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Investigating the causal impact of gut microbiota on glioblastoma: a bidirectional Mendelian randomization study



Currently, the influence of microbiota on the occurrence, progression, and treatment of cancer is a topic of considerable research interest. Therefore, based on the theory of the gut-brain axis proved by previous studies, our objective was to uncover the causal relationship between glioblastoma and the gut microbiome using Mendelian randomization analysis.


We conducted a bidirectional Mendelian randomization study using summary statistics of gut microbiota derived from the MiBioGen consortium, the largest database of gut microbiota. Summary statistics for glioblastoma were obtained from IEU OpenGWAS project, which included 91 cases and 218,701 controls. We assessed the presence of heterogeneity and horizontal pleiotropy in the analyzed data. We primarily employed the inverse variance weighting method to investigate the causal relationship between gut microbiota and glioblastoma after excluding cases of horizontal pleiotropy. Four other analysis methods were employed as supplementary. Excluding abnormal results based on leave-one-out sensitivity analysis. Finally, reverse Mendelian randomization analysis was performed.


Four genus-level taxa and one family-level taxa exhibited causal associations with glioblastoma. And these results of reverse Mendelian randomization analysis shown glioblastoma exhibited causal associations with three genus-level taxa and one family-level taxa. However, the Prevotella7(Forward, P=0.006, OR=0.34, 95%CI:0.158-0.732; Reverse, P=0.004, OR=0.972, 95%CI:0.953-0.991) shown the causal associations with glioblastoma in the bidirectional Mendelian randomization.


In this bidirectional Mendelian randomization study, we identified five gut microbiota species with causal associations to glioblastoma. However, additional randomized controlled trials are required to clarify the impact of gut microbiota on glioblastoma and to reveal its precise mechanisms.

Peer Review reports


Glioblastoma (GBM) stands out as one of the most malignant primary brain tumors, characterized by its exceptionally high fatality rate. The rapid growth and heterogeneity of this tumor are significant contributors to its aggressive progression, manifesting in symptoms such as neurological impairment and cognitive decline. The current standard treatment for newly diagnosed cases entails a combination of post-surgical radiotherapy and temozolomide, followed by adjuvant temozolomide therapy [1]. Nevertheless, the tumor's aggressiveness and its deep-seated location within brain tissue pose formidable challenges to achieving complete removal. Furthermore, post-successful surgery, the presence of residual tumor cells can lead to recurrence [2]. Moreover, glioblastoma frequently displays resistance to conventional radiotherapy and chemotherapy. The presence of the blood-brain barrier further hinders the delivery of therapeutic agents to tumor tissue, presenting a formidable therapeutic obstacle [3]. Furthermore, the genetic heterogeneity of tumor cells at different sites can result in diverse phenotypes and gene expression patterns, creating an additional therapeutic challenge. At present, the treatment of glioblastoma remains a pressing concern. Consequently, researchers are exploring innovative therapeutic approaches, including immunotherapy, gene therapy, and targeted therapy. Immunotherapy involves the stimulation of the patient's immune system to selectively target and attack tumor cells [4,5,6]. Therefore, given the unique nature of glioblastoma, the significance of prevention and early diagnosis becomes even more pronounced. Nevertheless, despite substantial progress in clinical and basic research over the years, the precise etiology of GBM remains elusive.

The brain was historically considered an “immune-privileged” organ due to the blood-brain barrier. However, the discovery of a functional lymphatic system and the presence of peripheral immune cells have substantiated the existence of an immune system in the brain [7]. Glioblastoma , characterized as cold tumors, inhibits the immune response to cancer, leading to immunotherapy failures [8, 9]. Recent studies have highlighted the multifaceted roles of the gut microbiota, encompassing regulation of nutrient absorption, synthesis of vitamins, metabolism of bile and hormones, and fermentation of carbohydrates [10, 11]. Moreover, the gut microbiota exerts systemic effects on immunity, inflammation, and metabolism [12,13,14]. Emerging evidence suggests that the gut microbiota can indirectly influence brain tumor metabolism and the brain's immune environment through the production of metabolites [15, 16]. This interaction can either promote or inhibit the malignant progression of GBM. As a result, researchers are increasingly focusing on the well-established gut-brain axis, a bidirectional link between the brain and the gut [17, 18].

