Skip to main content

Causal effect between gut microbiota and pancreatic cancer: a two-sample Mendelian randomization study



Gut microbiota (GM) comprises a vast and diverse community of microorganisms, and recent studies have highlighted the crucial regulatory roles of various GM and their secreted metabolites in pancreatic cancer (PC). However, the causal relationship between GM and PC has yet to be confirmed.


In the present study, we used two-sample Mendelian randomization (MR) analysis to investigate the causal effect between GM and PC, with genome-wide association study (GWAS) from MiBioGen consortium as an exposure factor and PC GWAS data from FinnGen as an outcome factor. Inverse variance weighted (IVW) was used as the primary method for this study.


At the genus level, we observed that Senegalimassilia (OR: 0.635, 95% CI: 0.403–0.998, P = 0.049) exhibited a protective effect against PC, while Odoribacter (OR:1.899, 95%CI:1.157–3.116, P = 0.011), Ruminiclostridium 9(OR:1.976,95%CI:1.128–3.461, P = 0.017), Ruminococcaceae (UCG011)(OR:1.433, 95%CI:1.072–1.916, P = 0.015), and Streptococcus(OR:1.712, 95%CI:1.071–1.736, P = 0.025) were identified as causative factors for PC. Additionally, sensitivity analysis, Cochran’s Q test, the Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and MR-Egger regression indicated no heterogeneity, horizontal pleiotropy, or reverse causality between GM and PC.


Our analysis establishes a causal effect between specific GM and PC, which may provide new insights into the potential pathogenic mechanisms of GM in PC and the assignment of effective therapeutic strategies.

Peer Review reports


PC represents one of the most malignant gastrointestinal tumors, characterized by its stealthiness, aggressiveness, and high lethality, with a meager 5-year survival rate of only 11% [1]. In the United States, PC accounts for over 62,210 annual diagnoses and more than 49,830 deaths [1]. By 2040, PC is projected to surpass lung cancer as the second leading cause of cancer-related deaths in the US [2].

Current studies have revealed that the occurrence and progression of PC are associated with genetic mutations, dietary habits, and environmental factors [3, 4]. Among them, through the advancements in metagenomic and 16 S ribosomal RNA gene sequencing, researchers have discovered a correlation between GM and pancreatic diseases, including PC [5]. GM dysbiosis not only affects intestinal diseases directly [6, 7], but also extends its influence to extraintestinal organs, such as the pancreas and liver [8]. Studies have observed multiple changes in the microbiota of the oral cavity, gastrointestinal tract, and pancreas in PC patients compared to healthy individuals, highlighting the role of GM in PC [9]. This alteration in GM exerts its influence on PC through multiple mechanisms. Specifically, GM can interact with various risk factors for PC, such as obesity and diabetes, triggering an inflammatory response. This activation of the inflammatory response can facilitate the entry of GM and its secretions into the pancreas through different routes, including hematogenous spread, lymphatic metastasis, and pancreatic retrograde flow [10]. Furthermore, GM influences the development and progression of PC by modulating inflammatory responses, immune cell infiltration, and other mechanisms [11,12,13]. However, despite this, their causal effect has yet to be discovered.

MR Study is a statistical method widely used to analyze the causal relationship between exposure and outcome factors, which follows Mendel’s second law and relies on the independent random assignment of genetic variants during meiosis to realize a similar randomization effect as in Randomized Controlled Trials. Thus, MR can effectively overcome the confounders that occur in traditional studies and avoid reverse causality [14, 15]. To our knowledge, current MR-based studies have explored the causal relationships between GM and autoimmune diseases [16], colorectal cancer [17], and psychiatric disorders [18], but no MR-based studies have been performed to investigate the relationship between GM and PC.

In this study, we employed a two-sample MR approach to evaluate the causal effect between GM and PC, providing insights into the etiology and mechanisms of PC.

Materials and methods

Overall study design

This study utilized pooled-level genetic data to conduct a two-sample MR analysis, aiming to investigate the causal effect between GM and PC. For this purpose, genetic variants that demonstrated significant associations with GM exposure were considered as instrumental variables (IVs), satisfying three crucial assumptions: the correlation assumption, independence assumption, and exclusion-limitation assumption (Fig. 1).

Fig. 1
figure 1

An overview of the study design. SNP: single nucleotide polymorphisms; IVs: instrumental variables

This study is based on publicly available abstract-level data from extensive genome-wide association study (GWAS) and consortia. Therefore, no additional ethical approval or consent to participate was required for this analysis.

