{"paper_id":"2f970ca0-a63c-4ece-a68f-9404ce866dbb","body_text":"Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies Haoqing Chen, Yan Gao, Tingting Chen, Yanxia He, Liqin Ping, Cheng Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3733715/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Previous research has revealed a significant association between the gut microbiome and diffuse large B-cell lymphoma (DLBCL). However, the findings of these studies have yet to be entirely consistent. Whether a causal relationship exists between gut bacterial and DLBCL remains elucidated. We performed two-sample mendelian randomization (MR) using genetic data from MiBioGen and DLBCL summary statistics from GWAS. The primary analysis used inverse variance weighted (IVW), the weighted median, MR-Egger regression, and pleiotropic residual sum and outlier tests. Reverse MR checked for reverse causality. Our study identified four bacterial genera can causally increase the risk of DLBCL disease: Ruminococcus torques.id.14377 (OR 0.44; p = 0.006), Ruminococcaceae UCG014.id.11371 (OR 1.69; p = 0.028), Ruminococcaceae UCG002.id.11360 (OR 0.62; p = 0.023), and Eubacterium oxidoreducens.id.11339 (OR 1.80; p = 0.033). In reverse MR analysis, we found no causal effect from DLBCL to gut bacterial. Our investigation offers indications of causal connections between the gut microbiome and the onset of DLBCL. Gut microbiota Diffuse large B-cell lymphoma Mendelian randomization Microbiome Causal influence Figures Figure 1 Figure 2 Figure 3 1 Introduction The human microbiome has been gaining increasing attention from researchers. Microbiome communities are characterized by unique diversity and variability, often coexisting with the body's barriers exposed to the external environment. The gut microbiome stands out as the most influential and prominent [ 1 , 2 ]. The gut microbiome has been shown to have a close connection with tumor development and tumor immune microenvironments [ 3 , 4 ]. Disruptions in the gut microbiota's ecological balance, leading to microbial dysbiosis, can induce tumorigenesis. Potential mechanisms by which bacteria induce carcinogenesis include but are not limited to, chronic inflammation, immune evasion, and immunosuppression [ 5 ]. Unique microbiota may enhance anti-tumor immune responses [ 6 , 7 ] or trigger systemic or local tumor immune suppression [ 8 ]. Consequently, the human microbiome is a potent external driving factor for acquiring hallmark tumor characteristics. The fascinating aspect of current microbiome research lies in its potential to modulate the molecular mechanisms of tumor immunobiology by regulating microbial balance and the abundance of beneficial probiotics. Lymphoma is a malignant tumor originating from the body's immune system, with diffuse large B-cell lymphoma (DLBCL) representing a consequential subset [ 9 ]. The cure rate for DLBCL patients receiving first-line treatment with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) can reach up to 60% [ 10 ]. However, the treatment outcomes for relapsed or refractory DLBCL patients are less favorable. To better understand the disease's mechanisms and prognosis, Staudt and colleagues proposed seven molecular subtypes based on the genetics of DLBCL in 2020. However, this classification method only covers 63.1% of the disease's spectrum [ 11 ]. Moreover, this classification method still needs to address the complex immunological features of the tumor. Therefore, the microbiome closely associated with immune responses may represent a valuable tool in studying the heterogeneity and immune microenvironment of DLBCL. Current research suggests a close link between gut microbiota and DLBCL, although significant differences exist between various studies. Several studies have shown that the phylum Bacteroidetes might dominate the gut microbiota in DLBCL patients. One study compared the gut flora of 35 DLBCL patients receiving immunotherapy with 20 healthy controls, and it found that Bacteroidetes dominated the disease group, while Firmicutes decreased significantly [ 12 ]. Similarly, the phylum Bacteroidetes as a dominant microbiome in DLBCL was also mentioned in the study by Li and colleagues [ 13 ]. Seok Jin Kim and co-authors reviewed the baseline gut microbiota of 189 newly diagnosed DLBCL patients, once again finding that the Bacteroidetes phylum dominated DLBCL, and the Enterobacteriaceae family was part of the dominant microbiota [ 14 ]. However, another study discovered significant differences in the abundance of six bacteria, including Streptococcus and Bacteroides , among DLBCL patients at baseline in different NCCN-IPI score groups [ 15 ]. Research also demonstrated that the phylum Proteobacteria significantly decreased in patients with primary gastrointestinal B-cell lymphoma, while microbiota lacking Proteobacteria interacted synergistically with the tumor necrosis factor (TNF) signaling pathway through lipopolysaccharides (LPS) and reinforced the NF-κB pathway through the MYD88-TLR4 signaling pathway, inducing the survival and malignant proliferation of intestinal B cells [ 16 ]. Additionally, gastric and intestinal lymphoma patients had significantly lower microbiota abundances such as Sporomusa, Rothia, Prevotella genera , and the Gemellaceae family compared to the control group [ 17 ]. Due to the considerable variability in the gut microbiome of DLBCL in existing studies and the inability to disentangle confounding factors and causal relationships in observational studies, the actual connection between gut microbiota and DLBCL remains elusive. Mendelian randomization (MR) stands as an innovative statistical approach that centers on genetic variations, utilizing single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to mitigate the impact of confounding factors. It assesses whether an exposure factor causally influences a disease outcome [ 18 ]. The onset and progression of diseases typically arise from intricate interactions involving numerous genes and environmental elements. As specific genetic factors, SNPs remain unaffected by common confounding factors like postnatal environmental conditions, dietary patterns, chemical exposures, and behavior. This characteristic ensures the temporal plausibility of causative relationships derived from Mendelian randomization (MR). Over the past few years, the application of MR analysis has extended to assess potential causal links between the gut microbiota and cancer risk [ 19 – 21 ]. This study employs MR analysis based on existing real-world observational studies and a genome-wide association study (GWAS) database to determine the causal relationship between gut microbiota and DLBCL. 2 Methods 2.1 Data Acquisition The schematic representation of this study's design framework is depicted in Fig. 1 (created using the Figdraw online platform). It comprises two datasets - the GWAS dataset and the MiBioGen dataset. The GWAS dataset includes genetic summary statistics about DLBCL derived from the GWAS database, incorporating 616 cases of European ancestry and 455,732 controls[ 22 ]. The MiBioGen dataset encompasses summary statistics of the human gut microbiome from the latest GWAS meta-analysis, including genomic genotyping and 16S gut microbiome information for 18,340 participants[ 23 ], spanning up to 24 cohorts from various regions. Since this study relies on publicly available aggregated data, no additional ethical approval or informed consent was required. The internet links to the dataset are outlined in Table S1 . 2.2 Instrumental Variable Acquisition First, after excluding bacterial traits with unknown names from the MiBioGen dataset, 196 bacterial traits were retained, including nine phyla, 16 classes, 20 orders, 32 families, and 119 genera. Subsequently, to better identify gut microbial species closely associated with DLBCL as IVs, we conducted a review of relevant real-world observational studies, literature, and conference abstracts in PubMed and Google Scholar to compile a list of gut bacteria potentially related to DLBCL risk[ 12 , 13 , 15 – 17 , 24 – 28 ]. The information of bacterial groups reported in the literature can be viewed in Table S2 and Table S3. We then searched for corresponding bacterial traits within the MiBioGen dataset for further analysis. We subsequently used a threshold of 1.0 x 10^-5 for selecting instrumental variables, with a linkage disequilibrium (LD) threshold set at 0.001 and a clumping distance of 10,000 kb. After obtaining specific bacterial species-associated SNPs, we calculated R2 and F-statistic: $${R}^{2}= \\frac{{{\\beta }}^{2}}{ {{\\beta }}^{2}+ \\text{N} \\text{*} {\\text{S}\\text{E}}^{2}}$$ $$F ={R}^{2}*\\frac{N-2}{1 - {R}^{2}}$$ where N signifies the sample size corresponding to the exposure factor, β represents effect size for the genetic variant of interest, and SE denotes a standard error for β. When F-statistic > 10, we had reason to exclude the hypothesis that the instrumental variable is a weak instrument. In the reverse MR analysis, we used the same methods to screen for IVs associated with DLBCL. 2.3 Two-Sample Mendelian Randomization Analysis We utilized the \"TwoSampleMR\" R package[ 29 ] for various methods to assess the potential causal relationship between gut bacteria and DLBCL. These methods included the fixed/random-effects inverse variance-weighted (IVW) method, the weighted median method, MR-Egger regression, and MR multi-effect residual and outlier (MR-PRESSO) tests. Since the IVW method provides the most precise effect estimates[ 30 , 31 ], we primarily employed the IVW method. The IVW method initially calculates the ratio estimate for individual SNPs using Wald estimators and the Delta method. The results of all specific bacterial community analyses are shown in Table S3. Then, it combines the estimates derived from each SNP to obtain the primary causal estimate[ 32 ]. Heterogeneity pertains to the variability in the causal estimates obtained for each SNP, and lower heterogeneity suggests increased reliability of MR estimates. We employed Cochran's Q test to assess heterogeneity among SNPs. If heterogeneity existed (p < 0.05), we opted for the random-effects IVW method; otherwise, we used the fixed-effects IVW method[ 33 ]. Furthermore, we conducted a sensitivity analysis to evaluate the robustness of the associations[ 34 ]. The weighted median (WM) method was used to estimate the weighted median, even when up to 50% of the IVs were invalid, and it provided effective causal effect estimates[ 35 ]. We utilized MR-Egger regression to test for potential horizontal pleiotropy. If the p-value for the intercept was less than 0.05, the instrumental variables may exhibit horizontal pleiotropy[ 36 ]. We also conducted MR-PRESSO testing to perform a global heterogeneity test for SNPs to identify potential outliers. Corrected association results were acquired after removing potential outliers[ 37 ]. The MR models generated in this study were deemed appropriate statistical power to detect moderately associated causal effects, as calculated using the power calculator implemented by Brion et al.