Genetically predicted blood metabolites mediate the association between circulating inflammation-related proteins and pancreatic cancer

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In the present study, we aimed to investigate the causal relationship of circulating inflammatory proteins and pancreatic cancer and identify the blood metabolites as potential mediators. Methods : We employed bidirectional two-sample Mendelian randomization analysis to examine the potential causal association between circulating inflammatory proteins, circulating metabolites, and PC using data from genome-wide association studies (GWAS). And two-step MR to discover potential mediating blood metabolites in this process. Results : MR analysis identified 4 types of circulating inflammation-related proteins causally associated with pancreatic cancer. Furthermore, there was no strong evidence that genetically predicted pancreatic cancer had an effect on these four types of circulating inflammatory proteins. Further two-step MR analysis found 11 types of blood metabolites were causally associated with pancreatic cancer and the associations between circulating Interleukin-15 receptor subunit alpha and pancreatic cancer were mediated by blood 5-methyluridine with proportions of 7.41%. Conclusion : The present study provides evidence supporting the causal relationships between various circulating inflammatory proteins, especially Interleukin-15 receptor subunit alpha, and pancreatic cancer, with a potential effect mediated by blood metabolites. Further research is needed on additional risk factors as potential mediators and establish a comprehensive inflammation-metabolism network in pancreatic cancer. pancreatic cancer circulating inflammatory proteins circulating metabolites genome-wide association studies Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Pancreatic cancer is one of the most lethal malignancies, with a 5-year survival rate of less than 5% [ 1 ] . Initiation and progression of this disease results from the interaction of genetic events combined. The potential role of inflammation in the development and growth of cancer was initially described in 1863 by Virchow, who observed that inflammatory cells infiltrate tumors [ 2 ] . In recent years, the advancements made in cancer biology have confirmed that systemic and local chronic inflammation might enhance the risk of PC [ 3 – 5 ] . Inflammatory processes have emerged as key mediators of pancreatic cancer development and progression [ 6 ] . Inflammation is a complex and highly coordinated cellular and biochemical process intended to resolve tissue injury and protect the host. However, when this “wound healing” process becomes chronic, derangement of normal cellular mechanisms occurs and paradoxically, tissue injury and neoplasia can ensue. In this regard, chronic inflammation is a hallmark of cancer, and inflammation can impair normal cellular and organ physiology as well as the efficacy of therapeutic interventions [ 7 ] . Inflammatory responses are orchestrated by a complex network of cells and mediators, including circulating proteins such as cytokines and soluble receptors. Therefore, discovery of the plasma level of the inflammation-related circulating proteins should yield valuable insights into both physiology and the etiology of PC. Reprogramming of cellular metabolism is another a hallmark of tumorigenesis. Metabolic reprogramming related to glucose, lipid, and amino acid metabolism in PC not only enables the cancer to thrive and survive under hypovascular, nutrient-poor and hypoxic microenvironments, but also confers chemoresistance, which contributes to the poor prognosis of PC [ 8 , 9 ] . To date, increasing evidence points to the influence of blood metabolomic profiling on pancreatic cancer risk [ 10 , 11 ] . Proteomic analysis of the pancreatic cancer cells demonstrated alteration in proteins involved in metabolic pathways including increased expression of glycolytic and reduced Krebs cycle enzymes, and accumulation of key proteins involved in glutamine metabolism, in support of Warburg effect [ 12 ] . These in turn play significant role in nucleotide and amino acid biosynthesis required for sustaining the proliferating cancer cells. Applications of sensitive mass spectrometric techniques in metabolomics study of PC detection biomarkers have led to identification of a set of small molecules or metabolites (or biochemical intermediates) that are potent discriminants of developing PC [ 13 , 14 ] . However, the relationship between inflammation-related circulating proteins, blood metabolites and pancreatic cancer still remains unknown. In addition, how blood metabolites are involved in the interaction between circulating inflammatory proteins and pancreatic cancer remains unclear. Therefore, clarifying the potential mediation effects of blood metabolites in the interaction between circulating inflammatory proteins and pancreatic cancer is crucial for establishing the comprehensive inflammation-metabolism network of pancreatic cancer. Mendelian randomization (MR) is a powerful tool for inferring causal effects between exposures and outcomes [ 15 ] . By leveraging genetic variants associated with the exposure, MR helps overcome the limitations of traditional observational studies, such as confounding and reverse causation. Therefore, we employed a two-sample MR approach to investigate the causal effects of 91 inflammation-related circulating proteins and 1400 human blood metabolites on PC, aiming to gain deeper molecular insights. Our findings shed new light on the intricate relationships between the inflammation-related circulating proteins, blood metabolites, and pancreatic cancer, which may have significant implications for preventing and treating pancreatic cancer. Materials and methods Data sources The exposure data, 91 inflammation-related circulating proteins, for this study were obtained from the the EBI GWAS Catalog, accession numbers GCST90274758 to GCST90274848 (The data could be downloaded from the homepage of the official website by entering the corresponding number at the following URL: https://www.ebi.ac.uk/gwas/home ) [ 16 ] . As for the intermediate variables, the study incorporated GWAS summary data for circulating metabolites levels from the EBI GWAS Catalog, accession numbers GCST90199621 to GCST90201020 (The data could be downloaded from the homepage of the official website by entering the corresponding number at the following URL: https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/ ). The outcome variable of interest, pancreatic cancer, was derived from GWAS summary data of the latest R10 version released by the FinnGen consortium, which included 1626 cases and 314193 controls. Instrument variable selection In this study, the selection of instrument variables was guided by relevant MR studies, using a threshold of p < 1 × 10 − 5 to preliminarily identify single nucleotide polymorphisms (SNP) loci with statistical significance from the GWAS summary data of 91 inflammation-related circulating proteins. Additionally, linkage disequilibrium coefficient r 2 = 0.001 and a region width of 10000 kb were set to minimize the impact of genetic pleiotropy on the results. F-statistics were employed to assess whether the selected instrument variables were weak instruments, with the criterion of F > 10, indicating the absence of weak instrument bias. To further evaluate whether individual SNPs are closely associated with pancreatic cancer and to mitigate potential confounding factors, each SNP’s secondary phenotype was manually checked in PhenoScanner. MR analysis MR analyses were combined with the “mr” functionality as well as with five methodologies: MR Egger, Weighted Median, Inverse Variance Weighted (IVW), Simple Mode and Weighted Mode. Central focus of this study was the results of the IVW method, which showed