Causal associations between gut microbiota and cancers

Causal associations between gut microbiota and cancers | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Causal associations between gut microbiota and cancers View ORCID Profile Zihan Yue , Junwei Yan , Linyuan Shen , Chunguang Zhao , Xiaopeng Yang , Yiying Yao , Dongmei Song , Chenyang Xu , Chenchen Bi , Zhongkui Xiong , Hongli Ma , Zheng Liu doi: https://doi.org/10.1101/2025.02.17.25322359 Zihan Yue a Department of Nursing, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China b Department of Pharmacology, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zihan Yue Junwei Yan c Department of Blood Transfusion, Affiliated Hospital of Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Linyuan Shen a Department of Nursing, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chunguang Zhao a Department of Nursing, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xiaopeng Yang b Department of Pharmacology, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yiying Yao b Department of Pharmacology, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dongmei Song d Zhejiang Chint Electrics Co.,Ltd , Wenzhou, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chenyang Xu a Department of Nursing, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chenchen Bi b Department of Pharmacology, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zhongkui Xiong e Department of Radiotherapy, Shaoxing Sencond Hospital , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: mahongli1007{at}163.com liuzheng1707{at}163.com Hongli Ma a Department of Nursing, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China f Department of Nursing, Shaoxing People’s Hospital , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: mahongli1007{at}163.com liuzheng1707{at}163.com Zheng Liu b Department of Pharmacology, School of Medicine, Shaoxing University , Shaoxing, Zhejiang, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: mahongli1007{at}163.com liuzheng1707{at}163.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Emerging evidence suggests that the gut microbiota is associated with various cancer-related outcomes. Recent studies using Mendelian randomization (MR) have indicated a causal relationship between gut microbiota and several types of cancer. However, these conclusions remain controversial. To clarify this relationship, we conducted a systematic review and meta-analysis of MR studies to investigate the potential causal links between gut microbiota and cancer. Method In this study, we searched PubMed, Embase, the Cochrane Library, and Web of Science up to May 2024 for eligible MR studies and performed meta-analyses. Results Our findings indicate that genetically determined gut microbiota is associated with 12 different types of neoplastic outcomes. We identified nine genera of gut microbiota that are generally associated with a reduced risk of developing cancer. However, we also found that the order Mollicutes RF9 (OR = 1.037, 95% CI 1.013-1.060) may be a potential risk factor for breast and lung cancer. Furthermore, we discovered that the families Alcaligenaceae (OR = 1.034, 95% CI 1.000-1.068), Enterobacteriaceae (OR = 1.077, 95% CI 1.020-1.135), and Lactobacillaceae (OR = 1.033, 95% CI 1.008-1.059) are negatively associated with the risk of breast cancer. On the other hand, the genetically predicted family Lactobacillaceae (OR = 0.892, 95% CI 0.848-0.937), along with other families and genera such as Veillonellaceae, Coprococcus, Dorea, Eubacterium, Lachnospiraceae, Ruminococcus, Ruminococcaceae, and FamilyXIII, were found to be correlated with a lower risk of lung cancer. Additionally, the family Peptostreptococcaceae, Veillonellaceae, Streptococcaceae, and certain genera including Eubacterium, Lachnospiraceae, and Ruminococcaceae, were identified as protective factors against glioblastoma. The evidence from published MR studies supports the notion that the gut microbiota plays a causal role in various neoplastic diseases. Conclusions In conclusion, there is a causal relationship between gut microbes and cancer, and it is related to microbial species and tumor