However, owing to the absence of evidence from randomized controlled trials, the existence of a definitive causal link between gut flora and glioblastoma remains uncertain. While randomized controlled trials serve as the gold standard for establishing causality in epidemiological investigations, conducting them can be challenging due to ethical constraints and substantial costs. To explore the potential association between the gut microbiota and GBM, we utilized Mendelian Randomization (MR) Analysis, a systematic method for assessing causality. MR employs genetic variation as an instrumental variable to model interventions, enhancing our ability to make more confident inferences regarding the influence of a factor on disease occurrence [19, 20]. In this study, we will employ MR methods to examine the potential causal connection between gut microbiota and GBM.

The objective of this study is to elucidate whether the gut microbiota's composition is linked to the risk of GBM and to delve deeper into potential underlying biological mechanisms. We anticipate that this study will offer novel insights and strategies for the future prevention and treatment of GBM. This endeavor will not only enhance our comprehension of GBM's etiology but may also offer substantial backing for the formulation of personalized therapeutic protocols, with the potential to enhance both patient survival and quality of life.


Study design

The entire study design is displayed in Fig. 1. MR was employed to analyze the causal relationship between the gut microbiota and GBM. We adhered to the three core principles of MR analysis: (1) Strong link between genetic variation and exposure factors [21]; (2) ensuring no correlation between genetic variation and confounders [22]; (3) affirming that genetic variation influences the outcomes solely through exposure factors, with no involvement of other pathways [23]. Concurrently, we conducted a reverse MR analysis utilizing the statistically significant findings from the initial MR analysis to obtain more robust results.

Fig. 1
figure 1

The whole study design

Data source and preparation

We sourced summary statistics of gut microbiota composition from the most extensive genome-wide meta-analysis to date, conducted by the MiBioGen consortium ( [24]. This analysis encompassed 18,340 participants of European ethnicity from 11 countries and included 122,110 loci of genetic variation. The summary statistics for the genome-wide association study (GWAS) related to GBM were acquired from the Medical Research Council Integrative Epidemiology Unit (IEU) Open GWAS project ( [25](updated to 2021.04.06, ncase=91, ncontrol=218,701, number of SNPs=16,380,466).

The selection criteria for instrumental variables (IVs) included the following steps: (1) Identification of single nucleotide polymorphisms (SNPs) associated with each genus at the locus-wide significance threshold (P<1.0 × 10-5) as potential IVs [18, 26]; (2) Conducting a linkage disequilibrium (LD) window analysis for all IVs (r2 < 0.001, clumping window size=10,000 kb); (3) Removal of SNPs related to exposure but lacking corresponding matches in the GWAS outcome statistics, calculated using the formula F=beta2exposure /SE 2exposure [23] ; (4) Exclusion of SNPs with a minor allele frequency (MAF) ≤ 0.01; and (5) In cases of palindromic SNPs, determination of forward strand alleles based on allele frequency information [27].

Statistical analysis

MR is employed to investigate causal relationships between bacterial taxa and GBM. Before conducting the analysis, we conducted a test for horizontal pleiotropy to eliminate statistics affected by horizontal pleiotropy. This ensures that the inverse variance weighting (IVW) method can serve as the primary approach for causality assessment in MR analysis [28]. Furthermore, we employed Cochrane's Q test to evaluate heterogeneity among IVs. In cases where heterogeneity was detected (P<0.05), we adopted a random-effects IVW (IVW-RE) model, which offers more conservative estimates. Conversely, in the absence of heterogeneity, we utilized a fixed-effects IVW (IVW-FE) model [18]. In case the IVW results yielded statistical significance (p<0.05), we introduced several additional MR methods, including MR-Egger regression, simple mode, weighted median, and weighted mode. Notably, weighted median (WM) and MR-Egger regression serve to complement the IVW method and offer broader CIs [29].

Finally, we performed a leave-one-out sensitivity analysis of statistically significant causal relationships to arrive at our final results. Subsequently, to enhance result credibility, we conducted an inverse MR analysis using the GWAS summary statistics from flora causally associated with GBM as the outcome and those from GBM as the exposure, applying the same MR analysis methods as previously described. All of the aforementioned analyses were conducted using the R programming language (R version 4.3.0) and the "TwoSampleMR" package in R [30, 31].


We utilized gut microbiota GAWs data obtained from the MiBioGen consortium, which encompassed 131 genus-level taxa, 35 family-level taxa, 20 order-level taxa, 9 phylum-level taxa, 16 class-level taxa, and a total of 2,620 SNPs, as instrumental variables. Detailed information regarding these SNPs can be found in Online Resource 1: Table S1.

