive biomarker candidate\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eMetabolomic profiling of serum and saliva further revealed robust metabolic reprogramming associated with cancer. The analysis of both biofluids underscores the potential of metabolic signatures for disease detection and monitoring.\\u003c/p\\u003e\\n\\u003cp\\u003eTogether, these immune, molecular, and metabolic alterations paint a cohesive picture of the systemic footprint of melanoma. Importantly, the detection of soluble P2X4 fragments and salivary/serum metabolite changes points to new opportunities for \\u003cstrong\\u003enon-invasive biomarker development\\u003c/strong\\u003e. While larger cohorts are needed to validate these findings, this work establishes a foundation for future studies integrating purinergic signalling, immune modulation, and metabolic profiling to support personalized monitoring and therapeutic strategies in melanoma\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by Personal research grants (PRG)1832 (Principal Investigator: Dr Ago Samoson, Tallinn University of Technology). Additional support for S.R.B. and A.R were provided by COST Action CA21130 (European Cooperation in Science and Technology).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors would like to thank\\u0026nbsp;the staff of the North Estonia Medical Centre, especially \\u003cstrong\\u003eSvetlana Bogoljubova\\u003c/strong\\u003e\\u003cstrong\\u003e,\\u0026nbsp;\\u003c/strong\\u003ewho collected patient samples, and \\u003cstrong\\u003eBirgit Truumees\\u003c/strong\\u003e\\u003cstrong\\u003e,\\u003c/strong\\u003e who performed the immunofluorescence staining and imaging of kidney epithelial cells.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting Interests\\u003c/strong\\u003e The authors declare that they have no financial or non-financial interests that are directly or indirectly related to the work submitted for publication.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data presented in this study are available on request from the corresponding author.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed Consent Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll samples were collected after written informed consent, in accordance with the Declaration of Helsinki (1975)\\u0026nbsp;and following approval from the relevant ethics committees.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contribution.\\u0026nbsp;\\u003c/strong\\u003eRMT and SRB conceived and designed the study. SRB, LK, AK, IK, CK and LT performed the experiments. RMT, SRB, and LK wrote the manuscript. JT contributed to data curation, supervision, and manuscript review and editing. IK, CK, AK, LT, and AR performed primary data analysis and contributed to figure preparation. AS and SRB supervised the study and acquired funding.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eWu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017 Feb 28;387:61-68. doi: 10.1016/j.canlet.2016.01.043. Epub 2016 Feb 1. PMID: 26845449.\\u003c/li\\u003e\\n\\u003cli\\u003eAlitalo A, Detmar M. Interaction of tumor cells and lymphatic vessels in cancer progression. Oncogene. 2012 Oct 18;31(42):4499-508. doi: 10.1038/onc.2011.602. Epub 2011 Dec 19. 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Eosinophils orchestrate cancer rejection by normalizing tumor vessels and enhancing infiltration of CD8(+) T cells. \\u003cem\\u003eNature Immunology\\u003c/em\\u003e, \\u003cem\\u003e16\\u003c/em\\u003e(6), 609\\u0026ndash;617. https://doi.org/10.1038/ni.3159\\u003c/li\\u003e\\n\\u003cli\\u003eVarricchi, G., Raap, U., Rivellese, F., Marone, G., \\u0026amp; Gibbs, B. F. (2018). Human mast cells and basophils\\u0026mdash;How are they similar how are they different? \\u003cem\\u003eImmunological Reviews\\u003c/em\\u003e, \\u003cem\\u003e282\\u003c/em\\u003e(1), 8\\u0026ndash;34. https://doi.org/10.1111/imr.12627\\u003c/li\\u003e\\n\\u003cli\\u003eTucci M, Passarelli A, Mannavola F, et al. Immune evasion in melanoma: From immune checkpoint inhibitors resistance to new therapeutic strategies. \\u003cem\\u003eFront Oncol\\u003c/em\\u003e 2019;9:123. doi:10.3389/fonc.2019.00123.\\u003c/li\\u003e\\n\\u003cli\\u003eHuang, P., Zou, Y., Zhong, X. Z., Cao, Q., Zhao, K., Zhu, M. 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(2015). \\u003cem\\u003eThe human saliva metabolome.\\u003c/em\\u003e\\u003cem\\u003eMetabolomics\\u003c/em\\u003e, \\u003cstrong\\u003e11\\u003c/strong\\u003e(6), 1864\\u0026ndash;1883. https://doi.org/10.1007/s11306-015-0840-5\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"purinergic-signalling\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pusi\",\"sideBox\":\"Learn more about [Purinergic Signalling](http://link.springer.com/journal/11302)\",\"snPcode\":\"11302\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11302/3\",\"title\":\"Purinergic Signalling\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Cancer metabolism, Immune dysregulation, Melanoma, P2X4 receptor, Purinergic signalling\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8396511/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8396511/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMelanoma progression involves coordinated immune suppression, altered receptor-mediated signalling, and tumour-driven metabolic reprogramming. To evaluate these systemic alterations, we integrated three datasets: flow-cytometric profiling of immune cell subsets and P2X4 expression in peripheral blood leukocytes from melanoma patients and healthy controls; molecular detection of P2X4 in plasma, leukocytes, and urine using Western blotting and immunoprecipitation; and NMR-based metabolomic profiling of serum and saliva. Melanoma patients exhibited reduced CD4⁺ T-helper cells, altered Tc/Treg balance, and eosinophil heterogeneity with elevated P2X4 expression. Truncated P2X4 receptor fragments were detected in plasma and urine of some melanoma patients but not in controls. Metabolomic analyses revealed tumour-associated metabolic shifts, including elevated branched-chain amino acids in both serum and saliva and many alterations associated with dysbiosis were detected in melanoma patients\\u0026rsquo; saliva. These findings highlight the convergence of immune dysregulation, purinergic P2X4 signalling, and systemic metabolic remodelling in melanoma. The presence of soluble P2X4 fragments, together with metabolomic fingerprints, supports their potential as minimally invasive biomarkers for disease monitoring.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Systemic purinergic dysregulation in melanoma revealed by soluble P2X4 receptor fragments\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-01-05 08:42:22\",\"doi\":\"10.21203/rs.3.rs-8396511/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-03-16T15:10:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-13T20:55:20+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"338226758483494053127209582714777588574\",\"date\":\"2026-02-19T18:52:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-08T18:27:40+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"243518291541596228269014492164139144903\",\"date\":\"2026-01-19T15:08:14+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-01-02T10:07:58+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-12-29T14:43:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-12-26T03:38:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Purinergic Signalling\",\"date\":\"2025-12-18T14:12:51+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"purinergic-signalling\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"pusi\",\"sideBox\":\"Learn more about [Purinergic Signalling](http://link.springer.com/journal/11302)\",\"snPcode\":\"11302\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11302/3\",\"title\":\"Purinergic Signalling\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"ad199c52-bcfc-4ce6-9b3e-3bdd1e7b2e8d\",\"owner\":[],\"postedDate\":\"January 5th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-07T10:38:53+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-01-05 08:42:22\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8396511\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8396511\",\"identity\":\"rs-8396511\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}