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Learn more: PMC Disclaimer | PMC Copyright Notice J Ovarian Res . 2026 Mar 5;19:145. doi: 10.1186/s13048-026-01963-9 Search in PMC Search in PubMed View in NLM Catalog Add to search Genetically predicted inflammatory cytokines mediate the associations between the gut microbiota and ovarian cancer: a bidirectional two-sample Mendelian randomization study Lili Chuan Lili Chuan 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China Find articles by Lili Chuan 1, # , Bo Yao Bo Yao 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China 2 Department of General Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou China Find articles by Bo Yao 1, 2, # , Haiming Zhang Haiming Zhang 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China Find articles by Haiming Zhang 1 , Shasha Luo Shasha Luo 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China Find articles by Shasha Luo 1 , Guangliang Lu Guangliang Lu 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China Find articles by Guangliang Lu 1 , Ying Wu Ying Wu 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China Find articles by Ying Wu 1 , Yanping Ren Yanping Ren 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China Find articles by Yanping Ren 1, ✉ Author information Article notes Copyright and License information 1 Department of Histology and Embryology, School of Preclinical Medicine, Zunyi Medical University, No.1 Xiaoyuan Road, Zunyi, Guizhou 563000 China 2 Department of General Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou China ✉ Corresponding author. # Contributed equally. Received 2025 Apr 17; Accepted 2026 Jan 7; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13072565 PMID: 41787531 Abstract Background Ovarian cancer (OC), the deadliest gynecologic malignancy, lacks effective strategies for early detection and treatment. Emerging evidence suggests that the gut microbiota and inflammatory cytokines may influence OC pathogenesis, but the causal mechanisms remain unclear. This study employed a bidirectional, two-sample Mendelian randomization (MR) analysis with genetic mediation analysis to investigate whether genetically predicted inflammatory cytokines mediate the associations between the gut microbiota and OC. Methods This study was based on an analysis of public genetic summary statistics from predominantly European populations. The data from a genome-wide association study (GWAS) and public databases included 473 gut microbiotas (FinnGen, n = 5,959), 91 inflammatory cytokines (GWAS, n = 14,824), and 1,218 OC cases (UK Biobank). Inverse-variance weighted (IVW) was the primary MR method, supplemented by multiple methods such as MR-Egger and other sensitivity analyses to evaluate the robustness of findings. Single-nucleotide polymorphisms (SNPs) associated with exposure were selected as instrumental variables (IVs). Mediation analysis was conducted to explore the potential mediation effect of inflammatory cytokines. Results Our analysis identified 19 gut microbiotas (9 risk, 10 protective) and 5 inflammatory cytokines (4 risk, 1 protective) with associations suggestive of causality with OC under MR assumptions (P FDR <0.05). Mediation analysis revealed that interleukin-6 (IL-6) mediated 14.67% and 16.63% of the effects of Bacillaceae A and Prevotella sp000434975 on OC risk, respectively. Similarly, T-cell surface glycoprotein CD6 and leukemia inhibitory factor receptor (LIFR) accounted for 6.06% and 11.16% of the mediating effects for Bacillaceae A and Gluconobacter , respectively. Reverse MR suggested potential bidirectional interactions, with OC being associated with alterations in 18 gut microbiotas and 3 inflammatory cytokines. Conclusion This MR study provides genetic evidence suggestive of a potential mechanism whereby the gut microbiota might influence OC risk partly through inflammatory cytokines, notably IL-6, CD6, and LIFR. These findings suggest these cytokines as potential risk markers and implicate pathways involving both microbiota modulation and cytokine activity. Supplementary Information The online version contains supplementary material available at 10.1186/s13048-026-01963-9. Keywords: Gut microbiota, Ovarian cancer, Inflammatory cytokines, Mendelian randomization, Mediation analysis Introduction Ovarian cancer (OC) is the gynecological malignancy with the highest mortality rate, with approximately 324,000 new cases and 207,000 deaths annually worldwide [ 1 , 2 ]. It represents a group of heterogeneous malignancies originating from diverse cell types within the ovary [ 2 , 3 ], all of which are included in this study. Approximately 90% of ovarian cancers are epithelial malignancies, with 70% to 80% of these being