Following the aforementioned steps, we conducted a horizontal pleiotropy test to exclude certain statistics influenced by horizontal pleiotropy. Subsequently, we employed different IVW analysis methods based on the Q-value obtained from the heterogeneity test. Notably, the Q-value for the heterogeneity test exceeded 0.05 in nearly all groups, suggesting the absence of statistical heterogeneity. As illustrated in Fig. 2, the IVW analysis method revealed that 8 genus-level flora (Eubacteriumbrachygroup, Eubacteriumruminantiumgroup, Anaerostipes, Faecalibacterium, LachnospiraceaeUCG004, Prevotella7, RikenellaceaeRC9gutgroup, Senegalimassilia) and 3 family-level flora (Bacteroidaceae, Peptostreptococcaceae, Ruminococcaceae, Victivallaceae) exhibited associations with GBM. Notably, Eubacteriumbrachygroup (Weighted median, P=0.007, OR=1.554, 95% CI: 1.554-15.890), Eubacteriumruminantiumgroup (Weighted median, P=0.036, OR=3.673, 95% CI: 1.087-12.411), Prevotella7 (Weighted median, P=0.034, OR=0.326, 95% CI: 0.116-0.917), and Peptostreptococcaceae (Weighted median, P=0.040, OR=6.121, 95% CI: 1.089-34.402) were confirmed in two MR methods to exhibit causality with GBM (IVW and weighted median). Additionally, Ruminococcaceae (MR-Egger regression, P=0.048, OR=0.009, 95% CI: 0.000-0.468; Weighted median, P=0.040, OR=0.094, 95% CI: 0.010-0.897) demonstrated causality with GBM in three distinct methods (IVW, MR-Egger regression, and weighted median).

Fig. 2
figure 2

Forest plot of GM taxa associated with GBM (P<0.05) identified by IVW-FE method

In addition, we conducted a leave-one-out sensitivity analysis for these 11 groups and presented the final results in Table 1, Figs. 3 and 4. And the details of SNPs were shown in Table 2. According to both the Inverse Variance Weighting and weighted median estimates, Eubacteriumbrachygroup exhibited a risk factor associated with GBM (IVW, P=0.011, OR=3.066, 95%CI=1.287-7.308; Weighted median, P=0.007, OR=4.969, 95%CI: 1.554-15.890). Furthermore, the IVW results for Anaerostipes (IVW, P=0.011, OR=0.145, 95%CI:0.033-0.642) , Faecalibacterium (IVW, P=0.005, OR=0.156, 95%CI=0.043-0.565), Prevotella7 (IVW, P=0.006, OR=0.340, 95%CI=0.158-0.732), and Ruminococcaceae (IVW, P=0.001, OR=0.058, 95%CI=0.011-0.304) with GBM remained causal associations even after leave-one-out sensitivity analysis, signifying a protective effect on GBM for all four.

Table 1 Results of all MR analyses with causality
Fig. 3
figure 3

Scatter plots for the causal association between gut microbiota and GBM identified by IVW-FE method

Fig. 4
figure 4

Leave-one-out plots for the causal association between gut microbiota and GBM identified by IVW-FE method

Table 2 Detailed information on SNPs used in MR analyses

Utilizing the taxa mentioned above, we carried out a reverse MR analysis, with the GWAS data of bacteria serving as the exposure. Detailed information regarding the SNPs used as IVs and the results of the reverse MR analysis can be found in Tables 3, and 4 and Fig. 5. Detailed information regarding these SNPs can be found in Online Resource 2: Table S2. Prevotella7, Anaerofilum, Subdoligranulum and Veillonellaceae and GBM have a reverse causal relationship. Notably, Glioblastoma was associated with Prevotella7, which, in combination with a forward Mendelian randomization analysis, suggests a bidirectional causal relationship between Prevotella7 and glioblastoma, raising the possibility that Prevotella7 may be of screening and therapeutic significance for glioblastoma.