Data sources

In this study, the data were derived from extensive GWAS and publicly available GWAS data from consortia.

The GWAS summary statistics for GM were obtained from a MiBioGen consortium meta-analysis [19,20,21,22,23], This meta-analysis included a total of 18,340 individuals from 24 cohorts, with a majority of individuals having European ancestry (n = 13,266). The microbial composition was analyzed by targeting variable regions V4, V3-V4, and V1-V2 of the 16 S rRNA gene, and classification was performed using direct taxonomic sub boxes. At the genus level, 131 genera with an average abundance higher than 1% were identified, including 12 unknown genera. Therefore, a total of 119 genus-level taxonomic units were included in the analysis and analyzed separately.

The GWAS summary statistics for PC were obtained from the FinnGen Consortium R9 release and included 377,277 patients (210,870 females and 166,407 males) with 20,175,454 variables. This study used the “Malignant neoplasm of pancreas” phenotype and included 1416 cases and 287,137 controls after adjusting for age, sex, ten significant components, and genotyping cohort [24,25,26].

Instrumental variables

The following selection criteria were used to select IVs: (1) At the beginning, the genome-wide significance threshold for single nucleotide polymorphisms (SNPs) associated with GM was set to P < 5 × 10− 8. Since the number of eligible IVs (P < 5 × 10− 8) was minimal, a relatively more comprehensive threshold ( P < 1.0 × 10− 5 ) was finally chosen [27], (2): IV1000 Genomes project European samples data were used as reference panel to calculate the linkage disequilibrium (LD) between SNPs, and the LD threshold was set to r2 < 0.001 with an aggregation window of 10,000 kb (clumping window size = 10,000 kb) [28], (3): remove SNPs with minor allele frequency (MAF) < 0.01, for MAF values no marked in the database, by querying the literature [27] as well as relevant databases( If the specific requested SNP does not exist in the resulting GWAS, the SNP(proxy) located in the LD with the requested SNP(target) will be searched (R2 > 0.8). LD proxies were defined using 1000 genomes of the European sample data. To avoid strand orientation or distortion of allele coding, we removed palindromic SNPs [18]. 5: In order to exclude potential associations between IVs and risk factors for PC, we identified the risk factors for PC as: smoking, diabetes, alcohol consumption, and chronic pancreatitis, according to the NCCN guidelines [29]. And the IVs were analysed with these risk factors using the PhenoScanner database and excluded SNPs that were potentially associated with PC risk factors.


The IVs included in the MR analysis should exhibit a significant association with the exposure. To assess the strength of the IVs, the F-statistic is commonly used. The F-statistic can be calculated using the formula: F =R2(n - k − 1) / k (1 -R2), where R2 is the proportion of the explained variance of the exposure by the genetic instrument, n is the sample size, and k denotes the number of IVs included. If the calculated F<10, it indicates a weak link between the IVs and the exposure, and such IVs were excluded from the analysis.


Our MR analysis was conducted following the guidelines outlined in the STROBE-MR statement [30](Table S1). The MR process flowchart is shown in Fig. 2.

Fig. 2
figure 2

The flow chart of MR analysis. SNP: single nucleotide polymorphisms; MR: Mendelian Randomization

To evaluate the causal estimates of GM on the risk of PC, we employed several MR methods, including inverse variance weighting (IVW) [31] the weighted median (WM) method [32], the MR-Egger test [33], Weighted mode (WMO) method, and robust adjusted profile score (RAPS) [34] method. The IVW method uses a meta-analysis method to combine Wald estimates of each SNP to obtain GM’s overall estimate of PC. If no horizontal pleiotropy is present, an unbiased result can be obtained by IVW linear regression [34, 35]. Therefore, in this study, IVW was employed as the primary method, while another four methods were used as complementary approaches.

Heterogeneity was assessed using Cochrane’s Q test, and IVs with P < 0.05 were considered heterogeneous. Additionally, the MR-Egger regression test was employed to examine the presence of horizontal pleiotropy in MR analysis. If P > 0.05, horizontal pleiotropy was considered not to be present. We would further analyze the pleiotropy using MR-PRESSO and remove possible outliers to ensure the accuracy of the results for GM taxa causally related to PC (based on IVW results), Furthermore, sensitivity analysis was conducted by iteratively removing each SNP to implement the leave-one-out method, aiming to verify the reliability and stability of the estimated causal effects [28].