[ 38 ]. Finally, we generated visual outputs for specific MR models, including scatter plots, forest plots, funnel plots, and leave-one-out plots. Following the analysis methods described, we conducted reverse MR analysis by treating gut bacteria as the outcome variable and DLBCL risk as the exposure factor. A specific bacterial species was assessed for inclusion in reverse MR analysis if it passed the IVW test and exhibited no deviations in sensitivity, heterogeneity, and horizontal pleiotropy checks. We performed all statistical analyses in R version 4.2.3, including R packages such as \"TwoSampleMR\" and \"ggplot2\". The association between exposure and outcome odds ratios (OR) and their corresponding 95% confidence intervals (CI). 3 Results 3.1 MR Analysis of Specific Microbiota and DLBCL Risk Based on the literature review of gut microbes, we anchored two phyla ( phylum Bacteroidetes et al. ), five families ( family Ruminococcaceae et al. ), and 52 genera in MiBioGen dataset (Table S2 ; Table S3). We performed MR analysis, treating these microbiota as exposure variables and DLBCL as the outcome variable. We used the IVW method to screen for potential causal links for the mentioned microbiota. Ultimately, we found that three genera of Ruminococcaceae ( Ruminococcus torques.id.14377, Ruminococcaceae UCG014.id.11371, Ruminococcaceae UCG002.id.11360 ) and one genus of Eubacterium ( Eubacterium oxidoreducens.id.11339 ) exhibited potential causal associations(Table S3; Table 1 ). For the 63 SNPs associated with these four specific microbiota, the F-statistic for each SNP exceeded 10 (Table S4). Within this specific microbiota, Ruminococcus torques.id.14377 showed a negative correlation with DLBCL risk (OR 0.44; p-value = 0.006; Table 1 , Fig. 2 ). This genus maintained its negative correlation with DLBCL in the weighted median method (p = 0.034). MR-PRESSO testing identified no outliers. In the heterogeneity test, MR Egger and IVW methods produced p-values greater than 0.05, indicating no heterogeneity in the causal associations between exposure and outcome. Furthermore, sensitivity analysis using the \"leave-one-out\" method and corresponding SNP forest plots showed no significant individual SNP driving drastic changes in the final results. The MR-Egger regression did not reveal any indications of directional pleiotropy, with an intercept p-value of 0.808. Similar link with DLBCL was observed for Ruminococcaceae UCG002.id.11360. (OR 0.62; p-value = 0.023; Table 1 , Fig. 2 ). Figure 2 , Fig. 3 and Figure S1 -S3 provides visualization images of these four microbiota MR analyses. On the other hand, the remaining two genera, Ruminococcaceae UCG014.id.11371 and Eubacterium oxidoreducens.id.11339 , both showed a positive causal relationship with DLBCL in the IVW method (Table 2 ), with OR values of 1.69 (p = 0.028) for the former and 1.80 (p = 0.033) for the latter. Additionally, Proteobacteria Phylum and Bacteroides Phylum did not achieve statistical significance in IVW analysis, we observed a numerical positive correlation between Proteobacteria Phylum and DLBCL risk and a negative correlation between Bacteroides Phylum and DLBCL risk(Fig. 2 ). 3.2 Reverse MR Analysis Results Subsequently, we conducted reverse MR analysis with DLBCL as the exposure variable and the four specific microbiota, as well as two popular phyla ( Proteobacteria Phylum, Bacteroides Phylum ) and a butyrate-producing genus ( genus Eubacterium rectale.id.14374 ) as outcome variables. We identified 23 DLBCL-related SNPs, each with F-statistics around 20 (Table S5). After analyzing with the IVW method, we found no statistically significant associations between DLBCL and those mentioned above four specific gut microbiota (Table 2 ). The interest taxa from the literature also did not exhibit significant differences ( Proteobacteria Phylum : OR 1.01, p = 0.56; Bacteroides Phylum : OR 0.99, p = 0.42; genus Eubacterium rectale.id.14374 : OR 1.01, p = 0.58). 4 Discussion In this twin-sample Mendelian randomization analysis study, we identified four microbiota taxonomic groups associated with the risk of DLBCL. These groups include Ruminococcus torques.id.14377, Ruminococcaceae UCG014.id.11371, Ruminococcaceae UCG002.id.11360 , all belonging to the Ruminococcus genus , and Eubacterium oxidoreducens.id.11339 from the Eubacterium genus . The investigation utilized Mendelian randomization to examine the potential causal association between the gut microbiota and DLBCL. While genetic and environmental factors can influence disease phenotypes in populations, our Mendelian randomization analysis directly used GWAS (Genome-Wide Association Studies) data based on genetic correlations to assess the potential causal relationship between the microbiota and DLBCL. Furthermore, we conducted strict quality control on the included SNPs to eliminate the influence of confounding factors and reverse causality. The human gut microbiota plays a crucial role in physiological regulation, and almost all human cells interact with it[ 39 ]. The gut microbiota can mediate the occurrence and development of tumors, increasing cancer susceptibility. The interaction between genomic abnormalities and microbial signals may be bidirectional, with host TET (Ten-Eleven Translocation) deficiencies making individuals more susceptible to the effects of microbial signals driving myelodysplastic syndromes[ 40 ]. Similar research also suggests that the loss of a symbiotic microbiota is crucial in promoting the accumulation of white blood cell abnormalities[ 41 ]. Helicobacter hepaticus could induce the aggregation of macrophages and neutrophils in the colon, resulting in the upregulation of inducible nitric oxide synthase (iNOS) and increased production of nitric oxide, thus promoting atypical hyperplasia and carcinogenesis in the colon[ 42 ]. Similarly, a study using a Rag2-deficient model of mice infected with colonic Helicobacter hepaticus significantly promoted breast cancer through a TNF-α-dependent mechanism[ 43 ]. Metabolites and other molecules produced by the gut microbiota could also affect the immune system, regulating the balance between pro-inflammatory and anti-inflammatory mechanisms[ 44 ]. For example, short-chain fatty acids (SCFAs), which are metabolites of bacteria, can promote the production of thymic regulatory T (Treg) cells, regulating the function of colonic Treg cells[ 44 , 45 ]. SCFAs also act as histone deacetylase (HDAC) inhibitors, which could prevent Epstein-Barr virus reactivation. Given the prevalence of the Epstein-Barr virus in lymphoma patients, further exploration of the interaction between SCFAs and the virus is warranted[ 46 ]. In our study, we identified two species belonging to the Ruminococcus genus as beneficial, with a substantial decrease in DLBCL risk (OR = 0.44) associated with an increased abundance of Ruminococcus torques.id.14377 . Similarly, Ruminococcaceae UCG002.id.11360 , classified as a probiotic, also contributed to approximately a 40% lower risk of lymphoma development. Previous studies have shown a deficiency of Ruminococcus in the gut microbiota of lymphoma patients[ 25 ]. Recent MR analysis has shown that enrichment of Ruminococcaceae and Bacteroidetes was associated with a reduced risk of liver cancer[ 19 ]. Another multi-tumor MR analysis showed that Ruminococcus genus UCG013 protectd against breast cancer[ 20 ]. The Ruminococcus genus was also closely related to the efficacy of immunotherapy. A study on hematologic CAR-T therapy showed that Ruminococcus, Prevotella , and Eubacterium were significantly associated with confirming the effectiveness of this immunotherapy[ 47 ]. Another study also suggested that a higher abundance of the Ruminococcus genus was associated with a higher complete response rate on day 100 after CAR-T therapy[ 48 ]. Moreover, patients with advanced liver cancer and melanoma who respond to PD-1 inhibitors typically had a higher abundance of the Ruminococcus genus [ 49 , 50 ]. However, some studies had contrasting findings. A study by Jin et al.[ 51 ] suggested that Ruminococcus unclassified was associated with PD-1 therapy ineffectiveness, and a study by Peters et al.[ 52 ] indicated that Ruminococcaceae was significantly related to poor lung cancer prognosis. On the other hand, Ruminococcaceae UCG014.id.11371 , a member of the Ruminococcus genus , substantially increased the risk of DLBCL (OR = 1.69). This observation suggests that different species or strains may exhibit different characteristics or traits even within the same genus. Such differences may be due to environmental conditions, growth conditions, genetic variations, or other factors. Further research into these specificities and diversities within genera is necessary. Additionally, Eubacterium oxidoreducens.id.11339 increased the risk of DLBCL (OR = 1.80). Lu et al.'s research indicated that Eubacterium , particularly Eubacterium rectale , was significantly deficient in gastrointestinal lymphoma and could produce high concentrations of butyrate[ 16 ]. Butyrate could induce apoptosis in lymphoma cells and inhibit the growth of lymphoma cells[ 53 ]. Furthermore, in Lu et al.'s in vitro experiments, Eubacterium rectale treatment reduced TNF levels and the incidence of lymphoma in mice[ 16 ]. Therefore, we also focused on this genus in our MR analysis. Unfortunately, Eubacterium rectale did not show a causal link with DLBCL risk in our MR analysis. However, Eubacterium oxidoreducens.id.11339 , another Eubacterium genus member, exhibited characteristics promoting DLBCL risk. Reports on this bacterium are relatively scarce, but a 2022 ASCO abstract suggests that patients who respond effectively to immune checkpoint inhibitors (ICIs) show enrichment in Eubacterium oxidoreducens [ 54 ]. Thus, there is a need for in-depth investigation and research on this genus. Considering that the phyla Firmicutes and Bacteroidetes have exhibited significant differences in multiple DLBCL-related observational studies[ 12 – 14 , 27 , 28 ], we also focused on these two phyla. In these studies, the Firmicutes phylum was highly enriched in DLBCL, and within the Firmicutes phylum , the family Enterobacteriaceae was significantly associated with the refractory outcomes of DLBCL[ 14 ]. On the other hand, the Bacteroidetes phylum exhibited reduced abundance in DLBCL patients[ 27 ]. Multiple studies have shown that an increase in Bacteroidetes and enhanced resistance to