high-grade serous ovarian carcinoma. Other epithelial subtypes such as endometrioid, clear cell, low-grade serous, mucinous, and carcinosarcoma are relatively uncommon [ 2 – 4 ]. Each subtype exhibits distinct molecular characteristics and clinical behaviors [ 2 – 4 ]. The remaining 10% are non-epithelial ovarian cancers, such as stromal tumors and germ cell tumors [ 2 – 4 ]. The heterogeneity of OC, combined with the absence of specific early symptoms and effective screening methods, leads to most patients being diagnosed at an advanced stage, thereby complicating treatment and resulting in an unfavorable prognosis [ 3 – 5 ]. Although standard treatments, including cytoreductive surgery and platinum-based chemotherapy, have improved outcomes for some patients, and the introduction of targeted therapies, particularly poly(ADP-ribose) polymerase (PARP) inhibitors, has opened new avenues for precision treatment, the overall survival of OC patients remains low due to drug resistance and disease relapse [ 2 , 6 – 9 ]. Risk prediction also faces challenges: established approaches such as genetic susceptibility models based on BRCA1/2 and screening strategies using CA-125 and transvaginal ultrasound have limited value in the general population and have not significantly reduced mortality [ 2 , 9 – 12 ]. Meanwhile, although machine learning (ML) and artificial intelligence (AI) show potential for improving diagnostic accuracy and risk stratification, their clinical translation remains challenging [ 12 – 14 ]. Therefore, elucidation of novel pathogenic mechanisms and modifiable risk factors is essential to improve early detection and develop innovative prevention and treatment strategies. The human gut is a “secret garden” of trillions of bacterial symbionts, collectively known as the “microbiota,” which vastly outnumber the somatic and germline cells of our bodies, and the collective genes of the microbiota, known as the “microbiome,” are 150 times larger than those of the human genome [ 15 , 16 ]. Changes in the composition of the microbiota can affect the relationship between the microbiota and its host, and many studies in recent years have highlighted the role of the gut microbiota in gynecological tumors, including OC, which is related not only to the development of OC but also to subsequent treatment and efficacy [ 17 – 20 ]. However, research on the underlying causal mechanisms and specific pathways between the gut microbiota and OC development remains limited. Furthermore, the gut microbiota plays an important role in modulating both local and systemic inflammation. Changes in the gut microbiota affect the immune system, often accompanied by changes in inflammatory cytokines [ 21 ]. Inflammation, while a biological response aimed at restoring homeostasis, can also promote multiple stages of tumorigenesis, with inflammatory cytokines serving as key mediators of these processes [ 22 ]. Therefore, we hypothesize that genetically predicted inflammatory cytokines mediate the causal relationship between the gut microbiota and OC. To test this hypothesis, we applied Mendelian randomization (MR), a genetic epidemiological method that uses genetic variants associated with an exposure as instrumental variables to infer causal relationships with an outcome, thereby reducing confounding and reverse causation [ 23 ]. When examining mechanisms, mediation analysis within an MR framework can decompose the total effect of an exposure on an outcome into direct and indirect effects mediated through an intermediate factor [ 24 ]. This approach allows us to explicitly test whether and which inflammatory cytokines mediate the pathway from the gut microbiota to OC risk. A conceptual overview of our hypothesized causal pathways is presented in Fig. 1 . Our study follows the STROBE-MR guidelines [ 25 ]. Fig. 1. Open in a new tab Schematic overview of the Mendelian randomization (MR) and mediation analysis design. A The three key assumptions for a valid two-sample MR study are illustrated: (1) Relevance: Genetic instruments (SNPs) are strongly associated with the exposure (gut microbiota or inflammatory cytokines); (2) Independence: SNPs are not associated with any known or unknown confounders; (3) Exclusion Restriction: SNPs influence the outcome (ovarian cancer) only through the exposure, not via alternative pathways. B Conceptual framework for the two-step MR mediation analysis. The total effect of the gut microbiota (exposure) on ovarian cancer (outcome) is decomposed into a direct effect and an indirect effect mediated by inflammatory cytokines. The path coefficients β(a) and β(b) represent the effect of the exposure on the mediator and the effect of the mediator on the outcome, respectively, which are