Statistical analysis

R software was used to conduct all statistical analyses (version 4.2.2). We performed MR of the causal link between GM and PC using the “TwoSample MR” package. 119 different MR Analyzes were conducted independently of each other, so we did not perform Bonferroni correction for multiple testing. P < 0.05 was considered statistically significant as evidence of a potential causal effect.


Selection of instrumental variables

Based on the principles of instrumental variable selection, a total of 119 genus-level GMs containing 1198 SNPs (P < 1 × 105) were finally identified as IVs in the MR analysis, and the details of all SNPs are detailed in Table S2.

MR analysis

IVW was chosen as the primary method for MR analysis because of its higher statistical efficacy. We identified six genus-level GMs (42 SNPs in total) that were causally associated with PC. Alloprevotella (OR: 0.752, 95% CI: 0.570–0.993, P = 0.045) was excluded because it was a weak instrumental variable (F = 9.8). We eventually identified five genus-level of GMs with the causal relationship with PC. Specifically, Senegalimassilia (OR: 0.635, 95% CI: 0.403–0.998, P = 0.049) was protective factors for PC. In contrast, Odoribacter (OR:1.899, 95%CI:1.157–3.116, P = 0.011), Ruminiclostridium 9(OR:1.976,95%CI:1.128–3.461, P = 0.017), Ruminococcaceae (UCG011)(OR:1.433, 95%CI:1.072–1.916, P = 0.015),and Streptococcus(OR:1.712, 95%CI:1.071–1.736, P = 0.025)were predisposing factors for PC(Fig. 3).

Fig. 3
figure 3

MR results of GM on PC. MR: Mendelian Randomization; GM: Gut microbiota; PC: Pancreatic cancer

Sensitivity analysis

In these five causal effects, the F-statistic for IV was between 12.77 and 122.64 (Table S3), eliminating the bias for weak IV. Cochran’s Q test for IVW showed no significant heterogeneity for these IVs (Table S4), and MR-Egger regression intercept analysis found no horizontal pleiotropy (Table S5). Based on the Scatter plots (Fig. 4) and the leave-one-out plot (Fig. 5), we detected potential outliers for all five IVs, but further MR-PRESSO analysis did not reveal any significant outliers (Table S6). However, it is noteworthy that, in certain instances, the slope from the MR-Egger method was inverse to that observed in the IVW method. Although this did not reach statistical significance, it could hint at the presence of some form of pleiotropy. Despite our MR-Egger regression intercept analysis not revealing evidence of horizontal pleiotropy, this inverse relationship between the two methods should be considered, suggesting that potential pleiotropic effects might subtly influence our results.

Fig. 4
figure 4

Scatter plots for MR analyses of the causal effect of GM on PC. A: Senegalimassilia;B: Odoribacter; C: Ruminiclostridium 9; D: Ruminococcaceae (UCG011);E: Streptococcus.SNP: single nucleotide polymorphisms; MR: Mendelian Randomization; PC: Pancreatic cancer

Fig. 5
figure 5

leave-one-out plots for MR analyses of the causal effect of GM on PC. A: Senegalimassilia;B: Odoribacter; C: Ruminiclostridium 9; D: Ruminococcaceae (UCG011);E: Streptococcus.SNP: single nucleotide polymorphisms; MR: Mendelian Randomization; PC: Pancreatic cancer


In this study, we performed a MR analysis using the GWAS database of GM and PC to investigate the causal effect between them. Our findings revealed that at the genus level, Senegalimassilia was identified as protective factors, while Odoribacter, Ruminiclostridium 9, Ruminococcaceae (UCG011), and Streptococcus were associated with increased risk for PC.

The human’s gastrointestinal tract contains more than 1014 microorganisms and more than 5,000,000 genes, which can affect the normal physiology of the body by influencing metabolism as well as regulating the immune system, and many studies have discovered that dysbiosis of the GM is closely associated with diseases such as cancer, cardiovascular diseases, and psychiatric disorders [36]. While the pancreas was traditionally believed to be in a sterile environment due to its lack of direct contact with the intestine. However, recent studies have detected that pancreatic cancers can affect not only the type and abundance of GM but also the presence of GM can be detected in the pancreas of PC [37]. The exact mechanism of pancreatic flora formation is not precise. However, current studies have identified direct translocation through the pancreatic duct, metastasis through mesenteric lymph nodes, and hematogenous infection as potential routes of spread [38]. In PC, GM and its metabolites may cause chronic inflammation, while an unhealthy lifestyle can exacerbate this condition and thus induce tumorigenesis. In addition, abnormal GM can affect local intestinal immunity, T-cell development, and immune system maturation [10], all of which are contributing factors to the development and progression of PC.

