The Causal Role of Esophageal Cancer and Gut Microbiota: A Bidirectional Mendelian Randomization Study

doi:10.21203/rs.3.rs-3149703/v1

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The Causal Role of Esophageal Cancer and Gut Microbiota: A Bidirectional Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-3149703/v1

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Aims

Esophageal cancer (EC) and gut microbiota are reported to be clinically relevant but their genetic association is unclear. We carried out a bidirectional MR analyses to assess the casual relationship between EC and gut microbiota from fecal samples.

Methods

The microbiome genome-wide association studies (GWAS) data of 18,340 individuals provided by MiBioGen consortium and the EC GWAS data (740 esophageal cancers cases and 372,016 controls) provided by UK Biobank were respectively utilized as exposure and/or outcome data. Reliable single nucleotide polymorphisms (SNPs) were obtained after rigorous screening. A bidirectional Mendelian randomization (MR) analysis was conducted using the inverse-variance weighted (IVW) method. The sensitivity analyses including the MR-Egger method, weighted median, MR-PRESSO and mode methods were performed to examine the stability, heterogeneity and pleiotropy of the results.

Results

Forward MR analysis revealed genetic liability to gut microbiota was associated with a higher risk of EC (Coprobacter (OR 1.001, 95%CI 1.000-1.002); Ruminococcus1 (OR 1.001, 95%CI 1.000-1.002); Turicibacter (OR 0.999, 95%CI 0.998-1.000); Senegalimassilia (OR 1.002, 95%CI 1.000-1.003); Veillonella (OR 1.001, 95%CI 1.000-1.002)) or a significantly lower risk of EC (Eubacterium oxidoreducens (OR 0.999, 95%CI 0.998-1.000); Lachnospira (OR 0.998, 95%CI 0.996-1.000); Romboutsia (OR 0.999, 95%CI 0.998-1.000)). Reverse MR analysis showed that genetic liability to EC was also causally linked to increased susceptibility of gut microbiota dysbiosis (genera Eggerthella (Beta 37.63, 95%CI 4.76–70.50); Coprococcus2 (Beta 23.90, 95%CI 1.65–46.15); ChristensenellaceaeR.7 (Beta 22.75, 95%CI 4.22–41.28); genera Intestinimonas (OR -33.24, 95%CI-54.90–11.58)).

Conclusions

Our findings supported a bidirectionally causal relationship between gut microbiota and EC, implying the potential of gut microbiota as novel biomarkers and therapeutic drugs for EC.

Gut microbiota

Esophageal cancer

Mendelian Randomization

Bidirectional

Esophageal cancer (EC) is one of the most common fatal cancers, which imposing significant worldwide health burdens. The global incidence and mortality of EC ranked tenth and sixth, respectively, in 2020 [1]. The majority of cases (85% [512,500 cases]) were Esophageal squamous cell carcinoma (ESCC) and Esophageal adenocarcinoma (EAC) accounted for 14% (85,700 cases). The distribution vary from regions and adenocarcinoma is the main type in in European and American countries. Moreover, the incidence of EAC has been rising at an alarming rate in Western world [2]. It is well recognized that host and environmental risk factors, such as obesity, gastroesophageal reflux diseases and Barrett's esophagus, contribute to EAC [3–5]. The symptoms of early esophageal cancer are often atypical and easy to be ignored, thus primary prevention and early screening play key roles in improving clinical outcome and survival of patients [6]. Despite researchers have done a lot of exploring, the exact pathogenic mechanism of esophageal cancer remains unknown to date, making it difficult to find potential noninvasive biomarkers and predictors.

Gastrointestinal tract (GIT) is colonized by trillions of microorganisms that comprise the gut microbiota. Gut microbiota not only takes part in protecting against pathogens and maintaining gut ecosystem homeostasis but also involves in several cancers [7]. According to a recent study, about 13% of all cancer cases in 2018 can be attributed to infectious etiology [8]. During the past few years, much attention has been paid to the association between esophageal cancers and gut microbes. Gut microbiota has a crucial role in the onset, development, and treatment efficacy of gastrointestinal malignancies, which may be caused by inflammation and immune disorders [9]. Several researches have revealed the different esophageal bacterial strains, from normal esophagus to ESCC and EAC [10–12]. Ajayi et al [12] showed that normal esophagus is mainly colonized by Streptococcus while esophagitis and BE are associated with gram-negative bacteria. Furthermore, certain G-bacteria, including Escherichia coli and Fusobacterium nucleatum, are linked to EA. Yamamura et al [13] revealed that Fusobacterium nucleatum aggravate the survival of ESCC patients. The limitation is, most of the sample were obtained from esophagus by invasive examination. It has been proven that intestinal microbiome involves in inflammatory bowel diseases [14],diabetes [15], colorectal cancer [16] and so on. Recently, Deng et al [17] demonstrated increased richness of gut microbes and significant change of the distribution proportion of some bacteria in EC compared with health individuals through 16S rDNA gene sequencing of fresh stool. While it is still unclear of the causal relationship between gut microbiota and esophageal cancer.

Mendelian randomization (MR) is an original approach to exploring the causality between exposure or risk factor and outcome, which has been widely applied to research in various fields. It screens for genetic variants that can represent exposure as an instrument to examine the relationship between instrument and outcome. Compared to traditional observational studies, which are prone to biases such as reverse causality and residual confounding, MR rules out these biases due to the law of independent assortment [18]. In this text, we utilize the genome-wide association study (GWAS) summary statistics from UK Biobank and MiBioGen consortium. A two-sample MR is carried out to evaluate the causal relationship between gut microbes and esophagus.

Figure 1 shows the flowchart of the MR analysis. A bidirectional two-sample MR study was designed to explore the causal effect of gut microbiota and esophageal cancer.

Data Sources

We obtained the genetic variants datasets from public-available GWAS. The summary statistics of esophageal cancer was provided by UK Biobank. The project is a population-based cohort study involving in 500,000 middle-aged participants. The study consisted of 740 esophageal cancers cases and 372,016 controls, and almost of them are European [19]. The summary statistics of gut microbes was acquired from the large-scale meta-analysis of 24 cohorts including 18,340 participants conducted by MiBioGen consortium. In this study, fecal microbial composition was analyzed targeting the V4, V3-V4, and V1-V2 variable regions of the 16S rRNA gene and classified by direct taxonomic classification. To identify host genetic variants that was mapped to the genetic loci impacting the relative abundance or presence of microbial taxa, microbiome quantitative trait loci (mbQTL) mapping analysis was carried out. A total of 211 taxa (131 genera, 35 families, 20 orders, 16 classes, and 9 phyla) were involved [20].

Selection of the instrumental variables

We set the threshold of genome-wide significance (p < 5*10^− 6) to filter the SNPs strongly associated with the exposure in the MR analysis. Then, on the basis of the European ancestral individuals from the 1000 Genomes Project, we assessed the linkage disequilibrium to ensure the independence ,and appropriate SNPs within 10,000kb clumping window with an r2 < 0.001 were kept [18]. In addition, Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) Test was implemented to test the presence of horizontal pleiotropy, the p value in each MR analysis should be greater than 0.05 [21]. Before harmonizing the exposure and outcome datasets, we excluded the palindromic sequence due to the uncertainty of the direction for exposure and outcome. Moreover, F-statistics was calculated to evaluate the strength of IVs by the formula: F = R2(n − 1−k)/(1 − R2) k, (R2 represents the proportion of variation explained by each IV, k represents the number of eligible SNPs, and n represents the sample size) [22]. When F statistics > 10, the IVs was considered as strongly-correlated instruments.

Statistical analysis

In this study, we performed the analysis in R 4.1.0 using TwoSampleMR [23] and MRPRESSO packages. To assess whether there is causal relationship between esophageal cancer and gut microbes, we applied multiple methods: Inverse variance weighted (IVW), MR Egger regression, Weighted median, Maximum likelihood, Weighted mode. The IVW method was predominate and the others was utilized as sensitivity analyses to further confirm the reliability of the IVW method. The IVW method calculated a weighted average of Wald ratio estimates of each SNP. If all of the SNPs are valid instrumental variables, there will be no bias in the results [24]. Besides, we used the Cochran Q test to quantify the heterogeneity of IVs. As the p value ≤ 0.05, which standing for the existence of heterogeneity, we carried out a random effects IVW MR analyses. Moreover, the Leave-one-out analysis demonstrated the meta effects of the remaining SNPs when a certain SNP was removed in turn. If the results changed greatly after the elimination, it indicated the heterogeneity of the removed SNP. We also performed the reverse MR analyses in the same methods of the forward MR analyses.

Genetic Association Between Gut microbiota and EC

A total of 208 SNPs were screened out to be valid instrumental variables for 211 bacterial genera according to the selection principles. Figure 2 and Figure S1 are the forest plot and the scatter plots of significant microbial taxa with risks of esophageal cancer. In the forward MR analyses, eight significant microbial taxa were found to be significantly associated with esophageal cancer: the genera Eubacterium, oxidoreducens, Coprobacter, Lachnospira, Romboutsia, Ruminococcus1, Senegalimassilia, Turicibacter, and Veillonella. The analyses suggests that Eubacterium and oxidoreducens (OR 0.999, 95%CI 0.998-1.000), Lachnospira (OR 0.998, 95%CI 0.996-1.000), Romboutsia (OR 0.999, 95%CI 0.998-1.000) and Turicibacter (OR 0.999, 95%CI 0.998-1.000) played a protective role in esophageal cancer, whereas Coprobacter (OR 1.001, 95%CI 1.000-1.002), Ruminococcus1 (OR 1.001, 95%CI 1.000-1.002), Senegalimassilia (OR 1.002, 95%CI 1.000-1.003) and Veillonella (OR 1.001, 95%CI 1.000-1.002) were concerned with the increased risk of EC. The results of all five statistical methods applied in the forward MR analyses have been displayed in Table S1. Figure S2 shows the results of MR leave-one-out sensitivity analyses, indicating the stability of the IVs. There is no presence of heterogeneity in Cochran Q test. Moreover, based on the results of MR-PRESSO test and MR Egger regression, no evidence of pleiotropy was identified.

Genetic Association Between EC and Gut microbiota

We extracted 14 strongly-associated and independent SNPs of esophageal cancer after excluding a palindromic SNP (rs3802909). The reverse MR analyses was carried out as prescribed. The forest plot (Fig. 3) and scatter plot (Figure S3) are clearly demonstrated the effect of esophageal cancer on gut microbes. We found esophageal cancer have causal relationship with the genera Intestinimonas, Eggerthella, Coprococcus2 and Christensenellaceae R.7. The occurrence of Esophageal carcinoma is consistent with the increased enrichment of the genera Eggerthella (Beta 37.63, 95%CI 4.76–70.50), Coprococcus2 (Beta 23.90, 95%CI 1.65–46.15) and ChristensenellaceaeR.7 (Beta 22.75, 95%CI 4.22–41.28). Nevertheless, the abundance of the genera Intestinimonas (Beta − 33.24, 95%CI -54.90–11.58) tends to decrease under the influence of EC. The results of all five statistical methods applied in the forward MR analyses have been displayed in Table S2. Moreover, the results of Cochran Q test verified no heterogeneity in the IVs. The MR Egger regression intercept which is close to zero also proved that there no pleiotropy existed (Figure S4).

The Two-Sample MR study we conducted clearly showed the bidirectional causal relationship between esophageal cancer and gut microbes without the influence of confounding factors. The forward MR revealed that genera Eubacterium oxidoreducens, Lachnospira, Romboutsia and Turicibacter had a protective effect on esophageal cancer, while Coprobacter, Ruminococcus1, Senegalimassilia and Veillonella might be the risk factors for the formation of esophageal malignant neoplasms. Meanwhile, in the reversed MR, Esophageal cancer enriched the abundance of the genera Eggerthella, Coprococcus2 and Christensenellaceae R.7, and the abundance of the genera Intestinimonas was found to be decreased.

In recent years, there has been an increasing focus on the correlation between microorganisms and digestive tract diseases in academic research. For example, Helicobacter pylori has been the well-recognized key causative factor for gastric cancer [25]. Previous researches have clarified that virus infection such as human papillomavirus (HPV) and Epstein-Barr virus, as well as the changes of gut microbial composition contribute to the carcinogenesis of the esophagus [26]. In a recent study, the tumor development in LFD-fed GF mice, which received the fecal microbiota from HFD-fed L2-IL1B mice, indicated the crucial role of intestinal microbes in esophageal cancer development and verified its linkage with the IL8/CXCL1 chemokine family [27]. In an observational study, which recruiting 23 EC patient and 23 health individuals from Huai'an First People's Hospital (Huai'an, China), the abundance of Lachnospira was observed to be reduced in the EC patients, and Lachnospira, of which the AUC = 1, showed a great discrimination ability between EC patients and the healthy [17]. Reinforcing our analyses, it is plausible that Lachnospira plays a protective role in the pathogenesis of esophageal cancer. The genera Lachnospira, belonging to the phylum Firmicutes and the family Lachnospiraceae, generates acetic acid and butyric acid. It has been recognized that SCFAs not only maintain the stability of Gastrointestinal homeostasis but also function through immunoregulation [28]. Studies have shown that Lachnospira might connect with nonalcoholic fatty liver disease [29], Asthma [30], gastric cancer [31], colorectal cancer [32], depression and anxiety in patients with active ulcerative colitis [33]. However, most of them are association studies, lacking in exploration of mechanisms. Veillonella, one of the major oral microorganisms, promoted the cancer development according to our analyses. The genera contain Veillonella parvum and Veillonella alkalinogens. Oral microbial colonization of the intestine is concerned with many diseases, such as inflammatory bowel disease (IBD) [34] and colorectal cancer [35]. A latest study illustrated that the Veillonella ectopically colonize the intestine by taking advantage of inflammatory nitrates derived from inducible nitric oxide synthase (iNOS) activity in host cells [36]. As a member of Type II bacteria (Gram-negative and anaerobic/microaerophilic bacteria) of the Esophageal Flora, the genera Veillonella may serve as collaborative mechanism to facilitate gastric reflux through activating the iNOS pathway. It is considered that the alteration of Veillonella promotes the progression of gastroesophageal reflux to Barrett's esophagus, which eventually leads to adenocarcinoma [37]. The studies mentioned above all further validated our analysis. Further studies are urgently needed to determine the mechanism of intestinal bacteria in the pathogenesis of esophageal cancer.

In our reverse MR analyses, we revealed that the alteration of gut microbes after suffering from esophageal cancer. Eggerthella is among the top five most abundant genera of the phylum Actinobacteria in the human gut. A study found that the colonization of Eggerthella activates intestinal Th17 by removing from inhibition of the Th17 transcription factor Rorγt, exacerbating the severity of colitis [38]. Moreover, with proinflammatory properties, Eggerthella is involved in the synthesis of key neurotransmitters in depression, such as glutamate, butyrate, serotonin, and γ-aminobutyric acid (GABA) [39]. In another study, Eggerthella lenta was found to promotes the accumulation of uremic toxin in serum and exacerbate the progression of kidney disease in a CKD rat model [40]. However, there is little research on the association of Eggerthella and EC, let alone the mechanism. Our analyses showed the decreased abundance of Intestinimonas under the exposure of EC. Intestinimonas produces butyrate from both sugars and amino acids, protecting the host from harmful metabolites. The enrichment of the genus Intestinimonas in gastric intraepithelial neoplasia [41] and the reduced abundance of this genera in the obesity [42] has been observed. Besides, there's already evidence that Intestinimonas showed a good distinction ability between the patients with chronic renal diseases (CKD) and the healthy [43] and correlated with the total functional ability score and the IL-4 level in patients with Huntington's disease [44]. Currently, there is limited research on the intestinal microbiota identified in our reverse study. Further investigation is necessary to determine whether alterations in gut microbiota following esophageal cancer have a positive or negative impact, which could enhance the prognosis and adjuvant therapy of EC.

Our study has several strengths. Previous studies are more focused on esophageal cancer and esophageal microbiota. Moreover, changes in the microbiota in observational studies are merely clinical manifestations. We analyzed GWAS data by MR to confirm the possible interaction and causal relationship between EC and intestinal microbiota. Bidirectional analyses guarantee causality inferred from both directions. The selected bacteria lay the foundation for further exploration in the future. It also has contributed to the screening of biomarkers for esophageal cancer and the improvement of prognosis. In the procedure of the MR analyses, we strictly screened for strong-associated SNPs as instrumental variables (F statistics > 10). MR-PRESSO test and MR Egger regression was conducted to exclude SNPS with pleiotropy. Despite our exploration is meaningful, there are several limitations. The population we obtained from the UK Biobank was restricted to the European, in which the most common type of esophageal cancer is adenocarcinoma. We did not get the original information on the patients with EC, making it difficult for us to perform subgroup analysis. Besides, genus was the lowest taxonomic level in the gut microbe datasets, further exploration at the species level was restricted.

In conclusion, this two sample MR found that the bilateral causality between esophageal cancer and intestinal microbes. Several genera such as Lachnospira and Veillonella had a protective or carcinogenic effect on esophageal cancer. Moreover, the alteration of the genera Eggerthella and Intestinimonas demonstrated the influence of EC on the gut microbiome. Further RCT study and animal experiments are in an urgent need to figure out the mechanisms involved, thus making the candidate intestinal microbes useful for clinical screening and treatment.

Ethics approval and consent to participate

The present MR analysis was based on summary data from previous studies that had gained written informed consent and ethics approval. No ethical permit is required for the secondary analysis of summary data.

Consent for publication

This manuscript has not been previously published. All authors have consented to the publication of the manuscript in this journal.

Competing interests

The authors declare no conflict of interest exists.

Funding

This study was funded by the National Natural Science Foundation of China (No. 82100594), China Postdoctoral Science Foundation (No. 2023M731417), Spring Sunshine Program from the Ministry of Education of China (No.202201552) and Jiangsu province Hospital (the First Affiliated Hospital with Nanjing Medical University) Clinical Capacity Enhancement Project (JSPH-MC-2022-29).

Authors' contributions

Z.X.Y. and S.W. drafted the manuscript. C.H. and L.X.L were responsible for conception and design the study. Z.X.Y. and Y.B.X. performed data analysis and interpretation. S.X.M. and Z.W.F. reviewed the data analysis. Z.G.X. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Acknowledgements

The authors thank all investigators and participants from GWAS databases for sharing these data.

Sung, H.; Ferlay, J.; Siegel, R. L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021, 71 (3), 209-249. DOI: 10.3322/caac.21660 PubMed.
Morgan, E.; Soerjomataram, I.; Rumgay, H.; Coleman, H. G.; Thrift, A. P.; Vignat, J.; Laversanne, M.; Ferlay, J.; Arnold, M. The Global Landscape of Esophageal Squamous Cell Carcinoma and Esophageal Adenocarcinoma Incidence and Mortality in 2020 and Projections to 2040: New Estimates From GLOBOCAN 2020. Gastroenterology 2022, 163 (3). DOI: 10.1053/j.gastro.2022.05.054 PubMed.
Thrift, A. P.; Shaheen, N. J.; Gammon, M. D.; Bernstein, L.; Reid, B. J.; Onstad, L.; Risch, H. A.; Liu, G.; Bird, N. C.; Wu, A. H.; et al. Obesity and risk of esophageal adenocarcinoma and Barrett's esophagus: a Mendelian randomization study. J Natl Cancer Inst 2014, 106 (11). DOI: 10.1093/jnci/dju252 PubMed.
Coleman, H. G.; Xie, S.-H.; Lagergren, J. The Epidemiology of Esophageal Adenocarcinoma. Gastroenterology 2018, 154 (2), 390-405. DOI: 10.1053/j.gastro.2017.07.046 PubMed.
Lagergren, J.; Bergström, R.; Lindgren, A.; Nyrén, O. Symptomatic gastroesophageal reflux as a risk factor for esophageal adenocarcinoma. N Engl J Med 1999, 340 (11), 825-831. PubMed.
Thrumurthy, S. G.; Chaudry, M. A.; Thrumurthy, S. S. D.; Mughal, M. Oesophageal cancer: risks, prevention, and diagnosis. BMJ 2019, 366, l4373. DOI: 10.1136/bmj.l4373 PubMed.
de Vos, W. M.; Tilg, H.; Van Hul, M.; Cani, P. D. Gut microbiome and health: mechanistic insights. Gut 2022, 71 (5), 1020-1032. DOI: 10.1136/gutjnl-2021-326789 PubMed.
de Martel, C.; Georges, D.; Bray, F.; Ferlay, J.; Clifford, G. M. Global burden of cancer attributable to infections in 2018: a worldwide incidence analysis. Lancet Glob Health 2020, 8 (2), e180-e190. DOI: 10.1016/S2214-109X(19)30488-7 PubMed.
LaCourse, K. D.; Johnston, C. D.; Bullman, S. The relationship between gastrointestinal cancers and the microbiota. Lancet Gastroenterol Hepatol 2021, 6 (6), 498-509. DOI: 10.1016/S2468-1253(20)30362-9 PubMed.
Kashyap, S.; Pal, S.; Chandan, G.; Saini, V.; Chakrabarti, S.; Saini, N. K.; Mittal, A.; Thakur, V. K.; Saini, A. K.; Saini, R. V. Understanding the cross-talk between human microbiota and gastrointestinal cancer for developing potential diagnostic and prognostic biomarkers. Semin Cancer Biol 2022, 86 (Pt 3), 643-651. DOI: 10.1016/j.semcancer.2021.04.020 PubMed.
Yang, L.; Francois, F.; Pei, Z. Molecular pathways: pathogenesis and clinical implications of microbiome alteration in esophagitis and Barrett esophagus. Clin Cancer Res 2012, 18 (8), 2138-2144. DOI: 10.1158/1078-0432.CCR-11-0934 PubMed.
Ajayi, T. A.; Cantrell, S.; Spann, A.; Garman, K. S. Barrett's esophagus and esophageal cancer: Links to microbes and the microbiome. PLoS Pathog 2018, 14 (12), e1007384. DOI: 10.1371/journal.ppat.1007384 PubMed.
Yamamura, K.; Baba, Y.; Nakagawa, S.; Mima, K.; Miyake, K.; Nakamura, K.; Sawayama, H.; Kinoshita, K.; Ishimoto, T.; Iwatsuki, M.; et al. Human Microbiome Fusobacterium Nucleatum in Esophageal Cancer Tissue Is Associated with Prognosis. Clin Cancer Res 2016, 22 (22), 5574-5581. DOI: 10.1158/1078-0432.CCR-16-1786 PubMed.
Kostic, A. D.; Xavier, R. J.; Gevers, D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 2014, 146 (6), 1489-1499. DOI: 10.1053/j.gastro.2014.02.009 PubMed.
Zhang, X.-S.; Yin, Y. S.; Wang, J.; Battaglia, T.; Krautkramer, K.; Li, W. V.; Li, J.; Brown, M.; Zhang, M.; Badri, M. H.; et al. Maternal cecal microbiota transfer rescues early-life antibiotic-induced enhancement of type 1 diabetes in mice. Cell Host Microbe 2021, 29 (8). DOI: 10.1016/j.chom.2021.06.014 PubMed.
Song, M.; Chan, A. T.; Sun, J. Influence of the Gut Microbiome, Diet, and Environment on Risk of Colorectal Cancer. Gastroenterology 2020, 158 (2), 322-340. DOI: 10.1053/j.gastro.2019.06.048 PubMed.
Deng, Y.; Tang, D.; Hou, P.; Shen, W.; Li, H.; Wang, T.; Liu, R. Dysbiosis of gut microbiota in patients with esophageal cancer. Microb Pathog 2021, 150, 104709. DOI: 10.1016/j.micpath.2020.104709 PubMed.
Sekula, P.; Del Greco M, F.; Pattaro, C.; Köttgen, A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol 2016, 27 (11), 3253-3265. PubMed.
Bycroft, C.; Freeman, C.; Petkova, D.; Band, G.; Elliott, L. T.; Sharp, K.; Motyer, A.; Vukcevic, D.; Delaneau, O.; O'Connell, J.; et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 2018, 562 (7726), 203-209. DOI: 10.1038/s41586-018-0579-z PubMed.
Kurilshikov, A.; Medina-Gomez, C.; Bacigalupe, R.; Radjabzadeh, D.; Wang, J.; Demirkan, A.; Le Roy, C. I.; Raygoza Garay, J. A.; Finnicum, C. T.; Liu, X.; et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet 2021, 53 (2), 156-165. DOI: 10.1038/s41588-020-00763-1 PubMed.
Verbanck, M.; Chen, C.-Y.; 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 (5), 693-698. DOI: 10.1038/s41588-018-0099-7 PubMed.
D, S. Instrumental variables regression with weak instruments. Econometrica 1997, 65:557–586.
Hemani, G.; Zheng, J.; Elsworth, B.; Wade, K. H.; Haberland, V.; Baird, D.; Laurin, C.; Burgess, S.; Bowden, J.; Langdon, R.; et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018, 7. DOI: 10.7554/eLife.34408 PubMed.
Burgess, S.; Dudbridge, F.; Thompson, S. G. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med 2016, 35 (11), 1880-1906. DOI: 10.1002/sim.6835 PubMed.
Amieva, M.; Peek, R. M. Pathobiology of Helicobacter pylori-Induced Gastric Cancer. Gastroenterology 2016, 150 (1), 64-78. DOI: 10.1053/j.gastro.2015.09.004 PubMed.
Meng, C.; Bai, C.; Brown, T. D.; Hood, L. E.; Tian, Q. Human Gut Microbiota and Gastrointestinal Cancer. Genomics Proteomics Bioinformatics 2018, 16 (1), 33-49. DOI: 10.1016/j.gpb.2017.06.002 PubMed.
Münch, N. S.; Fang, H.-Y.; Ingermann, J.; Maurer, H. C.; Anand, A.; Kellner, V.; Sahm, V.; Wiethaler, M.; Baumeister, T.; Wein, F.; et al. High-Fat Diet Accelerates Carcinogenesis in a Mouse Model of Barrett's Esophagus via Interleukin 8 and Alterations to the Gut Microbiome. Gastroenterology 2019, 157 (2). DOI: 10.1053/j.gastro.2019.04.013 PubMed.
Kaakoush, N. O.; Morris, M. J. The oesophageal microbiome: an unexplored link in obesity-associated oesophageal adenocarcinoma. FEMS Microbiol Ecol 2016, 92 (10). DOI: 10.1093/femsec/fiw161 PubMed.
Pataky, Z.; Genton, L.; Spahr, L.; Lazarevic, V.; Terraz, S.; Gaïa, N.; Rubbia-Brandt, L.; Golay, A.; Schrenzel, J.; Pichard, C. Impact of Hypocaloric Hyperproteic Diet on Gut Microbiota in Overweight or Obese Patients with Nonalcoholic Fatty Liver Disease: A Pilot Study. Dig Dis Sci 2016, 61 (9), 2721-2731. DOI: 10.1007/s10620-016-4179-1 PubMed.
Arrieta, M.-C.; Stiemsma, L. T.; Dimitriu, P. A.; Thorson, L.; Russell, S.; Yurist-Doutsch, S.; Kuzeljevic, B.; Gold, M. J.; Britton, H. M.; Lefebvre, D. L.; et al. Early infancy microbial and metabolic alterations affect risk of childhood asthma. Sci Transl Med 2015, 7 (307), 307ra152. DOI: 10.1126/scitranslmed.aab2271 PubMed.
Qi, Y.-F.; Sun, J.-N.; Ren, L.-F.; Cao, X.-L.; Dong, J.-H.; Tao, K.; Guan, X.-M.; Cui, Y.-N.; Su, W. Intestinal Microbiota Is Altered in Patients with Gastric Cancer from Shanxi Province, China. Dig Dis Sci 2019, 64 (5), 1193-1203. DOI: 10.1007/s10620-018-5411-y PubMed.
Lin, D.; Peters, B. A.; Friedlander, C.; Freiman, H. J.; Goedert, J. J.; Sinha, R.; Miller, G.; Bernstein, M. A.; Hayes, R. B.; Ahn, J. Association of dietary fibre intake and gut microbiota in adults. Br J Nutr 2018, 120 (9), 1014-1022. DOI: 10.1017/S0007114518002465 PubMed.
Yuan, X.; Chen, B.; Duan, Z.; Xia, Z.; Ding, Y.; Chen, T.; Liu, H.; Wang, B.; Yang, B.; Wang, X.; et al. Depression and anxiety in patients with active ulcerative colitis: crosstalk of gut microbiota, metabolomics and proteomics. Gut Microbes 2021, 13 (1), 1987779. DOI: 10.1080/19490976.2021.1987779 PubMed.
Shaw, K. A.; Bertha, M.; Hofmekler, T.; Chopra, P.; Vatanen, T.; Srivatsa, A.; Prince, J.; Kumar, A.; Sauer, C.; Zwick, M. E.; et al. Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease. Genome Med 2016, 8 (1), 75. DOI: 10.1186/s13073-016-0331-y PubMed.
Flemer, B.; Warren, R. D.; Barrett, M. P.; Cisek, K.; Das, A.; Jeffery, I. B.; Hurley, E.; O'Riordain, M.; Shanahan, F.; O'Toole, P. W. The oral microbiota in colorectal cancer is distinctive and predictive. Gut 2018, 67 (8), 1454-1463. DOI: 10.1136/gutjnl-2017-314814 PubMed.
Rojas-Tapias, D. F.; Brown, E. M.; Temple, E. R.; Onyekaba, M. A.; Mohamed, A. M. T.; Duncan, K.; Schirmer, M.; Walker, R. L.; Mayassi, T.; Pierce, K. A.; et al. Inflammation-associated nitrate facilitates ectopic colonization of oral bacterium Veillonella parvula in the intestine. Nat Microbiol 2022, 7 (10), 1673-1685. DOI: 10.1038/s41564-022-01224-7 PubMed.
Henderson, G.; Yilmaz, P.; Kumar, S.; Forster, R. J.; Kelly, W. J.; Leahy, S. C.; Guan, L. L.; Janssen, P. H. Improved taxonomic assignment of rumen bacterial 16S rRNA sequences using a revised SILVA taxonomic framework. PeerJ 2019, 7, e6496. DOI: 10.7717/peerj.6496 PubMed.
Alexander, M.; Ang, Q. Y.; Nayak, R. R.; Bustion, A. E.; Sandy, M.; Zhang, B.; Upadhyay, V.; Pollard, K. S.; Lynch, S. V.; Turnbaugh, P. J. Human gut bacterial metabolism drives Th17 activation and colitis. Cell Host Microbe 2022, 30 (1). DOI: 10.1016/j.chom.2021.11.001 PubMed.
Radjabzadeh, D.; Bosch, J. A.; Uitterlinden, A. G.; Zwinderman, A. H.; Ikram, M. A.; van Meurs, J. B. J.; Luik, A. I.; Nieuwdorp, M.; Lok, A.; van Duijn, C. M.; et al. Gut microbiome-wide association study of depressive symptoms. Nat Commun 2022, 13 (1), 7128. DOI: 10.1038/s41467-022-34502-3 PubMed.
Wang, X.; Yang, S.; Li, S.; Zhao, L.; Hao, Y.; Qin, J.; Zhang, L.; Zhang, C.; Bian, W.; Zuo, L.; et al. Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. Gut 2020, 69 (12), 2131-2142. DOI: 10.1136/gutjnl-2019-319766 PubMed.
Zhang, X.; Li, C.; Cao, W.; Zhang, Z. Alterations of Gastric Microbiota in Gastric Cancer and Precancerous Stages. Frontiers In Cellular and Infection Microbiology 2021, 11, 559148. DOI: 10.3389/fcimb.2021.559148 PubMed.
Thingholm, L. B.; Rühlemann, M. C.; Koch, M.; Fuqua, B.; Laucke, G.; Boehm, R.; Bang, C.; Franzosa, E. A.; Hübenthal, M.; Rahnavard, A.; et al. Obese Individuals with and without Type 2 Diabetes Show Different Gut Microbial Functional Capacity and Composition. Cell Host Microbe 2019, 26 (2). DOI: 10.1016/j.chom.2019.07.004 PubMed.
Hu, X.; Ouyang, S.; Xie, Y.; Gong, Z.; Du, J. Characterizing the gut microbiota in patients with chronic kidney disease. Postgrad Med 2020, 132 (6), 495-505. DOI: 10.1080/00325481.2020.1744335 PubMed.
Du, G.; Dong, W.; Yang, Q.; Yu, X.; Ma, J.; Gu, W.; Huang, Y. Altered Gut Microbiota Related to Inflammatory Responses in Patients With Huntington's Disease. Front Immunol 2020, 11, 603594. DOI: 10.3389/fimmu.2020.603594 PubMed.

Supplementary Figure 4 is not available with this version.

Supplementary Figure 4. Leave-one-out plots for the causal effects of esophageal cancer on gut microbiota.

No competing interests reported.

FigureS1.tif
Supplementary Figure 1. Scatter plots for the causal effects of significant microbial taxa on esophageal cancer.
FigureS2.tif
Supplementary Figure 2. Leave-one-out plots for the causal effects of gut microbiota on esophageal cancer.
FigureS3.tif
Supplementary Figure 3. Scatter plots for the causal effects of esophageal cancer on significant microbial taxa.
TableS1.xls
Supplementary Table 1. The causal effects of gut microbiota on esophageal cancer.
TableS2.xlsx
Supplementary Table 2. The causal effects of esophageal cancer on gut microbiota.
TableS3.STROBEMRchecklist.docx
Supplementary Table 3. STROBE-MR checklist of recommended items to address in reports of Mendelian randomization studies.

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The Causal Role of Esophageal Cancer and Gut Microbiota: A Bidirectional Mendelian Randomization Study

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Introduction

Methods

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Selection of the instrumental variables

Statistical analysis

Results

Genetic Association Between Gut microbiota and EC

Genetic Association Between EC and Gut microbiota

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