colitis[ 55 ], as well as the effectiveness of immunotherapy[ 47 , 54 , 56 , 57 ], was associated with this phylum. Although the numerical trends of these two phyla were visible in our results (Fig. 2 ), these differences did not exhibit statistical significance in the IVW analysis. As mentioned, specific microbial groups did not yield statistically significant results in the reverse MR analysis. Given the robust focus of MR on causal temporal relationships, DLBCL may not be amenable to specific microbial changes in the current analysis settings. This phenomenon may be due to interference from confounding factors or limitations in the study design. The lack of meaningful results at this stage may suggest that definitive causal conclusions cannot be drawn, and further research is needed. DLBCL, as a highly heterogeneous immunosystem tumor, exhibits different immune dysfunctions in different individuals, leading to considerable variability in microbial dysbiosis. Furthermore, DLBCL patients often use glucocorticoids or rituximab, which increases the instability of the immune microenvironment. These factors may have contributed to our inability to obtain meaningful results and present challenges to researchers in lymphoma microbiome research. To more accurately recapitulate the relationship between gut microbiota and DLBCL in the real world, we relied on existing observational studies of gut microbiota rather than analyzing microbiota included in all GWAS databases. This approach helps to delve deeper into the heterogeneity of current microbiota research. By analyzing existing observational studies of gut microbiota, we gain a more comprehensive understanding of the relationship between gut microbiota and DLBCL without being influenced by potential microbiota heterogeneity in GWAS databases. With existing research to help pinpoint relevant microbiota, this strategy allows us to focus on known microbiota data and more accurately explore their potential roles in DLBCL development. Additionally, we can more precisely assess the interactions between microbiota and their impact on DLBCL susceptibility. This analytical approach provides us with finer and more reliable results, further advancing our understanding of the correlation between gut microbiota and DLBCL. However, this study still has limitations. Firstly, the smallest analyzed taxonomic unit was at the genus level, and more precise information, such as species or strains, was unavailable, which may affect the depth of our analysis results. Secondly, the population included in GWAS summaries is predominantly of European ancestry, potentially limiting the generalizability of study results to other racial and ethnic groups. Thirdly, we used a SNP threshold of 1.0 × 10^−5 to obtain sufficient exposure variables for analysis, which is higher than the usual threshold of 5 × 10^–8. 5 Conclusion Our MR study results support a potential causal relationship between the gut microbiome and DLBCL. Future research can delve deeper into these specific microbial taxa to explore their potential in clinical prediction and treatment. Moreover, high-quality GWAS data and further investigations into potential mechanisms are needed to understand better the bidirectional causal relationship between the gut microbiome and DLBCL. Declarations Competing Interests The authors confirm that there are no relevant financial or non-financial competing interests to report. Ethical Approval This study is based on the analysis of biometric data and is not applicable for ethical approvals. Funding This study was conducted without any financial assistance or external funding. Author Contributions Haoqing Chen was primarily responsible for the research design, data analysis, visualization, and subsequent manuscript revisions. Yan Gao contributed to the research design and manuscript revisions. Tingting Chen was responsible for the compilation of public data and initial manuscript drafting. Yanxia He collected literature and contributed to the research discussions. Liqin Ping participated in the writing of R code and data visualization. Cheng Huang was involved in manuscript revisions. Huiqiang Huang, as the corresponding author, oversaw the research design, manuscript revisions, and team discussions. Data Availability The MiBioGen dataset for the gut microbiome was obtained from [https://mibiogen.gcc.rug.nl/], while the GWAS dataset for DLBCL was acquired from [http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90043001-GCST90044000/GCST90043906/]. Acknowledgments Special thanks to individuals involved in the design and creation of study. And we express our appreciation to the developers of GWAS, laying a solid foundation for a deeper understanding of the relevant field. References Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov 2022;12:31-46. Shi N, Li N, Duan X, Niu H. Interaction between the gut microbiome and mucosal immune system. Mil Med Res 2017;4:14. Qiu Q, Lin Y, Ma Y, et al. . Exploring the Emerging Role of the Gut Microbiota and Tumor Microenvironment in Cancer Immunotherapy. Front Immunol 2020;11:612202. Maynard CL, Elson CO, Hatton RD, Weaver CT. Reciprocal interactions of the intestinal microbiota and immune system. Nature 2012;489:231-241. Compare D, Nardone G. Contribution of gut microbiota to colonic and extracolonic cancer development. Dig Dis 2011;29:554-561. Tanoue T, Morita S, Plichta DR, et al. . A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature 2019;565:600-605. Mager LF, Burkhard R, Pett N, et al. . Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy. Science 2020;369:1481-1489. Pushalkar S, Hundeyin M, Daley D, et al. . The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov 2018;8:403-416. Alaggio R, Amador C, Anagnostopoulos I, et al. . The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia 2022;36:1720-1748. Coiffier B, Thieblemont C, Van Den Neste E, et al. . Long-term outcome of patients in the LNH-98.5 trial, the first randomized study comparing rituximab-CHOP to standard CHOP chemotherapy in DLBCL patients: a study by the Groupe d'Etudes des Lymphomes de l'Adulte. Blood 2010;116:2040-2045. Wright GW, Huang DW, Phelan JD, et al. . A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications. Cancer Cell 2020;37:551-568.e514. Yuan L, Wang W, Zhang W, et al. . Gut Microbiota in Untreated Diffuse Large B Cell Lymphoma Patients. Front Microbiol 2021;12:646361. Li Y, ZHANG Y, ZHANG W, et al. . Correlation of Gut Microbiota with Efficacy of Chemotherapy in Patients with Diffuse Large B-cell Lymphoma. 2021. Yoon SE, Kang W, Choi S, et al. . The influence of microbial dysbiosis on immunochemotherapy-related efficacy and safety in diffuse large B-cell lymphoma. Blood 2023;141:2224-2238. Zhang Y, Han S, Xiao X, et al. . Integration analysis of tumor metagenome and peripheral immunity data of diffuse large-B cell lymphoma. Front Immunol 2023;14:1146861. Lu H, Xu X, Fu D, et al. . Butyrate-producing Eubacterium rectale suppresses lymphomagenesis by alleviating the TNF-induced TLR4/MyD88/NF-κB axis. Cell Host Microbe 2022;30:1139-1150.e1137. Zeze K, Hirano A, Torisu T, et al. . Mucosal dysbiosis in patients with gastrointestinal follicular lymphoma. Hematol Oncol 2020;38:181-188. Morrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet 2020;52:740-747. Ma J, Li J, Jin C, et al. . Association of gut microbiome and primary liver cancer: A two-sample Mendelian randomization and case-control study. Liver Int 2023;43:221-233. Long Y, Tang L, Zhou Y, Zhao S, Zhu H. Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study. BMC Med 2023;21:66. Wei Z, Yang B, Tang T, et al. . Gut microbiota and risk of five common cancers: A univariable and multivariable Mendelian randomization study. Cancer Med 2023;12:10393-10405. Jiang L, Zheng Z, Fang H, Yang J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet 2021;53:1616-1621. Kurilshikov A, Medina-Gomez C, Bacigalupe R, et al. . Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 2021;53:156-165. Yoon SE, Kang W, Choi S, et al. . The influence of microbial dysbiosis on immunochemotherapy-related efficacy and safety in diffuse large B-cell lymphoma. Blood 2023;141:2224-2238. Casadei B, Guadagnuolo S, Barone M, et al. . Gut Microbiota Role in Response to Checkpoint Inhibitor Treatment in Patients with Relapsed/Refractory B-Cell Hodgkin Lymphoma: The MICRO-Linf Study. Blood 2021;138:2957-2957. Mahiddine FY, You I, Park H, Kim MJ. Microbiome Profile of Dogs with Stage IV Multicentric Lymphoma: A Pilot Study. Vet Sci 2022;9. Lin Z, Mao D, Jin C, et al. . The gut microbiota correlate with the disease characteristics and immune status of patients with untreated diffuse large B-cell lymphoma. Front Immunol 2023;14:1105293. Bae H, Lim SK, Jo HE, et al. . Fecal microbiome in dogs with lymphoid and nonlymphoid tumors. J Vet Intern Med 2023;37:648-659. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol 2017;46:1734-1739. Larsson SC, Traylor M, Malik R, Dichgans M, Burgess S, Markus HS. Modifiable pathways in Alzheimer's disease: Mendelian randomisation analysis. Bmj 2017;359:j5375. Larsson SC, Burgess S. Appraising the causal role of smoking in multiple diseases: A systematic review and meta-analysis of Mendelian randomization studies. EBioMedicine 2022;82:104154. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013;37:658-665. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 2015;34:2926-2940. Mikshowsky AA, Gianola D, Weigel KA. Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation. J Dairy Sci 2017;100:453-464. 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;40:304-314. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44:512-525. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50:693-698. Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 2013;42:1497-1501. Cani PD. Human gut microbiome: hopes, threats and promises. Gut 2018;67:1716-1725. Meisel M, Hinterleitner R, Pacis A, et al. . Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host. Nature 2018;557:580-584. Vicente-Dueñas C, Janssen S, Oldenburg M, et al. . An intact gut microbiome protects genetically predisposed mice against leukemia. Blood 2020;136:2003-2017. Erdman S, Rao V, Poutahidis T, et al. . Nitric oxide and TNF-α trigger colonic inflammation and carcinogenesis in Helicobacter hepaticus-infected, Rag2-deficient mice. Proceedings of the National Academy of Sciences 2009;106:1027-1032. Rao VP, Poutahidis T, Ge Z, et al. . Innate immune inflammatory response against enteric bacteria Helicobacter hepaticus induces mammary adenocarcinoma in mice. Cancer Res 2006;66:7395-7400. Uribe-Herranz M, Klein-González N, Rodríguez-Lobato LG, Juan M, de Larrea CF. Gut Microbiota Influence in Hematological Malignancies: From Genesis to Cure. Int J Mol Sci 2021;22. Smith PM, Howitt MR, Panikov N, et al. . The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 2013;341:569-573. Gorres KL, Daigle D, Mohanram S, Miller G. Activation and Repression of Epstein-Barr Virus and Kaposi's Sarcoma-Associated Herpesvirus Lytic Cycles by Short- and Medium-Chain Fatty Acids. Journal of Virology 2014;88:8028-8044. Stein-Thoeringer CK, Saini NY, Zamir E, et al. . A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy. Nat Med 2023;29:906-916. Smith M, Dai A, Ghilardi G, et al. . Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy. Nat Med 2022;28:713-723. Mao J, Wang D, Long J, et al. . Gut microbiome is associated with the clinical response to anti-PD-1 based immunotherapy in hepatobiliary cancers. J Immunother Cancer 2021;9. Gopalakrishnan V, Spencer CN, Nezi L, et al. . Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359:97-103. Jin Y, Dong H, Xia L, et al. . The Diversity of Gut Microbiome is Associated With Favorable Responses to Anti-Programmed Death 1 Immunotherapy in Chinese Patients With NSCLC. J Thorac Oncol 2019;14:1378-1389. Peters BA, Hayes RB, Goparaju C, Reid C, Pass HI, Ahn J. The Microbiome in Lung Cancer Tissue and Recurrence-Free Survival. Cancer Epidemiol Biomarkers Prev 2019;28:731-740. Wei W, Sun W, Yu S, Yang Y, Ai L. Butyrate production from high-fiber diet protects against lymphoma tumor. Leuk Lymphoma 2016;57:2401-2408. Bari S, Jain S, Yadav H, et al. . Gut microbiome/metabolome predicts response to immune checkpoint blockers (ICB) in patients with recurrent metastatic head and neck squamous cell cancer (RM HNSCC). Journal of Clinical Oncology 2022;40:6055-6055. Dubin K, Callahan MK, Ren B, et al. . Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat Commun 2016;7:10391. Vétizou M, Pitt JM, Daillère R, et al. . Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 2015;350:1079-1084. Pitt JM, Vétizou M, Gomperts Boneca I, Lepage P, Chamaillard M, Zitvogel L. Enhancing the clinical coverage and anticancer efficacy of immune checkpoint blockade through manipulation of the gut microbiota. Oncoimmunology 2017;6:e1132137. Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables.xlsx SupplementFigures.docx SupplementTablesv5.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-3733715\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":260461326,\"identity\":\"068dc3be-1b23-4ee3-b6c4-b4212d3ad945\",\"order_by\":0,\"name\":\"Haoqing Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Haoqing\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":260461327,\"identity\":\"d8ee2614-39ea-40d0-8944-0998d157cbcb\",\"order_by\":1,\"name\":\"Yan Gao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yan\",\"middleName\":\"\",\"lastName\":\"Gao\",\"suffix\":\"\"},{\"id\":260461328,\"identity\":\"50044ed8-1aa9-4dac-9218-43006e1fc892\",\"order_by\":2,\"name\":\"Tingting Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tingting\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":260461329,\"identity\":\"ee531f58-16bf-45de-95b8-da583ed6e8dd\",\"order_by\":3,\"name\":\"Yanxia He\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"the Third People's Hospital of Chengdu\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yanxia\",\"middleName\":\"\",\"lastName\":\"He\",\"suffix\":\"\"},{\"id\":260461330,\"identity\":\"7333389b-9886-4ec0-900d-9ad8936f6d95\",\"order_by\":4,\"name\":\"Liqin Ping\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Liqin\",\"middleName\":\"\",\"lastName\":\"Ping\",\"suffix\":\"\"},{\"id\":260461331,\"identity\":\"db6942bc-10e6-4c3d-90b5-831f7da6c8a0\",\"order_by\":5,\"name\":\"Cheng Huang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Cheng\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"},{\"id\":260461332,\"identity\":\"f1bb09bd-d777-4b23-902e-f58d1e678771\",\"order_by\":6,\"name\":\"Huiqiang Huang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACAxDB2MAgR7oWY9K1JDYQrcVcIvnZw687rNPnz0h/JvGjhkHOvH8B4+cCPFosZ6SZG8ueSc/dcCPHTLLnGIOxzI0HzNIz8DnsRoKZtGTb4dwNEjls0iAXzpA4wMbMg1dL+jeQlnR5oMOI1QJ0z8e2wwkMIOvAWvgbCGg586ZMmrEt3XDDmTfGlj3HJIwlJBibpfFqOZ6+TfJnm7W8fHv6wxs/amzkJPgPH/yMTwsIAJ3BzMAgkABiSwAREXHE+AOkhf8AlAtnjIJRMApGwSiAAADzkUkir++4PgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Sun Yat-sen University Cancer Center\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Huiqiang\",\"middleName\":\"\",\"lastName\":\"Huang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2023-12-10 10:44:15\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3733715/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3733715/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":48484448,\"identity\":\"6cd2ac2f-a1e1-44c0-ab81-352d8dd39ca8\",\"added_by\":\"auto\",\"created_at\":\"2023-12-19 19:21:23\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":452563,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eOverview of the Mendelian Randomization (MR) Analysis Process\\u003c/strong\\u003e. This figure provides an overview of the Mendelian Randomization (MR) analysis process employed in our study. Initially, we selected specific gut microbiota taxonomic groups as the exposure variables. Subsequently, we utilized GWAS data to identify single nucleotide polymorphisms (SNPs) associated with these microbiota. Then, we used these SNPs as instrumental variables to assess the causal relationship between gut microbiota (exposure variable) and DLBCL risk (outcome variable). We conducted statistical analysis to determine the presence of a causal relationship. Finally, we performed a reverse MR analysis using the microbiota as the outcome variable and the DLBCL as the exposure variable.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/c46bb66871004ab2eeb8fffc.png\"},{\"id\":48484921,\"identity\":\"558e0972-f563-444f-81f0-b0d00fdda70f\",\"added_by\":\"auto\",\"created_at\":\"2023-12-19 19:29:23\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":93081,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eForest Plot llustrating Causal Links Between Specific Microbiota and DLBCL by IVW\\u003c/strong\\u003e. Abbreviations: CI, Confidence interval; OR, Odds ratio; SNP, Single nucleotide polymorphism; DLBCL. Diffuse Large B-Cell Lymphoma; IVM, lnverse variance weighted.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/a101a80dbd4845041b1cc25f.png\"},{\"id\":48484449,\"identity\":\"47acb3f0-a043-470d-aff4-286c74dbc2bc\",\"added_by\":\"auto\",\"created_at\":\"2023-12-19 19:21:23\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":173763,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eScatter plots of MR Analysis on the Relationship Between Gut microbiota and DLBCL Risk.\\u003c/strong\\u003eScatter plot depicting the relationship between gut microbiota and DLBCL risk-associated SNPs. The regression slopes of the lines correspond to causal estimates using three Mendelian Randomization (MR) methods, including the inverse variance weighted (IVW), weighted median, and MR-Egger method.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/4038b8d7bf521577bf14a47e.png\"},{\"id\":51679426,\"identity\":\"5a546aa6-c949-475a-aa12-a7e4735629cb\",\"added_by\":\"auto\",\"created_at\":\"2024-02-27 06:03:01\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":953088,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/71116a26-fc71-4bfe-9193-49c88fbaee4d.pdf\"},{\"id\":48484451,\"identity\":\"4f6db38c-b76f-42b5-9a77-5e13752cd6c5\",\"added_by\":\"auto\",\"created_at\":\"2023-12-19 19:21:23\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":14367,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Tables.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/fe246ea6a7a661c2216f4ed6.xlsx\"},{\"id\":48484453,\"identity\":\"88a8779a-c2d4-4f3d-857c-a78ce8bcc8e3\",\"added_by\":\"auto\",\"created_at\":\"2023-12-19 19:21:23\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":4814555,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementFigures.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/69f3d02c5b7adb3f85405cde.docx\"},{\"id\":48484922,\"identity\":\"84a37dd2-612e-4761-b5fd-33c5a8826801\",\"added_by\":\"auto\",\"created_at\":\"2023-12-19 19:29:23\",\"extension\":\"xlsx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":33627,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementTablesv5.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3733715/v1/3472b3144e93fd5afbdcb009.xlsx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eThe human microbiome has been gaining increasing attention from researchers. Microbiome communities are characterized by unique diversity and variability, often coexisting with the body's barriers exposed to the external environment. The gut microbiome stands out as the most influential and prominent [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. The gut microbiome has been shown to have a close connection with tumor development and tumor immune microenvironments [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Disruptions in the gut microbiota's ecological balance, leading to microbial dysbiosis, can induce tumorigenesis. Potential mechanisms by which bacteria induce carcinogenesis include but are not limited to, chronic inflammation, immune evasion, and immunosuppression [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Unique microbiota may enhance anti-tumor immune responses [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e] or trigger systemic or local tumor immune suppression [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Consequently, the human microbiome is a potent external driving factor for acquiring hallmark tumor characteristics. The fascinating aspect of current microbiome research lies in its potential to modulate the molecular mechanisms of tumor immunobiology by regulating microbial balance and the abundance of beneficial probiotics.\\u003c/p\\u003e \\u003cp\\u003eLymphoma is a malignant tumor originating from the body's immune system, with diffuse large B-cell lymphoma (DLBCL) representing a consequential subset [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. The cure rate for DLBCL patients receiving first-line treatment with R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) can reach up to 60% [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. However, the treatment outcomes for relapsed or refractory DLBCL patients are less favorable. To better understand the disease's mechanisms and prognosis, Staudt and colleagues proposed seven molecular subtypes based on the genetics of DLBCL in 2020. However, this classification method only covers 63.1% of the disease's spectrum [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Moreover, this classification method still needs to address the complex immunological features of the tumor. Therefore, the microbiome closely associated with immune responses may represent a valuable tool in studying the heterogeneity and immune microenvironment of DLBCL.