The prognosis of PC is exceedingly poor, characterized by late-stage diagnosis, limited therapeutic efficacy, and high susceptibility to recurrence and metastasis (as cited in the literature). 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, GM has emerged as a novel and promising avenue in PC research. Beyond its potential as a biomarker for PC, recent studies have highlighted various GM-based therapeutic approaches, such as probiotic therapy and fecal microbiota transplantation, as promising future directions [39]. Our study analyzed the GM associated with PC using an MR method based on publicly available GWAS data. Our findings revealed Senegalimassilia as a protective factor against PC, suggesting a promising direction for GM-based therapies. On the other hand, we identified Odoribacter, Ruminiclostridium 9, Ruminococcaceae (UC Ruminococcaceae (UCG011)), and Streptococcus as risk factors for PC, providing valuable guidance for the development of GM-based predictive models for PC.

About Senegalimassilia, it has been found to exhibit a higher abundance in cirrhotic patients with exacerbated steatosis and cirrhotic patients without extracellular fluid [40]. In addition, MR analyses suggest that Senegalimassilia may be a protective factor against hypertension [41]. These results indicate that Senegalimassilia may play a key role in regulating metabolic and inflammatory processes, which may also explain its role as a protective factor against PC. In contrast, Odoribacter is known for producing essential short-chain fatty acids and modulating intestinal barrier function and inflammatory processes, making it a promising therapeutic candidate for inflammatory bowel disease [42]. Yet its abundance was elevated in hepatocellular carcinoma [43], highlighting the divergent roles that a single GM may play in different diseases. Similar discoveries apply to Streptococcus, which exhibits increased abundance in various diseases, including esophageal cancer [44], colon cancer [45], and chronic pancreatitis [46], while other studies suggest a potential link between Streptococcus and longevity [47], implying diverse roles for Streptococcus in different diseases. Concerning Ruminiclostridium 9, it has demonstrated its regulatory effects on lipid metabolism, inflammation reduction, enhancement of intestinal barrier function, weight gain reduction, and improved insulin sensitivity in mice, effectively countering obesity development [48]. However, its specific role in pancreatic cancer remains unstudied, similar to Ruminococcaceae (UCG011), and both warrant further research in the context of pancreatic cancer.

Despite the valuable insights gained from this study, we acknowledge certain limitations. Firstly, the Mibiogen database, the largest multi-ethnic genome-wide meta-analysis of GM, includes samples from diverse populations, not exclusively composed of individuals of European origin. This heterogeneity may have impacted the reliability and generalizability of our conclusions. Secondly, the inherent limitations of the Mibiogen database compelled us to utilize pooled statistics from all subjects, restricting our capacity to conduct more specific subgroup analyses. Consequently, some potentially findings might have been obscured. Furthermore, the database constraints necessitated analyzing GM at the genus level rather than the strain level, possibly limiting the granularity of our results. Thirdly, due to the constraints of sequencing technology, the number of patients included in our study for each specific GM species was relatively small. This limited sample size and the scarcity of instrumental variables meeting the traditional GWAS significance thresholds (P < 5 × 10− 8) led us to use a significance threshold of (P < 1 × 10− 5) to obtain more comprehensive results. However, this adjustment may introduce some bias in the conclusions. Lastly, it is essential to acknowledge that the conclusions drawn from this study have not been externally validated in clinical settings, which represents a limitation that should be recognized.


In conclusion, our two-sample Mendelian randomization (MR) study suggests a potential presence of a causal effect between GM and PC. Specifically, our findings revealed that at the genus level, Senegalimassilia was identified as protective factors, while Odoribacter, Ruminiclostridium 9, Ruminococcaceae (UCG011), and Streptococcus were associated with increased risk for PC. However, further original studies are needed to more comprehensively elucidate the underlying mechanisms that govern the relationship between GM and PC.

Data Availability

The datasets analyzed during the current study are available in the MiBioGen repository( [20] and the FinnGen repository ( [25, 26].


  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. Cancer J Clin. 2022;72(1):7–33.