type. Further research is necessary to understand the underlying mechanisms and to explore the potential for using gut microbiota in prediction, prevention, and therapeutic strategies for these diseases. Studying changes in the microbiome in cancer has significant implications for developing noninvasive diagnostic tools and innovative interventions that could alter the progression of these diseases. Introduction The human gut is home to approximately 3.8 trillion microorganisms, collectively weighing around 1.8 kilograms, and are collectively known as the gut microbiota. These microorganisms play a crucial role in maintaining health and physiological balance, influencing metabolism and modulating the immune system 1 . Recent research has highlighted the significant impact of the gut microbiota on cancer development, with different types of tumors potentially harboring unique microbial communities 2 . The gut microbiota within the tumor microenvironment is believed to actively contribute to cancer development and progression through various mechanisms, including promoting cell proliferation, evading growth-inhibiting mechanisms, resisting apoptosis, facilitating angiogenesis, and subverting immune surveillance 3 . Advancements in preclinical research have revealed the complex pathways through which microbes can influence the effectiveness of cancer treatments. This has been further supported by clinical trials, which have underscored the therapeutic potential of modulating the microbiota in oncology 4 . The intricate relationship between the gut microbiota and various cancer therapies—ranging from chemotherapy 5 , and radiotherapy 6 , to targeted therapy 7 and immunotherapy 8 is now a well-documented factor in the field. Unlike the fixed nature of host genetics, the adaptability of the microbiota offers a unique advantage. It can be modified through a range of strategies, including fecal microbiota transplantation (FMT), the use of probiotics, and targeted antibiotic therapies. This adaptability is paving the way for a more personalized approach to cancer treatment, marking a significant shift towards tailored therapies in the realm of personalized medicine 4 . Most previous research conclusions regarding the relationship between gut microbiota and cancers have been based on observations of the microbiota’s composition and changes in patients’ fecal samples, as well as the outcomes of trials involving the transplantation of gut microbiota into gnotobiotic mice 9 – 12 . However, the causal relationship between cancer and microbiota remains to be fully elucidated. MR is an instrumental variable analysis that mitigates bias from reverse causality 13 and addresses major limitations inherent in observational studies, such as unmeasured confounding, ascertainment bias, and small sample sizes 14 , 15 . It serves to test the causal relationship between the microbiota and cancer risk 16 . However, several MR studies have presented contradictory conclusions, particularly when characterizing the association between microorganisms and tumors. Therefore, to thoroughly evaluate and synthesize the evidence concerning the causal role of gut microbiota in various cancers, we undertook a systematic review and meta-analysis of published MR studies. Investigating the specific microbiota associated with different cancers offers novel insights and strategies for cancer prevention, treatment, and health management. Methods Search strategy and selection criteria We conducted an exhaustive literature search in the Cochrane, PubMed, Embase, and Web of Science databases from inception through May 31, 2024, to identify all MR studies exploring the association between genetically determined gut microbiota and cancer outcomes. The search terms included: “Gastrointestinal Microbiomes,” “Gut Microbiome,” “Intestinal Microbiota,” or “Microbiome” for the exposure; “Tumor,” “Neoplasm,” or “Cancer” for the outcome; and “Mendelian randomization” for the study design. The detailed search strategy is presented in the Supplementary Method. Additionally, we identified potentially eligible studies through the reference lists of included studies and relevant reviews. Inclusion