Table 3 The results of reverse MR analysis
Table 4 The detail information of SNPs in reverse MR analysis
Fig. 5
figure 5

Scatter plots and leave-one-out plots of reverse mendelian randomization analysis


In this study, our primary objective was to employ a Mendelian Randomization analysis to rigorously assess the causal relationship between gut microbiota and Glioblastoma. To accomplish this, we leveraged the aggregated gut microbiota statistics derived from the extensive GWAS meta-analysis conducted by the MiBioGen consortium. Simultaneously, we utilized aggregated GBM statistics, which were sourced from the IEU OpenGWAS project release data, thereby ensuring that our study was underpinned by a robust dataset. We identified four specific microbial taxa, namely Eubacteriumbrachygroup, Anaerostipes, Faecalibacterium, Prevotella7, and Ruminococcaceae, that exhibited significant associations with GBM. Remarkably, four of these taxa, Anaerostipes, Faecalibacterium, Prevotella7, and Ruminococcaceae, demonstrated a protective effect against GBM, suggesting their potential as therapeutic targets or indicators of reduced risk for this aggressive brain tumor. However, there are few studies on the effects of these microbiota and their metabolites on the development of GBM through specific pathways. Chronic inflammation has long been recognized as a factor associated with tumorigenesis, and GBM is no exception to this phenomenon. Therefore, our discussion is grounded in existing studies that investigate the responses of the flora within the organism, particularly focusing on inflammatory and immune responses.

The insights from previous research studies provide valuable context and support for our findings regarding Eubacteriumbrachygroup in the context of cancer. Wang et al. in 2021 highlighted the potential role of Eubacterium in cancer initiation by promoting inflammation. This observation underscores the complexity of microbial influences on cancer development and suggests that certain microbiota may create an inflammatory microenvironment that can contribute to carcinogenesis [32]. Moreover, the study conducted by Sama Rezasoltani et al. in 2022, which investigated saliva and fecal samples from colorectal cancer (CRC) patients compared to healthy controls, identified Eubacteriumbrachygroup as one of the top three genera showing differential abundance [33]. This finding strongly suggests that Eubacteriumbrachygroup may indeed have a role in cancer development and progression. Eubacterium has been identified as a producer of acetic acid and butyric acid [34]. Acetic acid and butyric acid, categorized as short-chain fatty acids (SCFAs), play pivotal roles in cellular processes. Acetic acid, in conjunction with glucose, participates in the tricarboxylic acid (TCA) cycle, influencing the production of acetyl-CoA [15]. Acetyl-CoA, an active substance, can drive GBM proliferation and survival through the acetylation of RICTOR by mTORC2 [16]. Moreover, SCFAs, including acetic acid and butyric acid, have been shown to stimulate the production of regulatory T cells [35]. These cells contribute to the immunosuppressive environment of GBM by producing interleukin-10 (IL-10) and transforming growth factor-beta (TGF-β) [36]. The findings from our MR analysis align with this, suggesting that Eubacterium is a potential risk factor for GBM.

However, the 2023 study by Reza N et al. introduces a perspective that contrasts with our findings [37]. According to their research, Eubacterium is associated with the release of a peptide recognized by TCC88. TCC88 is demonstrated to target glioblastoma neoantigens and exhibit a strong response to various peptides derived from glioblastoma. Additionally, it shows a robust response to a broad range of bacterial sources and targets derived from the intestinal microbiota. This capacity enables TCC88 to trigger substantial Tumor-Infiltrating Lymphocytes (TIL) responses and even elicit cross-reactive T cell responses against tumor targets in peripheral blood memory T cells based on the peptides secreted by the intestinal microbiota, ultimately playing an anti-tumor role.

Anaerostipes, identified for its potential role in inhibiting colorectal cancer (CRC) progression by regulating the immune response, aligns with our present findings indicating its protective effect against glioblastoma [38]. Its appearance as a differential genus in both GBM and CRC studies underscores its potential significance as a common microbial factor across different cancer types, emphasizing its relevance in cancer biology. In contrast, Ruminococcaceae, another microbial taxon of interest, has showcased diverse implications in health and disease. The discovery of the metabolite isoamylamine (IAA) produced by Ruminococcaceae, with its potential to induce S100A8 and result in microglial cell death, adds a layer of complexity [39, 40]. Microglia, innate immune cells in the brain, play a crucial role in glioblastoma, polarizing between pro-inflammatory (M1) and anti-inflammatory (M2) phenotypic profiles. The M2 cells' secretion of cytokines such as IL10, EGF, and VEGF can inhibit T cell proliferation and promote tumor growth and angiogenesis [41]. A higher M2/M1 ratio in GBM often indicates a poorer survival rate [42]. Considering the combined performance of Anaerostipes and Ruminococcaceae in previous studies and the results of this study, it is speculated that these genera may influence M2-type microglial polarization and potentially lead to M2-type microglial death in the tumor microenvironment of GBM patients by releasing specific metabolites through the damaged blood-brain barrier. Moreover, the higher relative abundances of Ruminococcaceae observed in melanoma patients who responded positively to anti-PD-1 immunotherapy raise intriguing questions about the potential role of the Ruminococcaceae family in modulating the immune response and influencing outcomes in cancer treatment. The MR results indicating Ruminococcaceae as a protective factor for GBM prompt further exploration into whether increasing the abundance of Ruminococcaceae microbiota could enhance the efficacy of immunotherapy for GBM. This observation underscores the need for additional research to unravel the specific mechanisms through which the Ruminococcaceae family may impact the immune response and contribute to improved outcomes in GBM treatment.