    Article  Google Scholar 

  2. Rahib L, Wehner MR, Matrisian LM, Nead KT. Estimated projection of US Cancer incidence and death to 2040. JAMA NETW OPEN. 2021;4(4):e214708.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Liu M. Arid1a: a gatekeeper in the Development of Pancreatic Cancer from a Rare Precursor Lesion. Gastroenterology. 2022;163(2):371–3.

    Article  PubMed  Google Scholar 

  4. Cai J, Chen H, Lu M, Zhang Y, Lu B, You L, Zhang T, Dai M, Zhao Y. Advances in the epidemiology of pancreatic cancer: Trends, risk factors, screening, and prognosis. CANCER LETT. 2021;520:1–11.

    Article  CAS  PubMed  Google Scholar 

  5. Tong Y, Gao H, Qi Q, Liu X, Li J, Gao J, Li P, Wang Y, Du L, Wang C. High fat diet, gut microbiome and gastrointestinal cancer. THERANOSTICS. 2021;11(12):5889–910.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Cohen LJ, Cho JH, Gevers D, Chu H. Genetic factors and the intestinal Microbiome Guide Development of Microbe-Based therapies for inflammatory Bowel Diseases. Gastroenterology. 2019;156(8):2174–89.

    Article  PubMed  Google Scholar 

  7. Wong SH, Yu J. Gut microbiota in colorectal cancer: mechanisms of action and clinical applications. NAT REV GASTRO HEPAT. 2019;16(11):690–704.

    Article  CAS  Google Scholar 

  8. Ma C, Han M, Heinrich B, Fu Q, Zhang Q, Sandhu M, Agdashian D, Terabe M, Berzofsky JA, Fako V et al. Gut microbiome–mediated bile acid metabolism regulates liver cancer via NKT cells. SCIENCE 2018, 360(6391).

  9. Ertz-Archambault N, Keim P, Von Hoff D. Microbiome and pancreatic cancer: a comprehensive topic review of literature. WORLD J GASTROENTERO. 2017;23(10):1899.

    Article  CAS  Google Scholar 

  10. Yang Q, Zhang J, Zhu Y. Potential roles of the gut microbiota in pancreatic carcinogenesis and therapeutics. FRONT CELL INFECT MI 2022, 12.

  11. Pushalkar S, Hundeyin M, Daley D, Zambirinis CP, Kurz E, Mishra A, Mohan N, Aykut B, Usyk M, Torres LE, et al. The pancreatic Cancer Microbiome promotes oncogenesis by induction of Innate and Adaptive Immune suppression. CANCER DISCOV. 2018;8(4):403–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yu Q, Newsome RC, Beveridge M, Hernandez MC, Gharaibeh RZ, Jobin C, Thomas RM. Intestinal microbiota modulates pancreatic carcinogenesis through intratumoral natural killer cells. Gut Microbes 2022, 14(1).

  13. Sethi V, Kurtom S, Tarique M, Lavania S, Malchiodi Z, Hellmund L, Zhang L, Sharma U, Giri B, Garg B, et al. Gut microbiota promotes Tumor Growth in mice by modulating Immune Response. Gastroenterology. 2018;155(1):33–7.

    Article  CAS  PubMed  Google Scholar 

  14. Qu Z, Yang F, Yan Y, Huang J, Zhao J, Hong J, Li S, Jiang G, Wang W, Yan S. A mendelian randomization study on the role of serum parathyroid hormone and 25-hydroxyvitamin D in osteoarthritis. OSTEOARTHR Cartil. 2021;29(9):1282–90.

    Article  CAS  Google Scholar 

  15. Baurecht H, Freuer D, Welker C, Tsoi LC, Elder JT, Ehmke B, Leitzmann MF, Holtfreter B, Baumeister SE. Relationship between periodontitis and psoriasis: a two-sample mendelian randomization study. J CLIN PERIODONTOL. 2022;49(6):573–9.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Xu Q, Ni J, Han B, Yan S, Wei X, Feng G, Zhang H, Zhang L, Li B, Pei Y. Causal relationship between Gut Microbiota and Autoimmune Diseases: a two-sample mendelian randomization study. FRONT IMMUNOL 2022, 12.

  17. Ni J, Li X, Zhang H, Xu Q, Wei X, Feng G, Zhao M, Zhang Z, Zhang L, Shen G et al. Mendelian randomization study of causal link from gut microbiota to colorectal cancer. BMC Cancer 2022, 22(1).