\\u003c/p\\u003e \\u003cp\\u003eCurrent research suggests a close link between gut microbiota and DLBCL, although significant differences exist between various studies. Several studies have shown that the \\u003cem\\u003ephylum Bacteroidetes\\u003c/em\\u003e might dominate the gut microbiota in DLBCL patients. One study compared the gut flora of 35 DLBCL patients receiving immunotherapy with 20 healthy controls, and it found that Bacteroidetes dominated the disease group, while \\u003cem\\u003eFirmicutes\\u003c/em\\u003e decreased significantly [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Similarly, the \\u003cem\\u003ephylum Bacteroidetes\\u003c/em\\u003e as a dominant microbiome in DLBCL was also mentioned in the study by Li and colleagues [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Seok Jin Kim and co-authors reviewed the baseline gut microbiota of 189 newly diagnosed DLBCL patients, once again finding that the \\u003cem\\u003eBacteroidetes phylum\\u003c/em\\u003e dominated DLBCL, and the \\u003cem\\u003eEnterobacteriaceae family\\u003c/em\\u003e was part of the dominant microbiota [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. However, another study discovered significant differences in the abundance of six bacteria, including \\u003cem\\u003eStreptococcus\\u003c/em\\u003e and \\u003cem\\u003eBacteroides\\u003c/em\\u003e, among DLBCL patients at baseline in different NCCN-IPI score groups [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. Research also demonstrated that the \\u003cem\\u003ephylum Proteobacteria\\u003c/em\\u003e significantly decreased in patients with primary gastrointestinal B-cell lymphoma, while microbiota lacking \\u003cem\\u003eProteobacteria\\u003c/em\\u003e interacted synergistically with the tumor necrosis factor (TNF) signaling pathway through lipopolysaccharides (LPS) and reinforced the NF-κB pathway through the MYD88-TLR4 signaling pathway, inducing the survival and malignant proliferation of intestinal B cells [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Additionally, gastric and intestinal lymphoma patients had significantly lower microbiota abundances such as \\u003cem\\u003eSporomusa, Rothia, Prevotella genera\\u003c/em\\u003e, and the \\u003cem\\u003eGemellaceae family\\u003c/em\\u003e compared to the control group [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Due to the considerable variability in the gut microbiome of DLBCL in existing studies and the inability to disentangle confounding factors and causal relationships in observational studies, the actual connection between gut microbiota and DLBCL remains elusive.\\u003c/p\\u003e \\u003cp\\u003eMendelian randomization (MR) stands as an innovative statistical approach that centers on genetic variations, utilizing single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to mitigate the impact of confounding factors. It assesses whether an exposure factor causally influences a disease outcome [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. The onset and progression of diseases typically arise from intricate interactions involving numerous genes and environmental elements. As specific genetic factors, SNPs remain unaffected by common confounding factors like postnatal environmental conditions, dietary patterns, chemical exposures, and behavior. This characteristic ensures the temporal plausibility of causative relationships derived from Mendelian randomization (MR). Over the past few years, the application of MR analysis has extended to assess potential causal links between the gut microbiota and cancer risk [\\u003cspan additionalcitationids=\\\"CR20\\\" citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. This study employs MR analysis based on existing real-world observational studies and a genome-wide association study (GWAS) database to determine the causal relationship between gut microbiota and DLBCL.\\u003c/p\\u003e\"},{\"header\":\"2 Methods\",\"content\":\"\\u003cp\\u003e2.1 Data Acquisition\\u003c/p\\u003e \\u003cp\\u003eThe schematic representation of this study's design framework is depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e (created using the Figdraw online platform). It comprises two datasets - the GWAS dataset and the MiBioGen dataset. The GWAS dataset includes genetic summary statistics about DLBCL derived from the GWAS database, incorporating 616 cases of European ancestry and 455,732 controls[\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. The MiBioGen dataset encompasses summary statistics of the human gut microbiome from the latest GWAS meta-analysis, including genomic genotyping and 16S gut microbiome information for 18,340 participants[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e], spanning up to 24 cohorts from various regions.\\u003c/p\\u003e\\u003cp\\u003eSince this study relies on publicly available aggregated data, no additional ethical approval or informed consent was required. The internet links to the dataset are outlined in Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e2.2 Instrumental Variable Acquisition\\u003c/p\\u003e \\u003cp\\u003eFirst, after excluding bacterial traits with unknown names from the MiBioGen dataset, 196 bacterial traits were retained, including nine phyla, 16 classes, 20 orders, 32 families, and 119 genera. Subsequently, to better identify gut microbial species closely associated with DLBCL as IVs, we conducted a review of relevant real-world observational studies, literature, and conference abstracts in PubMed and Google Scholar to compile a list of gut bacteria potentially related to DLBCL risk[\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR16\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR25 CR26 CR27\\\" citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. The information of bacterial groups reported in the literature can be viewed in Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e and Table S3.\\u003c/p\\u003e \\u003cp\\u003eWe then searched for corresponding bacterial traits within the MiBioGen dataset for further analysis. We subsequently used a threshold of 1.0 x 10^-5 for selecting instrumental variables, with a linkage disequilibrium (LD) threshold set at 0.001 and a clumping distance of 10,000 kb. After obtaining specific bacterial species-associated SNPs, we calculated R2 and F-statistic:\\u003cdiv id=\\\"Equa\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equa\\\" name=\\\"EquationSource\\\"\\u003e\\n$${R}^{2}= \\\\frac{{{\\\\beta }}^{2}}{ {{\\\\beta }}^{2}+ \\\\text{N} \\\\text{*} {\\\\text{S}\\\\text{E}}^{2}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Equb\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equb\\\" name=\\\"EquationSource\\\"\\u003e\\n$$F ={R}^{2}*\\\\frac{N-2}{1 - {R}^{2}}$$\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003ewhere N signifies the sample size corresponding to the exposure factor, β represents effect size for the genetic variant of interest, and SE denotes a standard error for β. When F-statistic\\u0026thinsp;\\u0026gt;\\u0026thinsp;10, we had reason to exclude the hypothesis that the instrumental variable is a weak instrument. In the reverse MR analysis, we used the same methods to screen for IVs associated with DLBCL.\\u003c/p\\u003e \\u003cp\\u003e2.3 Two-Sample Mendelian Randomization Analysis\\u003c/p\\u003e \\u003cp\\u003eWe utilized the \\\"TwoSampleMR\\\" R package[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e] for various methods to assess the potential causal relationship between gut bacteria and DLBCL. These methods included the fixed/random-effects inverse variance-weighted (IVW) method, the weighted median method, MR-Egger regression, and MR multi-effect residual and outlier (MR-PRESSO) tests.\\u003c/p\\u003e \\u003cp\\u003eSince the IVW method provides the most precise effect estimates[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e], we primarily employed the IVW method. The IVW method initially calculates the ratio estimate for individual SNPs using Wald estimators and the Delta method. The results of all specific bacterial community analyses are shown in Table S3.\\u003c/p\\u003e \\u003cp\\u003eThen, it combines the estimates derived from each SNP to obtain the primary causal estimate[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Heterogeneity pertains to the variability in the causal estimates obtained for each SNP, and lower heterogeneity suggests increased reliability of MR estimates. We employed Cochran's Q test to assess heterogeneity among SNPs. If heterogeneity existed (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), we opted for the random-effects IVW method; otherwise, we used the fixed-effects IVW method[\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eFurthermore, we conducted a sensitivity analysis to evaluate the robustness of the associations[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. The weighted median (WM) method was used to estimate the weighted median, even when up to 50% of the IVs were invalid, and it provided effective causal effect estimates[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. We utilized MR-Egger regression to test for potential horizontal pleiotropy. If the p-value for the intercept was less than 0.05, the instrumental variables may exhibit horizontal pleiotropy[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eWe also conducted MR-PRESSO testing to perform a global heterogeneity test for SNPs to identify potential outliers. Corrected association results were acquired after removing potential outliers[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]. The MR models generated in this study were deemed appropriate statistical power to detect moderately associated causal effects, as calculated using the power calculator implemented by Brion et al.[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Finally, we generated visual outputs for specific MR models, including scatter plots, forest plots, funnel plots, and leave-one-out plots.\\u003c/p\\u003e \\u003cp\\u003eFollowing the analysis methods described, we conducted reverse MR analysis by treating gut bacteria as the outcome variable and DLBCL risk as the exposure factor. A specific bacterial species was assessed for inclusion in reverse MR analysis if it passed the IVW test and exhibited no deviations in sensitivity, heterogeneity, and horizontal pleiotropy checks.\\u003c/p\\u003e \\u003cp\\u003eWe performed all statistical analyses in R version 4.2.3, including R packages such as \\\"TwoSampleMR\\\" and \\\"ggplot2\\\". The association between exposure and outcome odds ratios (OR) and their corresponding 95% confidence intervals (CI).