criteria for studies were: (1) utilization of MR; (2) use of gut microbiota as the exposure variable; and (3) measurement of the causal association between the exposure and one or more cancer outcomes. Studies were excluded if they met any of the following criteria: (1) non-human studies; (2) conference abstracts, editorials, or reviews; (3) those not written in English or Chinese; or (4) if the full text was not available. In cases where multiple studies reported the same outcome in the same population, we prioritized the study with the largest participant sample. After the literature search, all articles were imported into NoteExpress, and duplicate records were eliminated. Two independent reviewers assessed the articles based on the predefined inclusion and exclusion criteria, with any disagreements resolved by a third reviewer. The selection process began with titles and abstracts, followed by a full-text review. Data extraction and quality assessment Data extracted from each study included the surname of the first author and the publication year. We also recorded the consortium or study that provided the genetic variants for gut microbiota exposure and the one that supplied the genetic association estimates for cancers. Additionally, we noted the sample size, defined as the number of cases and non-cases. We extracted the relative risk estimate (reported as the odds ratio [OR]) along with the corresponding 95% confidence interval (CI) for the association of gut microbiota. One investigator was responsible for extracting the data, and four other investigator verified the accuracy of these data. Two researchers assessed the quality of the research by adopting a modified version of the Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomization (STROBE-MR) guidelines. The guidelines were adapted based on articles reporting quality assessment approaches used to document MR studies. The results from the evaluation of study quality are provided in Additional file 1: Table S1. Statistical analysis Stata (version 17.0, Stata Corporation) software was utilized to conduct the meta-analysis of the included MR associations. The odds ratios (ORs) per 1 standard deviation (SD) increase in gut microbiota, with their respective 95% CIs, were pooled for each cancer outcome using a random-effects model. Results Literature search and study selection The literature search yielded 223 records, of which 71 original articles reported findings on genetic liability to gut microbiota in relation to one or more specific outcomes. After excluding studies that used overlapping or identical outcome data, 58 articles based on non-overlapping populations were deemed eligible for inclusion in one or more meta-analyses 17 – 30 . Figure 1 provides a summary of the study selection process and the designs of the included studies. Download figure Open in new tab Figure 1 Flow chart of the systematic review and meta-analysis. Causal relationship between cancers and gut microbiota at phylum, class and order level At the phylum level, we identified nine associations with cancers. Through meta-analysis, we combined these findings and determined that the phyla Proteobacteria (OR = 0.963, 95% CI 0.935-0.990), Tenericutes (OR = 0.968, 95% CI 0.941-0.996), and Verrucomicrobia (OR = 0.983, 95% CI 0.969-0.997) were associated with a reduced risk of cancer. However, we found no significant associations between the other phyla and cancer. Under the phylum Proteobacteria, classes Alphaproteobacteria (OR = 0.977, 95% CI 0.964-0.990), Gammaproteobacteria (OR = 0.967, 95% CI 0.939-0.995) and the order Rhodospirillales (OR = 0.963, 95% CI 0.937-0.989) were associated with a decreased risk of cancers. Under the phylum Tenericutes, the class Mollicutes (OR = 0.967, 95% CI 0.938-0.995) was associated with a decreased risk of cancers. However, the order MollicutesRF9 (OR = 1.037, 95% CI 1.013-1.060) was associated with an increased risk of cancers. Under the phylum Verrucomicrobia, the class Verrucomicrobiae (OR = 0.983, 95% CI 0.972-0.994) and the order Verrucomicrobiales (OR = 0.988, 95% CI 0.980-0.997) were associated with a decreased risk of cancers. (See Figure 2 ) Download figure