  18. Ni J, Xu Q, Yan S, Han B, Zhang H, Wei X, Feng G, Zhao M, Pei Y, Zhang L. Gut Microbiota and Psychiatric Disorders: a two-sample mendelian randomization study. FRONT MICROBIOL 2022, 12.

  19. Swertz MA, Jansen RC. Beyond standardization: dynamic software infrastructures for systems biology. NAT REV GENET. 2007;8(3):235–43.

    Article  CAS  PubMed  Google Scholar 

  20. MiBioGen MC. 16.

  21. Kurilshikov A, Medina-Gomez C, Bacigalupe R, Radjabzadeh D, Wang J, Demirkan A, Le Roy CI, Raygoza Garay JA, Finnicum CT, Liu X, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. NAT GENET. 2021;53(2):156–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. van der Velde KJ, Imhann F, Charbon B, Pang C, van Enckevort D, Slofstra M, Barbieri R, Alberts R, Hendriksen D, Kelpin F, et al. MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians. Bioinformatics. 2019;35(6):1076–8.

    Article  PubMed  Google Scholar 

  23. Swertz MA, Dijkstra M, Adamusiak T, van der Velde JK, Kanterakis A, Roos ET, Lops J, Thorisson GA, Arends D, Byelas G, et al. The MOLGENIS toolkit: rapid prototyping of biosoftware at the push of a button. BMC Bioinformatics. 2010;11:12.

    Article  Google Scholar 

  24. Liu K, Zou J, Fan H, Hu H, You Z. Causal effects of gut microbiota on diabetic retinopathy: a mendelian randomization study. FRONT IMMUNOL 2022, 13.

  25. FinnGen. : Unique genetic insights from combining isolated population and national health register data.

  26. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner KM, Reeve MP, Laivuori H, Aavikko M, Kaunisto MA, et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Li P, Wang H, Guo L, Gou X, Chen G, Lin D, Fan D, Guo X, Liu Z. Association between gut microbiota and preeclampsia-eclampsia: a two-sample mendelian randomization study. BMC MED 2022, 20(1).

  28. Xiang K, Wang P, Xu Z, Hu Y, He Y, Chen Y, Feng Y, Yin K, Huang J, Wang J et al. Causal Effects of Gut Microbiome on systemic lupus erythematosus: a two-sample mendelian randomization study. FRONT IMMUNOL 2021, 12.

  29. Tempero MA, Malafa MP, Al-Hawary M, Behrman SW, Benson AB, Cardin DB, Chiorean EG, Chung V, Czito B, Del Chiaro M, et al. Pancreatic adenocarcinoma, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J NATL COMPR CANC NE. 2021;19(4):439–57.

    Article  CAS  Google Scholar 

  30. Skrivankova VW, Richmond RC, Woolf BAR, Yarmolinsky J, Davies NM, Swanson SA, VanderWeele TJ, Higgins JPT, Timpson NJ, Dimou N, et al. Strengthening the reporting of Observational Studies in Epidemiology using mendelian randomization. JAMA-J AM MED ASSOC. 2021;326(16):1614.

    Article  Google Scholar 

  31. Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity analyses for robust causal inference from mendelian randomization analyses with multiple genetic variants. EPIDEMIOLOGY. 2017;28(1):30–42.

    Article  PubMed  Google Scholar 

  32. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some Invalid Instruments using a weighted median estimator. GENET EPIDEMIOL. 2016;40(4):304–14.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. INT J EPIDEMIOL. 2015;44(2):512–25.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wu F, Huang Y, Hu J, Shao Z. Mendelian randomization study of inflammatory bowel disease and bone mineral density. BMC MED 2020, 18(1).

  35. Jin T, Huang W, Cao F, Yu X, Guo S, Ying Z, Xu C. Causal association between systemic lupus erythematosus and the risk of dementia: a mendelian randomization study. FRONT IMMUNOL 2022, 13.

  36. Akshintala VS, Talukdar R, Singh VK, Goggins M. The gut Microbiome in Pancreatic Disease. CLIN GASTROENTEROL H. 2019;17(2):290–5.

    Article  CAS  Google Scholar 

  37. Fan X, Alekseyenko AV, Wu J, Peters BA, Jacobs EJ, Gapstur SM, Purdue MP, Abnet CC, Stolzenberg-Solomon R, Miller G, et al. Human oral microbiome and prospective risk for pancreatic cancer: a population-based nested case-control study. Gut. 2017;67(1):120–7.