The multifaceted roles of the Faecalibacterium genus in human health and disease have garnered increasing attention in recent years. Notably, Faecalibacterium consists of two distinct phylogroups, and while their precise physiological functions remain partially understood, research has pointed toward their involvement in crucial processes, particularly in the context of inflammatory bowel disease (IBD) [43, 44]. Faecalibacterium's association with IBD suggests its potential role in modulating the anti-inflammatory response, which is relevant in various disease contexts, including cancer. Indeed, studies have begun to unveil intriguing links between Faecalibacterium abundance and other forms of cancer, such as prostate cancer [45]. The connection between Faecalibacterium and prostate cancer highlights the intricate interplay between the gut microbiota and cancer development. These findings suggest that alterations in the relative abundance of Faecalibacterium may be linked to the pathogenesis of certain cancers, opening avenues for further investigation into the mechanistic underpinnings of these associations.

The identification of Prevotella7 as being associated with glioblastoma through both forward and reverse Mendelian Randomization analyses is a noteworthy discovery. Prevotella7 is a specific strain or subgenus of gut microorganisms belonging to the Prevotella genus, and it has previously been recognized for its roles in dietary and intestinal health [46]. In 2022, Arsenij U et al. found Prevotella in mouse glioblastoma tissue and found that Prevotella can produce Alpha-galactosylceramide (α-GalCer), a metabolite that stimulates invariant natural killer T (iNKT) cells to exert anticancer effects [47]. This finding is consistent with our findings suggesting that increasing the abundance of Prevonella may play a role in immunotherapy for glioblastoma. The consistent association of Prevotella7 with GBM in the study implies its potential as a valuable biomarker for early identification and treatment of GBM. This finding is particularly intriguing as it aligns with advanced research conducted in other disease contexts. For example, Prevotella7 has shown promise in improving the prognosis of CRC, suggesting that it might have broader implications in cancer biology beyond GBM. Additionally, its utility as a diagnostic marker for oral squamous cell carcinoma (OSCC), with the ability to predict 80% of cases, further underscores its potential as a versatile biomarker in various cancer types [48, 49]. The identification of Prevotella7 as a common factor in multiple cancer types suggests its significance in the broader context of cancer research and diagnosis. However, it's essential to conduct further research to understand the mechanistic underpinnings of Prevotella7's involvement in these different cancer types and to evaluate its clinical utility as a diagnostic or prognostic marker.

The theoretical basis of this study is the gut-brain axis proposed in previous studies [17]. Human gut microbiota can modulate the development and function of the central nervous system (CNS) through gut-brain axis [50,51,52]. And this study has several advantages. MR analysis is a method used to establish causal inferences by leveraging existing genetic variations in nature. It employs randomization simulation, treating assignment to a control group, thereby enhancing our ability to formulate causal hypotheses with increased confidence. MR analysis employs genetic variation as an instrumental variable, effectively mitigating issues related to confounding and reverse causation [53]. This approach contributes to a clearer elucidation of relationships between variables. Observational studies frequently encounter numerous limitations, including confounding, selection bias, and memory bias. To some extent, MR analysis can circumvent these issues and offer more dependable causal inferences [54]. Genetic variation in the gut microbiota was derived from the most extensive meta-analysis of global genomic studies, ensuring robust instrumental variables for MR analysis. It identifies causal relationships between gut microbiota and GBM through MR analysis, reducing confounding factors and reversing causality in causal inference. A two-sample MR design was used and non-overlapping exposure and outcome pooled data were utilized to reduce bias [55].