Open in new tab Figure 2 Cladogram of odds ratio (OR) of the association between gut microbiome and the risk of cancer outcomes. From the inner ring to the outer ring are the kingdom, phylum, class, order, family and genus. Since we have explored some meaningful results in phyla, classes, and orders of taxa respectively, we continue to explore the levels of intestinal flora at the family and genus levels. However, due to the large amount of data, we analyzed the top ten families and genera with the highest data volume, respectively. Interestingly, most of the families and genera are concentrated in the order Clostridiales under the Firmicutes taxa. Under the order Clostridiales, the family Ruminococcaceae (OR = 0.997, 95% CI 0.995-0.998) was associated with a decreased risk of cancers. Among them, Ruminococcaceae is derived from the aggregate effect of its subtaxa. Under other orders, the families Alcaligenaceae.id.2875 (OR = 0.963, 95% CI 0.937-0.989) and Lactobacillaceae.id.1836 (OR = 0.948, 95% CI 0.921-0.975) were associated with a decreased risk of cancers. Under the family Eubacterium taxa, the genera Eubacterium brachy group.id.11296 , Eubacterium hallii group.id.11338 , Eubacterium rectale group.id.14374 , Eubacterium ruminantium group.id.11340 , and Eubacterium xylanophilum group.id.14375 were associated with a decreased risk of cancers. Under the family Hungateiclostridiaceae taxa, the genus Ruminiclostridium 6.id.11356 was associated with a decreased risk of cancers, while the genus Ruminiclostridium 9 .id.11357 was associated with an increased risk of cancers. Under the family Lachnospiraceae taxa, several genera including Coprococcus 2.id.11302 , Coprococcus 3.id.11303 , Dorea.id.1997 , Lachnospiraceae FCS020 group.id.11314 , Lachnospiraceae NK4A136 group.id.11319 , Lachnospiraceae UCG001.id.11321 , and Lachnospiraceae UCG004.id.11324 were associated with a decreased risk of cancers. The genus Lachnospiraceae UCG010.id.11330 was associated with an increased risk of cancers. Under the family Ruminococcaceae taxa, several genera including Ruminococcaceae NK4A214 group.id.11358 , Ruminococcaceae UCG004.id.11362 , Ruminococcaceae UCG005.id.11363 , Ruminococcaceae UCG011.id.11368 , Ruminococcaceae UCG014.id.11371 , Ruminococcus 2.id.11374 , and Ruminococcus gauvreauii group.id.11342 were associated with a decreased risk of cancers. The genus Ruminococcaceae UCG010.id.11367 was associated with an increased risk of cancers. Under other family taxa, the Family XIII AD3011 group .id.11293 and Adlercreutzia.id.812 were associated with a decreased risk of cancers. Because this is a causal study that excludes confounding factors, even though the taxa are related on the branch plot, they are all statistically independent. (See Figure 2 ) Causal effects of gut microbiota in the family and genera level on the ten cancer types At the phylum level, high heterogeneity between studies was only observed in the analyses of the phyla Actinobacteria and Verrucomicrobia. We analyzed them in subgroups by cancer type and found that the heterogeneity of Actinobacteria was mainly concentrated in thyroid cancer and digestive cancers, significantly reducing other heterogeneity. It was found to be a protective factor against digestive cancers (OR = 0.81, 95% CI 0.625, 0.996), thyroid cancer (OR = 0.634, 95% CI 0.470, 0.799), gynecological cancers (OR = 0.998, 95% CI 0.997, 0.999), and lymphoma (OR = 0.819, 95% CI 0.656, 0.981). This result may indicate insufficient data volume, necessitating further analysis. In Verrucomicrobia, heterogeneity was significantly reduced, and it was found to be a protective factor against lymphoma (OR = 0.765, 95% CI 0.632, 0.897) and lung cancers (OR = 0.862, 95% CI 0.796, 0.928), but a risk factor for glioblastoma (OR = 1.268, 95% CI 1.07, 1.466). (Table 2) To further analyze the causal relationship between gut microbiota and specific cancers, we selected the top ten families and genera based on data volume for further analysis and found that different cancers had closer relationships with specific families and genera. ( Figure 3 ) Download figure Open in new tab Figure 3 Forest plot of meta-analysis results of the association between gut microbiome and risk of cancer. The effect on the x-axis is the odds ratio of cancer per 1 standard deviation change in the exposure. Download figure Open in new tab Figure 4 Heatmap of the ORs. Color in the heatmap indicates the OR of gut metabolite and cancers. Darker red color indicates higher OR values. The asterisk indicates statistically significant OR. BCC, Basal Cell Carcinoma; BRC, Breast Cancer; DC, Digestive Cancers; GBM, Glioblastoma; GC, Gynecological cancer; LC, Lung Cancer; LYM, Lymphoma; MM, Multiple Myeloma; TC, Thyroid Cancer; UC, Urinary Cancers Basal Cell Carcinoma The genus Ruminococcaceae (OR = 0.887, 95% CI 0.786-0.988) and genus Ruminiclostridium (OR = 0.864, 95% CI 0.744-0.984) were associated with a decreased risk of basal cell carcinoma. Conversely, the genus Family XIII (OR = 1.124, 95% CI 1.001-1.247) and genus Turicibacter (OR = 1.178, 95% CI 1.064-1.291) were associated with an increased risk of basal cell carcinoma. Breast Cancer The family Streptococcaceae.id.1850 (OR = 0.929, 95% CI 0.899-0.959), the genus Adlercreutzia (OR = 0.907, 95% CI 0.866-0.949), genus Dorea (OR = 0.911, 95% CI 0.871-0.951), genus FamilyXIII (OR = 0.970, 95% CI 0.945-0.995), genus Lachnospiraceae (OR = 0.976, 95% CI 0.966-0.987), genus Ruminococcus (OR = 0.968, 95% CI 0.950-0.986), genus Ruminococcaceae (OR = 0.987, 95% CI 0.982-0.991) and genus Ruminiclostridium (OR = 0.965, 95% CI 0.947-0.983) were associated with a decreased risk of breast cancer. Conversely, the family Alcaligenaceae.id.2875 (OR = 1.034, 95% CI 1.000-1.068), the family Enterobacteriaceae.id.3469 (OR = 1.077, 95% CI 1.020-1.135) and family Lactobacillaceae.id.1836 (OR = 1.033, 95% CI 1.008-1.059) were associated with an increased risk of breast cancer. Digestive cancers The family Enterobacteriaceae.id.3469 (OR = 0.786, 95% CI 0.637-0.935), family Peptostreptococcaceae.id.2042 (OR = 0.818, 95% CI 0.711-0.925), and genus Dorea (OR = 0.756, 95% CI 0.582-0.929) were associated with a decreased risk of digestive cancers. The family Bifidobacteriaceae.id.433 (OR = 1.001, 95% CI 1.000-1.002) was associated with an increased risk of digestive cancers. Glioblastoma The family Peptostreptococcaceae.id.2042 (OR = 0.742, 95% CI 0.638-0.846), family Veillonellaceae.id.2172 (OR = 0.389, 95% CI −0.187-0.965), family Streptococcaceae.id.1850 (OR = 0.231, 95% CI −0.230-0.692), the genus Eubacterium (OR = 0.455, 95% CI 0.216-0.693), genus Lachnospiraceae (OR = 0.238, 95% CI 0.018-0.459) and genus Ruminococcaceae (OR = 0.500, 95% CI 0.255-0.745) were associated with a decreased risk of glioblastoma. Gynecological cancers The genus Coprococcus (OR = 0.761, 95% CI 0.655-0.867), genus Dorea (OR = 0.735, 95% CI 0.667-0.804), genus Family XIII (OR = 0.799, 95% CI 0.745-0.854) and genus Turicibacter (OR = 0.842, 95% CI 0.789-0.896) were associated with a decreased risk of gynecological cancers. Lung Cancer The family Lactobacillaceae.id.1836 (OR = 0.892, 95% CI 0.848-0.937), family Veillonellaceae.id.2172 (OR = 0.878, 95% CI 0.830-0.926), the genus Coprococcus (OR = 0.850, 95% CI 0.800-0.900), genus Dorea (OR = 0.856, 95% CI 0.761-0.951), genus Eubacterium (OR = 0.908, 95% CI 0.884-0.933), genus Lachnospiraceae (OR = 0.937, 95% CI 0.909-0.964), genus Ruminococcus (OR = 0.824, 95% CI 0.783-0.866), genus Ruminococcaceae (OR = 0.913, 95% CI 0.890-0.937) and genus Family XIII (OR = 0.923, 95% CI 0.856-0.990) were causally associated with lung cancer. However, the OR values were all less than 1, suggesting a potential tumor suppressor effect. Conversely, the family Enterobacteriaceae.id.3469 (OR = 1.267, 95% CI 1.112-1.423) was associated with an increased risk of lung cancer. Lymphoma The genus Coprococcus (OR = 0.775, 95% CI 0.669-0.881), the genus Dorea (OR = 0.737, 95% CI 0.480-0.994), genus Eubacterium (OR = 0.955, 95% CI 0.915-0.996), genus Family XIII (OR = 0.626, 95% CI 0.522-0.729), genus Lachnospiraceae (OR = 0.878, 95% CI 0.826-0.930), genus Ruminococcus (OR = 0.840, 95% CI 0.776-0.903), genus Ruminococcaceae (OR = 0.818, 95% CI 0.771-0.864) and genus Turicibacter (OR = 0.683, 95% CI 0.585-0.781) were associated with a decreased risk of lymphoma. Oropharyngeal Cancer The genus Dorea (OR = 1.004, 95% CI 1.001-1.007) and genus Ruminococcaceae (OR = 1.004, 95% CI 1.001-1.007) were associated with an increased risk of oropharyngeal cancer. Thyroid Carcinoma The family Bifidobacteriaceae.id.433 (OR = 0.881, 95% CI 0.806-0.956), family Oxalobacteraceae.id.2966 (OR = 0.945, 95% CI 0.893-0.997), family Peptostreptococcaceae.id.2042 (OR = 0.919, 95% CI 0.846-0.992), family Streptococcaceae.id.1850 (OR = 0.772, 95% CI 0.699-0.845), family Porphyromonadaceae.id.943 (OR = 0.839, 95% CI 0.715-0.964), the genus Coprococcus (OR = 0.918, 95% CI 0.860-0.977), genus Eubacterium (OR = 0.913, 95% CI 0.891-0.935) and genus Family XIII (OR = 0.836, 95% CI 0.771-0.900) were associated with a decreased risk of thyroid carcinoma. The genus Dorea (OR = 1.127, 95% CI 1.011-1.243) was associated with an increased risk of thyroid carcinoma. Urinary cancers The genus Adlercreutzia (OR = 0.883, 95% CI 0.857-0.910), the genus Coprococcus (OR = 0.630, 95% CI 0.405-0.855) and genus Lachnospiraceae (OR = 0.740, 95% CI 0.560-0.920) were associated with a decreased risk of urinary cancers. The family Porphyromonadaceae.id.943 (OR = 1.082, 95% CI 1.017-1.146) and genus Eubacterium (OR = 1.037, 95% CI 1.001-1.073) were associated with an increased risk of urinary cancers. Discussion In this systematic review and meta-analysis of MR studies, we have conducted the most extensive analysis to date, encompassing 58 studies and 22,818 data. Utilizing taxonomical methods, we have delved into the causal relationship between gut microbiota and the spectrum of cancers. Our findings reveal that the phyla Proteobacteria, Tenericutes, and Verrucomicrobia exert protective effects against cancer, while the order MollicutesRF9.id.11579 emerges as a potential risk factor for breast and lung cancer. Furthermore, we identified the family Alcaligenaceae, Lactobacillaceae , and genera Adlercreutzia, Dorea, Eubacterium brachy group , Lachnospiraceae FCS020 group, and Ruminococcaceae UCG004 as being associated with a reduced cancer risk. Expanding our investigation to the phylum level, Actinobacteria demonstrated a protective role in digestive, thyroid, gynecological cancers, and lymphoma. Intriguingly, Verrucomicrobia showed a protective effect for lung cancer and lymphoma, yet not for glioblastoma, underscoring the nuanced influence of microbial taxa on cancer risk. At the familial and generic levels, our analysis uncovered a more complex picture. The family Peptostreptococcaceae was identified as a risk factor for gynecological cancers but offered protection against digestive cancers, glioblastoma, and lymphoma. Similarly, the genus Turicibacter presented as a risk for basal cell carcinoma and multiple myeloma, yet it was protective for gynecological cancers and lymphoma. Notably, the family Enterobacteriaceae , while a risk factor for breast and lung cancers, paradoxically acts as a protective factor against digestive cancers. Moreover, the families Lactobacillaceae, Veillonellaceae , and the genus Eubacterium , along with Lachnospiraceae, Ruminococcaceae, Ruminococcus, Coprococcus, Dorea , and Turicibacter , were found to be protective against lung cancer. In contrast, Enterobacteriaceae was identified as a risk factor for this disease. In our comprehensive analysis, the phylum Proteobacteria has emerged as a significant factor in cancer prognosis. Our findings indicate that higher levels of Proteobacteria in pancreatic tumors are associated with extended survival times, positing these bacteria as potentially beneficial in pancreatic cancer contexts 31 , This correlation is mirrored in ovarian cancer, where an increased presence of Proteobacteria is also noted 32 . In the context of pancreatic ductal adenocarcinoma, Proteobacteria may contribute to immune evasion by upregulating immunosuppressive cytokines such as IL-10, which can diminish type 1 helper T cell activity and drive tumor-associated macrophages toward an M2 phenotype, thereby fostering a more immunosuppressive tumor microenvironment 33 . Although preliminary experimental evidence points to a role for gut microbiota in tumor promotion and development 34 , the specific mechanisms through which this occurs have yet to be fully established. Further research is essential to uncover the detailed mechanisms by which Proteobacteria could influence host immune responses and cancer treatment efficacy. The potential mechanisms we propose for investigation include: (1) Microbial Ecosystem Effects: The influence of Proteobacteria on the gut microbiota’s composition and function 35 , (2) Intestinal Barrier Modulation: Impacts on the intestinal epithelium and associated lymphoid tissues, potentially via autophagy and apoptosis induction 36 , 37 , (3)Pattern-Recognition Receptor Activation: The stimulation of immune receptors by microbial components 38 , (4) Neuroendocrine System Interactions: The influence on neuroendocrine pathways through gut hormone secretion 39 , (5) Systemic Metabolic Impact: The role in systemic metabolism via the synthesis of polyamines and B vitamins 40 , and (6) Cross-Reactive Immune Responses: The potential to induce immune reactions against microbial antigens that mimic tumor-associated antigens 41 , 42 . These insights underscore the importance of exploring the microbiota’s mechanistic links to cancer. Such knowledge is vital for the development of microbiota-targeted therapeutic strategies that could enhance the efficacy of cancer treatments by leveraging the protective effects of Proteobacteria. Our MR analysis has identified the phylum Actinobacteria as a protective factor against a range of cancers, including those of the digestive system, thyroid, gynecological organs, and lymphoma. This is further supported by previous findings highlighting a high prevalence of Actinobacteria in breast cancer tissue samples 43 . Notably, the family Bifidobacteriaceae appears to offer protection against digestive and thyroid cancers. Variations in Bifidobacterium levels have been linked to different clinical stages of breast cancer, suggesting a role for the microbiome in disease progression 44 . Consistent with this, elevated Bifidobacterium levels have been observed in patients with colorectal adenomas and in mouse models of advanced pancreatic cancer 33 , 45 . Moreover, Bifidobacterium has been correlated with improved responses to anti-PD-L1 therapies 46 . However, it is important to note that while generally beneficial, certain Bifidobacterium species may act as potential pathogens, indicating a complex relationship with human health 47 , 48 . Ruminococcus, an early-identified gut bacterium with key metabolic functions, including the breakdown of cellulose, has been identified as a protective factor in breast, lung, and lymphoma. Beyond its metabolic role, Ruminococcus contributes to intestinal barrier stabilization, diarrhea prevention, reduction in kidney stone formation, and decreased risk of colorectal cancer 49 . Conversely, the family Peptostreptococcaceae presents a risk for gynecological cancers but interestingly, acts protectively against digestive cancers, glioblastoma, and lymphoma 17 .These findings underscore the intricate and sometimes paradoxical nature of microbial associations with cancer, emphasizing the need for a nuanced understanding of the microbiome’s role in cancer development and progression. In summary, the gut microbiota’s diverse species exert variable influences on the tumor microenvironment, with potential to be either beneficial or detrimental to health 50 . Our review indicates that the compositional differences in gut microbiota between healthy individuals and those with cancer may not solely indicate causation, but could also be a bystander effect or even contribute to carcinogenesis 51 . Diet significantly shapes the gut microbiota, thereby indirectly influencing cancer risk 52 . Strategically, probiotics offer a practical avenue for modulating the gut microbiota 53 . Prebiotics, which are non-living substances, and probiotics, which are live bacteria, can enhance the growth and activity of beneficial species. For instance, probiotic supplementation with Bifidobacterium lactis and Lactobacillus acidophilus in colorectal cancer (CRC) patients has been shown to increase butyrate production and reduce CRC-promoting bacteria 54 .Such interventions can regulate