used to calculate the indirect (mediation) effect. The mediation proportion is calculated as (Mediation Effect/Total Effect) × 100% In summary, this study utilized a two-sample MR design to explore the potential causal relationships between the gut microbiota, inflammatory cytokines, and OC. We further employed causal mediation analysis to determine whether genetically predicted inflammatory cytokines mediate the pathway from the gut microbiota to OC risk. This study aims to provide novel genetic evidence to address the current knowledge gaps regarding the role of genetically mediated immune-inflammatory mechanisms in the gut microbiotic-OC pathway. Materials and methods Study design The research framework and analytical workflow of this study are shown in Figs. 1 and 2 . First, we obtained summary statistics that included characteristics of 473 gut microbiotas, 91 inflammatory cytokines and OC samples from a genome-wide association study (GWAS) and existing databases and compiled a comprehensive dataset. The dataset was subsequently subjected to two-sample Mendelian randomization (MR) analysis to investigate the potential causal relationships between the gut microbiota, inflammatory cytokines and the presence of OC. Finally, two-step MR analysis and mediation analysis were conducted to explore the potential mediating effects of inflammatory cytokines in the pathway from the gut microbiota to OC. Fig. 2. Open in a new tab The flow chart of this study. This flowchart outlines the data sources and analytical steps. Summary-level genetic data for 473 gut microbial taxa ( N = 5,959), 91 inflammatory cytokines ( N = 14,824), and ovarian cancer were obtained from public databases (UK Biobank). The analysis comprised three main components: (1) Bidirectional two-sample MR to assess potential causal relationships between gut microbiota and ovarian cancer, and between inflammatory cytokines and ovarian cancer; (2) MR to assess the effect of gut microbiota on inflammatory cytokines; (3) Two-step MR mediation analysis to investigate whether significant inflammatory cytokines mediate the causal pathways from gut microbiota to ovarian cancer. Arrows indicate the direction of the causal hypotheses tested Genetic variants were utilized to estimate causal relationships in the MR analyses. We selected single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs), which must follow three main assumptions: (1) Assumption 1: IVs are strongly associated with the exposure of interest (relevance); (2) Assumption 2: IVs are not associated with any known or unknown confounders that may affect the outcomes (independence); (3) Assumption 3: IVs influence the outcome only through the exposure, not via alternative pathways (exclusion) (Fig. 1 A) [ 26 ]. As all the samples in this study were obtained from publicly available data, this study was a secondary analysis of published data, and no additional ethical approval was needed. Data sources Summary statistical data for the gut microbiota were collected from the FinnGen database ( https://www.finngen.fi/en ), which includes 5,959 participants and reports a total of 473 different genomic classifications of the gut microbiota [ 27 ]. The summary data of 91 inflammatory cytokines were derived from a study on the genetics of circulating inflammatory proteins, which included 14,824 participants [ 28 ]. Detailed information on their data processing is available in the original literature. Additionally, summary statistics for OC were sourced from the OpenGWAS database (ID: ieu-b-4963). This dataset originates from the UK Biobank, a large-scale prospective cohort containing genetic, lifestyle, and health data from approximately 500,000 participants [ 29 ]. Our analysis included 1,218 OC cases and 198,523 controls (total N = 199,741), all of European ancestry. The OC phenotype was defined according to the International Classification of Diseases, Tenth Revision (ICD-10) codes. Selection of instrumental variables (IVs) Single-nucleotide polymorphisms (SNPs) significantly associated with each exposure (gut microbiota taxa or inflammatory cytokines) were selected as IVs. Given the typically modest genetic effects for these traits and a limited number of SNPs being detected with the more stringent threshold of P < 5 × 10 − 8 , a relaxed genome-wide significance threshold of P < 5 × 10 − 6 was applied to ensure sufficient instrumental strength and statistical power. In the field of MR research, the relaxation of this statistical threshold combined with strict downstream validation is a common practice to incorporate a broader set of genetic instruments [ 30 – 32 ]. To ensure IV quality and minimize bias, the following quality control steps were implemented: (1) SNPs were clumped