\\u003c/p\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cp\\u003e3.1 MR Analysis of Specific Microbiota and DLBCL Risk\\u003c/p\\u003e \\u003cp\\u003eBased on the literature review of gut microbes, we anchored two phyla (\\u003cem\\u003ephylum Bacteroidetes et al.\\u003c/em\\u003e), five families (\\u003cem\\u003efamily Ruminococcaceae et al.\\u003c/em\\u003e), and 52 genera in MiBioGen dataset (Table \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003e; Table S3). We performed MR analysis, treating these microbiota as exposure variables and DLBCL as the outcome variable. We used the IVW method to screen for potential causal links for the mentioned microbiota. Ultimately, we found that three genera of \\u003cem\\u003eRuminococcaceae\\u003c/em\\u003e (\\u003cem\\u003eRuminococcus torques.id.14377, Ruminococcaceae UCG014.id.11371, Ruminococcaceae UCG002.id.11360\\u003c/em\\u003e) and one genus of Eubacterium (\\u003cem\\u003eEubacterium oxidoreducens.id.11339\\u003c/em\\u003e) exhibited potential causal associations(Table S3; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). For the 63 SNPs associated with these four specific microbiota, the F-statistic for each SNP exceeded 10 (Table S4).\\u003c/p\\u003e\\u003cp\\u003eWithin this specific microbiota, \\u003cem\\u003eRuminococcus torques.id.14377\\u003c/em\\u003e showed a negative correlation with DLBCL risk (OR 0.44; p-value\\u0026thinsp;=\\u0026thinsp;0.006; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This genus maintained its negative correlation with DLBCL in the weighted median method (p\\u0026thinsp;=\\u0026thinsp;0.034). MR-PRESSO testing identified no outliers. In the heterogeneity test, MR Egger and IVW methods produced p-values greater than 0.05, indicating no heterogeneity in the causal associations between exposure and outcome. Furthermore, sensitivity analysis using the \\\"leave-one-out\\\" method and corresponding SNP forest plots showed no significant individual SNP driving drastic changes in the final results. The MR-Egger regression did not reveal any indications of directional pleiotropy, with an intercept p-value of 0.808. Similar link with DLBCL was observed for \\u003cem\\u003eRuminococcaceae UCG002.id.11360.\\u003c/em\\u003e (OR 0.62; p-value\\u0026thinsp;=\\u0026thinsp;0.023; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e and Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e-S3 provides visualization images of these four microbiota MR analyses.\\u003c/p\\u003e \\u003cp\\u003eOn the other hand, the remaining two genera, \\u003cem\\u003eRuminococcaceae UCG014.id.11371\\u003c/em\\u003e and \\u003cem\\u003eEubacterium oxidoreducens.id.11339\\u003c/em\\u003e, both showed a positive causal relationship with DLBCL in the IVW method (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), with OR values of 1.69 (p\\u0026thinsp;=\\u0026thinsp;0.028) for the former and 1.80 (p\\u0026thinsp;=\\u0026thinsp;0.033) for the latter. Additionally, \\u003cem\\u003eProteobacteria Phylum\\u003c/em\\u003e and \\u003cem\\u003eBacteroides Phylum\\u003c/em\\u003e did not achieve statistical significance in IVW analysis, we observed a numerical positive correlation between \\u003cem\\u003eProteobacteria Phylum\\u003c/em\\u003e and DLBCL risk and a negative correlation between \\u003cem\\u003eBacteroides Phylum\\u003c/em\\u003e and DLBCL risk(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e3.2 Reverse MR Analysis Results\\u003c/p\\u003e \\u003cp\\u003eSubsequently, we conducted reverse MR analysis with DLBCL as the exposure variable and the four specific microbiota, as well as two popular phyla (\\u003cem\\u003eProteobacteria Phylum, Bacteroides Phylum\\u003c/em\\u003e) and a butyrate-producing genus (\\u003cem\\u003egenus Eubacterium rectale.id.14374\\u003c/em\\u003e) as outcome variables. We identified 23 DLBCL-related SNPs, each with F-statistics around 20 (Table S5). After analyzing with the IVW method, we found no statistically significant associations between DLBCL and those mentioned above four specific gut microbiota (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The interest taxa from the literature also did not exhibit significant differences (\\u003cem\\u003eProteobacteria Phylum\\u003c/em\\u003e: OR 1.01, p\\u0026thinsp;=\\u0026thinsp;0.56; \\u003cem\\u003eBacteroides Phylum\\u003c/em\\u003e: OR 0.99, p\\u0026thinsp;=\\u0026thinsp;0.42; \\u003cem\\u003egenus Eubacterium rectale.id.14374\\u003c/em\\u003e: OR 1.01, p\\u0026thinsp;=\\u0026thinsp;0.58).\\u003c/p\\u003e\"},{\"header\":\"4 Discussion\",\"content\":\"\\u003cp\\u003eIn this twin-sample Mendelian randomization analysis study, we identified four microbiota taxonomic groups associated with the risk of DLBCL. These groups include \\u003cem\\u003eRuminococcus torques.id.14377, Ruminococcaceae UCG014.id.11371, Ruminococcaceae UCG002.id.11360\\u003c/em\\u003e, all belonging to the \\u003cem\\u003eRuminococcus genus\\u003c/em\\u003e, and \\u003cem\\u003eEubacterium oxidoreducens.id.11339\\u003c/em\\u003e from the \\u003cem\\u003eEubacterium genus\\u003c/em\\u003e. The investigation utilized Mendelian randomization to examine the potential causal association between the gut microbiota and DLBCL. While genetic and environmental factors can influence disease phenotypes in populations, our Mendelian randomization analysis directly used GWAS (Genome-Wide Association Studies) data based on genetic correlations to assess the potential causal relationship between the microbiota and DLBCL. Furthermore, we conducted strict quality control on the included SNPs to eliminate the influence of confounding factors and reverse causality.\\u003c/p\\u003e \\u003cp\\u003eThe human gut microbiota plays a crucial role in physiological regulation, and almost all human cells interact with it[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. The gut microbiota can mediate the occurrence and development of tumors, increasing cancer susceptibility. The interaction between genomic abnormalities and microbial signals may be bidirectional, with host \\u003cem\\u003eTET\\u003c/em\\u003e (Ten-Eleven Translocation) deficiencies making individuals more susceptible to the effects of microbial signals driving myelodysplastic syndromes[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. Similar research also suggests that the loss of a symbiotic microbiota is crucial in promoting the accumulation of white blood cell abnormalities[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. \\u003cem\\u003eHelicobacter hepaticus\\u003c/em\\u003e could induce the aggregation of macrophages and neutrophils in the colon, resulting in the upregulation of inducible nitric oxide synthase (iNOS) and increased production of nitric oxide, thus promoting atypical hyperplasia and carcinogenesis in the colon[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. Similarly, a study using a Rag2-deficient model of mice infected with colonic \\u003cem\\u003eHelicobacter hepaticus\\u003c/em\\u003e significantly promoted breast cancer through a TNF-α-dependent mechanism[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]. Metabolites and other molecules produced by the gut microbiota could also affect the immune system, regulating the balance between pro-inflammatory and anti-inflammatory mechanisms[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. For example, short-chain fatty acids (SCFAs), which are metabolites of bacteria, can promote the production of thymic regulatory T (Treg) cells, regulating the function of colonic Treg cells[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. SCFAs also act as histone deacetylase (HDAC) inhibitors, which could prevent Epstein-Barr virus reactivation. Given the prevalence of the Epstein-Barr virus in lymphoma patients, further exploration of the interaction between SCFAs and the virus is warranted[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn our study, we identified two species belonging to the \\u003cem\\u003eRuminococcus genus\\u003c/em\\u003e as beneficial, with a substantial decrease in DLBCL risk (OR\\u0026thinsp;=\\u0026thinsp;0.44) associated with an increased abundance of \\u003cem\\u003eRuminococcus torques.id.14377\\u003c/em\\u003e. Similarly, \\u003cem\\u003eRuminococcaceae UCG002.id.11360\\u003c/em\\u003e, classified as a probiotic, also contributed to approximately a 40% lower risk of lymphoma development. Previous studies have shown a deficiency of \\u003cem\\u003eRuminococcus\\u003c/em\\u003e in the gut microbiota of lymphoma patients[\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eRecent MR analysis has shown that enrichment of \\u003cem\\u003eRuminococcaceae\\u003c/em\\u003e and \\u003cem\\u003eBacteroidetes\\u003c/em\\u003e was associated with a reduced risk of liver cancer[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Another multi-tumor MR analysis showed that \\u003cem\\u003eRuminococcus genus UCG013\\u003c/em\\u003e protectd against breast cancer[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. The \\u003cem\\u003eRuminococcus genus\\u003c/em\\u003e was also closely related to the efficacy of immunotherapy. A study on hematologic CAR-T therapy showed that \\u003cem\\u003eRuminococcus, Prevotella\\u003c/em\\u003e, and \\u003cem\\u003eEubacterium\\u003c/em\\u003e were significantly associated with confirming the effectiveness of this immunotherapy[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. Another study also suggested that a higher abundance of the \\u003cem\\u003eRuminococcus genus\\u003c/em\\u003e was associated with a higher complete response rate on day 100 after CAR-T therapy[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. Moreover, patients with advanced liver cancer and melanoma who respond to PD-1 inhibitors typically had a higher abundance of the \\u003cem\\u003eRuminococcus genus\\u003c/em\\u003e[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]. However, some studies had contrasting findings. A study by Jin et al.[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e] suggested that \\u003cem\\u003eRuminococcus unclassified\\u003c/em\\u003e was associated with PD-1 therapy ineffectiveness, and a study by Peters et al.[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e] indicated that \\u003cem\\u003eRuminococcaceae\\u003c/em\\u003e was significantly related to poor lung cancer prognosis.\\u003c/p\\u003e \\u003cp\\u003eOn the other hand, \\u003cem\\u003eRuminococcaceae UCG014.id.11371\\u003c/em\\u003e, a member of the \\u003cem\\u003eRuminococcus genus\\u003c/em\\u003e, substantially increased the risk of DLBCL (OR\\u0026thinsp;=\\u0026thinsp;1.69). This observation suggests that different species or strains may exhibit different characteristics or traits even within the same genus. Such differences may be due to environmental conditions, growth conditions, genetic variations, or other factors. Further research into these specificities and diversities within genera is necessary.