However, Since the number of SNPs screened by the significance threshold (P < 5 × 10-8) of the conventional GWAS was too small, we raised the significance threshold accordingly for sensitivity analysis and to avoid horizontal pleiotropy. Moreover, MR analysis is affected by demographic and genetic sequencing errors, and the present study population is European, which makes it limited. Finally, although MR analysis can provide evidence of causality, explaining the biological mechanisms may still be complex and requires further experimental studies.


In this bidirectional Mendelian randomization study, we identified five gut microbiota species with causal associations to glioblastoma. Especially significant was the bidirectional causal relationship observed with Prevotella7, suggesting potential implications for glioblastoma screening and treatment. To comprehensively comprehend Prevotella7's protective role against glioblastoma and unveil its precise protective mechanisms, additional randomized controlled trials are necessary.

Availability of data and materials

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.



Mendelian randomization




inverse variance weighting


genome-wide association study


Integrative Epidemiology Unit


Single nucleotide polymorphisms


instrumental variables


Odds Ratio


95% confidence interval


Colorectal cancer


Inflammatory bowel disease


Oral squamous cell carcinoma


Central nervous system


  1. McKinnon C, Nandhabalan M, Murray SA, Plaha P. Glioblastoma: clinical presentation, diagnosis, and management. BMJ. 2021;374:n1560.

    Article  PubMed  Google Scholar 

  2. Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–96.

    Article  PubMed  CAS  Google Scholar 

  3. Tan AC, Ashley DM, López GY, Malinzak M, Friedman HS, Khasraw M. Management of glioblastoma: State of the art and future directions. CA Cancer J Clin. 2020;70(4):299–312.

    Article  PubMed  Google Scholar 

  4. Reardon DA, Brandes AA, Omuro A, et al. Effect of Nivolumab vs bevacizumab in patients with recurrent glioblastoma: the checkMate 143 phase 3 randomized clinical trial. JAMA Oncol. 2020;6(7):1003–10.

    Article  PubMed  Google Scholar 

  5. Yu MW, Quail DF. Immunotherapy for glioblastoma: current progress and challenges. Front Immunol. 2021;12: 676301.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Qiu Z, Zhao L, Shen JZ, et al. Transcription Elongation Machinery Is a Druggable Dependency and Potentiates Immunotherapy in Glioblastoma Stem Cells. Cancer Discov. 2022;12(2):502–21.

    Article  PubMed  CAS  Google Scholar 

  7. Da Mesquita S, Fu Z, Kipnis J. The meningeal lymphatic system: a new player in neurophysiology. Neuron. 2018;100(2):375–88.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Broekman ML, Maas SLN, Abels ER, Mempel TR, Krichevsky AM, Breakefield XO. Multidimensional communication in the microenvirons of glioblastoma. Nat Rev Neurol. 2018;14(8):482–95.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lim M, Xia Y, Bettegowda C, Weller M. Current state of immunotherapy for glioblastoma. Nat Rev Clin Oncol. 2018;15(7):422–42.

    Article  PubMed  CAS  Google Scholar 

  10. Dzutsev A, Badger JH, Perez-Chanona E, et al. Microbes and cancer. Annu Rev Immunol. 2017;35:199–228.

    Article  PubMed  CAS  Google Scholar 

  11. Gopalakrishnan V, Helmink BA, Spencer CN, Reuben A, Wargo JA. The influence of the gut microbiome on cancer, immunity, and cancer immunotherapy. Cancer Cell. 2018;33(4):570–80.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Queen J, Shaikh F, Sears CL. Understanding the mechanisms and translational implications of the microbiome for cancer therapy innovation. Nat Cancer. 2023;4(8):1083–94.

    Article  PubMed  Google Scholar 

  13. Ni JJ, Xu Q, Yan SS, et al. Gut microbiota and psychiatric disorders: a two-sample mendelian randomization study. Front Microbiol. 2021;12:737197.

    Article  PubMed  Google Scholar 

  14. Lyu Y, Yang H, Chen L. Metabolic regulation on the immune environment of glioma through gut microbiota. Semin Cancer Biol. 2022;86(Pt 2):990–7.