to ensure independence (linkage disequilibrium threshold R 2 < 0.001, window size = 10,000 kb); (2) Palindromic SNPs with ambiguous allele strands were removed; (3) The strength of each IV was assessed using the F-statistic, calculated as F = R 2 ( N − 2)/(1 − R 2 ), where R 2 is the proportion of variance in the exposure explained by the SNP and N is the sample size of the GWAS for the exposure. IVs with an F-statistic < 10 were excluded to mitigate weak instrument bias. The final set of IVs for each exposure ranged from 4 to 20 SNPs, all satisfying the three core MR assumptions detailed in the Study Design section (Fig. 1 A). MR analysis and reverse MR analysis To assess the potential causal relationships between the gut microbiota, inflammatory cytokines and OC, we conducted two-sample MR analysis. For exposures with only one IV, we used the Wald ratio method to infer causality, whereas for exposures with multiple IVs in this study, we used a variety of methods, including inverse variance weighted (IVW), MR Egger, weighted median, simple mode and weighted mode analysis. Among them, IVW is the main method, as it can avoid the influence of confounders in the absence of heterogeneity and horizontal pleiotropy. The intercept term of the MR-Egger method can be used to evaluate horizontal pleiotropy, so MR-Egger regression was used to evaluate whether the included SNPs had potential horizontal pleiotropy. Additionally, the weighted median and weighted mode methods could provide consistent causal effect estimates, improving the accuracy of the results [ 33 ]. The results of the MR analysis are presented as odds ratios (ORs) with their respective 95% confidence intervals (CIs). The False Discovery Rate (FDR) was controlled to account for multiple testing, with an FDR-corrected P-value < 0.05 considered statistically significant [ 34 ]. We also conducted reverse MR analysis using SNPs associated with OC as IVs to explore whether OC had a potential causal relationship with the gut microbiota or inflammatory cytokines, where OC was the exposure factor and the gut microbiota or inflammatory cytokines were the outcome factors. The reverse MR analysis process was similar to the process used for MR analysis. Mediation analysis We employed a two-step MR approach for potential causal mediation analysis, with the conceptual framework illustrated in Fig. 3 . The statistical model decomposes the total effect of gut microbiota (exposure) on ovarian cancer (outcome) into direct and indirect effects mediated by inflammatory cytokines (mediator) [ 35 ]. Fig. 3. Open in a new tab Two-step MR framework for mediation analysis. This figure illustrates the conceptual model used to assess whether inflammatory cytokines mediate the causal effect of gut microbiota on ovarian cancer. IV_Exposure: Genetic instruments (single-nucleotide polymorphisms, SNPs) associated with the exposure (gut microbiota). IV_Mediator: Genetic instruments (SNPs) associated with the potential mediator (inflammatory cytokines). The total effect of the gut microbiota on ovarian cancer can be decomposed into a direct effect and an indirect (mediation) effect. The indirect effect is calculated as the product of the causal effect of the gut microbiota on the inflammatory cytokines and the causal effect of the inflammatory cytokines on ovarian cancer (β(a)×β(b)). The proportion of the effect mediated by the cytokine is given by (Indirect Effect/Total Effect) × 100%. This design leverages genetic variants to minimize confounding and infer potential causal pathways The mediation analysis proceeded as follows: First, we estimated the total effect of the gut microbiota on OC using genetic instruments for gut microbiota. Second, we assessed the effect of gut microbiota on inflammatory cytokines and the effect of these cytokines on OC, using genetic instruments for each. The indirect effect, direct effect, and mediation proportion were computed using the following formulae: We primarily interpreted mediation effects when the directions of the direct and indirect effects were concordant. However, in cases where the direct and indirect effects were discordant, we still reported these results as they may indicate the presence of suppressor effects or more complex causal pathways [ 36 ]. Sensitivity analysis Comprehensive sensitivity analyses were performed to evaluate the robustness of the MR findings. Heterogeneity among IVs was assessed using Cochran’s Q statistic for the IVW and MR-Egger methods; Horizontal pleiotropy was evaluated via the MR-Egger intercept test; The MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) global test was applied to identify and correct for potential outliers due to pleiotropy; Leave-one-out analysis was conducted to determine if causal estimates were driven by any single influential SNP; Scatter plots and funnel plots were generated for visual inspection of the results. All statistical analyses were performed using R software (version 4.4.0). MR analyses were conducted primarily using the “Two Sample MR” package [ 37 ].The analysis was not blinded to prior results as it was based on prespecified hypotheses and an analytical plan. Results Sensitivity analyses support the robustness of the findings Supplementary MR analyses yielded results consistent with the IVW method in assessing the gut microbiota, inflammatory cytokines, and OC. Moreover, the robustness of the primary findings was supported by comprehensive sensitivity analyses. For both the gut microbiota and inflammatory cytokines, these analyses consistently indicated the absence of significant heterogeneity (Cochran’s Q test, all P > 0.05), horizontal pleiotropy (MR-Egger intercept test, all P > 0.05), and influential outliers (MR-PRESSO and leave-one-out analyses). The consistency of the effect estimates was further visually confirmed via scatter and funnel plots (Fig. 4 , Supplementary Table S1-S9, Figure S1-S9). Fig. 4. Open in a new tab Potential causal associations between gut microbiota, inflammatory cytokines, and ovarian cancer identified by MR analysis. Forest plots display the causal associations of ( a ) gut microbial taxa and ( b ) inflammatory cytokines with ovarian cancer risk, using the inverse-variance weighted (IVW) method as the primary analysis. Points represent the odds ratio (OR), and horizontal lines represent the 95% confidence interval (CI) for each exposure. A red vertical dotted line is plotted at OR = 1, which serves as the line of null effect. Taxa/cytokines whose confidence intervals fall to the left of this line (OR < 1) are considered protective factors, while those whose intervals fall to the right (OR > 1) are considered risk factors ( a ) A total of 19 gut microbial taxa showing suggestive evidence of a potential causal relationship with ovarian cancer (P FDR < 0.05). Column headings: id, Unique identifier for the gut microbial taxon; Exposure, taxon name of the gut microbe; Outcome, all refer to OC (Ovarian cancer) in this study; nSNP, number of single-nucleotide polymorphisms used as instrumental variables; OR(95%CI), Odds Ratio with 95% Confidence Interval; Heter_pval, P-value for Cochran’s Q test of heterogeneity (a non-significant P > 0.05 suggests no substantial heterogeneity); egger_intercept_pval, P-value for the MR-Egger regression intercept test (a non-significant P > 0.05 suggests no evidence of directional horizontal pleiotropy); P_FDR, the False Discovery Rate adjusted P-value. Associations with P_FDR < 0.05 are considered statistically significant after multiple testing correction ( b ) A total of 5 inflammatory cytokines showing suggestive evidence of a potential causal relationship with ovarian cancer (P FDR < 0.05). The interpretation of columns is identical to panel ( a ) Causal effects between the gut microbiota and OC We employed a two-sample MR approach and identified 19 gut microbial taxa exhibiting potential causal relationships with OC (P FDR < 0.05). As illustrated in Fig. 4 (a), 9 of these taxa were identified as risk factors for OC (OR > 1), while the remaining 10 were classified as protective factors (OR < 1). The gut microbial taxa potentially associated with an increased risk of OC include several well-defined genera/families such as Blautia , Psychroserpens , Flavonifractor , Victivallis , and Bacillaceae, as well as candidate taxa (not yet formally named taxonomic groups) such as CAG-349, CAG-552, UBA7748 and UBA8621. Among the protective factors were several well-defined taxa, including the genera Lachnoanaerobaculum , Enorma , Clostridium , Prevotella , and Gluconobacter , along with two taxa classified at the family level, Planococcaceae and Erysipelatoclostridiaceae. Additionally, several candidate taxa, namely QALR01, CAG-977, and UBA11471 , were identified. Causal effects between inflammatory cytokines and OC The potential causal relationship between 91 inflammatory cytokines and OC was also revealed in this study, with only 5 inflammatory cytokines associated with OC (P FDR < 0.05). As shown in Fig. 4 (b), IVW analysis identified 1 protective factor (OR < 1), leukemia inhibitory factor receptor (LIFR), and 4 risk factors (OR > 1), including Artemin (ARTN), T-cell surface glycoprotein CD6 isoform (CD6), interleukin-2 receptor subunit beta (IL-2Rβ)and interleukin-6 (IL-6). Mediation analysis results of the gut microbiota, inflammatory cytokines and OC Given that 19 gut microbiotas and 5 inflammatory cytokines exhibited potential causal relationships with OC, we further assessed whether inflammatory cytokines play a mediating role in the pathway from the gut microbiota to the OC. The MR analysis between the 19 significant gut microbiotas and the 5 inflammatory cytokines yielded several significant associations. Subsequent two-step mediation analysis revealed multiple potential mediated pathways (Table 1 ). Table 1. Mediating effects of inflammatory cytokines on the potential causal pathways from gut microbiota to ovarian cancer ID.Exposure Exposure ID.Mediator Mediator outcome Total_effect Mediation_effect Direct_effect Mediation_proportion GCST90032198 Bacillaceae A GCST90274774 T-cell surface glycoprotein CD6 isoform levels OC 0.0136 0.0008 0.0128 6.06% GCST90032198 Bacillaceae A GCST90274815 Interleukin-6 levels OC 0.0136 0.0020 0.0116 14.67% GCST90032419 Gluconobacter GCST90274760 Artemin levels OC −0.0072 0.0011 −0.0082 −14.71% GCST90032419 Gluconobacter GCST90274820 Leukemia inhibitory factor receptor levels OC −0.0072 −0.0008 −0.0064 11.16% GCST90032512 Planococcaceae GCST90274774 T-cell surface glycoprotein CD6 isoform levels OC −0.0116 0.0006 −0.0123 −5.44% GCST90032518 Prevotella sp000434975 GCST90274815 Interleukin-6 levels OC −0.0022 −0.0004 −0.0018 16.63% Open in a new tab Total effect: The overall causal effect of the gut microbiota on ovarian cancer risk Mediation effect: The portion of the total effect that is mediated by the inflammatory cytokine, calculated as the product of the path coefficients (β exposure→mediator × β mediator→outcome ) Direct Effect: The portion of the total effect that is not mediated by the inflammatory cytokine, calculated as Total Effect - Mediation Effect Mediation Proportion: The percentage of the total effect mediated by the inflammatory cytokine, calculated as (Mediation Effect / Total Effect) × 100%. A positive value indicates that the mediation effect is in the same direction as the total effect (i.e., consistent with a risk-increasing or protective role). A negative value suggests a inconsistent mediation, which may indicate the presence of a suppressor effect or a more complex relationship. Effects are derived from inverse-variance weighted (IVW) estimates * Abbreviation : OC ovarian cancer The final results revealed three inflammatory cytokines that mediate the pathway from specific gut microbiota to OC: (1) T-cell surface glycoprotein CD6 isoform levels (CD6) mediate the pathway from Bacillaceae A to OC (6.06% of the effect is mediated); (2) interleukin-6 levels (IL-6) mediate the pathway from Bacillaceae A to OC (14.67% of the effect is mediated) and from Prevotella sp000434975 to OC (16.63% of the effect is mediated); and (3) leukemia inhibitory factor receptor (LIFR) levels mediate the pathway from Gluconobacter to OC (11.16% of the effect is mediated). Additionally, we observed two pathways with negative mediation proportions, indicating inconsistent mediation (1) Artemin mediated the effect of Gluconobacter (−14.71%), and (2) CD6 mediated the effect of Planococcaceae (−5.44%). Reverse MR analysis between the gut microbiota and OC To investigate potential bidirectional relationships, we performed reverse MR analyses with OC as the exposure. Notably, this analysis identified potential causal effects of OC on 18 gut microbial taxa and 3 inflammatory cytokines (Supplementary Table S10-11). Among the 18 gut microbial taxa, those potentially exhibiting increased abundance in response to OC (OR > 1) included the genera-level Blautia , Eubacterium , Faecalicatena , Massiliomicrobiota , and Provencibacterium , along with the order-level taxon Sporomusales . Conversely, taxa potentially exhibiting decreased abundance (OR < 1) comprised the genera Ruminococcus and Succinivibrio , the family-level taxon Succinivibrionaceae, and two taxa that remain candidate taxa (CAG-145 and CAG-177). Furthermore, among the 3 inflammatory cytokines, OC exposure was potentially associated with elevated levels of C-C motif chemokine 28 (CCL28) and Fms-related tyrosine kinase 3 ligand (FLT3LG) (OR > 1), whereas it was linked to suppressed levels of Signaling lymphocytic activation molecule (SLAM) (OR < 1). Discussion The gut microbiota constitutes a complex ecosystem that is intricately linked to the pathogenesis of host diseases, immune system regulation, and other physiological processes [ 38 , 39 ]. Recent systematic reviews on the microbiota and ovarian cancer (OC) have indicated that gut dysbiosis may accelerate OC progression through pro-inflammatory cytokines, although causal evidence remains limited [ 40 , 41 ]. In this study, we leveraged a two-sample Mendelian randomization (MR) method to systematically investigate the potential causal relationships between the gut microbiota, inflammatory cytokines, and