\\u003c/p\\u003e \\u003cp\\u003eAdditionally, \\u003cem\\u003eEubacterium oxidoreducens.id.11339\\u003c/em\\u003e increased the risk of DLBCL (OR\\u0026thinsp;=\\u0026thinsp;1.80). Lu et al.'s research indicated that \\u003cem\\u003eEubacterium\\u003c/em\\u003e, particularly \\u003cem\\u003eEubacterium rectale\\u003c/em\\u003e, was significantly deficient in gastrointestinal lymphoma and could produce high concentrations of butyrate[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Butyrate could induce apoptosis in lymphoma cells and inhibit the growth of lymphoma cells[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]. Furthermore, in Lu et al.'s in vitro experiments, \\u003cem\\u003eEubacterium rectale\\u003c/em\\u003e treatment reduced TNF levels and the incidence of lymphoma in mice[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. Therefore, we also focused on this genus in our MR analysis. Unfortunately, \\u003cem\\u003eEubacterium rectale\\u003c/em\\u003e did not show a causal link with DLBCL risk in our MR analysis. However, \\u003cem\\u003eEubacterium oxidoreducens.id.11339\\u003c/em\\u003e, another Eubacterium genus member, exhibited characteristics promoting DLBCL risk. Reports on this bacterium are relatively scarce, but a 2022 ASCO abstract suggests that patients who respond effectively to immune checkpoint inhibitors (ICIs) show enrichment in \\u003cem\\u003eEubacterium oxidoreducens\\u003c/em\\u003e [\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]. Thus, there is a need for in-depth investigation and research on this genus.\\u003c/p\\u003e \\u003cp\\u003eConsidering that the \\u003cem\\u003ephyla Firmicutes\\u003c/em\\u003e and \\u003cem\\u003eBacteroidetes\\u003c/em\\u003e have exhibited significant differences in multiple DLBCL-related observational studies[\\u003cspan additionalcitationids=\\\"CR13\\\" citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e], we also focused on these two phyla. In these studies, the \\u003cem\\u003eFirmicutes phylum\\u003c/em\\u003e was highly enriched in DLBCL, and within the \\u003cem\\u003eFirmicutes phylum\\u003c/em\\u003e, the \\u003cem\\u003efamily Enterobacteriaceae\\u003c/em\\u003e was significantly associated with the refractory outcomes of DLBCL[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. On the other hand, the \\u003cem\\u003eBacteroidetes phylum\\u003c/em\\u003e exhibited reduced abundance in DLBCL patients[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Multiple studies have shown that an increase in \\u003cem\\u003eBacteroidetes\\u003c/em\\u003e and enhanced resistance to colitis[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e], as well as the effectiveness of immunotherapy[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e], was associated with this phylum. Although the numerical trends of these two phyla were visible in our results (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e), these differences did not exhibit statistical significance in the IVW analysis.\\u003c/p\\u003e \\u003cp\\u003eAs mentioned, specific microbial groups did not yield statistically significant results in the reverse MR analysis. Given the robust focus of MR on causal temporal relationships, DLBCL may not be amenable to specific microbial changes in the current analysis settings. This phenomenon may be due to interference from confounding factors or limitations in the study design. The lack of meaningful results at this stage may suggest that definitive causal conclusions cannot be drawn, and further research is needed. DLBCL, as a highly heterogeneous immunosystem tumor, exhibits different immune dysfunctions in different individuals, leading to considerable variability in microbial dysbiosis. Furthermore, DLBCL patients often use glucocorticoids or rituximab, which increases the instability of the immune microenvironment. These factors may have contributed to our inability to obtain meaningful results and present challenges to researchers in lymphoma microbiome research.\\u003c/p\\u003e \\u003cp\\u003eTo more accurately recapitulate the relationship between gut microbiota and DLBCL in the real world, we relied on existing observational studies of gut microbiota rather than analyzing microbiota included in all GWAS databases. This approach helps to delve deeper into the heterogeneity of current microbiota research. By analyzing existing observational studies of gut microbiota, we gain a more comprehensive understanding of the relationship between gut microbiota and DLBCL without being influenced by potential microbiota heterogeneity in GWAS databases. With existing research to help pinpoint relevant microbiota, this strategy allows us to focus on known microbiota data and more accurately explore their potential roles in DLBCL development. Additionally, we can more precisely assess the interactions between microbiota and their impact on DLBCL susceptibility. This analytical approach provides us with finer and more reliable results, further advancing our understanding of the correlation between gut microbiota and DLBCL.\\u003c/p\\u003e \\u003cp\\u003eHowever, this study still has limitations. Firstly, the smallest analyzed taxonomic unit was at the genus level, and more precise information, such as species or strains, was unavailable, which may affect the depth of our analysis results. Secondly, the population included in GWAS summaries is predominantly of European ancestry, potentially limiting the generalizability of study results to other racial and ethnic groups. Thirdly, we used a SNP threshold of 1.0 \\u0026times; 10^\\u0026minus;5 to obtain sufficient exposure variables for analysis, which is higher than the usual threshold of 5 \\u0026times; 10^\\u0026ndash;8.\\u003c/p\\u003e\"},{\"header\":\"5 Conclusion\",\"content\":\"\\u003cp\\u003eOur MR study results support a potential causal relationship between the gut microbiome and DLBCL. Future research can delve deeper into these specific microbial taxa to explore their potential in clinical prediction and treatment. Moreover, high-quality GWAS data and further investigations into potential mechanisms are needed to understand better the bidirectional causal relationship between the gut microbiome and DLBCL.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eCompeting Interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors confirm that there are no relevant financial or non-financial competing interests to report.\\u003c/p\\u003e\\n\\u003cp\\u003eEthical Approval\\u003c/p\\u003e\\n\\u003cp\\u003eThis study is based on the analysis of biometric data and is not applicable for ethical approvals.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was conducted without any financial assistance or external funding.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor Contributions\\u003c/p\\u003e\\n\\u003cp\\u003eHaoqing Chen was primarily responsible for the research design, data analysis, visualization, and subsequent manuscript revisions. Yan Gao contributed to the research design and manuscript revisions. Tingting Chen was responsible for the compilation of public data and initial manuscript drafting. Yanxia He collected literature and contributed to the research discussions. Liqin Ping participated in the writing of R code and data visualization. Cheng Huang was involved in manuscript revisions. Huiqiang Huang, as the corresponding author, oversaw the research design, manuscript revisions, and team discussions.\\u003c/p\\u003e\\n\\u003cp\\u003eData Availability\\u003c/p\\u003e\\n\\u003cp\\u003eThe MiBioGen dataset for the gut microbiome was obtained from [https://mibiogen.gcc.rug.nl/], while the GWAS dataset for DLBCL was acquired from [http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90043001-GCST90044000/GCST90043906/].\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgments\\u003c/p\\u003e\\n\\u003cp\\u003eSpecial thanks to individuals involved in the design and creation of study. And we express our appreciation to the developers of GWAS, laying a solid foundation for a deeper understanding of the relevant field. \\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eHanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov 2022;12:31-46.\\u003c/li\\u003e\\n\\u003cli\\u003eShi N, Li N, Duan X, Niu H. Interaction between the gut microbiome and mucosal immune system. Mil Med Res 2017;4:14.\\u003c/li\\u003e\\n\\u003cli\\u003eQiu Q, Lin Y, Ma Y, et al. . Exploring the Emerging Role of the Gut Microbiota and Tumor Microenvironment in Cancer Immunotherapy. Front Immunol 2020;11:612202.\\u003c/li\\u003e\\n\\u003cli\\u003eMaynard CL, Elson CO, Hatton RD, Weaver CT. Reciprocal interactions of the intestinal microbiota and immune system. Nature 2012;489:231-241.\\u003c/li\\u003e\\n\\u003cli\\u003eCompare D, Nardone G. Contribution of gut microbiota to colonic and extracolonic cancer development. Dig Dis 2011;29:554-561.\\u003c/li\\u003e\\n\\u003cli\\u003eTanoue T, Morita S, Plichta DR, et al. . A defined commensal consortium elicits CD8 T cells and anti-cancer immunity. Nature 2019;565:600-605.\\u003c/li\\u003e\\n\\u003cli\\u003eMager LF, Burkhard R, Pett N, et al. . Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy. Science 2020;369:1481-1489.\\u003c/li\\u003e\\n\\u003cli\\u003ePushalkar S, Hundeyin M, Daley D, et al. . The Pancreatic Cancer Microbiome Promotes Oncogenesis by Induction of Innate and Adaptive Immune Suppression. Cancer Discov 2018;8:403-416.\\u003c/li\\u003e\\n\\u003cli\\u003eAlaggio R, Amador C, Anagnostopoulos I, et al. . The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia 2022;36:1720-1748.\\u003c/li\\u003e\\n\\u003cli\\u003eCoiffier B, Thieblemont C, Van Den Neste E, et al. . Long-term outcome of patients in the LNH-98.5 trial, the first randomized study comparing rituximab-CHOP to standard CHOP chemotherapy in DLBCL patients: a study by the Groupe d\\u0026apos;Etudes des Lymphomes de l\\u0026apos;Adulte. Blood 2010;116:2040-2045.\\u003c/li\\u003e\\n\\u003cli\\u003eWright GW, Huang DW, Phelan JD, et al. . A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications. Cancer Cell 2020;37:551-568.e514.\\u003c/li\\u003e\\n\\u003cli\\u003eYuan L, Wang W, Zhang W, et al. . Gut Microbiota in Untreated Diffuse Large B Cell Lymphoma Patients. Front Microbiol 2021;12:646361.\\u003c/li\\u003e\\n\\u003cli\\u003eLi Y, ZHANG Y, ZHANG W, et al. . Correlation of Gut Microbiota with Efficacy of Chemotherapy in Patients with Diffuse Large B-cell Lymphoma. 2021.