    Article  PubMed  CAS  Google Scholar 

  15. Mashimo T, Pichumani K, Vemireddy V, et al. Acetate is a bioenergetic substrate for human glioblastoma and brain metastases. Cell. 2014;159(7):1603–14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Masui K, Tanaka K, Ikegami S, et al. Glucose-dependent acetylation of Rictor promotes targeted cancer therapy resistance. Proc Natl Acad Sci U S A. 2015;112(30):9406–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Foster JA, McVey Neufeld KA. Gut-brain axis: how the microbiome influences anxiety and depression. Trends Neurosci. 2013;36(5):305–12.

    Article  PubMed  CAS  Google Scholar 

  18. Zeng Y, Cao S, Yang H. Roles of gut microbiome in epilepsy risk: a mendelian randomization study. Front Microbiol. 2023;14:1115014.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Saunders CN, Kinnersley B, Culliford R, Cornish AJ, Law PJ, Houlston RS. Relationship between genetically determined telomere length and glioma risk. Neuro Oncol. 2022;24(2):171–81.

    Article  PubMed  Google Scholar 

  20. Porcu E, Rüeger S, Lepik K, Santoni FA, Reymond A, Kutalik Z. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits. Nat Commun. 2019;10(1):3300.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Mendelian Randomization as an Approach to Assess Causality Using Observational Data - PubMed. Accessed 20 Sept 2023.

  22. Jia Y, Yao P, Li J, et al. Causal associations of Sjögren’s syndrome with cancers: a two-sample Mendelian randomization study. Arthritis Res Ther. 2023;25(1):171.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Jin Q, Ren F, Dai D, Sun N, Qian Y, Song P. The causality between intestinal flora and allergic diseases: Insights from a bi-directional two-sample Mendelian randomization analysis. Front Immunol. 2023;14:1121273.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. van der Velde KJ, Imhann F, Charbon B, et al. MOLGENIS research: advanced bioinformatics data software for non-bioinformaticians. Bioinformatics. 2019;35(6):1076–8.

    Article  PubMed  CAS  Google Scholar 

  25. IEU Open GWAS project. Brain glioblastoma Dataset: finn-b-C3_GBM. Published 2021.

  26. Liu K, Zou J, Fan H, Hu H, You Z. Causal effects of gut microbiota on diabetic retinopathy: a mendelian randomization study. Front Immunol. 2022;13:930318.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Li P, Wang H, Guo L, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20(1):443.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Burgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med. 2016;35(11):1880–906.

    Article  PubMed  Google Scholar 

  29. Slob EAW, Burgess S. A comparison of robust Mendelian randomization methods using summary data. Genet Epidemiol. 2020;44(4):313–29.

    Article  PubMed  PubMed Central  Google Scholar 

  30. The MR-Base platform supports systematic causal inference across the human phenome - PubMed. Accessed 18 Sept 2023.

  31. Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13(11):e1007081.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Wang Y, Wan X, Wu X, Zhang C, Liu J, Hou S. Eubacterium rectale contributes to colorectal cancer initiation via promoting colitis. Gut Pathog. 2021;13(1):2.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Rezasoltani S, Aghdaei HA, Jasemi S, et al. Oral microbiota as novel biomarkers for colorectal cancer screening. Cancers (Basel). 2022;15(1):192.

    Article  PubMed  CAS  Google Scholar 

  34. de Vos WM, Tilg H, Van Hul M, Cani PD. Gut microbiome and health: mechanistic insights. Gut. 2022;71(5):1020–32.

    Article  PubMed  CAS  Google Scholar 

  35. Simpson RC, Shanahan ER, Batten M, et al. Diet-driven microbial ecology underpins associations between cancer immunotherapy outcomes and the gut microbiome. Nat Med. 2022;28(11):2344–52.

    Article  PubMed  CAS  Google Scholar 

  36. Zitvogel L, Galluzzi L, Viaud S, et al. Cancer and the gut microbiota: an unexpected link. Sci Transl Med. 2015;7(271):271ps1.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Naghavian R, Faigle W, Oldrati P, et al. Microbial peptides activate tumour-infiltrating lymphocytes in glioblastoma. Nature. 2023;617(7962):807–17.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Montalban-Arques A, Katkeviciute E, Busenhart P, et al. Commensal Clostridiales strains mediate effective anti-cancer immune response against solid tumors. Cell Host Microbe. 2021;29(10):1573-1588.e7.

    Article  PubMed  CAS  Google Scholar 

  39. Gopalakrishnan V, Spencer CN, Nezi L, et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science. 2018;359(6371):97–103.