the strongest ability to test for causal relationships in MR analyses [ 17 ] . In the event that the IVW method yielded a p-value of less than 0.05, the result was incorporated into the subsequent study [ 18 ] . Then the odds ratio (OR) was calculated, where more than 1 was a danger element and less than 1 was a protection element. To assure the precision of the analyses, several kinds of susceptibility tests, consisting of heterogeneity tests, horizontal multivariate tests, as well as leave-one-out sensitivity tests (LOO), were employed in order to fully assess the reliability concerning these findings. Study design This study employs a two-sample two-step MR design based on GWAS summary data. The study follows the latest guidelines for MR analysis, as outlined in the STROBE-MR guidelines [ 19 ] , and operates under three fundamental assumptions: (1) There exist robust correlations of instrumental variables (IVs) with the exposure (association); (2) IVs are irrespective of the confounders (independence); (3) the effect of IVs on outcomes is achievable solely through exposure (exclusion restriction criteria). Figure 1 shows a schematic summary of the analysis. We conducted a two-sample bidirectional MR to evaluate the mutual causality between 91 inflammation-related circulating proteins and Pancreatic cancer (Fig. 1 A), which was designated as the total effect. We further performed a mediation analysis using a two-step MR design to explore whether 1400 human blood metabolites mediate the causal pathway from inflammation-related circulating proteins to PC outcome (Fig. 1 B). The overall effect can be decomposed into an indirect effect (through mediators) and a direct- effect (without mediators) effect. The total effect of inflammation-related circulating proteins on PC was decomposed into 1) direct effects of inflammation-related circulating proteins on PC (c’ in Fig. 1 B) and 2) indirect effects mediated by inflammation-related circulating proteins through the mediator (a * b in Fig. 1 B). We calculated the percentage mediated by the mediating effect by dividing the indirect effect by the total effect. Meanwhile, 95% confidence intervals were calculated with the delta method. All MR analyses were conducted using R (version 4.0.2) and the TwoSampleMR R package version 0.5.6 [ 20 ] . Results MR results of inflammatory proteins and pancreatic cancer The MR analyses uncovered a causal relationship between four inflammation-related circulating proteins and pancreatic cancer. Specifically, we observed that the genetically predicted C-X-C motif chemokine 10 (Odds Ratio (OR): 0.8209, 95% Confidence Interval (95%CI): 0.6797–0.9915; p = 0.0405) were associated with a reduced risk of developing PC. Conversely, the Interleukin-15 receptor subunit alpha (OR: 1.1736, 95% CI: 1.0267–1.3415; p = 0.0190), Interleukin-1-alpha (OR: 1.2406, 95% CI: 1.0015–1.5367; p = 0.0484), Interleukin-2 receptor subunit beta (OR: 1.2605, 95% CI: 1.0285–1.5448; p = 0.0257) were linked to an elevated risk of PC (Fig. 2 and Supplementary Table 1). The MR-Egger test and Cochrane’ Q test did not reveal significant horizontal pleiotropy and heterogeneity (Supplementary Tables 2 and 3). The leave-one-out analysis method did not reveal any interference with the results attributable to a single SNP (Supplementary Fig. 1). When evaluating the causal effects of pancreatic cancer on the inflammation-related circulating proteins, it was observed that all p values were greater than 0.05, suggesting that pancreatic cancer does not have an effect on the inflammation-related circulating proteins under consideration (Supplementary Table 4). MR results of circulating metabolites and PC MR results of 1400 circulating metabolites and the PC suggested that there was a correlation between a total of 11 metabolites (Fig. 3 and Supplementary Table 5). Resulting in 4 positive circulating metabolites associated with PC, which included 2-linoleoylglycerol (18:2), Cortisone, X-18888 and X-21742 levels were positively correlated with PC, and Oxalate (ethanedioate), Adrenate(22:4n6), 1-palmitoleoyl-GPC (16:1), 5-methyluridine (ribothymidine), Glycodeoxycholate 3-sulfate, Branched-chain (straight-chain, or cyclopropyl 12:1 fatty acid) and Palmitate (16:0) were negatively correlated with PC. Sensitivity analyses were performed in order to verify of the above findings (Supplementary Tables 6 and 7). Leave-one-out plots showed that the MR results were not affected by any single SNP (Supplementary Fig. 2). MR results of inflammatory proteins and circulating metabolites Previously, we identified 4 inflammation-related circulating proteins and 11 circulating metabolites to PC. Then, we investigated the causal role of 4 inflammation-related circulating proteins on 11 circulating metabolites. The MR analysis revealed that only Interleukin-15 receptor subunit alpha (OR = 0.9237, 95% CI: 0.8583–0.9940, p = 0.0340) was highly associated with 5-methyluridine (ribothymidine) (Table 1 ). At the same time, no significant causal effect was determined with other approaches. Table 1 MR analysis showed the causality of Interleukin-15 receptor subunit alpha on 5-methyluridine was significant. exposure outcome method nsnp b se pval lo_ci up_ci or or_lci95 or_uci95 Interleukin-15 receptor subunit alpha levels 5-methyluridine MR Egger 24 -0.09 0.06 0.18 -0.21 0.04 0.92 0.81 1.04 Interleukin-15 receptor subunit alpha levels 5-methyluridine Weighted median 24 -0.06 0.04 0.12 -0.14 0.01 0.94 0.87 1.02 Interleukin-15 receptor subunit alpha levels 5-methyluridine Inverse variance weighted 24 -0.08 0.04 0.03 -0.15 -0.01 0.92 0.86 0.99 Interleukin-15 receptor subunit alpha levels 5-methyluridine Simple mode 24 -0.07 0.10 0.49 -0.25 0.12 0.94 0.78 1.13 Interleukin-15 receptor subunit alpha levels 5-methyluridine Weighted mode 24 -0.07 0.04 0.07 -0.14 0.00 0.93 0.87 1.00 Intermediary effect Two-step MR and bidirectional MR analyses were performed to examine the mediating pathways from Interleukin-15 receptor subunit alpha through 5-methyluridine (ribothymidine) to PC. The Two-step MR results indicated that 5-methyluridine may act as a mediator in the causal relationship between Interleukin-15 receptor subunit alpha and PC, with a mediation ratio of 7.41% (Fig. 4 ). Discussion In this comprehensive mediated MR study, we identified 4 inflammatory proteins and 11 circulating metabolites in a causal relationship to pancreatic cancer. Mediated MR results indicated that 5-methyluridine may be a mediator of the causal relationship between Interleukin-15 receptor subunit alpha and pancreatic cancer, with a mediation ratio of 7.41%. This analysis highlighted the link from the inflammation-related circulating proteins to PC, highlighting the mediating role of the metabolites. PC stands as an ongoing challenge to global health due to its aggressive behavior and bleak prognosis [ 21 , 22 ] . Addressing these challenges and finding ways to improve therapeutic efficacy, overcome treatment tolerance, identify high-risk groups and discover appropriate biomarkers for PC has become paramount research directions for PC researchers. In this context, the research direction has begun to shift to the value of inflammatory markers in the human body on the survival and prognosis of PC [ 23 ] . Systemic and local chronic inflammation might enhance the risk of pancreatic cancer, and PC-associated inflammatory infiltrate in the tumor microenvironment concurs in enhancing tumor growth and metastasis. Inflammatory mediators, including interleukin 1β (IL-1β), interleukin-18 (IL-18), Interleukin-15(IL-15) and tumor necrosis factor-α, which are prevalent in pancreatic cancer, are implicated in promoting cellular proliferation, thwarting programmed cell death, stimulating new blood vessel formation, and fostering genetic instability – all of which can contribute to the progression of PC [ 2 , 24 – 26 ] . Interleikin-15 (IL-15) is a pleiotropic proinflammatory cytokine produced by activated dendritic cells, macrophages, and monocytes that essential for cell survival, cell proliferation, and functional activity of immune cells such as natural killer (NK) cells, memory T cells, monocytes, macrophages, and dendritic cells. IL-15 exerts its effect by binding to a membrane receptor composed of high affinity binding alpha chain (IL-15Rα) that forms a heterotrimeric receptor complex with IL-15Rβ and IL-15Rγ [ 27 ] . The findings of this study suggest that IL-15Rα may increase the risk of PC and as an independent risk factor for PC. The metabolic features of PC drive disease aggression and therapeutic resistance and present new opportunities for therapy. Nutrients in the form of carbohydrates, amino acids, and lipids are used by cells to maintain energy balance, assist in detoxification, and support biosynthesis. The pathways that perform this function are collectively referred to as intermediary metabolism. Pancreatic cancer cells rewire intermediary metabolism to support different energetic and biosynthetic demands compared with normal cells. Much of what has been described for this reprogramming is driven by mutations in the oncogene KRAS, which is nearly universally mutated in PC. Pancreatic tumors are hypovascular and thus in a state of constant nutrient deprivation. In our investigation, we discerned a spectrum of 11 peripheral metabolites intricately linked with the risk of PC. Interestingly, our findings underscore the role of 5-methyluridine as a significant potential mediating blood metabolites in PC progression. A recent study has identified that uridine as a fuel for PC in glucose-deprived conditions. They demonstrated that liberates uridine-derived ribose to fuel central carbon metabolism and thereby support redox balance, survival and proliferation in glucose-restricted pancreatic ductal carcinoma cells [ 28 ] . In conclusion, 5-methyluridine, serving as an intermediary in pyrimidine metabolism, plays a crucial role in promoting our understanding of tumor metabolism and its interplay with the inflammation. Therefore, our results reveal a potential mediator effect of 5-methyluridine in the casual association between circulating inflammation and pancreatic cancer. The present study has several strengths to guide future directions in pancreatic cancer researches. First, our work comprises the first systematic investigation to examine the causal relationship between circulating inflammation-related circulating proteins, blood metabolites and pancreatic cancer. This unique contribution is underscored by the comprehensive analysis of 91 inflammation-related circulating proteins and 1400 blood metabolites. By using a MR design, the study effectively mitigated issues related to reverse causality and residual confounding variables. Furthermore, we have identified a set of circulating inflammatory proteins and blood metabolites that exhibit a strong correlation with the risk of pancreatic cancer. These findings present potential biomarkers for non-invasive liquid biopsy in pancreatic cancer that warrant validation through subsequent experiments. Finally, we have proposed a potential axis of circulating inflammatory proteins–blood metabolites levels–pancreatic cancer, which may serve as a foundation for drug development strategies in pancreatic cancer research. Further investigation utilizing in vitro and in vivo models is necessary to validate this proposed axis and to develop targeted therapies accordingly. Meanwhile, our study also has several limitations. First, our analysis is based on a European population, limiting its generalizability. Second, there is a relative scarcity of GWAS datasets for PC, and we hope for larger datasets in the future for validation. Third, despite measures taken to identify and eliminate outlier variants, we cannot exclude the possibility of pleiotropy affecting our results. Last, the impact of our intermediary factors on the outcomes is relatively low, necessitating further research to quantify the influence of other mediators. Conclusion Overall, the present study is the first to comprehensively assess the causal relationships between inflammation-related circulating proteins, blood metabolites and pancreatic cancer, emphasizing the disruption of the inflammation–metabolism network in this disease. The results offer important insights into the etiology of pancreatic cancer and present novel insights for therapeutic and preventive strategies. Furthermore, these findings highlight the importance of elucidating the potential mechanisms mediated by blood metabolites between the circulating inflammatory proteins and pancreatic cancer. These results provide novel insights into inflammatory response and metabolite-targeted interventions for pancreatic cancer. Abbreviations PC pancreatic cancer CI Confidence Interval MR Mendelian randomization OR odds ratio SNP Single nucleotide polymorphism IVs Instrumental variables IVW Inverse variance weighted Declarations Funding This work was supported by the National Natural Science Foundation of China (grants 82172919). Author Contributions Conception, design, interpretation, and manuscript writing and revision: Yanheng Duan and Fen zhang. Data acquisition, statistical and computational analysis, and technical support; Yanheng Duan, Fen Zhang, Xiaojing Zhang, Liang Jin, Kun He, Yufan Guan, Xiaotian Dong, Yong Chen, Jiaze An. Study supervision: Jiaze An and Yong Chen. All authors read and approved the final manuscript. Acknowledgments The authors gratefully acknowledge the invaluable contributions of all the participants to the GWAS. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Availability of data and materials The datasets analyzed during the present study are available in summary data https://www.ebi.ac.uk/gwas/home and https://ftp.ebi.a c.uk/pub/databases/gwas/summary_statistics/). References SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2018 [J]. CA Cancer J Clin, 2018, 68(1): 7-30. PADOAN A, PLEBANI M, BASSO D. Inflammation and Pancreatic Cancer: Focus on Metabolism, Cytokines, and Immunity [J]. Int J Mol Sci, 2019, 20(3). HAMADA S, MASAMUNE A, SHIMOSEGAWA T. Inflammation and pancreatic cancer: disease promoter and new therapeutic target [J]. J Gastroenterol, 2014, 49(4): 605-17. MICHAUD D, MIRLEKAR B, BISCHOFF S, et al. Pancreatic cancer-associated inflammation drives dynamic regulation of p35 and Ebi3 [J]. Cytokine, 2020, 125: 154817. STONE M L, BEATTY G L. Cellular determinants and therapeutic implications of inflammation in pancreatic cancer [J]. Pharmacol Ther, 2019, 201: 202-13. SHADHU K, XI C. Inflammation and pancreatic cancer: An updated review [J]. Saudi J Gastroenterol, 2019, 25(1): 3-13. HANAHAN D. Hallmarks of Cancer: New Dimensions [J]. Cancer Discov, 2022, 12(1): 31-46. SOUSA C M, KIMMELMAN A C. The complex landscape of pancreatic cancer metabolism [J]. Carcinogenesis, 2014, 35(7): 1441-50. PERAZZOLI G, GARCIA-VALDEAVERO O M, PENA M, et al. Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review [J]. Metabolites, 2023, 13(7). URAYAMA S. Pancreatic cancer early detection: expanding higher-risk group with clinical and metabolomics parameters [J]. World J Gastroenterol, 2015, 21(6): 1707-17. LI X, DU Y, JIANG W, et al. Integrated transcriptomics, proteomics and metabolomics-based analysis uncover TAM2-associated glycolysis and pyruvate metabolic remodeling in pancreatic cancer [J]. Front Immunol, 2023, 14: 1170223. ZHOU W, CAPELLO M, FREDOLINI C, et al. Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells [J]. J Proteome Res, 2012, 11(2): 554-63. ZHONG H, LIU S, ZHU J, et al. Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk [J]. Int J Cancer, 2023, 153(1): 103-10. LI Y, TANG S, SHI X, et al. Metabolic classification suggests the GLUT1/ALDOB/G6PD axis as a therapeutic target in chemotherapy-resistant pancreatic cancer [J]. Cell Rep Med, 2023, 4(9): 101162. DAVIES N M, HOLMES M V, DAVEY SMITH G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians [J]. BMJ, 2018, 362: k601. ZHAO J H, STACEY D, ERIKSSON N, et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets [J]. Nat Immunol, 2023, 24(9): 1540-51. PALMER T M, LAWLOR D A, HARBORD R M, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors [J]. Stat Methods Med Res, 2012, 21(3): 223-42. CHEN Y, CHEN C. Gut microbiota, inflammatory proteins and COVID-19: a Mendelian randomisation study [J]. Front Immunol, 2024, 15: 1406291. SKRIVANKOVA V W, RICHMOND R C, WOOLF B A R, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement [J]. JAMA, 2021, 326(16): 1614-21. HEMANI G, ZHENG J, ELSWORTH B, et al. The MR-Base platform supports systematic causal inference across the human phenome [J]. Elife, 2018, 7. QIN C, YANG G, YANG J, et al. Metabolism of pancreatic cancer: paving the way to better anticancer strategies [J]. Mol Cancer, 2020, 19(1): 50. CASOLINO R, PAIELLA S, AZZOLINA D, et al. Homologous Recombination Deficiency in Pancreatic Cancer: A Systematic Review and Prevalence Meta-Analysis [J]. J Clin Oncol, 2021, 39(23): 2617-31. YUAN C, MORALES-OYARVIDE V, KHALAF N, et al. Prediagnostic Inflammation and Pancreatic Cancer Survival [J]. J Natl Cancer Inst, 2021, 113(9): 1186-93. CARONNI N, LA TERZA F, VITTORIA F M, et al. IL-1beta(+) macrophages fuel pathogenic inflammation in pancreatic cancer [J]. Nature, 2023, 623(7986): 415-22. LI Z, YU X, WERNER J, et al. The role of interleukin-18 in pancreatitis and pancreatic cancer [J]. Cytokine Growth Factor Rev, 2019, 50: 1-12. KURZ E, HIRSCH C A, DALTON T, et al. Exercise-induced engagement of the IL-15/IL-15Ralpha axis promotes anti-tumor immunity in pancreatic cancer [J]. Cancer Cell, 2022, 40(7): 720-37 e5. ISLAM S M S, CHOI B, CHOI J, et al. Frequencies of IL-15Ralpha+ cells in patients with Behcet's disease and the effects of overexpressing IL-15Ralpha+ on disease symptoms in mice [J]. Cytokine, 2018, 110: 257-66. NWOSU Z C, WARD M H, SAJJAKULNUKIT P, et al. Uridine-derived ribose fuels glucose-restricted pancreatic cancer [J]. Nature, 2023, 618(7963): 151-8. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif SupplementaryFigure2.tif SupplementaryTable.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5783157","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399437996,"identity":"b3ba4edb-a760-4adc-8832-f5b1b6ea07d0","order_by":0,"name":"Yanheng Duan","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanheng","middleName":"","lastName":"Duan","suffix":""},{"id":399437997,"identity":"ebdf1d38-8586-4481-b8b1-7cf41dede186","order_by":1,"name":"Fen Zhang","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Zhang","suffix":""},{"id":399437998,"identity":"5f93b92a-8e67-48f3-8a1a-124b3601e172","order_by":2,"name":"Xiaojing Zhang","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Zhang","suffix":""},{"id":399438000,"identity":"61bb16bc-8b56-41ee-962e-d80058c5b8fd","order_by":3,"name":"Liang Jin","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Jin","suffix":""},{"id":399438001,"identity":"11e5b10f-b1ee-4333-b123-2fc45062ca9c","order_by":4,"name":"Kun He","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"He","suffix":""},{"id":399438002,"identity":"60f1111f-9591-424e-a9c3-b6bb680ea851","order_by":5,"name":"Yufan Guan","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yufan","middleName":"","lastName":"Guan","suffix":""},{"id":399438003,"identity":"f5e5b0fb-2c49-4180-81a9-9c28be9a43f4","order_by":6,"name":"Xiaotian Dong","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaotian","middleName":"","lastName":"Dong","suffix":""},{"id":399438004,"identity":"f15c4d76-d203-413c-81ac-00590c09d19c","order_by":7,"name":"Yong Chen","email":"","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Chen","suffix":""},{"id":399438005,"identity":"dbd5345f-52f5-4f65-89fb-8c2dbaf3aafd","order_by":8,"name":"Jiaze An","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYHCChANAQo6fvbHx4QdStBhL9hxuNpYgxapEgxvpbQI8xCjln93w8MCPPzYJkjMftjFIMNjJ6TYQ0CJx50DCwR6etDx+6cS2BwUMycZmBwhZcyMh4QCPxOFiydmJ7QYSDAcStxHSIg/UcvCPwf/EDTcPtknwEKPFAKjlME/CgcQNNxiJ1GII0iJzIBkYyInAQDYgwi9yN3KSP775YweMyuMPH36osJMj7H0GngRkdxJUDgLshE0dBaNgFIyCEQ4A0+5JyMCnUTcAAAAASUVORK5CYII=","orcid":"","institution":"Xijing Hospital, Air Force Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiaze","middleName":"","lastName":"An","suffix":""}],"badges":[],"createdAt":"2025-01-07 16:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5783157/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5783157/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73456696,"identity":"48bd38fd-d127-4902-b573-5037ef57243c","added_by":"auto","created_at":"2025-01-10 07:10:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45324,"visible":true,"origin":"","legend":"\u003cp\u003eDiagrams illustrating associations examined in this study. (A) The total effect between inflammation-related circulating proteins and PC.\u003cstrong\u003e \u003c/strong\u003ec is the total effect using genetically predicted inflammation-related circulating proteins as exposure and PC as outcome. d is the total effect using genetically predicted PC as\u003cstrong\u003e \u003c/strong\u003eexposure and inflammation-related circulating proteins as outcome. (B) The total effect was decomposed into: (1) indirect effect using a two-step approach (where a is the total effect\u003c/p\u003e\n\u003cp\u003eof inflammation-related circulating proteins on circulating metabolites, and b is the effect of circulating metabolites on PC) and the product method (a * b) and (2) direct effect (c′ = c – a * b). Proportion mediated was the indirect effect divided by the total effect.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/8e9c0bf30f1033bf9fa44256.png"},{"id":73456932,"identity":"da2993ae-7c45-4ef8-a315-755159907c13","added_by":"auto","created_at":"2025-01-10 07:18:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58254,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis showed the causality of 4 inflammation-related circulating proteins on pancreatic cancer were significant. nsnp: number of single nucleotide polymorphisms; OR: odds ratio; CI: Confidence Interval;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/0a9c114f9d3ab85034730b1c.png"},{"id":73456698,"identity":"80d7afe5-beea-40d5-99e2-5b4a6eaedf61","added_by":"auto","created_at":"2025-01-10 07:10:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110176,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis showed the causality of 15 circulating metabolites on pancreatic cancer were significant. nsnp: number of single nucleotide polymorphisms; OR: odds ratio; CI: Confidence Interval;\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/decb0902ab0b0cc95f640abd.png"},{"id":73456703,"identity":"3e576acf-c459-4600-9d10-c59c415fb87e","added_by":"auto","created_at":"2025-01-10 07:10:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136477,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot showing the mediation effect of Interleukin-15 receptor subunit alpha on PC via 5-methyluridine.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/11801fa88d94e016eeb1151e.png"},{"id":74401539,"identity":"3c76fd5e-7dc8-4c59-9a45-758b73373d64","added_by":"auto","created_at":"2025-01-22 03:16:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876700,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/b6dfed4d-2cce-42a2-a56b-4d7ff22b1076.pdf"},{"id":73456699,"identity":"7af74108-a4f8-4734-954e-fa262dbfb356","added_by":"auto","created_at":"2025-01-10 07:10:34","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1019548,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/b7b652ca7529188552dba4e2.tif"},{"id":73456705,"identity":"a318033c-b1ef-4bc7-99ec-e35e57d09382","added_by":"auto","created_at":"2025-01-10 07:10:34","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1649994,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/af1455a2058cb1130b597bff.tif"},{"id":73456700,"identity":"8a6ca197-c134-41fb-be6b-bab5085fac0b","added_by":"auto","created_at":"2025-01-10 07:10:34","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1321129,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5783157/v1/4b46c05c093fe048fb9f09c6.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetically predicted blood metabolites mediate the association between circulating inflammation-related proteins and pancreatic cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer is one of the most lethal malignancies, with a 5-year survival rate of less than 5% \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Initiation and progression of this disease results from the interaction of genetic events combined. The potential role of inflammation in the development and growth of cancer was initially described in 1863 by Virchow, who observed that inflammatory cells infiltrate tumors\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. In recent years, the advancements made in cancer biology have confirmed that systemic and local chronic inflammation might enhance the risk of PC\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Inflammatory processes have emerged as key mediators of pancreatic cancer development and progression \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Inflammation is a complex and highly coordinated cellular and biochemical process intended to resolve tissue injury and protect the host. However, when this \u0026ldquo;wound healing\u0026rdquo; process becomes chronic, derangement of normal cellular mechanisms occurs and paradoxically, tissue injury and neoplasia can ensue. In this regard, chronic inflammation is a hallmark of cancer, and inflammation can impair normal cellular and organ physiology as well as the efficacy of therapeutic interventions\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Inflammatory responses are orchestrated by a complex network of cells and mediators, including circulating proteins such as cytokines and soluble receptors. Therefore, discovery of the plasma level of the inflammation-related circulating proteins should yield valuable insights into both physiology and the etiology of PC.\u003c/p\u003e \u003cp\u003eReprogramming of cellular metabolism is another a hallmark of tumorigenesis. Metabolic reprogramming related to glucose, lipid, and amino acid metabolism in PC not only enables the cancer to thrive and survive under hypovascular, nutrient-poor and hypoxic microenvironments, but also confers chemoresistance, which contributes to the poor prognosis of PC\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. To date, increasing evidence points to the influence of blood metabolomic profiling on pancreatic cancer risk\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Proteomic analysis of the pancreatic cancer cells demonstrated alteration in proteins involved in metabolic pathways including increased expression of glycolytic and reduced Krebs cycle enzymes, and accumulation of key proteins involved in glutamine metabolism, in support of Warburg effect\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. These in turn play significant role in nucleotide and amino acid biosynthesis required for sustaining the proliferating cancer cells. Applications of sensitive mass spectrometric techniques in metabolomics study of PC detection biomarkers have led to identification of a set of small molecules or metabolites (or biochemical intermediates) that are potent discriminants of developing PC\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. However, the relationship between inflammation-related circulating proteins, blood metabolites and pancreatic cancer still remains unknown. In addition, how blood metabolites are involved in the interaction between circulating inflammatory proteins and pancreatic cancer remains unclear. Therefore, clarifying the potential mediation effects of blood metabolites in the interaction between circulating inflammatory proteins and pancreatic cancer is crucial for establishing the comprehensive inflammation-metabolism network of pancreatic cancer. Mendelian randomization (MR) is a powerful tool for inferring causal effects between exposures and outcomes\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. By leveraging genetic variants associated with the exposure, MR helps overcome the limitations of traditional observational studies, such as confounding and reverse causation. Therefore, we employed a two-sample MR approach to investigate the causal effects of 91 inflammation-related circulating proteins and 1400 human blood metabolites on PC, aiming to gain deeper molecular insights. Our findings shed new light on the intricate relationships between the inflammation-related circulating proteins, blood metabolites, and pancreatic cancer, which may have significant implications for preventing and treating pancreatic cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe exposure data, 91 inflammation-related circulating proteins, for this study were obtained from the the EBI GWAS Catalog, accession numbers GCST90274758 to GCST90274848 (The data could be downloaded from the homepage of the official website by entering the corresponding number at the following URL: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/home\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. As for the intermediate variables, the study incorporated GWAS summary data for circulating metabolites levels from the EBI GWAS Catalog, accession numbers GCST90199621 to GCST90201020 (The data could be downloaded from the homepage of the official website by entering the corresponding number at the following URL: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/\u003c/span\u003e\u003cspan address=\"https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The outcome variable of interest, pancreatic cancer, was derived from GWAS summary data of the latest R10 version released by the FinnGen consortium, which included 1626 cases and 314193 controls.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstrument variable selection\u003c/h3\u003e\n\u003cp\u003eIn this study, the selection of instrument variables was guided by relevant MR studies, using a threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e to preliminarily identify single nucleotide polymorphisms (SNP) loci with statistical significance from the GWAS summary data of 91 inflammation-related circulating proteins. Additionally, linkage disequilibrium coefficient r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.001 and a region width of 10000 kb were set to minimize the impact of genetic pleiotropy on the results. F-statistics were employed to assess whether the selected instrument variables were weak instruments, with the criterion of F\u0026thinsp;\u0026gt;\u0026thinsp;10, indicating the absence of weak instrument bias. To further evaluate whether individual SNPs are closely associated with pancreatic cancer and to mitigate potential confounding factors, each SNP\u0026rsquo;s secondary phenotype was manually checked in PhenoScanner.\u003c/p\u003e\n\u003ch3\u003eMR analysis\u003c/h3\u003e\n\u003cp\u003eMR analyses were combined with the \u0026ldquo;mr\u0026rdquo; functionality as well as with five methodologies: MR Egger, Weighted Median, Inverse Variance Weighted (IVW), Simple Mode and Weighted Mode. Central focus of this study was the results of the IVW method, which showed the strongest ability to test for causal relationships in MR analyses\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In the event that the IVW method yielded a p-value of less than 0.05, the result was incorporated into the subsequent study\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Then the odds ratio (OR) was calculated, where more than 1 was a danger element and less than 1 was a protection element. To assure the precision of the analyses, several kinds of susceptibility tests, consisting of heterogeneity tests, horizontal multivariate tests, as well as leave-one-out sensitivity tests (LOO), were employed in order to fully assess the reliability concerning these findings.\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eThis study employs a two-sample two-step MR design based on GWAS summary data. The study follows the latest guidelines for MR analysis, as outlined in the STROBE-MR guidelines\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, and operates under three fundamental assumptions: (1) There exist robust correlations of instrumental variables (IVs) with the exposure (association); (2) IVs are irrespective of the confounders (independence); (3) the effect of IVs on outcomes is achievable solely through exposure (exclusion restriction criteria).\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows a schematic summary of the analysis. We conducted a two-sample bidirectional MR to evaluate the mutual causality between 91 inflammation-related circulating proteins and Pancreatic cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), which was designated as the total effect. We further performed a mediation analysis using a two-step MR design to explore whether 1400 human blood metabolites mediate the causal pathway from inflammation-related circulating proteins to PC outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The overall effect can be decomposed into an indirect effect (through mediators) and a direct- effect (without mediators) effect. The total effect of inflammation-related circulating proteins on PC was decomposed into 1) direct effects of inflammation-related circulating proteins on PC (c\u0026rsquo; in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and 2) indirect effects mediated by inflammation-related circulating proteins through the mediator (a * b in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We calculated the percentage mediated by the mediating effect by dividing the indirect effect by the total effect. Meanwhile, 95% confidence intervals were calculated with the delta method.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll MR analyses were conducted using R (version 4.0.2) and the TwoSampleMR R package version 0.5.6\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMR results of inflammatory proteins and pancreatic cancer\u003c/h2\u003e \u003cp\u003eThe MR analyses uncovered a causal relationship between four inflammation-related circulating proteins and pancreatic cancer. Specifically, we observed that the genetically predicted C-X-C motif chemokine 10 (Odds Ratio (OR): 0.8209, 95% Confidence Interval (95%CI): 0.6797\u0026ndash;0.9915; p\u0026thinsp;=\u0026thinsp;0.0405) were associated with a reduced risk of developing PC. Conversely, the Interleukin-15 receptor subunit alpha (OR: 1.1736, 95% CI: 1.0267\u0026ndash;1.3415; p\u0026thinsp;=\u0026thinsp;0.0190), Interleukin-1-alpha (OR: 1.2406, 95% CI: 1.0015\u0026ndash;1.5367; p\u0026thinsp;=\u0026thinsp;0.0484), Interleukin-2 receptor subunit beta (OR: 1.2605, 95% CI: 1.0285\u0026ndash;1.5448; p\u0026thinsp;=\u0026thinsp;0.0257) were linked to an elevated risk of PC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;1). The MR-Egger test and Cochrane\u0026rsquo; Q test did not reveal significant horizontal pleiotropy and heterogeneity (Supplementary Tables\u0026nbsp;2 and 3). The leave-one-out analysis method did not reveal any interference with the results attributable to a single SNP (Supplementary Fig.\u0026nbsp;1). When evaluating the causal effects of pancreatic cancer on the inflammation-related circulating proteins, it was observed that all p values were greater than 0.05, suggesting that pancreatic cancer does not have an effect on the inflammation-related circulating proteins under consideration (Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMR results of circulating metabolites and PC\u003c/h3\u003e\n\u003cp\u003eMR results of 1400 circulating metabolites and the PC suggested that there was a correlation between a total of 11 metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;5). Resulting in 4 positive circulating metabolites associated with PC, which included 2-linoleoylglycerol (18:2), Cortisone, X-18888 and X-21742 levels were positively correlated with PC, and Oxalate (ethanedioate), Adrenate(22:4n6), 1-palmitoleoyl-GPC (16:1), 5-methyluridine (ribothymidine), Glycodeoxycholate 3-sulfate, Branched-chain (straight-chain, or cyclopropyl 12:1 fatty acid) and Palmitate (16:0) were negatively correlated with PC. Sensitivity analyses were performed in order to verify of the above findings (Supplementary Tables\u0026nbsp;6 and 7). Leave-one-out plots showed that the MR results were not affected by any single SNP (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMR results of inflammatory proteins and circulating metabolites\u003c/h3\u003e\n\u003cp\u003ePreviously, we identified 4 inflammation-related circulating proteins and 11 circulating metabolites to PC. Then, we investigated the causal role of 4 inflammation-related circulating proteins on 11 circulating metabolites. The MR analysis revealed that only Interleukin-15 receptor subunit alpha\u003c/p\u003e \u003cp\u003e(OR\u0026thinsp;=\u0026thinsp;0.9237, 95% CI: 0.8583\u0026ndash;0.9940, p\u0026thinsp;=\u0026thinsp;0.0340) was highly associated with 5-methyluridine (ribothymidine) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At the same time, no significant causal effect was determined with other approaches.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMR analysis showed the causality of Interleukin-15 receptor subunit alpha on 5-methyluridine was significant.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ensnp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003elo_ci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eup_ci\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eor_lci95\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eor_uci95\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-15 receptor subunit alpha levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-methyluridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-15 receptor subunit alpha levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-methyluridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-15 receptor subunit alpha levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-methyluridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-15 receptor subunit alpha levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-methyluridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-15 receptor subunit alpha levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-methyluridine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntermediary effect\u003c/h2\u003e \u003cp\u003eTwo-step MR and bidirectional MR analyses were performed to examine the mediating pathways from Interleukin-15 receptor subunit alpha through 5-methyluridine (ribothymidine) to PC. The Two-step MR results indicated that 5-methyluridine may act as a mediator in the causal relationship between Interleukin-15 receptor subunit alpha and PC, with a mediation ratio of 7.41% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this comprehensive mediated MR study, we identified 4 inflammatory proteins and 11 circulating metabolites in a causal relationship to pancreatic cancer. Mediated MR results indicated that 5-methyluridine may be a mediator of the causal relationship between Interleukin-15 receptor subunit alpha and pancreatic cancer, with a mediation ratio of 7.41%. This analysis highlighted the link from the inflammation-related circulating proteins to PC, highlighting the mediating role of the metabolites.