inflammation, bolster immune responses, and enhance antitumor immunity 55 . Furthermore, the combination of immunotherapy with probiotics represents a promising research frontier, allowing for the development of targeted treatments that leverage the synergistic effects of multiple microorganisms 2 . This study’s strength lies in its systematic review and meta-analysis of MR studies, which provides a robust investigation into the causal links between gut microbiota and cancer. However, it is not without limitations. The analysis was conducted at the genus level, which does not capture species or strain-level nuances. Additionally, the heterogeneity among the studies included could introduce some bias into our estimates of causality. Lastly, future research should explore the relationship between specific gut microbiota and various cancer subtypes to build upon these findings and advance cancer prevention and treatment strategies. Conclusion In conclusion, this study delivers a sweeping evaluation of the potential causal links between the gut microbiota and the pan-cancer risk landscape. Our findings not only shed new light on the etiology of cancer but also set the stage for leveraging the gut microbiome as a biomarker, heralding a new era in personalized cancer therapy and patient prognosis. By elucidating how the gut microbiota influences tumor onset and progression, our research provides substantial evidence that could facilitate the incorporation of microbiome assessments into cancer management strategies. Early detection through fecal testing could identify high-risk individuals, while microbiota modulation presents a promising preventive and therapeutic avenue. Looking ahead, the integration of gut microbiota analysis into routine cancer surveillance, diagnostics, and therapeutics is poised to transform cancer care, enhancing both its efficacy and personalization. Data Availability All data produced in the present study are available upon reasonable request to the authors All data produced in the present work are contained in the manuscript All data produced are available online at Author Contributions All authors were involved in the conception of this review, Zihan Yue: collected data, interpreted the results, wrote the manuscript and drew the figures. Zheng Liu: designed and supervised the study and revised the manuscript. Hongli Ma: checked and reviewed the manuscript. Junwei Yan: interpreted the results and revised the manuscript. Dongmei Song: drew the figures. Linyuan Shen, Chunguang Zhao, Xiaopeng Yang and Yiying Yao: assisted data collection and checked data. Chenyang Xu, Chenchen Bi, Zhongkui Xion: assisted data collection. Disclosure of potential conflicts of interest No potential conflict of interest was reported by the authors. Funding Statement This study was supported by grants from the Program for Cultivation of New Medical Talents of Zhejiang Provence (no. zheweifa 2015-70) and the Science and Technology Project about Social Development of Keqiao District, Shaoxing City (no. 2022KZ17), and Shaoxing University Enterprise Important Horizontal Topic (No. 2024USXH287). View this table: View inline View popup References 1. ↵ Panebianco C , Andriulli A , Pazienza V . 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Share Causal associations between gut microbiota and cancers Zihan Yue , Junwei Yan , Linyuan Shen , Chunguang Zhao , Xiaopeng Yang , Yiying Yao , Dongmei Song , Chenyang Xu , Chenchen Bi , Zhongkui Xiong , Hongli Ma , Zheng Liu medRxiv 2025.02.17.25322359; doi: https://doi.org/10.1101/2025.02.17.25322359 Share This Article: Copy Citation Tools Causal associations between gut microbiota and cancers Zihan Yue , Junwei Yan , Linyuan Shen , Chunguang Zhao , Xiaopeng Yang , Yiying Yao , Dongmei Song , Chenyang Xu , Chenchen Bi , Zhongkui Xiong , Hongli Ma , Zheng Liu medRxiv 2025.02.17.25322359; doi: https://doi.org/10.1101/2025.02.17.25322359 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Oncology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5432) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ff4b03ef98cc13d',t:'MTc3OTM3ODExOQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();