    Article  Google Scholar 

  38. Thomas RM, Jobin C. Microbiota in pancreatic health and disease: the next frontier in microbiome research. NAT REV GASTRO HEPAT. 2020;17(1):53–64.

    Article  Google Scholar 

  39. Li Q, Jin M, Liu Y, Jin L. Gut microbiota: its potential roles in pancreatic Cancer. FRONT CELL INFECT MI 2020, 10.

  40. Maslennikov R, Ivashkin V, Alieva A, Poluektova E, Kudryavtseva A, Krasnov G, Zharkova M, Zharikov Y. Gut dysbiosis and body composition in cirrhosis. WORLD J HEPATOL. 2022;14(6):1210–25.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Li Y, Fu R, Li R, Zeng J, Liu T, Li X, Jiang W. Causality of gut microbiome and hypertension: a bidirectional mendelian randomization study. FRONT CARDIOVASC MED 2023, 10.

  42. Zhou J, Li M, Chen Q, Li X, Chen L, Dong Z, Zhu W, Yang Y, Liu Z, Chen Q. Programmable probiotics modulate inflammation and gut microbiota for inflammatory bowel disease treatment after effective oral delivery. NAT COMMUN 2022, 13(1).

  43. Piñero F, Vazquez M, Baré P, Rohr C, Mendizabal M, Sciara M, Alonso C, Fay F, Silva M. A different gut microbiome linked to inflammation found in cirrhotic patients with and without hepatocellular carcinoma. ANN HEPATOL. 2019;18(3):480–7.

    Article  PubMed  Google Scholar 

  44. Deng Y, Tang D, Hou P, Shen W, Li H, Wang T, Liu R. Dysbiosis of gut microbiota in patients with esophageal cancer. MICROB PATHOGENESIS. 2021;150:104709.

    Article  CAS  Google Scholar 

  45. Uchino Y, Goto Y, Konishi Y, Tanabe K, Toda H, Wada M, Kita Y, Beppu M, Mori S, Hijioka H et al. Colorectal Cancer Patients Have Four Specific Bacterial Species in Oral and Gut Microbiota in Common—A Metagenomic Comparison with Healthy Subjects. CANCERS 2021, 13(13):3332.

  46. Hamada S, Masamune A, Nabeshima T, Shimosegawa T. Differences in gut microbiota profiles between autoimmune pancreatitis and chronic pancreatitis. Tohoku J Exp Med. 2018;244(2):113–7.

    Article  CAS  PubMed  Google Scholar 

  47. Liu X, Zou L, Nie C, Qin Y, Tong X, Wang J, Yang H, Xu X, Jin X, Xiao L et al. Mendelian randomization analyses reveal causal relationships between the human microbiome and longevity. SCI REP-UK 2023, 13(1).

  48. Wang P, Li D, Ke W, Liang D, Hu X, Chen F. Resveratrol-induced gut microbiota reduces obesity in high-fat diet-fed mice. INT J OBESITY. 2020;44(1):213–25.

    Article  Google Scholar 

Download references


The authors want to acknowledge the participants and investigators of the FinnGen study and the authors also appreciate the MiBioGen consortium for releasing the gut microbiota GWAS summary statistics.


This study was supported by Scientific research fund of national health commision of China, Key health science and technology program of Zhejiang Province(WKJ-ZJ-2201) , the Zhejiang Provincial Basic Technology Foundation of China (Grant No. LGC20H160003), and Key Project of social welfare program of Zhejiang Science and Technology Department,’Lingyan’Program(2022C03099).

Author information

Authors and Affiliations



Zhichen Jiang and Weiwei Jin designed the research. Zhichen Jiang collected and analyzed the data and drafted the manuscript. Huiju Wang and Li Li supervised the study. Zhichen Jiang, Yiping Mou, He Wang, and Tianyu Jin were involved in writing the manuscript. Mingyang Liu and Zhichen Jiang revised the manuscript and embellished the language. All authors contributed to the article and approved the submitted version.

Corresponding authors

Correspondence to Mingyang Liu or Weiwei Jin.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Only publicly available data were used for this study. Ethical approval for all data used can be found in the original publication.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Supplementary Material 3

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, Z., Mou, Y., Wang, H. et al. Causal effect between gut microbiota and pancreatic cancer: a two-sample Mendelian randomization study. BMC Cancer 23, 1091 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: