Gut microbiota, human blood metabolites and esophageal cancer: a Mendelian randomization study

Gut microbiota, human blood metabolites and esophageal cancer: a Mendelian randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gut microbiota, human blood metabolites and esophageal cancer: a Mendelian randomization study Xiuzhi LI, Bingchen Xu, Han Yang, Zhihua Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4153773/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Unbalances in the gut microbiota have been proposed as a possible cause of esophageal cancer, yet the exact causal relationship remains unclear. Objectives: To investigate the potential causal relationship between the gut microbiota and esophageal cancer with Mendelian randomization (MR) analysis. Methods: Genome-wide association studies (GWAS) of 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) and 205 gut microbiota metabolic pathways conducted by the Dutch Microbiome Project (DMP) and a FinnGen cohort GWASs of esophageal specified the summary statistics. To investigate the possibility of a mediation effect between the gut microbiota and esophageal cancer, mediation MR analyses were performed for 1,091 blood metabolites and 309 metabolite ratios. Results: MR analysis indicated that the relative abundance of 10 gut microbial taxa was associated with esophageal cancer but all the 12 gut microbiota metabolic pathways with esophageal cancer indicated no statistically significant association existing. Two blood metabolites and a metabolite ratio were discovered to be mediating factors in the pathway from gut microbiota to esophageal cancer. Conclusion: This research indicated the potential mediating effects of blood metabolites and offered genetic evidence in favor of a causal correlation between gut microbiota and esophageal cancer. Esophageal cancer Gut microbiota Human blood metabolites Mendelian randomization analysis Mediation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction A major global disease, esophageal cancer (ESCA) ranks sixth among all cancers in terms of mortality based on global cancer statistics [ 1 ]. In the United States, about 17,000 new cases are diagnosed each year [ 2 ]. ESCA is mainly composed of two epidemiologically and pathologically distinct diseases, namely adenocarcinoma (OAC) and esophageal squamous cell carcinoma (OSCC) [ 3 , 4 ]. The incidence of OAC is significant and sharply rising in Western countries [ 5 ]. Individuals with early-stage ESCA may not recognize the symptoms of obstruction or stricture due to the dilated and muscular nature of the esophagus. Symptoms only appear after the tumor has progressed locally or even metastatically [ 6 ]. The majority of patients with ESCA in the United States and Europe are diagnosed with locally advanced or metastatic disease, which is ineligible for curative treatment [ 7 ]. In the UK, more than 70% of patients are diagnosed with lymph node metastases or distant metastases, of which distant metastases account for about 40% [ 7 ]. Patients with advanced ESCA typically encounter an unfavorable prognosis. Due to etiological, molecular, and histological heterogeneity, advanced ESCA patients often acquire innate resistance to systemic therapy, significantly reducing treatment efficacy [ 8 ]. Surgical intervention remains the most effective way to treat ESAC, but for patients with advanced ESCA, the 5-year survival rate remains less than 25% even after surgery [ 9 ]. Generally, the 5-year overall survival (OS) rates for ESCA patients remain low, ranging from 12–20% [ 10 ]. Surgical treatment or palliative care for ESCA can contribute to reduced quality of life and a significant symptom burden [ 11 , 12 ]. Early adoption of preventative strategies and a detailed understanding of the etiology of ESCA is critical in reducing the incidence of ESCA. Throughout the digestive system, the intestinal microbiota is a group of microorganisms indigenous to the human intestines, which has flourished in the human intestine over an extended period of time interacting with the human body [ 13 , 14 ]. With approximately 10 14 species of microorganisms, it is regarded as the largest microbial reservoir in our body [ 15 ]. As an important regulator of human health [ 16 ], the gut microbiota plays an integral role in the development of the human immune system and the maintenance of intestinal homeostasis [ 17 ]. An increasing corpus of studies has demonstrated the intricate relationships between ESCA and the human gut flora in recent years [ 18 – 21 ]. The composition and abundance of fecal microorganisms in ESCA patients are closely related to the severity of the disease [ 22 ]. In addition, the genome-wide methylation level of ESCA can be regulated by the gut microbiota, which affects the occurrence, development, and metastasis of ESCA. The intestinal microbiota can function through a variety of bioactive metabolites that systematically affect the internal microenvironment, including bile acids, short-chain fatty acids, and lipopolysaccharides, to regulate the function of the corresponding target organs [ 23 , 24 ]. Deficiency or disorder of intestinal flora significantly affects polysaccharide decomposition and lipid absorption, resulting in liver and adipose tissue dysfunction, leading to cardiovascular and cerebrovascular diseases, type 2 diabetes, and obesity, among other metabolic-related diseases [ 25 ]. Recent studies have shown that there were notable variations in the concentration of amino acids such as tryptophan and tyrosine, as well as lipids such as oleic acid and palmitoleic acid, between ESCA patients and healthy controls [ 26 – 28 ]. Hydroxytryptamine (5-HT) was discovered to be a risk factor for lymph node metastases [ 29 ]. This evidence suggests that human circulating metabolites are integral components in the development of ESCA. Tumor cells can disrupt the entire metabolism in the process of continuously adapting to the dynamic metabolic microenvironment, thus affecting the distribution and content of metabolites in blood circulation, such as upregulating the glycolytic pathway under adequate oxygen conditions, resulting in rapid growth [ 30 ]. In recent years, for determining possible causal associations between various exposures and clinical outcomes, Mendelian randomization (MR) analysis has become widely applied, which is an approach for inferring causality between exposure and outcome from genetic variations, especially single nucleotide polymorphisms (SNPs) [ 31 ]. The majority of epidemiologic analyses conducted on the association between gut microbiota, human blood metabolites, and ESCA are based on traditional methods (e.g., cross-sectional, case-control, cohort). However, they are subject to various limitations, such as confounding and reverse causation bias, which can affect the estimates of effect [ 32 , 33 ].By virtue of the fact that allelic randomization occurs prior to the onset of disease, MR analysis has an advantage over conventional observational studies in reducing reverse causation bias. Furthermore, the independent assortment and random segregation of genetic polymorphisms at conception mitigate the confounding bias, as genetic markers are used as instrumental variables (IVs) in MR analysis [ 34 , 35 ]. Given the absence of research examining the causal association between gut microbiota and ESCA mediated by human blood metabolites, we undertook a two-sample, two-step MR analysis to reveal the relationship. 2. Method 2.1. Study design The study design is shown in Fig. 1 . More than 647,920 participants were selected from summary-level, publicly available datasets to conduct a large two-step, two-sample MR study using a two-step strategy to investigate the relationship between genetic prediction of gut microbiota and oesophageal carcinoma and to determine whether plasma metabolites could mediate this association. A two-sample MR analysis utilizing different datasets is performed to assess correlations of the same genetic variants with exposure (e.g., gut microbiota) and outcome (e.g., oesophageal carcinoma). Initially, the causal effects of genetic prediction of 412 gut microbiota with a genetic disposition to oesophageal carcinoma were analyzed, and the two-step approach utilized in mediating analysis examined the association between genetically predicted gut microbiota and each potential mediator. Subsequently, we investigate and quantify the mediation effects of potential mediators in the pathway from the 412 gut microbiota to oesophageal carcinoma. 2.2. Data sources 2.2.1. Genetic instrumental variable for gut microbiome A large-scale genome-wide association study (GWAS) conducted by the Dutch Microbiome Project (DMP) provided the species-level dataset for the gut microbiota [ 36 ]. 7738 participants of European descent were involved in the analysis of this dataset, which is the hitherto largest species-level genomics study on the human gut microbiota. An analysis of stool samples was performed utilizing shotgun metagenomic sequencing to determine the gut microbiome, ultimately identifying 207 taxonomies (105 species, 48 genera, 26 families, 13 orders, 10 classes, 5 phyla) and 205 gut microbiota metabolic pathways related to microbial functions. This GWAS dataset is described in more detail in its original publication [ 36 ]. The GWAS data is publicly available at https://mibiogen.gcc.rug.nl . SNPs with genome-wide significance (P < 1 × 10 –5 ) and clumping at a linkage disequilibrium (LD) threshold of r 2 < 0.001 (clumping distance: 10,000 kb) were used in the analyses as instrumental variables (IVs) for gut microbiota. To exclude weak instrumental variable bias, the calculated F-statistics for exposure used to quantify the IVs ranged from 19 to 57, which is consistent with the hypothesis of F > 10 for MR analyses [ 37 ]. 2.2.2. Genetic Instrumental Variables for Potential Mediators A recent GWAS carried out on the Canadian Longitudinal Study on Aging (CLSA) cohort involved a total of 8,299 individuals and approximately 15.4 million SNPs explored the association of SNPs with human metabolite levels. A genome-wide association study of 1,091 blood metabolites and 309 metabolite ratios was performed in this research [ 38 ]. 2.2.3. Mediators Genetic Instrumental Variables for CVD Oesophageal carcinoma GWAS summary data were derived from the tenth version of the FinnGen consortium ( https://r10.finngen.fi/ ). Oesophageal carcinoma was identified using International Classification of Diseases (ICD) diagnosis codes in this prospective cohort study, involving 619 cases and 314,193 controls originating from European ancestry. 2.3. Statistical analyses We examined the potential association between genetically predicted gut microbiota and genetically predicted oesophageal carcinoma by applying a bidirectional two-sample MR analysis. Additionally, to examine the possible mediation effects of human blood metabolites in the causal relationship, a two-step MR analysis was carried out using summary statistical data. In both forward and reverse directions of MR analyses, the inverse-variance weighted (IVW) method was utilized as the primary analytical approach to estimate odds ratios and P-values, widely recognized as the most robust methodology for generating reliable causal estimates in MR studies. The IVW method, analyzing the causal effects of exposure SNPs on outcome data, was employed as the primary approach [ 39 ]. In the absence of effective instruments, the weighted median method was employed, as it is capable of offering reliable causal effect estimates even if less than fifty percent of the information is derived from valid instruments [ 40 ]. To discover the anomalies in the analysis due to the large horizontal pleiotropy during the MR analysis and to account for the weak effects and uncertainties of the weak horizontal pleiotropy, we performed further analysis using Bayesian weighted Mendelian randomization (BWMR) [ 41 ]. When the P value was less than 0.05 with the IVW approach but greater than 0.05 with the weighted median method, it was considered suggestive of the potential association. 2.4. Sensitivity Analyses An assessment of the heterogeneity between SNPs was carried out using Cochran's Q statistics [ 42 ]. Unless evidence of substantial heterogeneity (P < 0.05), fixed-effects models were employed; otherwise, random-effects models were applied. As well as determining whether instrumental SNPs are multi-effect, we used the MR-Egger method to identify the multi-effects. The p value of its intercept was calculated as part of an MR-Egger regression analysis for uncovering possible horizontal pleiotropy [ 43 ]. Furthermore, we also performed MR pleiotropy residual sum and outlier (MR-PRESSO), thus removing possible outliers from multi-effects estimates [ 44 ] and rectifying potential confounding factors [ 45 ]. The odds ratio (OR) and 95% confidence interval (CI) per standard deviation were calculated as the result. The mediation proportions were determined based on the formula: (beta1 × beta2) / beta_all, beta_all represents the total causal effects of gut microbiota on oesophageal carcinoma derived from the main analysis, beta1 represents the estimated effect of gut microbiota-related traits on potential blood metabolites mediators, and beta 2 represents the causal effects of blood metabolites mediators on oesophageal carcinoma. 3. Results 3.1. Bidirectional Two-Sample MR Analyses 3.1.1. Causal Effects of gut microbiota on oesophageal carcinoma In total, 10 gut microbiota taxa (including one phylum, one family, two genera, and six species) and twelve gut microbiota metabolic pathways were associated with oesophageal carcinoma. In Table S2 and Table S3, 85 SNPs for 10 gut microbiota and 117 SNPs for 12 gut microbiota metabolic pathways are presented in detail. Based on the MR analyses, Fig. 2 illustrates the correlation of 4 gut microbiota (genus Phascolarctobacterium, species Phascolarctobacterium succinatutens, species Bifidobacterium adolescentis and phylum Proteobacteria) with the increased risk of oesophageal carcinoma. The genus Phascolarctobacterium (OR = 1.426, 95%CI = 1.092 ~ 1.862, P = 0.009), species Phascolarctobacterium succinatutens (OR = 1.426, 95%CI = 1.093 ~ 1.861, P = 0.009), species Bifidobacterium adolescentis (OR = 1.426, 95%CI = 1.012 ~ 2.139, P = 0.043) and phylum Proteobacteria (OR = 1.724, 95%CI = 1.132 ~ 2.626, P = 0.011) significantly increased the risk of oesophageal carcinoma. Genetically predicted six gut microbiota (family Ruminococcaceae, species Streptococcus thermophilus, species Clostridium leptum, genus Erysipelotrichaceae no name, species Eubacterium hallii and species Holdemania unclassified) were associated with the decreased risk of oesophageal carcinoma. The family Ruminococcaceae (OR = 0.446, 95%CI = 0.258 ~ 0.770, P = 0.004), species Streptococcus thermophilus (OR = 0.586, 95%CI = 0.402 ~ 0.855, P = 0.006) and species Clostridium leptum (OR = 0.621, 95%CI = 0.436 ~ 0.885, P = 0.008), genus Erysipelotrichaceae no name (OR = 0.716, 95%CI = 0.552 ~ 0.930, P = 0.012), species Eubacterium hallii (OR = 0.719, 95%CI = 0.532 ~ 0.973, P = 0.033) and species Holdemania unclassified (OR = 0.687, 95%CI = 0.504 ~ 0.937, P = 0.018) remarkably decreased the risk of oesophageal carcinoma. Other robust MR approaches, including the weighted median method, MR-Egger, and BWMR, also indicate similar results (In addition to family Ruminococcaceae, the P value of the weighted median method is greater than 0.05, which is considered to exist a potential causal relationship). MR analysis of all the 12 gut microbiota metabolic pathways with oesophageal carcinoma indicated no statistically significant association existing (Table S5). 3.1.2. Causal Effects of oesophageal carcinoma on gut microbiota The reverse MR analysis revealed no significant causal effects of genetic prediction of oesophageal carcinoma on the 10 gut microbiota as mentioned above with the P value higher than 0.05 shown in the IVW method. 3.2. Causal Effects of the selected gut microbiota on the human blood metabolites Figure 3 identifies that family Ruminococcaceae was causally associated with higher 1-arachidonoyl-gpc (20:4n6) levels (β = 0.265, 95%CI = 0.103 ~ 0.427, P = 0.001), Phosphate levels (β = 0.302, 95%CI = 0.143 ~ 0.461, P = 0.0002), X-23648 levels (β = 0.274, 95%CI = 0.105 ~ 0.443, P = 0.001), Phosphate to glucose ratio (β = 0.278, 95%CI = 0.120 ~ 0.437, P = 0.0006) and Arachidonate (20:4n6) to caffeine ratio (β = 0.262, 95%CI = 0.091 ~ 0.433, P = 0.003). We identified that family Ruminococcaceae, genus Phascolarctobacterium, species Clostridium leptum, and species Phascolarctobacterium succinatutens were causally associated with 69, 59, 47, and 61 metabolites respectively primarily applying the IVW approach (Supplementary Table 3). The weighted median method and BWMR supported the robustness of the results. 3.3. Causal Effects of the selected human blood metabolites on oesophageal carcinoma After examining the causal association between gut microbial taxa and above significant metabolites through the weighted median method and BWMR, we found that Perfluorooctanoate (PFOA) levels (OR = 0.713, 95%CI = 0.508 ~ 1.000, P = 0.0498) was a significant risk factor in the causal pathway from species Clostridium leptum to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio (OR = 1.298, 95%CI = 1.010 ~ 1.668, P = 0.041) was a significant risk factor in the causal pathway from genus Phascolarctobacterium to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio (OR = 1.298, 95%CI = 1.010 ~ 1.668, P = 0.041) was also a significant risk factor in the causal pathway from species Phascolarctobacterium succinatutens to oesophageal carcinoma. Genetic prediction of 1-arachidonoyl-gpc (20:4n6) levels (OR = 0.814, 95%CI = 0.664 ~ 0.997, P = 0.047) was a significant risk factor in the causal pathway from family Ruminococcaceae to oesophageal carcinoma as shown in Fig. 4 . However, the causal relationship between family Ruminococcaceae and 1-arachidonoyl-gpc (20:4n6) levels was not identified by the weighted median method with a P value higher than 0.05. MR analysis of the rest of the four metabolites with oesophageal carcinoma indicated no statistically significant association existing. The detailed information on the results is presented in Table S4. 3.4. Mediation Effects of the selected human blood metabolites on oesophageal carcinoma For the mediation analysis illustrated in Fig. 5 , we excluded mediating factors that were not causally affected by gut microbiota and those that did not causally influence oesophageal carcinoma. Finally, our results indicated that Perfluorooctanoate (PFOA) levels, Cholate to bilirubin (Z, Z) ratio, and 1-arachidonoyl-gpc (20:4n6) levels were significant risk factors mediating the correlation of gut microbiota-related traits with oesophageal carcinoma. The overall effect can be separated into direct effect (via mediators) and indirect effect (without mediators). Our results demonstrated that Perfluorooctanoate (PFOA) levels accounted for 9.74% in the causal pathway from species Clostridium leptum to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio accounted for 11.45% in the causal pathway from genus Phascolarctobacterium to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio accounted for 11.42% in the causal pathway from species Phascolarctobacterium succinatutens to oesophageal carcinoma; 1-arachidonoyl-gpc (20:4n6) levels accounted for 6.75% in the causal pathway from family Ruminococcaceae to oesophageal carcinoma. 