Relationship between gut microbiota and breast cancer: a Mendelian randomization analysis of mediating effects based on inflammatory proteins

doi:10.21203/rs.3.rs-4667151/v1

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Relationship between gut microbiota and breast cancer: a Mendelian randomization analysis of mediating effects based on inflammatory proteins

https://doi.org/10.21203/rs.3.rs-4667151/v1

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Background: Breast cancer is associated with dysbiosis of dysbiosis of gut flora. However, the mechanisms of how gut microbiota mediate breast cancer disease are not clear, and exploring possible mediating mechanisms is key to investigating the study of the mechanisms that gut microbiota contribute to breast cancer development.

Methods: A two-sample two-way Mendelian randomization (MR) analysis was employed, with publicly available genome-wide association study (GWAS) data, to investigate role of abnormal gut microbiota in breast cancer development. It focuses on assessing the potential mediation by circulating inflammatory proteins. The primary methodology employed for identifying gut microbiota (GM) associated with breast cancer and examining the impact of inflammatory markers is the variance inverse weighting method, supplemented by the MR-Egger method and weighted median method. This research aims to offer novel insights into potential therapeutic interventions for breast cancer.

Result: Seven genera and three inflammatory proteins were determined to be associated with breast cancer. The inflammatory protein Fibroblast growth factor 21 levels mediated the effect of the intestinal bacterium Adlercreutzia on the course of breast cancer in 11.1% of cases, and the inflammatory protein CD40L receptor levels mediated the effect on the gut microbiotaParabacteroides on the course of breast cancer in 9% of cases.

Biological sciences/Cancer

Biological sciences/Computational biology and bioinformatics

Biological sciences/Genetics

Gut microbiota (GM)

Mendelian randomization (MR)

Circulating inflammatory proteins

With the and steadily increasingly global incidence of breast cancer (BC) over the past few decades, comes the significant health burden and concern over worldwide women. It has emerged as the primary culprit of cancer-induing mortality among women, accounting for 684,996 deaths in 2020 due to BC[1, 2].

The fact that gut microbiota enlightens an important influence on the health of the host has been proven [3]. It has been demonstrated to be pivotal in lipid metabolism[4], immune system disorders[5], psychiatric disorders[6]. Also, gut microbiota flora has been extensively studied in cancer[7]. A recent Mendelian randomization investigation uncovered a link between Ruminococcaceae, Porphyromonadaceae, Bacteroidetes, and a lower risk of liver carcinomous (HCC or ICC)[8]. This offers vital insights for prevention and management of liver cancer. Similarly, gut microbiota has been a target in study and exploration of breast cancer, with its dual role in breast cancer being summarised by Álvarez-Mercado AI et al[9]. In the study of De,Cecco,L et al. gut microbiota was directly involved in trastuzumab treatment of HER2-positive breast cancer, which may become an important breast cancer biomarker in the future[10]. However, it should be emphasized that certain gut microbiota may also contribute to breast cancer development, as consistent with the results uncovered by Nejman et al. They explored an abundance of fungal thiol-producing bacteria in breast cancer cases[11].

In summary, participation of gut microbiota in breast cancer development remains intricate. MR is a robust access utilizing genetic variation as instrumental variables (IVs) for exposure, thus mitigating confounding bias and addressing issue of reverse causality inherent in observational studies[12]. In this study, we attempted to explore mediating function of gut microbiota in tumorigenesis and development of breast cancer based on effect of inflammatory proteins, as well as aim to provide potential research ideas for the BC-targeting intervention and treatment.

Ethical approval

The datasets employed were exclusively obtained from publicly accessible repositories and underscoring our commitment to transparency and data accessibility. Comprehensive documentation of ethical clearance and approvals for each constituent study analyzed in our research is readily available in corresponding primary articles[13-15].

The selection of data sources

Table S1 presents statistics of genome-wide association studies (GWAS) utilized our statistical analyses. Kurilshikov et al., through the establishment of the MiBioGen consortium, curated available GWAS data on gut microbiota to interrogate interplay between genetic variations and microbial components. This consortium collectively enrolled 18,340 participants (24 cohorts) ranging from 11 countries, with both 16S rRNA gene sequencing profiles and genotyping data of microbial composition[13]. A dataset comprising 211 taxa with 122,110 mutation loci was compiled, with 119 genera featuring an average abundance exceeding 1% being selected for further analysis in our study. Figure 1 shows a schematic diagram of a Mendelian randomised study of gut flora-mediated circulating inflammatory proteins in breast cancer progression. We aimed to explore a more in-depth interaction between breast cancer and gut microbiota, thereby further analysing the relationship through mediating role of circulating inflammatory proteins. Our study is adheres to the STROBE-MR guidelines.

Figure 1. Schematic design of a two-sample Mendelian randomisation study of intestinal flora and breast cancer. The design hypothesises that gut microbiota influences the course of breast cancer by modulating circulating inflammatory proteins.

2.1 Data source for inflammatory proteins

We utilized Circulating Inflammatory Proteins GWAS dataset as reported by Zhao JH et al., encompassing 14,824 participants across 11 cohorts. This comprehensive dataset identified a total of 180 protein quantitative trait loci (PQTLs), comprising 59 cis and 121 trans variants[14]. GWAS analysis was independently conducted for each cohort using linear regression to establish additive genetic association model. The influence of inflammatory proteins was quantified as alterations in inverse-normalized protein levels per dose of effect allele. To mitigate potential confounding factors, adjustments were made for population substructure using genetic principal components, as well as covariates (e.g., age and sex) were incorporated into model.

2.2 Data source for breast cancer

We utilized the findings from a GWAS on breast cancer conducted by Michailidou K et al[15]. This study encompassed a large cohort of individuals, consisting of 228,951 individuals of European ancestry as well as 27,172 individuals of Asian ancestry. Through this analysis, a total of 65 novel loci correlated with overall breast cancer threats were identified. Additionally, by integrating computational data to identify target driver genes within breast cells at each locus, their study demonstrated a significant intersection between candidate target genes and somatic driver genes .

2.3 Instrumental variable

The threshold for determining the significance of IVs linked to breast cancer was established at P < 5 × 10^-8.

Due to restricted quantity of single nucleotide polymorphisms (SNPs) associated with both GM genus and circulating inflammatory proteins at the P < 5 × 10^-5 threshold, P < 1 × 10^-5 thresholds in screening IVs for GM genus as well as circulating cytokines were opted, respectively. The rationale behind the selection of these thresholds has been extensively elucidated in prior studies[16, 17]. To ensure reliability and precision, additional criteria were applied to select all IVs: (i) implementation of chain imbalance analysis to fulfill the MR assumptions (R2 < 0.001, aggregation window size = 10,000 kb) utilizing data from European subset of 1000 Genomes Project; (ii) exclusion of palindromic SNPs to mitigate potential allele influence; (iii) elimination of outliers and ensuring data robustness through the Radial-MR method.

2.4 Statistical analysis

2.4.1 Mendelian randomization

Initially, we conducted MR analyses to evaluate the potential pathway involving gut flora, inflammatory proteins, and breast cancer. We identified: (i) 119 GM genera associated with breast cancer; (ii) metrics for inflammatory proteins; and (iii) for the GM genera linked with breast cancer, we further examined their influence on inflammatory metrics. Our primary method for assessing univariable Mendelian randomization (UVMR) estimates was inverse variance weighting IVW, complemented by weighted median (WM) and MR-Egger methods. IVW estimates are displyed by slope of a weighted regression of SNP outcome effect on SNP exposure effect, with intercept constrained to zero[18]. The WM method was utilized, considering outcomes as relevant even with half of IVs being below the mark. Additionally, MR-Egger method incorporated an intercept term to assess presence of horizontal pleiotropy (p < 0.05). Further details on these MR methods can be found [19, 20]. Several sensitivity analyses were conducted. Initially, Cochran's Q test was employed to examine the heterogeneity of the results, with significance set at P < 0.05 indicating heterogeneity. Additionally, the MR-PRESSO method was utilized to identify potential SNP outliers for multiplicity analysis and to validate robustness of the data. False discovery rate (FDR) correction was applied to each UVMR analysis, with FDR threshold set at Pfdr < 0.05[21]. Exposure was considered to be positively associated with the results of this study if P < 0.05.

2.4.2 Multivariable Mendelian randomization

MVMR was conducted to interrogate potential mediation from GM to breast cancer. MVMR, an extension of UVMR, allows for decomposition of the overall effect between exposure and outcome into direct and indirect effects[22, 23]. In our study, we utilized UVMR to estimate the overall impact between exposure and outcome. Additionally, MVMR analyses were employed to evaluate the direct influence of exposure on outcome and to account for potential mediation effects.

2.4.3 Statistical analysis

Statistical analyses were performed by R software (version 4.2.1, R Foundation for Statistical Computing, Vienna, Austria), with effect sizes presented as odds ratios (ORs) and corresponding 95% confidence intervals. "TwosampleMR" and "MendelianRandomization" packages were employed for MR analyses.

3.1 Genetic effects of gut microbiota on breast cancer

IVW method identified seven genera associated with breast cancer (P < 0.05) (Table S2, Figure 2). Melainabacteria significantly increased breast cancer risk (OR = 1.072, 95% CI = 1.007-1.141, P = 0.029).Romboutsia (OR = 1.125, 95% CI = 1.017 - 1.245 , P = 0.022) and Roseburia (OR = 1.115, 95% CI = 1.007-1.235, P = 0.036) showed positive correlation with breast cancer.Rikenellaceae (OR = 0.865, 95% CI = 0.788-0.950, P = 0.002), Adlercreutzia ( OR = 0.919, 95%CI = 0.845-0.999, P = 0.049), Adlercreutzia (OR = 0.877, 95%CI = 0.790-0.972, P = 0.013) and Parabacteroides (OR = 0.869, 95%CI = 0.756-0.999, P = 0.048) were protective against breast cancer development.

Figure 2. The forest plot shows the relationship between gut microbiota and breast cancer in different ways

3.2 Genetic effects of inflammatory proteins on breast cancer

The IVW method detected 9 inflammatory proteins associated with breast cancer expression (P<0.05) (Table S3, Figure 3). Caspase 8 levels (OR = 0.911, 95%CI = 0.843-0.985, P = 0.019), Fibroblast growth factor 19 levels (OR = 0.937, 95%CI = 0.883-0.995 , P = 0.033 ), Fibroblast growth factor 21 levels (OR = 0.921, 95%CI = 0.856,0.991, P = 0.027) and Fms-related tyrosine kinase 3 ligand levels (OR = 0.931, 95%CI = 0.877-0.988, P = 0.018) were protective against breast carcinogenesis. CD40L receptor levels (OR = 1.049, 95%CI = 1.010-1.089 , P = 0.013), T-cell surface glycoprotein CD5 levels (OR = 1.077, 95%CI = 1.002-1.158, P = 0.044), C-X-C motif chemokine 6 levels (OR = 1.102, 95%CI = 1.023-1.188, P = 0.010), Interferon gamma levels (OR = 1.112, 95%CI = 1.015-1.218, P = 0.022) and STAM binding protein levels (OR = 1.115, 95%CI = 1.024-1.214, P = 0.012) were positively correlated with breast cancer development and progression.

Figure 3. The forest plot shows the relationship between circulating inflammatory proteins and breast cancer in different ways

3.3 Gut microbiota flora and inflammatory proteins

Among seven gut microbiota and nine inflammatory proteins linked with breast carcinogenesis, genus Adlercreutzia and Fibroblast growth factor 21 levels were associated (OR = 1.190, 95%CI = 1.006-1.447, P = 0.042), and genus Parabacteroides and CD40L receptor levels were associated (OR = 1.128, 95%CI = 1.041-1.450, P = 0.015) (Table S4).

3.4 MVMR Result

We employed MVMR method to assess direct impact of inflammatory protein-mediated GM on breast cancer-mediated by inflammatory proteins. After adjusting for CRP, we reassessed the genetic associations between two inflammatory proteins associated with two GMs ( Adlercreutzia with Fibroblast growth factor 21 levels and Parabacteroides with CD40L receptor levels) and breast cancer, and based on the IVW approach, the estimates of Adlercreutzia and Parabacteroides on breast cancer changed significantly (P > 0.05), suggesting Fibroblast growth factor 21 levels and CD40L receptor levels involving in these pathways (Table S5).

In this study of inflammatory protein-mediated effects of gut microbiota flora on breast cancer progression, we obtained seven gut microbiota flora and nine inflammatory proteins thought to be correlated with breast cancer, as well as in subsequent validation found that Adlercreutzia modulated the inflammatory proteins Fibroblast growth factor 21 levels. Parabacteroides modulated CD40L receptor levels. The mediation of Fibroblast growth factor 21 levels accounted for 11.1% and CD40L receptor levels accounted for 9%. Therefore, this study successfully confirmed our previous hypothesis that there is a gut flora-inflammatory protein-breast cancer pathway, and that inflammatory proteins play a mediating role in the pathway.

There have been two previous studies on Mendelian randomisation of breast cancer and gut microbiota flora [24, 25]. Considering the differences in the databases chosen, there were slight differences in the results. However, all findings consistently demonstrated a protective effect of higher Adlercreutzia genus abundance against breast cancer, which aligns with our own results. Genus Adlercreutzia were first isolated from human faeces by Maruo et al. as strains shown to metabolise soy isoflavones to estramustrol[26]. Genus Adlercreutzia as a gut microbiota flora class, in a Mendelian randomization study of intestinal flora, the genus Adlercreutzia was reported to have an increased risk for diabetic retinopathy [27]. It was also observed that abundance of Adlercreutzia genus diminishes progressively in patients with liver disease, declining as the disease advances and ultimately disappearing in its advanced stages[28].

Fibroblast growth factor 21 (FGF21) exert diverse metabolic activities in a autocrine, paracrine, and/or endocrine manner. It belongs to the FGF subfamily of hormones[29]. Previous studies have shown that FGF21 exhibits antioxidative effects in animal models and in vitro cell culture experiments[30]. FGF21 has currently been proven its crucial role in the maintenance of myocardial function and has been referred to as a ‘cardiomyokine’[31], in addition, FGF21 has been shown to be resistant to obesity and has been widely used in the study of obesity disorders[32]. One review systematically summarized that FGF21 could be a therapeutic goal for non-alcoholic fatty liver disease[33], while another experiment exhibited that FGF21 is closely associated with fatty pancreas and could serve as a screening marker for individuals with this condition[34]. In our current study, we established that the presence of the Adlercreutzia genus can mitigate breast cancer risk through its modulation of the FGF21 protein, and although the exact mechanism is currently unknown, this finding is consistent with previous demonstrations that an increase in genus Adlercreutzia leads to a reduced risk of some diseases.

The genus Parabacteroides is Gram-negative and exclusively anaerobic. In an earlier study, the analysis of database tests revealed that Parabacteroides genus is one of the most common bacteria in human body, 91.21% of nearly 4000 individuals with good physical signs were present with the presence of Parabacteroides genus, whereas and it was detected in samples from different periods of time, which indicates that this bacterium is closely related to different stages of human health[35]. The presence of the Parabacteroides genus has been shown to exert a beneficial impact on the human digestive system. Research indicates that an elevation in Parabacteroides abundance correlates with a decreased risk of conditions such as obesity[36], and inflammatory bowel disease in humans [37]. However, its presence may also elevate the risk of certain diseases, for example, it can lead to metabolic syndrome[38], psoriasis[39], and ankylosing spondylitis[40]. These studies highlight the intricate and intimate relationship between the Parabacteroides genus and human health.