\\u003c/li\\u003e\\n\\u003cli\\u003eYoon SE, Kang W, Choi S, et al. . The influence of microbial dysbiosis on immunochemotherapy-related efficacy and safety in diffuse large B-cell lymphoma. Blood 2023;141:2224-2238.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Y, Han S, Xiao X, et al. . Integration analysis of tumor metagenome and peripheral immunity data of diffuse large-B cell lymphoma. Front Immunol 2023;14:1146861.\\u003c/li\\u003e\\n\\u003cli\\u003eLu H, Xu X, Fu D, et al. . Butyrate-producing Eubacterium rectale suppresses lymphomagenesis by alleviating the TNF-induced TLR4/MyD88/NF-\\u0026kappa;B axis. Cell Host Microbe 2022;30:1139-1150.e1137.\\u003c/li\\u003e\\n\\u003cli\\u003eZeze K, Hirano A, Torisu T, et al. . Mucosal dysbiosis in patients with gastrointestinal follicular lymphoma. Hematol Oncol 2020;38:181-188.\\u003c/li\\u003e\\n\\u003cli\\u003eMorrison J, Knoblauch N, Marcus JH, Stephens M, He X. Mendelian randomization accounting for correlated and uncorrelated pleiotropic effects using genome-wide summary statistics. Nat Genet 2020;52:740-747.\\u003c/li\\u003e\\n\\u003cli\\u003eMa J, Li J, Jin C, et al. . Association of gut microbiome and primary liver cancer: A two-sample Mendelian randomization and case-control study. Liver Int 2023;43:221-233.\\u003c/li\\u003e\\n\\u003cli\\u003eLong Y, Tang L, Zhou Y, Zhao S, Zhu H. Causal relationship between gut microbiota and cancers: a two-sample Mendelian randomisation study. BMC Med 2023;21:66.\\u003c/li\\u003e\\n\\u003cli\\u003eWei Z, Yang B, Tang T, et al. . Gut microbiota and risk of five common cancers: A univariable and multivariable Mendelian randomization study. Cancer Med 2023;12:10393-10405.\\u003c/li\\u003e\\n\\u003cli\\u003eJiang L, Zheng Z, Fang H, Yang J. A generalized linear mixed model association tool for biobank-scale data. Nat Genet 2021;53:1616-1621.\\u003c/li\\u003e\\n\\u003cli\\u003eKurilshikov A, Medina-Gomez C, Bacigalupe R, et al. . Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 2021;53:156-165.\\u003c/li\\u003e\\n\\u003cli\\u003eYoon SE, Kang W, Choi S, et al. . The influence of microbial dysbiosis on immunochemotherapy-related efficacy and safety in diffuse large B-cell lymphoma. Blood 2023;141:2224-2238.\\u003c/li\\u003e\\n\\u003cli\\u003eCasadei B, Guadagnuolo S, Barone M, et al. . Gut Microbiota Role in Response to Checkpoint Inhibitor Treatment in Patients with Relapsed/Refractory B-Cell Hodgkin Lymphoma: The MICRO-Linf Study. Blood 2021;138:2957-2957.\\u003c/li\\u003e\\n\\u003cli\\u003eMahiddine FY, You I, Park H, Kim MJ. Microbiome Profile of Dogs with Stage IV Multicentric Lymphoma: A Pilot Study. Vet Sci 2022;9.\\u003c/li\\u003e\\n\\u003cli\\u003eLin Z, Mao D, Jin C, et al. . The gut microbiota correlate with the disease characteristics and immune status of patients with untreated diffuse large B-cell lymphoma. Front Immunol 2023;14:1105293.\\u003c/li\\u003e\\n\\u003cli\\u003eBae H, Lim SK, Jo HE, et al. . Fecal microbiome in dogs with lymphoid and nonlymphoid tumors. J Vet Intern Med 2023;37:648-659.\\u003c/li\\u003e\\n\\u003cli\\u003eYavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol 2017;46:1734-1739.\\u003c/li\\u003e\\n\\u003cli\\u003eLarsson SC, Traylor M, Malik R, Dichgans M, Burgess S, Markus HS. Modifiable pathways in Alzheimer\\u0026apos;s disease: Mendelian randomisation analysis. Bmj 2017;359:j5375.\\u003c/li\\u003e\\n\\u003cli\\u003eLarsson SC, Burgess S. Appraising the causal role of smoking in multiple diseases: A systematic review and meta-analysis of Mendelian randomization studies. EBioMedicine 2022;82:104154.\\u003c/li\\u003e\\n\\u003cli\\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 2013;37:658-665.\\u003c/li\\u003e\\n\\u003cli\\u003eGreco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 2015;34:2926-2940.\\u003c/li\\u003e\\n\\u003cli\\u003eMikshowsky AA, Gianola D, Weigel KA. Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation. J Dairy Sci 2017;100:453-464.\\u003c/li\\u003e\\n\\u003cli\\u003eBowden 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;40:304-314.\\u003c/li\\u003e\\n\\u003cli\\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 2015;44:512-525.\\u003c/li\\u003e\\n\\u003cli\\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 2018;50:693-698.\\u003c/li\\u003e\\n\\u003cli\\u003eBrion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol 2013;42:1497-1501.\\u003c/li\\u003e\\n\\u003cli\\u003eCani PD. Human gut microbiome: hopes, threats and promises. Gut 2018;67:1716-1725.\\u003c/li\\u003e\\n\\u003cli\\u003eMeisel M, Hinterleitner R, Pacis A, et al. . Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host. Nature 2018;557:580-584.\\u003c/li\\u003e\\n\\u003cli\\u003eVicente-Due\\u0026ntilde;as C, Janssen S, Oldenburg M, et al. . An intact gut microbiome protects genetically predisposed mice against leukemia. Blood 2020;136:2003-2017.\\u003c/li\\u003e\\n\\u003cli\\u003eErdman S, Rao V, Poutahidis T, et al. . Nitric oxide and TNF-\\u0026alpha; trigger colonic inflammation and carcinogenesis in Helicobacter hepaticus-infected, Rag2-deficient mice. Proceedings of the National Academy of Sciences 2009;106:1027-1032.\\u003c/li\\u003e\\n\\u003cli\\u003eRao VP, Poutahidis T, Ge Z, et al. . Innate immune inflammatory response against enteric bacteria Helicobacter hepaticus induces mammary adenocarcinoma in mice. Cancer Res 2006;66:7395-7400.\\u003c/li\\u003e\\n\\u003cli\\u003eUribe-Herranz M, Klein-Gonz\\u0026aacute;lez N, Rodr\\u0026iacute;guez-Lobato LG, Juan M, de Larrea CF. Gut Microbiota Influence in Hematological Malignancies: From Genesis to Cure. Int J Mol Sci 2021;22.\\u003c/li\\u003e\\n\\u003cli\\u003eSmith PM, Howitt MR, Panikov N, et al. . The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 2013;341:569-573.\\u003c/li\\u003e\\n\\u003cli\\u003eGorres KL, Daigle D, Mohanram S, Miller G. Activation and Repression of Epstein-Barr Virus and Kaposi\\u0026apos;s Sarcoma-Associated Herpesvirus Lytic Cycles by Short- and Medium-Chain Fatty Acids. Journal of Virology 2014;88:8028-8044.\\u003c/li\\u003e\\n\\u003cli\\u003eStein-Thoeringer CK, Saini NY, Zamir E, et al. . A non-antibiotic-disrupted gut microbiome is associated with clinical responses to CD19-CAR-T cell cancer immunotherapy. Nat Med 2023;29:906-916.\\u003c/li\\u003e\\n\\u003cli\\u003eSmith M, Dai A, Ghilardi G, et al. . Gut microbiome correlates of response and toxicity following anti-CD19 CAR T cell therapy. Nat Med 2022;28:713-723.\\u003c/li\\u003e\\n\\u003cli\\u003eMao J, Wang D, Long J, et al. . Gut microbiome is associated with the clinical response to anti-PD-1 based immunotherapy in hepatobiliary cancers. J Immunother Cancer 2021;9.\\u003c/li\\u003e\\n\\u003cli\\u003eGopalakrishnan V, Spencer CN, Nezi L, et al. . Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018;359:97-103.\\u003c/li\\u003e\\n\\u003cli\\u003eJin Y, Dong H, Xia L, et al. . The Diversity of Gut Microbiome is Associated With Favorable Responses to Anti-Programmed Death 1 Immunotherapy in Chinese Patients With NSCLC. J Thorac Oncol 2019;14:1378-1389.\\u003c/li\\u003e\\n\\u003cli\\u003ePeters BA, Hayes RB, Goparaju C, Reid C, Pass HI, Ahn J. The Microbiome in Lung Cancer Tissue and Recurrence-Free Survival. Cancer Epidemiol Biomarkers Prev 2019;28:731-740.\\u003c/li\\u003e\\n\\u003cli\\u003eWei W, Sun W, Yu S, Yang Y, Ai L. Butyrate production from high-fiber diet protects against lymphoma tumor. Leuk Lymphoma 2016;57:2401-2408.\\u003c/li\\u003e\\n\\u003cli\\u003eBari S, Jain S, Yadav H, et al. . Gut microbiome/metabolome predicts response to immune checkpoint blockers (ICB) in patients with recurrent metastatic head and neck squamous cell cancer (RM HNSCC). Journal of Clinical Oncology 2022;40:6055-6055.\\u003c/li\\u003e\\n\\u003cli\\u003eDubin K, Callahan MK, Ren B, et al. . Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat Commun 2016;7:10391.\\u003c/li\\u003e\\n\\u003cli\\u003eV\\u0026eacute;tizou M, Pitt JM, Daill\\u0026egrave;re R, et al. . Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 2015;350:1079-1084.\\u003c/li\\u003e\\n\\u003cli\\u003ePitt JM, V\\u0026eacute;tizou M, Gomperts Boneca I, Lepage P, Chamaillard M, Zitvogel L. Enhancing the clinical coverage and anticancer efficacy of immune checkpoint blockade through manipulation of the gut microbiota. Oncoimmunology 2017;6:e1132137.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003cp\\u003eTables 1 and 2 are available in the Supplementary Files section.\\u003c/p\\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\":\"info@researchsquare.com\",\"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\":\"Gut microbiota, Diffuse large B-cell lymphoma, Mendelian randomization, Microbiome, Causal influence\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3733715/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3733715/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003ePrevious research has revealed a significant association between the gut microbiome and diffuse large B-cell lymphoma (DLBCL). However, the findings of these studies have yet to be entirely consistent. Whether a causal relationship exists between gut bacterial and DLBCL remains elucidated. We performed two-sample mendelian randomization (MR) using genetic data from MiBioGen and DLBCL summary statistics from GWAS. The primary analysis used inverse variance weighted (IVW), the weighted median, MR-Egger regression, and pleiotropic residual sum and outlier tests. Reverse MR checked for reverse causality. Our study identified four bacterial genera can causally increase the risk of DLBCL disease: \\u003cem\\u003eRuminococcus torques.id.14377\\u003c/em\\u003e (OR 0.44; p\\u0026thinsp;=\\u0026thinsp;0.006), \\u003cem\\u003eRuminococcaceae UCG014.id.11371\\u003c/em\\u003e (OR 1.69; p\\u0026thinsp;=\\u0026thinsp;0.028), \\u003cem\\u003eRuminococcaceae UCG002.id.11360\\u003c/em\\u003e (OR 0.62; p\\u0026thinsp;=\\u0026thinsp;0.023), and \\u003cem\\u003eEubacterium oxidoreducens.id.11339\\u003c/em\\u003e (OR 1.80; p\\u0026thinsp;=\\u0026thinsp;0.033). In reverse MR analysis, we found no causal effect from DLBCL to gut bacterial. Our investigation offers indications of causal connections between the gut microbiome and the onset of DLBCL.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2023-12-19 19:21:18\",\"doi\":\"10.21203/rs.3.rs-3733715/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"0c5de167-6e67-419f-a051-c5afccab9177\",\"owner\":[],\"postedDate\":\"December 19th, 2023\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-02-27T06:02:40+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2023-12-19 19:21:18\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3733715\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3733715\",\"identity\":\"rs-3733715\",\"version\":[\"v1\"]},\"buildId\":\"J0_U0BvcaRcwD8yVFaRlm\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}