\u003c/p\u003e \u003cp\u003ePC stands as an ongoing challenge to global health due to its aggressive behavior and bleak prognosis\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Addressing these challenges and finding ways to improve therapeutic efficacy, overcome treatment tolerance, identify high-risk groups and discover appropriate biomarkers for PC has become paramount research directions for PC researchers. In this context, the research direction has begun to shift to the value of inflammatory markers in the human body on the survival and prognosis of PC\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSystemic and local chronic inflammation might enhance the risk of pancreatic cancer, and PC-associated inflammatory infiltrate in the tumor microenvironment concurs in enhancing tumor growth and metastasis. Inflammatory mediators, including interleukin 1β (IL-1β), interleukin-18 (IL-18), Interleukin-15(IL-15) and tumor necrosis factor-α, which are prevalent in pancreatic cancer, are implicated in promoting cellular proliferation, thwarting programmed cell death, stimulating new blood vessel formation, and fostering genetic instability \u0026ndash; all of which can contribute to the progression of PC\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Interleikin-15 (IL-15) is a pleiotropic proinflammatory cytokine produced by activated dendritic cells, macrophages, and monocytes that essential for cell survival, cell proliferation, and functional activity of immune cells such as natural killer (NK) cells, memory T cells, monocytes, macrophages, and dendritic cells. IL-15 exerts its effect by binding to a membrane receptor composed of high affinity binding alpha chain (IL-15Rα) that forms a heterotrimeric receptor complex with IL-15Rβ and IL-15Rγ\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. The findings of this study suggest that IL-15Rα may increase the risk of PC and as an independent risk factor for PC.\u003c/p\u003e \u003cp\u003eThe metabolic features of PC drive disease aggression and therapeutic resistance and present new opportunities for therapy. Nutrients in the form of carbohydrates, amino acids, and lipids are used by cells to maintain energy balance, assist in detoxification, and support biosynthesis. The pathways that perform this function are collectively referred to as intermediary metabolism. Pancreatic cancer cells rewire intermediary metabolism to support different energetic and biosynthetic demands compared with normal cells. Much of what has been described for this reprogramming is driven by mutations in the oncogene KRAS, which is nearly universally mutated in PC. Pancreatic tumors are hypovascular and thus in a state of constant nutrient deprivation. In our investigation, we discerned a spectrum of 11 peripheral metabolites intricately linked with the risk of PC. Interestingly, our findings underscore the role of 5-methyluridine as a significant potential mediating blood metabolites in PC progression. A recent study has identified that uridine as a fuel for PC in glucose-deprived conditions. They demonstrated that liberates uridine-derived ribose to fuel central carbon metabolism and thereby support redox balance, survival and proliferation in glucose-restricted pancreatic ductal carcinoma cells\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In conclusion, 5-methyluridine, serving as an intermediary in pyrimidine metabolism, plays a crucial role in promoting our understanding of tumor metabolism and its interplay with the inflammation. Therefore, our results reveal a potential mediator effect of 5-methyluridine in the casual association between circulating inflammation and pancreatic cancer.\u003c/p\u003e \u003cp\u003eThe present study has several strengths to guide future directions in pancreatic cancer researches. First, our work comprises the first systematic investigation to examine the causal relationship between circulating inflammation-related circulating proteins, blood metabolites and pancreatic cancer. This unique contribution is underscored by the comprehensive analysis of 91 inflammation-related circulating proteins and 1400 blood metabolites. By using a MR design, the study effectively mitigated issues related to reverse causality and residual confounding variables. Furthermore, we have identified a set of circulating inflammatory proteins and blood metabolites that exhibit a strong correlation with the risk of pancreatic cancer. These findings present potential biomarkers for non-invasive liquid biopsy in pancreatic cancer that warrant validation through subsequent experiments. Finally, we have proposed a potential axis of circulating inflammatory proteins\u0026ndash;blood metabolites levels\u0026ndash;pancreatic cancer, which may serve as a foundation for drug development strategies in pancreatic cancer research. Further investigation utilizing in vitro and in vivo models is necessary to validate this proposed axis and to develop targeted therapies accordingly.\u003c/p\u003e \u003cp\u003eMeanwhile, our study also has several limitations. First, our analysis is based on a European population, limiting its generalizability. Second, there is a relative scarcity of GWAS datasets for PC, and we hope for larger datasets in the future for validation. Third, despite measures taken to identify and eliminate outlier variants, we cannot exclude the possibility of pleiotropy affecting our results. Last, the impact of our intermediary factors on the outcomes is relatively low, necessitating further research to quantify the influence of other mediators.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, the present study is the first to comprehensively assess the causal relationships between inflammation-related circulating proteins, blood metabolites and pancreatic cancer, emphasizing the disruption of the inflammation\u0026ndash;metabolism network in this disease. The results offer important insights into the etiology of pancreatic cancer and present novel insights for therapeutic and preventive strategies. Furthermore, these findings highlight the importance of elucidating the potential mechanisms mediated by blood metabolites between the circulating inflammatory proteins and pancreatic cancer. These results provide novel insights into inflammatory response and metabolite-targeted interventions for pancreatic cancer.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePC\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003epancreatic cancer\u003c/p\u003e\n\u003cp\u003eCI \u0026nbsp; \u0026nbsp;Confidence Interval\u003c/p\u003e\n\u003cp\u003eMR \u0026nbsp; Mendelian randomization\u003c/p\u003e\n\u003cp\u003eOR \u0026nbsp; \u0026nbsp;odds ratio\u003c/p\u003e\n\u003cp\u003eSNP \u0026nbsp; Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eIVs \u0026nbsp; \u0026nbsp;Instrumental variables\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; Inverse variance weighted\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (grants 82172919).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception, design, interpretation, and manuscript writing and revision: Yanheng Duan and Fen zhang. Data acquisition, statistical and computational analysis, and technical support; Yanheng Duan, Fen Zhang, Xiaojing Zhang, Liang Jin, Kun He, Yufan Guan, Xiaotian Dong, Yong Chen, Jiaze An.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy supervision: Jiaze An and Yong Chen. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the invaluable contributions of all the participants to the GWAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during the present study are available in summary data https://www.ebi.ac.uk/gwas/home \u0026nbsp;and \u0026nbsp; https://ftp.ebi.a c.uk/pub/databases/gwas/summary_statistics/). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2018 [J]. CA Cancer J Clin, 2018, 68(1): 7-30.