CD40/CD40L serves as a crucial immune checkpoint, extensively investigated since its discovery. CD40L, also referred to as TNFSF5, was initially identified in 1992 on a mouse clone of activated T helper cells (Th) capable of binding CD40. In humans, CD40L is characterized as a type II membrane glycoprotein with a dissociative cytoplasmic tail (containing 22 amino acids (aa)), a 24-aa transmembrane domain, as well as an extracellular structural domain spanning 215 aa[41]. CD40L has been demonstrated in bioinformatics analyses and database mining to have relatively high mRNA levels in T cells and platelets. A human lung single-cell RNA-seq dataset analysis revealed elevated CD40 levels in myeloid and B cells, while T lymphocytes exhibited heightened CD40L expression[42]. The current comprehension of the CD40/CD40L engagement underscores its pivotal role in T cell-dependent B cell differentiation. This process is fundamental to the humoral response and facilitates a bidirectional interaction between T cells and monocytes/macrophages (MCs/M), thereby forming an expansion loop in cellular immunity. Moreover, it also contributes to T cell-mediated platelet activation[43]. In our study, it was demonstrated that the genus Parabacteroides reduces the risk of breast carcinogenesis by modulating CD40L receptor levels, which corresponds to the per se activation of CD40L receptor levels, and we speculate that it may be due to the fact that Parabacteroides upregulation of CD40L receptor levels, Parabacteroides may activate humoral and cellular immune functions and increase anti-tumour ability, resulting in a lower risk of breast cancer. Of course, this is only a speculation, and more experimental evidence is needed to prove the specific pathway mechanism.

With regard to the strengths in our study, it represents the pioneering Mendelian randomization analysis interrogating involvement of human gut microbiota in breast cancer, while exploring mediating effects of inflammatory proteins. Furthermore, we utilized MVMR analysis to explore mediating effects of these circulating inflammatory proteins, although our findings warrant cautious interpretation. However, certain limitations exist in our study. The scope was inherently constrained by the availability of original datasets, limiting our ability to delve into finer distinctions necessary for identifying specific breast cancer subtypes or capturing more complex characteristics. The majority of participants had European ancestry, potentially restricting the applicability and generalizability of our findings to individuals of non-European ancestry. Moreover, as our data were analyzed at the genus level, deeper insights could be gained by exploring data at the species level. In this case, extended investigation with larger sample sizes is warranted to refine these results.

Our study provides better understanding of involvement of altered human gut microbiota in breast cancer development and provides evidence for a mediating function of inflammatory proteins. Increased abundance of Adlercreutzia regulates Fibroblast growth factor 21 levels inflammatory proteins have an inhibitory effect on breast cancer progression, and abundance of Parabacteroides decreases CD40L receptor levels have a promotional effect on breast cancer progression. These associations may provide new targets and novel indicators or biomarkers for breast cancer treatment and prognosis analysis.

MR: Mendelian randomization; GWAS: genome-wide association study; GM: gut microbiota; BC: breast cancer; IVs: instrumental variables; PQTLs: protein quantitative trait loci; SNPs: single nucleotide polymorphisms; UVMR: univariable Mendelian randomization; WM: weighted median; FDR: False discovery rate; FGF21: Fibroblast growth factor 21; Th: T helper cells; MCs/M: monocytes/macrophages.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

Data on gut flora can be obtained from the MiBioGene database (https://mibiogen.gcc.rug.nl/), data on circulating inflammatory proteins can be obtained from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10457199/; breast cancer data can be obtained from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798588/.

Competing interests

The authors declare that they have no competing interests.

Author contributions

Hongtao Li: Conceptualization, Methodology, Software. Haipeng Li: Data curation, Writing - Original draft preparation, Writing- Reviewing and Editing. Hui Li and Yuan Liu :Visualization, Investigation. Chen Chen and Xinyu Liu: Supervision, Software, Validation.

Funding

Anhui Provincial Health Research Project (AHWJ2023BAc20063); Open Subjects of Scientific Research Platform of Anhui Biochemical Engineering Centre (2023SYKFZ06); Key Projects of Natural Science Research in Higher Education Institutions in Anhui Province (KJ2021A0815).

Acknowledgements

Not applicable.

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Relationship between gut microbiota and breast cancer: a Mendelian randomization analysis of mediating effects based on inflammatory proteins

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1. INTRODUCTION

2. METHODS AND MATERIALS

Ethical approval

The selection of data sources

3. RESULT

4. DISCUSSION

5. CONCLUSION

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