3.5. Sensitivity Analyses To assess the heterogeneity of our estimates, we calculated Cochrane's Q and p values derived from Cochrane's Q test (Table S4; Table S6; Table S7). No evidence of significant heterogeneity was found in our analysis. To test and correct for the directional pleiotropy in causal estimates, a series of sensitivity analyses were carried out. No significant intercept was discovered in the other aforementioned MR analyses, indicating the null findings of the directional pleiotropy. Additionally, a leave-one-out analysis was performed to determine whether any single SNP significantly violated the causal estimate, indicating that monocyte count and eGFR are positively associated genetically (Figure S1 -S4). All of our positive results were consistent after removing the outliers in the original MR-PRESSO global test, which was utilized to ascertain and exclude outliers, as well as decrease heterogeneity in our analysis. 4. Discussion To the best of our knowledge, based on statistical approaches that account for directional pleiotropy, this is the first study to investigate the likelihood of metabolite traits mediating a causal path between gut microbial taxa and ESCA. In this comprehensive and large-scale MR analysis, we affirmed that Perfluorooctanoate (PFOA) levels and 1-arachidonoyl-gpc (20:4n6) levels respectively mediate the causal influence of species Clostridium leptum and family Ruminococcaceae on ESCA, while Cholate to bilirubin (Z, Z) ratio mediates the pathway from genus Phascolarctobacterium and species Phascolarctobacterium succinatutens to ESCA. There is a large microbial population in the stool of adults, of which Cluster IV (Clostridium leptum group) occupies a dominant position, and its abundance is generally higher than 15%. These bacteria tend to be associated with multiple metabolic pathways in the body. These bacteria are involved in a variety of metabolic pathways that maintain the balance of the intestinal microecological. One of the main sources of energy for colonic epithelial cells to regulate intestinal epithelial function is short-chain fatty acids (SCFAs) produced by Clostridium leptum [ 46 – 48 ]. Clostridium leptum ferments polysaccharides through acetyl-CoA and pyruvate pathways to produce propionate and butyrate [ 49 ], thereby controlling glucose concentration in the intestinal microenvironment. Butyrate can act on free fatty acid receptor 2 and promote GLP-1 secretion [ 50 ]. Recent studies have shown that the intestinal flora produces some metabolites with weight loss effects in the process of fermenting polysaccharides [ 51 , 52 ]. Li et al. found that Clostridium leptum can alleviate obesity by fermenting metabolites produced by FP [ 53 ]. Obesity is considered one of the risk factors for ESCA [ 54 , 55 ]. It is plausible to speculate that this may be one of the mechanisms by which Clostridium leptum can lower the risk of ESCA. In addition, Clostridium leptum plays a significant role in mediating the upregulation of the expansion and differentiation of regulatory Treg cells and recruits them into the intestinal epithelial mucosa [ 56 ]. Treg cells are considered to be associated with ESCA [ 57 , 58 ]. The metabolite salicylic acid produced by prausnitzii faecalibacterium as the dominant microbiota in the Clostridium leptum group during the fermentation of salicin can inhibit the secretion of the pro-inflammatory cytokine IL-8 [ 59 ]. Additionally, this type of microbiota can inhibit the upregulation of interleukin-17, thereby mitigating inflammation [ 60 ]. The extracellular polymer matrix (EPM) released by Prausnitzii faecalibacterium stimulates the secretion of IL-10 and IL-12, which can also achieve anti-inflammatory effects [ 61 ]. These anti-inflammatory properties presumably reduce the incidence of ESCA. [ 62 ]. PFOA, one of the four types of polyfluoroalkyl substances (PFASs), is a newly discovered environmental contaminant that can cause health problems as an endocrine disruptor [ 63 ]. Four PFAS (PFOA, perfluorooctane sulfonic acid [PFOS], perfluorohexane sulfonic acid [PFHxS], and perfluorononanoic acid [PFNA]) have been reported to be detected in the serum among individuals over 12 in the United States, with a positive rate of more than 98%, indicating the prevalence of PFOA exposure [ 64 ]. Several studies have elucidated the mechanism of action of PFOA in the development of various types of cancer, such as thyroid cancer, rhabdomyosarcoma, prostate cancer, and breast cancer [ 65 , 66 ]. Additionally, high levels of PFOA are strongly associated with ESCA [ 67 , 68 ]. Phascolarctobacterium is a fecal-phase intestinal bacterium extracted from koala excrement by Del Dot et al [ 69 ]. This bacterium is classified as gram-negative, pleomorphic rod-shaped cells composed of P. faecium and Phascolarctobacterium succinatutens [ 70 ], which has an abundance of more than 2% in the human adult fecal microbial population [ 71 ]. A recent study has shown that Phascolarctobacterium is widespread in the human gastrointestinal tract and can produce SCFAs, including acetic acid, propionic acid, isobutyric acid, butyric acid, and isovaleric acid [ 72 ]. Phascolarctobacterium stimulates growth by succinic acid and decomposes it into propionic acid [ 73 ], which is involved in significant metabolic pathways, such as hepatic gluconeogenesis [ 74 ], and can activate anti-inflammatory effects [ 75 – 77 ]. Maintaining intestinal homeostasis is thought to be facilitated by an increase in butyrate [ 74 ]. An estimated one-fifth of human feces contain Phascolarctobacterium succinatutens, which uses succinate as its sole energy source [ 70 ]. An elevated abundance of Phascolarctobacterium and Phascolarctobacterium Succinatutens is associated with colorectal cancer growth [ 78 ]. Clinically substantial reductions in Phascolarctobacterium abundance are observed in patients with head and neck cancer [ 79 ], while pancreatic and prostate cancer patients exhibit significantly larger abundances compared to healthy controls [ 80 , 81 ]. A bile acid receptor, the G-protein coupled bile acid receptor Gpbar1 (TGR5) is widely distributed in muscles, adipose tissue, immune systems, enteric nervous systems, etc., and plays a crucial role in maintaining energy homeostasis by regulating glucose metabolism and lipid metabolism processes mediated by bile acids [ 82 – 84 ]. Activation of epidermal growth factor receptor EGFR can inhibit the occurrence and progression of pancreatic ductal adenocarcinoma and colorectal cancer, a process that can be prevented by TGR5 [ 85 ]. TGR5 has recently been reported overexpressed in gastric cancer and activates the oncogenic pathway of gastric cancer cell lines [ 84 ]. Bile acids can modulate the expression of TGR5 in the EAC FLO cell line and the BE BAR-T cell line [ 86 ], and possibly affect progression from BE to EAC [ 87 – 89 ]. One of the bile acid receptors known as the vitamin D receptor (VDR) is overexpressed in precancerous lesions and EAC [ 90 ], in addition to being implicated in the formation of malignancies such as liver, colon, and breast cancer[ 91 – 93 ]. These findings imply that bile acids may be involved in the early carcinogenesis process through TGR5 and VDR. Bilirubin levels in serum reveal liver dysfunction as a result of chronic viral hepatitis, alcohol consumption, and chemoradiotherapy. The albumin-bilirubin (ALBI) score was proved to be a predictive prognostic factor in patients with EACC, as the 5 years survival rate of in the albumin-bilirubin ratio low group was significantly higher than that in the albumin-bilirubin ratio high group [ 94 , 95 ]. In healthy individuals, the colonic mucosal biofilm contains Ruminococcaceae bacteria which are strictly anaerobic [ 96 ]. SCFAs generated by Ruminococcaceae metabolism can stabilize the homeostasis of the intestinal microenvironment [ 72 ]. Dysfunction of the colonic mucosa frequently coexists with osmotic diarrhea and is generally brought on by a deficiency in SCFAs. [ 97 ]. Butyrate exerts anti-inflammatory effects by upregulating the tight junctions between colonocytes to strengthen the intestinal barrier and prevent lipopolysaccharide (LPS) from being transported into the systemic circulation [ 98 ]. Therefore, the abundance of Ruminococcaceae typically declines in patients with inflammatory bowel diseases such as Crohn's disease or ulcerative colitis [ 47 , 99 , 100 ], as well as in patients with inflammatory diseases such as hepatic encephalopathy [ 101 ]. As an essential lysophosphatidylcholine, 1-Arachidonoyl-GPC hinders the migration of CXCR3 + T cells to the inflammatory microenvironment by inhibiting autoimmunity [ 102 , 103 ]. According to the Phe-MR analysis of 655 diseases conducted by Jia et al., 1-arachidonol-gpc supplementation may lead to an increased risk of benign neoplasm of the colon and impaired thyroid function [ 104 ]. The mechanism of 1-arachidonol-GPC in the prevention of ESCA is still poorly understood, and research on it should be further conducted. Overall, despite some evidence supporting the association of species Clostridium leptum, genus Phascolarctobacterium, species Phascolarctobacterium succinatutens and family Ruminococcaceae with ESCA, the evidence is apparently inadequate and of relatively low quality. Hence, clinical trials with larger sample sizes, as well as studies of cellular mechanisms are necessary to confirm the health effects and mechanisms underlying these bacteria. Several crucial strengths have been identified in our study. First, our study closes a knowledge vacuum in this area by examining whether gut microbial taxa are causally associated with ESCA through metabolite traits, as no other study has done so yet. Second, to move forward with animal experiments and mechanism research, we analyzed gut microbiota taxa at the species level. Finally, as part of our attempt to uncover the possible mechanisms responsible for the association between gut microbiota and ESCA, we employed a mediating analysis. However, we must acknowledge certain limitations in our research. First, since our analysis was carried out mostly among European populations, the results should be extrapolated with caution since the correlation between the gut microbial taxa and the host genomes may vary based on ethnicity. Second, as the gut microbial taxa of different populations vary considerably in terms of their gut microbiota composition, the sample size of the GWAS summary data may not have been adequate for all potential causal relationships to be revealed. Third, our study identified independent variants associated with gut microbiota traits at the genome-wide significance of P < 1×10 − 5 , as the criteria applied in the primary GWAS and MR analysis of gut microbiota in other literature. As indicated by large F-statistics, however, genetic instruments were significantly correlated to exposure in this study. At last, the assumptions underlying MR may be untestable, especially if potential confounding variables remain unmeasured. The presence of pleiotropy can be observed when genetic variants independently affect traits other than those investigated. MR-Egger and weighted median approaches do not completely protect findings from pleiotropic effects when used individually, but the consistency of effect estimates derived from multiple sensitivity analyses reinforces the plausibility of true causal effects. The findings of this study may aid in facilitating the development of novel strategies to the management and prevention of esophageal cancer from the standpoint of clinical practice. For instance, altering the balance of the gut microbiota and blood metabolites may lessen the possibility of esophageal cancer developing, or it may diminish the severity of existing conditions. This can be accomplished in a variety of ways, including dietary modifications and probiotic supplements. More research is necessary to corroborate these preliminary findings and identify the most effective treatment alternatives, as this field of study is still in its early stages. 5. Conclusion We found four gut microbial taxa in our MR analysis that may be causally related to ESCA. Our research offers genetic evidence that alterations in the gut flora could be an essential risk factor for the progression of ESCA, which could be mediated by several human blood metabolites. These findings provide novel perspectives on the pathogenesis of ESCA and propose possible EACA intervention targets. To validate these results and comprehend the underlying mechanisms involved, further investigation is required. Declarations The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions H.Y. and Z.Z. contributed to the study conception and design. Data collection and analysis were performed by Z.Z. The first draft of the manuscript was written by X.L. and X.L. and B.X. commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability GWAS summary data for gut microbiota from Dutch Microbiome project is available at https://dutchmicrobiomeproject.molgeniscloud.org/; The GWAS summary statistics of human blood metabolites was obtained from GWAS Catalog (https://www.ebi.ac.uk/gwas/); GWAS summary statistics for the esophageal cancer is available at https://www.finngen.fi/en/access results. Ethics approval Ethical approval and consent to participate were obtained in all original studies. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4153773","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284426088,"identity":"b03fadf5-a2b9-4421-a84c-1d185b6ce6a3","order_by":0,"name":"Xiuzhi LI","email":"","orcid":"","institution":"Guangdong Provincial Clinical Research Center for Cancer","correspondingAuthor":false,"prefix":"","firstName":"Xiuzhi","middleName":"","lastName":"LI","suffix":""},{"id":284426089,"identity":"90247f11-d048-4aba-a338-260a50496b31","order_by":1,"name":"Bingchen Xu","email":"","orcid":"","institution":"Sun Yat-sen University Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Bingchen","middleName":"","lastName":"Xu","suffix":""},{"id":284426090,"identity":"eda36a91-f428-4482-a13d-fb1297b5f06e","order_by":2,"name":"Han Yang","email":"","orcid":"","institution":"Guangdong Provincial Clinical Research Center for Cancer","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Yang","suffix":""},{"id":284426092,"identity":"28e8edb1-e9f1-40b7-882a-e64cd98226a0","order_by":3,"name":"Zhihua Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYDACCQYGZsYGG9K1pJGu5TAJOuRn9xh/Ltxx3t7g/BnDDz8YbPLlHZifPcCnhXHOGQPjmWduJ264kWMs2cOQZrnxAJu5AT4tzBI5Bsm8bbcTDG7wmDEzMBw2MGzgYZPAp4UNqOUwb9s5kMOI1MIjkWPYzNt2gHHDgRyIFnkGAlokJNKKmXnbkhNn3kgrluwxSDMwYGYzw6tFfkby5s+8bXb2fOcPb/zwo8LGQL69+RleLXCgcABEAoPKgOg4km9AZ4yCUTAKRsEogAIAj5xAGbdJwHkAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Provincial Clinical Research Center for Cancer","correspondingAuthor":true,"prefix":"","firstName":"Zhihua","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-03-23 09:45:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4153773/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4153773/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53872759,"identity":"ad800c8b-397a-4118-8641-445944e48ec3","added_by":"auto","created_at":"2024-04-01 15:48:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61770,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design. An overview of our two-stage study design is displayed in the diagram. First, to discover putative causal gut microbial taxa of esophageal cancer, we conducted a bidirectional two-sample Mendelian randomization (MR) study mainly with the inverse variance weighted approach, along with several sensitivity analyses. Second, an MR analysis of mediation was carried out. We assessed the causal association between several human blood metabolites and gut microbial taxa, as well as the degree to which these blood metabolites modulate the influence of gut microbial taxa on esophageal cancer.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/73cf9c76387a00c81fac27a2.jpg"},{"id":53873127,"identity":"9ea7c118-5944-42e0-8830-49482877345b","added_by":"auto","created_at":"2024-04-01 15:56:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":111088,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis of causal effects between vital gut microbiotas and esophageal cancer.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/3089b5cec5aae9bd9964334e.jpg"},{"id":53872758,"identity":"692e01bf-2af3-43a5-b922-ee68b7938aae","added_by":"auto","created_at":"2024-04-01 15:48:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121735,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis of causal effects between vital gut microbiotas and mediated blood metabolites.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/9f2eea99049206bda9032f94.jpg"},{"id":53872761,"identity":"ce5d3718-3f2f-49fa-95cc-241f1585fdff","added_by":"auto","created_at":"2024-04-01 15:48:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":49471,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis of causal effects between mediated blood metabolites and esophageal cancer.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/2ec7b00973b7d8ca6882b7b5.jpg"},{"id":53872762,"identity":"98b082b5-152e-4104-a46a-9feac476775f","added_by":"auto","created_at":"2024-04-01 15:48:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":56063,"visible":true,"origin":"","legend":"\u003cp\u003eThe proportions of each significant blood metabolite mediating from corresponding gut microbial taxa to esophageal cancer.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/79fd3990717cf36eb3dd9211.jpg"},{"id":54229311,"identity":"b38fc7f0-c04d-4531-b451-43498ceda1a4","added_by":"auto","created_at":"2024-04-07 04:52:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2886795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/34c12b12-ebe4-45f3-a935-1a39a14eab75.pdf"},{"id":53872763,"identity":"6584ffa4-cf9a-4bdb-bdb4-813e254cbeab","added_by":"auto","created_at":"2024-04-01 15:48:32","extension":"zip","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":792320,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfiles.zip","url":"https://assets-eu.researchsquare.com/files/rs-4153773/v1/9b7edf2ab36d3df2324de132.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut microbiota, human blood metabolites and esophageal cancer: a Mendelian randomization study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA major global disease, esophageal cancer (ESCA) ranks sixth among all cancers in terms of mortality based on global cancer statistics [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In the United States, about 17,000 new cases are diagnosed each year [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ESCA is mainly composed of two epidemiologically and pathologically distinct diseases, namely adenocarcinoma (OAC) and esophageal squamous cell carcinoma (OSCC) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The incidence of OAC is significant and sharply rising in Western countries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Individuals with early-stage ESCA may not recognize the symptoms of obstruction or stricture due to the dilated and muscular nature of the esophagus. Symptoms only appear after the tumor has progressed locally or even metastatically [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The majority of patients with ESCA in the United States and Europe are diagnosed with locally advanced or metastatic disease, which is ineligible for curative treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In the UK, more than 70% of patients are diagnosed with lymph node metastases or distant metastases, of which distant metastases account for about 40% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Patients with advanced ESCA typically encounter an unfavorable prognosis. Due to etiological, molecular, and histological heterogeneity, advanced ESCA patients often acquire innate resistance to systemic therapy, significantly reducing treatment efficacy [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Surgical intervention remains the most effective way to treat ESAC, but for patients with advanced ESCA, the 5-year survival rate remains less than 25% even after surgery [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Generally, the 5-year overall survival (OS) rates for ESCA patients remain low, ranging from 12\u0026ndash;20% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Surgical treatment or palliative care for ESCA can contribute to reduced quality of life and a significant symptom burden [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Early adoption of preventative strategies and a detailed understanding of the etiology of ESCA is critical in reducing the incidence of ESCA.\u003c/p\u003e \u003cp\u003eThroughout the digestive system, the intestinal microbiota is a group of microorganisms indigenous to the human intestines, which has flourished in the human intestine over an extended period of time interacting with the human body [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. With approximately 10\u003csup\u003e14\u003c/sup\u003e species of microorganisms, it is regarded as the largest microbial reservoir in our body [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As an important regulator of human health [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], the gut microbiota plays an integral role in the development of the human immune system and the maintenance of intestinal homeostasis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. An increasing corpus of studies has demonstrated the intricate relationships between ESCA and the human gut flora in recent years [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The composition and abundance of fecal microorganisms in ESCA patients are closely related to the severity of the disease [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, the genome-wide methylation level of ESCA can be regulated by the gut microbiota, which affects the occurrence, development, and metastasis of ESCA. The intestinal microbiota can function through a variety of bioactive metabolites that systematically affect the internal microenvironment, including bile acids, short-chain fatty acids, and lipopolysaccharides, to regulate the function of the corresponding target organs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Deficiency or disorder of intestinal flora significantly affects polysaccharide decomposition and lipid absorption, resulting in liver and adipose tissue dysfunction, leading to cardiovascular and cerebrovascular diseases, type 2 diabetes, and obesity, among other metabolic-related diseases [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Recent studies have shown that there were notable variations in the concentration of amino acids such as tryptophan and tyrosine, as well as lipids such as oleic acid and palmitoleic acid, between ESCA patients and healthy controls [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Hydroxytryptamine (5-HT) was discovered to be a risk factor for lymph node metastases [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This evidence suggests that human circulating metabolites are integral components in the development of ESCA. Tumor cells can disrupt the entire metabolism in the process of continuously adapting to the dynamic metabolic microenvironment, thus affecting the distribution and content of metabolites in blood circulation, such as upregulating the glycolytic pathway under adequate oxygen conditions, resulting in rapid growth [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, for determining possible causal associations between various exposures and clinical outcomes, Mendelian randomization (MR) analysis has become widely applied, which is an approach for inferring causality between exposure and outcome from genetic variations, especially single nucleotide polymorphisms (SNPs) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The majority of epidemiologic analyses conducted on the association between gut microbiota, human blood metabolites, and ESCA are based on traditional methods (e.g., cross-sectional, case-control, cohort). However, they are subject to various limitations, such as confounding and reverse causation bias, which can affect the estimates of effect [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].By virtue of the fact that allelic randomization occurs prior to the onset of disease, MR analysis has an advantage over conventional observational studies in reducing reverse causation bias. Furthermore, the independent assortment and random segregation of genetic polymorphisms at conception mitigate the confounding bias, as genetic markers are used as instrumental variables (IVs) in MR analysis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the absence of research examining the causal association between gut microbiota and ESCA mediated by human blood metabolites, we undertook a two-sample, two-step MR analysis to reveal the relationship.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study design\u003c/h2\u003e \u003cp\u003eThe study design is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. More than 647,920 participants were selected from summary-level, publicly available datasets to conduct a large two-step, two-sample MR study using a two-step strategy to investigate the relationship between genetic prediction of gut microbiota and oesophageal carcinoma and to determine whether plasma metabolites could mediate this association. A two-sample MR analysis utilizing different datasets is performed to assess correlations of the same genetic variants with exposure (e.g., gut microbiota) and outcome (e.g., oesophageal carcinoma). Initially, the causal effects of genetic prediction of 412 gut microbiota with a genetic disposition to oesophageal carcinoma were analyzed, and the two-step approach utilized in mediating analysis examined the association between genetically predicted gut microbiota and each potential mediator. Subsequently, we investigate and quantify the mediation effects of potential mediators in the pathway from the 412 gut microbiota to oesophageal carcinoma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data sources\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Genetic instrumental variable for gut microbiome\u003c/h2\u003e \u003cp\u003eA large-scale genome-wide association study (GWAS) conducted by the Dutch Microbiome Project (DMP) provided the species-level dataset for the gut microbiota [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. 7738 participants of European descent were involved in the analysis of this dataset, which is the hitherto largest species-level genomics study on the human gut microbiota. An analysis of stool samples was performed utilizing shotgun metagenomic sequencing to determine the gut microbiome, ultimately identifying 207 taxonomies (105 species, 48 genera, 26 families, 13 orders, 10 classes, 5 phyla) and 205 gut microbiota metabolic pathways related to microbial functions. This GWAS dataset is described in more detail in its original publication [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The GWAS data is publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mibiogen.gcc.rug.nl\u003c/span\u003e\u003cspan address=\"https://mibiogen.gcc.rug.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eSNPs with genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026ndash;5\u003c/sup\u003e) and clumping at a linkage disequilibrium (LD) threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (clumping distance: 10,000 kb) were used in the analyses as instrumental variables (IVs) for gut microbiota. To exclude weak instrumental variable bias, the calculated F-statistics for exposure used to quantify the IVs ranged from 19 to 57, which is consistent with the hypothesis of F\u0026thinsp;\u0026gt;\u0026thinsp;10 for MR analyses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Genetic Instrumental Variables for Potential Mediators\u003c/h2\u003e \u003cp\u003eA recent GWAS carried out on the Canadian Longitudinal Study on Aging (CLSA) cohort involved a total of 8,299 individuals and approximately 15.4\u0026nbsp;million SNPs explored the association of SNPs with human metabolite levels. A genome-wide association study of 1,091 blood metabolites and 309 metabolite ratios was performed in this research [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Mediators Genetic Instrumental Variables for CVD\u003c/h2\u003e \u003cp\u003eOesophageal carcinoma GWAS summary data were derived from the tenth version of the FinnGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r10.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r10.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Oesophageal carcinoma was identified using International Classification of Diseases (ICD) diagnosis codes in this prospective cohort study, involving 619 cases and 314,193 controls originating from European ancestry.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analyses\u003c/h2\u003e \u003cp\u003eWe examined the potential association between genetically predicted gut microbiota and genetically predicted oesophageal carcinoma by applying a bidirectional two-sample MR analysis. Additionally, to examine the possible mediation effects of human blood metabolites in the causal relationship, a two-step MR analysis was carried out using summary statistical data.\u003c/p\u003e \u003cp\u003eIn both forward and reverse directions of MR analyses, the inverse-variance weighted (IVW) method was utilized as the primary analytical approach to estimate odds ratios and P-values, widely recognized as the most robust methodology for generating reliable causal estimates in MR studies.\u003c/p\u003e \u003cp\u003eThe IVW method, analyzing the causal effects of exposure SNPs on outcome data, was employed as the primary approach [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In the absence of effective instruments, the weighted median method was employed, as it is capable of offering reliable causal effect estimates even if less than fifty percent of the information is derived from valid instruments [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To discover the anomalies in the analysis due to the large horizontal pleiotropy during the MR analysis and to account for the weak effects and uncertainties of the weak horizontal pleiotropy, we performed further analysis using Bayesian weighted Mendelian randomization (BWMR) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. When the P value was less than 0.05 with the IVW approach but greater than 0.05 with the weighted median method, it was considered suggestive of the potential association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eAn assessment of the heterogeneity between SNPs was carried out using Cochran's Q statistics [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Unless evidence of substantial heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), fixed-effects models were employed; otherwise, random-effects models were applied. As well as determining whether instrumental SNPs are multi-effect, we used the MR-Egger method to identify the multi-effects. The p value of its intercept was calculated as part of an MR-Egger regression analysis for uncovering possible horizontal pleiotropy [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, we also performed MR pleiotropy residual sum and outlier (MR-PRESSO), thus removing possible outliers from multi-effects estimates [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and rectifying potential confounding factors [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The odds ratio (OR) and 95% confidence interval (CI) per standard deviation were calculated as the result. The mediation proportions were determined based on the formula: (beta1 \u0026times; beta2) / beta_all, beta_all represents the total causal effects of gut microbiota on oesophageal carcinoma derived from the main analysis, beta1 represents the estimated effect of gut microbiota-related traits on potential blood metabolites mediators, and beta 2 represents the causal effects of blood metabolites mediators on oesophageal carcinoma.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Bidirectional Two-Sample MR Analyses\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. Causal Effects of gut microbiota on oesophageal carcinoma\u003c/h2\u003e \u003cp\u003eIn total, 10 gut microbiota taxa (including one phylum, one family, two genera, and six species) and twelve gut microbiota metabolic pathways were associated with oesophageal carcinoma. In Table S2 and Table S3, 85 SNPs for 10 gut microbiota and 117 SNPs for 12 gut microbiota metabolic pathways are presented in detail.\u003c/p\u003e \u003cp\u003eBased on the MR analyses, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the correlation of 4 gut microbiota (genus Phascolarctobacterium, species Phascolarctobacterium succinatutens, species Bifidobacterium adolescentis and phylum Proteobacteria) with the increased risk of oesophageal carcinoma. The genus Phascolarctobacterium (OR\u0026thinsp;=\u0026thinsp;1.426, 95%CI\u0026thinsp;=\u0026thinsp;1.092\u0026thinsp;~\u0026thinsp;1.862, P\u0026thinsp;=\u0026thinsp;0.009), species Phascolarctobacterium succinatutens (OR\u0026thinsp;=\u0026thinsp;1.426, 95%CI\u0026thinsp;=\u0026thinsp;1.093\u0026thinsp;~\u0026thinsp;1.861, P\u0026thinsp;=\u0026thinsp;0.009), species Bifidobacterium adolescentis (OR\u0026thinsp;=\u0026thinsp;1.426, 95%CI\u0026thinsp;=\u0026thinsp;1.012\u0026thinsp;~\u0026thinsp;2.139, P\u0026thinsp;=\u0026thinsp;0.043) and phylum Proteobacteria (OR\u0026thinsp;=\u0026thinsp;1.724, 95%CI\u0026thinsp;=\u0026thinsp;1.132\u0026thinsp;~\u0026thinsp;2.626, P\u0026thinsp;=\u0026thinsp;0.011) significantly increased the risk of oesophageal carcinoma. Genetically predicted six gut microbiota (family Ruminococcaceae, species Streptococcus thermophilus, species Clostridium leptum, genus Erysipelotrichaceae no name, species Eubacterium hallii and species Holdemania unclassified) were associated with the decreased risk of oesophageal carcinoma. The family Ruminococcaceae (OR\u0026thinsp;=\u0026thinsp;0.446, 95%CI\u0026thinsp;=\u0026thinsp;0.258\u0026thinsp;~\u0026thinsp;0.770, P\u0026thinsp;=\u0026thinsp;0.004), species Streptococcus thermophilus (OR\u0026thinsp;=\u0026thinsp;0.586, 95%CI\u0026thinsp;=\u0026thinsp;0.402\u0026thinsp;~\u0026thinsp;0.855, P\u0026thinsp;=\u0026thinsp;0.006) and species Clostridium leptum (OR\u0026thinsp;=\u0026thinsp;0.621, 95%CI\u0026thinsp;=\u0026thinsp;0.436\u0026thinsp;~\u0026thinsp;0.885, P\u0026thinsp;=\u0026thinsp;0.008), genus Erysipelotrichaceae no name (OR\u0026thinsp;=\u0026thinsp;0.716, 95%CI\u0026thinsp;=\u0026thinsp;0.552\u0026thinsp;~\u0026thinsp;0.930, P\u0026thinsp;=\u0026thinsp;0.012), species Eubacterium hallii (OR\u0026thinsp;=\u0026thinsp;0.719, 95%CI\u0026thinsp;=\u0026thinsp;0.532\u0026thinsp;~\u0026thinsp;0.973, P\u0026thinsp;=\u0026thinsp;0.033) and species Holdemania unclassified (OR\u0026thinsp;=\u0026thinsp;0.687, 95%CI\u0026thinsp;=\u0026thinsp;0.504\u0026thinsp;~\u0026thinsp;0.937, P\u0026thinsp;=\u0026thinsp;0.018) remarkably decreased the risk of oesophageal carcinoma. Other robust MR approaches, including the weighted median method, MR-Egger, and BWMR, also indicate similar results (In addition to family Ruminococcaceae, the P value of the weighted median method is greater than 0.05, which is considered to exist a potential causal relationship).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMR analysis of all the 12 gut microbiota metabolic pathways with oesophageal carcinoma indicated no statistically significant association existing (Table S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Causal Effects of oesophageal carcinoma on gut microbiota\u003c/h2\u003e \u003cp\u003eThe reverse MR analysis revealed no significant causal effects of genetic prediction of oesophageal carcinoma on the 10 gut microbiota as mentioned above with the P value higher than 0.05 shown in the IVW method.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Causal Effects of the selected gut microbiota on the human blood metabolites\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e identifies that family Ruminococcaceae was causally associated with higher 1-arachidonoyl-gpc (20:4n6) levels (β\u0026thinsp;=\u0026thinsp;0.265, 95%CI\u0026thinsp;=\u0026thinsp;0.103\u0026thinsp;~\u0026thinsp;0.427, P\u0026thinsp;=\u0026thinsp;0.001), Phosphate levels (β\u0026thinsp;=\u0026thinsp;0.302, 95%CI\u0026thinsp;=\u0026thinsp;0.143\u0026thinsp;~\u0026thinsp;0.461, P\u0026thinsp;=\u0026thinsp;0.0002), X-23648 levels (β\u0026thinsp;=\u0026thinsp;0.274, 95%CI\u0026thinsp;=\u0026thinsp;0.105\u0026thinsp;~\u0026thinsp;0.443, P\u0026thinsp;=\u0026thinsp;0.001), Phosphate to glucose ratio (β\u0026thinsp;=\u0026thinsp;0.278, 95%CI\u0026thinsp;=\u0026thinsp;0.120\u0026thinsp;~\u0026thinsp;0.437, P\u0026thinsp;=\u0026thinsp;0.0006) and Arachidonate (20:4n6) to caffeine ratio (β\u0026thinsp;=\u0026thinsp;0.262, 95%CI\u0026thinsp;=\u0026thinsp;0.091\u0026thinsp;~\u0026thinsp;0.433, P\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified that family Ruminococcaceae, genus Phascolarctobacterium, species Clostridium leptum, and species Phascolarctobacterium succinatutens were causally associated with 69, 59, 47, and 61 metabolites respectively primarily applying the IVW approach (Supplementary Table\u0026nbsp;3). The weighted median method and BWMR supported the robustness of the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Causal Effects of the selected human blood metabolites on oesophageal carcinoma\u003c/h2\u003e \u003cp\u003eAfter examining the causal association between gut microbial taxa and above significant metabolites through the weighted median method and BWMR, we found that Perfluorooctanoate (PFOA) levels (OR\u0026thinsp;=\u0026thinsp;0.713, 95%CI\u0026thinsp;=\u0026thinsp;0.508\u0026thinsp;~\u0026thinsp;1.000, P\u0026thinsp;=\u0026thinsp;0.0498) was a significant risk factor in the causal pathway from species Clostridium leptum to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio (OR\u0026thinsp;=\u0026thinsp;1.298, 95%CI\u0026thinsp;=\u0026thinsp;1.010\u0026thinsp;~\u0026thinsp;1.668, P\u0026thinsp;=\u0026thinsp;0.041) was a significant risk factor in the causal pathway from genus Phascolarctobacterium to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio (OR\u0026thinsp;=\u0026thinsp;1.298, 95%CI\u0026thinsp;=\u0026thinsp;1.010\u0026thinsp;~\u0026thinsp;1.668, P\u0026thinsp;=\u0026thinsp;0.041) was also a significant risk factor in the causal pathway from species Phascolarctobacterium succinatutens to oesophageal carcinoma. Genetic prediction of 1-arachidonoyl-gpc (20:4n6) levels (OR\u0026thinsp;=\u0026thinsp;0.814, 95%CI\u0026thinsp;=\u0026thinsp;0.664\u0026thinsp;~\u0026thinsp;0.997, P\u0026thinsp;=\u0026thinsp;0.047) was a significant risk factor in the causal pathway from family Ruminococcaceae to oesophageal carcinoma as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. However, the causal relationship between family Ruminococcaceae and 1-arachidonoyl-gpc (20:4n6) levels was not identified by the weighted median method with a P value higher than 0.05. MR analysis of the rest of the four metabolites with oesophageal carcinoma indicated no statistically significant association existing. The detailed information on the results is presented in Table S4.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Mediation Effects of the selected human blood metabolites on oesophageal carcinoma\u003c/h2\u003e \u003cp\u003eFor the mediation analysis illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we excluded mediating factors that were not causally affected by gut microbiota and those that did not causally influence oesophageal carcinoma. Finally, our results indicated that Perfluorooctanoate (PFOA) levels, Cholate to bilirubin (Z, Z) ratio, and 1-arachidonoyl-gpc (20:4n6) levels were significant risk factors mediating the correlation of gut microbiota-related traits with oesophageal carcinoma. The overall effect can be separated into direct effect (via mediators) and indirect effect (without mediators). Our results demonstrated that Perfluorooctanoate (PFOA) levels accounted for 9.74% in the causal pathway from species Clostridium leptum to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio accounted for 11.45% in the causal pathway from genus Phascolarctobacterium to oesophageal carcinoma; Cholate to bilirubin (Z, Z) ratio accounted for 11.42% in the causal pathway from species Phascolarctobacterium succinatutens to oesophageal carcinoma; 1-arachidonoyl-gpc (20:4n6) levels accounted for 6.75% in the causal pathway from family Ruminococcaceae to oesophageal carcinoma.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Sensitivity Analyses\u003c/h2\u003e \u003cp\u003eTo assess the heterogeneity of our estimates, we calculated Cochrane's Q and p values derived from Cochrane's Q test (Table S4; Table S6; Table S7). No evidence of significant heterogeneity was found in our analysis. To test and correct for the directional pleiotropy in causal estimates, a series of sensitivity analyses were carried out. No significant intercept was discovered in the other aforementioned MR analyses, indicating the null findings of the directional pleiotropy. Additionally, a leave-one-out analysis was performed to determine whether any single SNP significantly violated the causal estimate, indicating that monocyte count and eGFR are positively associated genetically (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4). All of our positive results were consistent after removing the outliers in the original MR-PRESSO global test, which was utilized to ascertain and exclude outliers, as well as decrease heterogeneity in our analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, based on statistical approaches that account for directional pleiotropy, this is the first study to investigate the likelihood of metabolite traits mediating a causal path between gut microbial taxa and ESCA. In this comprehensive and large-scale MR analysis, we affirmed that Perfluorooctanoate (PFOA) levels and 1-arachidonoyl-gpc (20:4n6) levels respectively mediate the causal influence of species Clostridium leptum and family Ruminococcaceae on ESCA, while Cholate to bilirubin (Z, Z) ratio mediates the pathway from genus Phascolarctobacterium and species Phascolarctobacterium succinatutens to ESCA.\u003c/p\u003e \u003cp\u003eThere is a large microbial population in the stool of adults, of which Cluster IV (Clostridium leptum group) occupies a dominant position, and its abundance is generally higher than 15%. These bacteria tend to be associated with multiple metabolic pathways in the body. These bacteria are involved in a variety of metabolic pathways that maintain the balance of the intestinal microecological. One of the main sources of energy for colonic epithelial cells to regulate intestinal epithelial function is short-chain fatty acids (SCFAs) produced by Clostridium leptum [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Clostridium leptum ferments polysaccharides through acetyl-CoA and pyruvate pathways to produce propionate and butyrate [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], thereby controlling glucose concentration in the intestinal microenvironment. Butyrate can act on free fatty acid receptor 2 and promote GLP-1 secretion [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Recent studies have shown that the intestinal flora produces some metabolites with weight loss effects in the process of fermenting polysaccharides [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Li et al. found that Clostridium leptum can alleviate obesity by fermenting metabolites produced by FP [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Obesity is considered one of the risk factors for ESCA [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. It is plausible to speculate that this may be one of the mechanisms by which Clostridium leptum can lower the risk of ESCA. In addition, Clostridium leptum plays a significant role in mediating the upregulation of the expansion and differentiation of regulatory Treg cells and recruits them into the intestinal epithelial mucosa [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Treg cells are considered to be associated with ESCA [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The metabolite salicylic acid produced by prausnitzii faecalibacterium as the dominant microbiota in the Clostridium leptum group during the fermentation of salicin can inhibit the secretion of the pro-inflammatory cytokine IL-8 [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Additionally, this type of microbiota can inhibit the upregulation of interleukin-17, thereby mitigating inflammation [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The extracellular polymer matrix (EPM) released by Prausnitzii faecalibacterium stimulates the secretion of IL-10 and IL-12, which can also achieve anti-inflammatory effects [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. These anti-inflammatory properties presumably reduce the incidence of ESCA. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePFOA, one of the four types of polyfluoroalkyl substances (PFASs), is a newly discovered environmental contaminant that can cause health problems as an endocrine disruptor [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Four PFAS (PFOA, perfluorooctane sulfonic acid [PFOS], perfluorohexane sulfonic acid [PFHxS], and perfluorononanoic acid [PFNA]) have been reported to be detected in the serum among individuals over 12 in the United States, with a positive rate of more than 98%, indicating the prevalence of PFOA exposure [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Several studies have elucidated the mechanism of action of PFOA in the development of various types of cancer, such as thyroid cancer, rhabdomyosarcoma, prostate cancer, and breast cancer [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Additionally, high levels of PFOA are strongly associated with ESCA [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhascolarctobacterium is a fecal-phase intestinal bacterium extracted from koala excrement by Del Dot et al [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. This bacterium is classified as gram-negative, pleomorphic rod-shaped cells composed of P. faecium and Phascolarctobacterium succinatutens [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], which has an abundance of more than 2% in the human adult fecal microbial population [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. A recent study has shown that Phascolarctobacterium is widespread in the human gastrointestinal tract and can produce SCFAs, including acetic acid, propionic acid, isobutyric acid, butyric acid, and isovaleric acid [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Phascolarctobacterium stimulates growth by succinic acid and decomposes it into propionic acid [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], which is involved in significant metabolic pathways, such as hepatic gluconeogenesis [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], and can activate anti-inflammatory effects [\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Maintaining intestinal homeostasis is thought to be facilitated by an increase in butyrate [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. An estimated one-fifth of human feces contain Phascolarctobacterium succinatutens, which uses succinate as its sole energy source [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. An elevated abundance of Phascolarctobacterium and Phascolarctobacterium Succinatutens is associated with colorectal cancer growth [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Clinically substantial reductions in Phascolarctobacterium abundance are observed in patients with head and neck cancer [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], while pancreatic and prostate cancer patients exhibit significantly larger abundances compared to healthy controls [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. A bile acid receptor, the G-protein coupled bile acid receptor Gpbar1 (TGR5) is widely distributed in muscles, adipose tissue, immune systems, enteric nervous systems, etc., and plays a crucial role in maintaining energy homeostasis by regulating glucose metabolism and lipid metabolism processes mediated by bile acids [\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Activation of epidermal growth factor receptor EGFR can inhibit the occurrence and progression of pancreatic ductal adenocarcinoma and colorectal cancer, a process that can be prevented by TGR5 [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. TGR5 has recently been reported overexpressed in gastric cancer and activates the oncogenic pathway of gastric cancer cell lines [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Bile acids can modulate the expression of TGR5 in the EAC FLO cell line and the BE BAR-T cell line [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], and possibly affect progression from BE to EAC [\u003cspan additionalcitationids=\"CR88\" citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. One of the bile acid receptors known as the vitamin D receptor (VDR) is overexpressed in precancerous lesions and EAC [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], in addition to being implicated in the formation of malignancies such as liver, colon, and breast cancer[\u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. These findings imply that bile acids may be involved in the early carcinogenesis process through TGR5 and VDR.\u003c/p\u003e \u003cp\u003eBilirubin levels in serum reveal liver dysfunction as a result of chronic viral hepatitis, alcohol consumption, and chemoradiotherapy. The albumin-bilirubin (ALBI) score was proved to be a predictive prognostic factor in patients with EACC, as the 5 years survival rate of in the albumin-bilirubin ratio low group was significantly higher than that in the albumin-bilirubin ratio high group [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn healthy individuals, the colonic mucosal biofilm contains Ruminococcaceae bacteria which are strictly anaerobic [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. SCFAs generated by Ruminococcaceae metabolism can stabilize the homeostasis of the intestinal microenvironment [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Dysfunction of the colonic mucosa frequently coexists with osmotic diarrhea and is generally brought on by a deficiency in SCFAs. [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Butyrate exerts anti-inflammatory effects by upregulating the tight junctions between colonocytes to strengthen the intestinal barrier and prevent lipopolysaccharide (LPS) from being transported into the systemic circulation [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Therefore, the abundance of Ruminococcaceae typically declines in patients with inflammatory bowel diseases such as Crohn's disease or ulcerative colitis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], as well as in patients with inflammatory diseases such as hepatic encephalopathy [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. As an essential lysophosphatidylcholine, 1-Arachidonoyl-GPC hinders the migration of CXCR3\u0026thinsp;+\u0026thinsp;T cells to the inflammatory microenvironment by inhibiting autoimmunity [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. According to the Phe-MR analysis of 655 diseases conducted by Jia et al., 1-arachidonol-gpc supplementation may lead to an increased risk of benign neoplasm of the colon and impaired thyroid function [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. The mechanism of 1-arachidonol-GPC in the prevention of ESCA is still poorly understood, and research on it should be further conducted.\u003c/p\u003e \u003cp\u003eOverall, despite some evidence supporting the association of species Clostridium leptum, genus Phascolarctobacterium, species Phascolarctobacterium succinatutens and family Ruminococcaceae with ESCA, the evidence is apparently inadequate and of relatively low quality. Hence, clinical trials with larger sample sizes, as well as studies of cellular mechanisms are necessary to confirm the health effects and mechanisms underlying these bacteria.\u003c/p\u003e \u003cp\u003eSeveral crucial strengths have been identified in our study. First, our study closes a knowledge vacuum in this area by examining whether gut microbial taxa are causally associated with ESCA through metabolite traits, as no other study has done so yet. Second, to move forward with animal experiments and mechanism research, we analyzed gut microbiota taxa at the species level. Finally, as part of our attempt to uncover the possible mechanisms responsible for the association between gut microbiota and ESCA, we employed a mediating analysis.\u003c/p\u003e \u003cp\u003eHowever, we must acknowledge certain limitations in our research. First, since our analysis was carried out mostly among European populations, the results should be extrapolated with caution since the correlation between the gut microbial taxa and the host genomes may vary based on ethnicity. Second, as the gut microbial taxa of different populations vary considerably in terms of their gut microbiota composition, the sample size of the GWAS summary data may not have been adequate for all potential causal relationships to be revealed. Third, our study identified independent variants associated with gut microbiota traits at the genome-wide significance of P\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, as the criteria applied in the primary GWAS and MR analysis of gut microbiota in other literature. As indicated by large F-statistics, however, genetic instruments were significantly correlated to exposure in this study. At last, the assumptions underlying MR may be untestable, especially if potential confounding variables remain unmeasured. The presence of pleiotropy can be observed when genetic variants independently affect traits other than those investigated. MR-Egger and weighted median approaches do not completely protect findings from pleiotropic effects when used individually, but the consistency of effect estimates derived from multiple sensitivity analyses reinforces the plausibility of true causal effects.\u003c/p\u003e \u003cp\u003eThe findings of this study may aid in facilitating the development of novel strategies to the management and prevention of esophageal cancer from the standpoint of clinical practice. For instance, altering the balance of the gut microbiota and blood metabolites may lessen the possibility of esophageal cancer developing, or it may diminish the severity of existing conditions. This can be accomplished in a variety of ways, including dietary modifications and probiotic supplements. More research is necessary to corroborate these preliminary findings and identify the most effective treatment alternatives, as this field of study is still in its early stages.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe found four gut microbial taxa in our MR analysis that may be causally related to ESCA. Our research offers genetic evidence that alterations in the gut flora could be an essential risk factor for the progression of ESCA, which could be mediated by several human blood metabolites. These findings provide novel perspectives on the pathogenesis of ESCA and propose possible EACA intervention targets. To validate these results and comprehend the underlying mechanisms involved, further investigation is required.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.Y. and Z.Z. contributed to the study conception and design. Data collection and analysis were performed by Z.Z. The first draft of the manuscript was written by X.L. and X.L. and B.X. commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS summary data for gut microbiota from Dutch Microbiome project is available at https://dutchmicrobiomeproject.molgeniscloud.org/; \u0026nbsp;The GWAS summary statistics of human blood metabolites was obtained from GWAS Catalog (https://www.ebi.ac.uk/gwas/); \u0026nbsp;GWAS summary statistics for the esophageal cancer is available at https://www.finngen.fi/en/access results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval and consent to participate were obtained in all original studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide 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\u003cstrong\u003e49\u003c/strong\u003e(2):428-443.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Esophageal cancer, Gut microbiota, Human blood metabolites, Mendelian randomization analysis, Mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-4153773/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4153773/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eUnbalances in the gut microbiota have been proposed as a possible cause of esophageal cancer, yet the exact causal relationship remains unclear. \u003cstrong\u003eObjectives: \u003c/strong\u003eTo investigate the potential causal relationship between the gut microbiota and esophageal cancer with Mendelian randomization (MR) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eGenome-wide association studies (GWAS) of 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) and 205 gut microbiota metabolic pathways conducted by the Dutch Microbiome Project (DMP) and a FinnGen cohort GWASs of esophageal specified the summary statistics. To investigate the possibility of a mediation effect between the gut microbiota and esophageal cancer, mediation MR analyses were performed for 1,091 blood metabolites and 309 metabolite ratios.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMR analysis indicated that the relative abundance of 10 gut microbial taxa was associated with esophageal cancer but all the 12 gut microbiota metabolic pathways with esophageal cancer indicated no statistically significant association existing. Two blood metabolites and a metabolite ratio were discovered to be mediating factors in the pathway from gut microbiota to esophageal cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis research indicated the potential mediating effects of blood metabolites and offered genetic evidence in favor of a causal correlation between gut microbiota and esophageal cancer.\u003c/p\u003e","manuscriptTitle":"Gut microbiota, human blood metabolites and esophageal cancer: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 15:48:27","doi":"10.21203/rs.3.rs-4153773/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eaa9afbc-18bd-4e78-89b3-41ffe207e457","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-07T04:44:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 15:48:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4153773","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4153773","identity":"rs-4153773","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}