    Article  PubMed  CAS  Google Scholar 

  40. Teng Y, Mu J, Xu F, et al. Gut bacterial isoamylamine promotes age-related cognitive dysfunction by promoting microglial cell death. Cell Host Microbe. 2022;30(7):944-960.e8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Lisi L, Ciotti GMP, Braun D, et al. Expression of iNOS, CD163 and ARG-1 taken as M1 and M2 markers of microglial polarization in human glioblastoma and the surrounding normal parenchyma. Neurosci Lett. 2017;645:106–12.

    Article  PubMed  CAS  Google Scholar 

  42. Prosniak M, Harshyne LA, Andrews DW, et al. Glioma grade is associated with the accumulation and activity of cells bearing M2 monocyte markers. Clin Cancer Res. 2013;19(14):3776–86.

    Article  PubMed  CAS  Google Scholar 

  43. Miquel S, Martín R, Rossi O, et al. Faecalibacterium prausnitzii and human intestinal health. Curr Opin Microbiol. 2013;16(3):255–61.

    Article  PubMed  CAS  Google Scholar 

  44. Sokol H, Pigneur B, Watterlot L, et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc Natl Acad Sci U S A. 2008;105(43):16731–6.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Golombos DM, Ayangbesan A, O’Malley P, et al. The role of gut microbiome in the pathogenesis of prostate cancer: a prospective pilot study. Urology. 2018;111:122–8.

    Article  PubMed  Google Scholar 

  46. Tett A, Pasolli E, Masetti G, Ercolini D, Segata N. Prevotella diversity, niches and interactions with the human host. Nat Rev Microbiol. 2021;19(9):585–99.

    Article  PubMed  CAS  Google Scholar 

  47. Ustjanzew A, Sencio V, Trottein F, Faber J, Sandhoff R, Paret C. Interaction between bacteria and the immune system for cancer immunotherapy: the α-GalCer alliance. Int J Mol Sci. 2022;23(11):5896.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Huh JW, Kim MJ, Kim J, et al. Enterotypical Prevotella and three novel bacterial biomarkers in preoperative stool predict the clinical outcome of colorectal cancer. Microbiome. 2022;10(1):203.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Karpiński TM. Role of Oral Microbiota in Cancer Development. Microorganisms. 2019;7(1):20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Dehhaghi M, Kazemi Shariat Panahi H, Heng B, Guillemin GJ. The Gut Microbiota, Kynurenine Pathway, and Immune System Interaction in the Development of Brain Cancer. Front Cell Dev Biol. 2020;8:562812.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Dehhaghi M, Kazemi Shariat Panahi H, Guillemin GJ. Microorganisms’ Footprint in Neurodegenerative Diseases. Front Cell Neurosci. 2018;12:466.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Linking circadian rhythms to microbiome-gut-brain axis in aging-associated neurodegenerative diseases - PubMed. Accessed 20 Sept 2023.

  53. Hong W, Huang G, Wang D, et al. Gut microbiome causal impacts on the prognosis of breast cancer: a Mendelian randomization study. 2023.

    Google Scholar 

  54. Birney E. Mendelian Randomization. Cold Spring Harb Perspect Med. 2022;12(4):a041302.

    Article  PubMed  Google Scholar 

  55. 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(1):66.

    Article  PubMed  PubMed Central  Google Scholar 

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Thanks to all researchers and editors who contributed to this study.


This study was supported by the Research Initiation Project of First Affiliated Hospital of Gannan Medical University (QD202316), Jiangxi Provincial Natural Science Foundation (20232BAB206108), Jiangxi Provincial Health Commission science and technology plan (SKJP220236456) and 2023 "Science and Technology+Medical" joint plan project-Key research and development plan-First Affiliated Hospital of Gannan Medical College (2023LNS36663).

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Authors and Affiliations



HC and ZC contributed to the study conception and design. Material preparation, data collection and analysis were performed by ZC and ZCL. The first draft of the manuscript was written by ZC, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Chunming He or Haimin Song.

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This is a study based on GAWs data analysis. Ethical approval and consent to participate is not required.

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Supplementary Information

Additional file 1: Table S1.

Detailed information of SNPs from different taxa (exposure).

Addtiional file 2: TableS2.

The detail information of SNP on Glioblastoma (exposure).

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Zeng, C., Zhang, C., He, C. et al. Investigating the causal impact of gut microbiota on glioblastoma: a bidirectional Mendelian randomization study. BMC Genomics 24, 784 (2023).

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