\u003c/li\u003e\n\u003cli\u003ePADOAN A, PLEBANI M, BASSO D. Inflammation and Pancreatic Cancer: Focus on Metabolism, Cytokines, and Immunity [J]. Int J Mol Sci, 2019, 20(3).\u003c/li\u003e\n\u003cli\u003eHAMADA S, MASAMUNE A, SHIMOSEGAWA T. Inflammation and pancreatic cancer: disease promoter and new therapeutic target [J]. J Gastroenterol, 2014, 49(4): 605-17.\u003c/li\u003e\n\u003cli\u003eMICHAUD D, MIRLEKAR B, BISCHOFF S, et al. Pancreatic cancer-associated inflammation drives dynamic regulation of p35 and Ebi3 [J]. Cytokine, 2020, 125: 154817.\u003c/li\u003e\n\u003cli\u003eSTONE M L, BEATTY G L. Cellular determinants and therapeutic implications of inflammation in pancreatic cancer [J]. Pharmacol Ther, 2019, 201: 202-13.\u003c/li\u003e\n\u003cli\u003eSHADHU K, XI C. Inflammation and pancreatic cancer: An updated review [J]. Saudi J Gastroenterol, 2019, 25(1): 3-13.\u003c/li\u003e\n\u003cli\u003eHANAHAN D. Hallmarks of Cancer: New Dimensions [J]. Cancer Discov, 2022, 12(1): 31-46.\u003c/li\u003e\n\u003cli\u003eSOUSA C M, KIMMELMAN A C. The complex landscape of pancreatic cancer metabolism [J]. Carcinogenesis, 2014, 35(7): 1441-50.\u003c/li\u003e\n\u003cli\u003ePERAZZOLI G, GARCIA-VALDEAVERO O M, PENA M, et al. Evaluating Metabolite-Based Biomarkers for Early Diagnosis of Pancreatic Cancer: A Systematic Review [J]. Metabolites, 2023, 13(7).\u003c/li\u003e\n\u003cli\u003eURAYAMA S. Pancreatic cancer early detection: expanding higher-risk group with clinical and metabolomics parameters [J]. World J Gastroenterol, 2015, 21(6): 1707-17.\u003c/li\u003e\n\u003cli\u003eLI X, DU Y, JIANG W, et al. Integrated transcriptomics, proteomics and metabolomics-based analysis uncover TAM2-associated glycolysis and pyruvate metabolic remodeling in pancreatic cancer [J]. Front Immunol, 2023, 14: 1170223.\u003c/li\u003e\n\u003cli\u003eZHOU W, CAPELLO M, FREDOLINI C, et al. Proteomic analysis reveals Warburg effect and anomalous metabolism of glutamine in pancreatic cancer cells [J]. J Proteome Res, 2012, 11(2): 554-63.\u003c/li\u003e\n\u003cli\u003eZHONG H, LIU S, ZHU J, et al. Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk [J]. Int J Cancer, 2023, 153(1): 103-10.\u003c/li\u003e\n\u003cli\u003eLI Y, TANG S, SHI X, et al. Metabolic classification suggests the GLUT1/ALDOB/G6PD axis as a therapeutic target in chemotherapy-resistant pancreatic cancer [J]. Cell Rep Med, 2023, 4(9): 101162.\u003c/li\u003e\n\u003cli\u003eDAVIES N M, HOLMES M V, DAVEY SMITH G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians [J]. BMJ, 2018, 362: k601.\u003c/li\u003e\n\u003cli\u003eZHAO J H, STACEY D, ERIKSSON N, et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets [J]. Nat Immunol, 2023, 24(9): 1540-51.\u003c/li\u003e\n\u003cli\u003ePALMER T M, LAWLOR D A, HARBORD R M, et al. Using multiple genetic variants as instrumental variables for modifiable risk factors [J]. Stat Methods Med Res, 2012, 21(3): 223-42.\u003c/li\u003e\n\u003cli\u003eCHEN Y, CHEN C. Gut microbiota, inflammatory proteins and COVID-19: a Mendelian randomisation study [J]. Front Immunol, 2024, 15: 1406291.\u003c/li\u003e\n\u003cli\u003eSKRIVANKOVA V W, RICHMOND R C, WOOLF B A R, et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement [J]. JAMA, 2021, 326(16): 1614-21.\u003c/li\u003e\n\u003cli\u003eHEMANI G, ZHENG J, ELSWORTH B, et al. The MR-Base platform supports systematic causal inference across the human phenome [J]. Elife, 2018, 7.\u003c/li\u003e\n\u003cli\u003eQIN C, YANG G, YANG J, et al. Metabolism of pancreatic cancer: paving the way to better anticancer strategies [J]. Mol Cancer, 2020, 19(1): 50.\u003c/li\u003e\n\u003cli\u003eCASOLINO R, PAIELLA S, AZZOLINA D, et al. Homologous Recombination Deficiency in Pancreatic Cancer: A Systematic Review and Prevalence Meta-Analysis [J]. J Clin Oncol, 2021, 39(23): 2617-31.\u003c/li\u003e\n\u003cli\u003eYUAN C, MORALES-OYARVIDE V, KHALAF N, et al. Prediagnostic Inflammation and Pancreatic Cancer Survival [J]. J Natl Cancer Inst, 2021, 113(9): 1186-93.\u003c/li\u003e\n\u003cli\u003eCARONNI N, LA TERZA F, VITTORIA F M, et al. IL-1beta(+) macrophages fuel pathogenic inflammation in pancreatic cancer [J]. Nature, 2023, 623(7986): 415-22.\u003c/li\u003e\n\u003cli\u003eLI Z, YU X, WERNER J, et al. The role of interleukin-18 in pancreatitis and pancreatic cancer [J]. Cytokine Growth Factor Rev, 2019, 50: 1-12.\u003c/li\u003e\n\u003cli\u003eKURZ E, HIRSCH C A, DALTON T, et al. Exercise-induced engagement of the IL-15/IL-15Ralpha axis promotes anti-tumor immunity in pancreatic cancer [J]. Cancer Cell, 2022, 40(7): 720-37 e5.\u003c/li\u003e\n\u003cli\u003eISLAM S M S, CHOI B, CHOI J, et al. Frequencies of IL-15Ralpha+ cells in patients with Behcet\u0026apos;s disease and the effects of overexpressing IL-15Ralpha+ on disease symptoms in mice [J]. Cytokine, 2018, 110: 257-66.\u003c/li\u003e\n\u003cli\u003eNWOSU Z C, WARD M H, SAJJAKULNUKIT P, et al. Uridine-derived ribose fuels glucose-restricted pancreatic cancer [J]. Nature, 2023, 618(7963): 151-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pancreatic cancer, circulating inflammatory proteins, circulating metabolites, genome-wide association studies, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-5783157/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5783157/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: While extensive research highlighted the involvement of circulating inflammatory proteins and metabolism in pancreatic cancer (PC), causality remains unestablished. In the present study, we aimed to investigate the causal relationship of circulating inflammatory proteins and pancreatic cancer and identify the blood metabolites as potential mediators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We employed bidirectional two-sample Mendelian randomization analysis to examine the potential causal association between circulating inflammatory proteins, circulating metabolites, and PC using data from genome-wide association studies (GWAS). \u0026nbsp;And two-step MR to discover potential mediating blood metabolites in this process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: MR analysis identified 4 types of circulating inflammation-related proteins causally associated with pancreatic cancer. Furthermore, there was no strong evidence that genetically predicted pancreatic cancer had an effect on these four types of circulating inflammatory proteins. Further two-step MR analysis found 11 types of blood metabolites were causally associated with pancreatic cancer and the associations between circulating Interleukin-15 receptor subunit alpha and pancreatic cancer were mediated by blood 5-methyluridine with proportions of 7.41%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The present study provides evidence supporting the causal\u003c/p\u003e\n\u003cp\u003erelationships between various circulating inflammatory proteins, especially Interleukin-15 receptor subunit alpha, and pancreatic cancer, with a potential effect mediated by blood metabolites. Further research is needed on additional risk factors as potential mediators and establish a comprehensive inflammation-metabolism network in pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"Genetically predicted blood metabolites mediate the association between circulating inflammation-related proteins and pancreatic cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 07:10:29","doi":"10.21203/rs.3.rs-5783157/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e077500d-0215-41c6-b377-805a712aba11","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-22T03:08:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 07:10:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5783157","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5783157","identity":"rs-5783157","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}