Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies

doi:10.21203/rs.3.rs-3733715/v1

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Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies

https://doi.org/10.21203/rs.3.rs-3733715/v1

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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

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.

2.1 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).

3.1 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).

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.

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.

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.

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Tables 1 and 2 are available in the Supplementary Files section.

No competing interests reported.

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Exploring the Causal Relationship between Gut Bacteria and DLBCL through Comprehensive Integration of Prior Studies

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