the risk of ovarian cancer (OC). Under MR assumptions, our findings suggest potential causal links for 19 gut microbial taxa and 5 inflammatory cytokines with OC. Further mediation analysis indicated that inflammatory cytokines, specifically IL-6, CD6, and LIFR, might partially mediate the influence of specific gut microbiota on OC pathogenesis. In addition, the reverse MR analysis hinted at potential bidirectional interactions. Our findings indicate that increased abundances of well-characterized genera/families, including Blautia , Psychroserpens , Flavonifractor , Victivallis , and Bacillaceae, as well as candidate taxa (referring to bacterial groups that have not yet been assigned formal scientific names) such as CAG-349, CAG-552, UBA7748, and UBA8621, may be associated with an elevated risk of OC. Although somewhat controversial, Blautia , a core commensal genus within the Firmicutes phylum, has been traditionally associated with the biosynthesis of short-chain fatty acids (SCFAs) and the maintenance of gut homeostasis [ 42 , 43 ]. In contrast to the decreased abundance of Blautia observed in colorectal cancer studies, our MR analysis identified a positive association with OC, which may be attributed to strain-specific effects [ 44 , 45 ]. The risk effect of Psychroserpens , a taxon primarily associated with marine environments [ 46 , 47 ], on OC may be due to opportunistic colonization. Flavonifractor is involved in the metabolism of flavonoids. A study linked the degradation of beneficial flavonoids to cancer progression, suggesting a plausible mechanism for the observed risk effect of Flavonifractor in OC [ 48 ]. The potential mechanistic link between Victivallis and OC risk may involve inflammatory pathways, as Victivallis was found to be positively correlated with NLRP3 inflammasome expression [ 49 ]. Furthermore, a MR study on lung squamous cell carcinoma (LUSC) reported a similar increase in Victivallis abundance [ 50 ]. A proposed mechanism linking Bacillaceae A to OC risk involves the activation of Toll-like receptor 2 (TLR2) by Gram-positive bacterial cell wall components such as peptidoglycan and lipoteichoic acid [ 51 – 53 ]. This activation triggers the NF-κB pathway, leading to the production of pro-inflammatory cytokines that facilitate tumorigenesis [ 51 – 53 ]. Nonetheless, this mechanism is currently hypothetical. Additionally, given the lack of research on uncultured candidate taxa, their association with OC risk may currently be attributed to common oncogenic pathways, such as immune modulation or metabolic processes [ 40 ]. On the other hand, this study also identified several gut microbial taxa associated with a reduced risk of OC, indicating their potential protective roles. These include the genera Lachnoanaerobaculum , Enorma , Clostridium , Prevotella , and Gluconobacter at the genus level; the families Planococcaceae and Erysipelatoclostridiaceae at the family level; and candidate taxa such as QALR01, CAG-977, and UBA11471 . Notably, several genera identified in our study, including Lachnoanaerobaculum , Clostridium , and Prevotella , have been reported to be associated with the production of SCFAs [ 54 – 57 ], which are known to modulate signaling pathways involved in tumor progression [ 40 ]. Specifically, in models of OC, butyrate functions as a histone deacetylase (HDAC) inhibitor, inducing apoptosis, suppressing proliferation, and reprogramming the epigenetic landscape, thereby exerting antitumor effects [ 40 , 58 ]. These established mechanisms align with our findings. Furthermore, our results are consistent with previous reports on Enorma , which is recognized as a beneficial microbe involved in the metabolism of triglycerides, glucose, and glycogen in the host [ 59 , 60 ]. Its protective role in OC may be mediated through the improvement of systemic energy metabolic homeostasis. Separately, the protective effect of Gluconobacter , which oxidizes glucose to gluconic acid, may involve improved insulin resistance via glucose homeostasis regulation, potentially countering IGF-driven oncogenesis [ 61 , 62 ]. At the family level, members of Planococcaceae are predominantly environmental and potentially pathogenic [ 63 – 65 ], while increased abundance of Erysipelatoclostridiaceae (formerly Erysipelotrichaceae) is typically linked to gut inflammation, immune dysregulation, and metabolic disorders [ 44 , 66 – 68 ]. The paradoxical association of these taxa with protection effects in our study suggests that their biological functions are likely context-dependent, being shaped by the specific pathological milieu of OC. In addition, the biological functions of the other candidate protective taxa require further investigation. Our analysis of inflammatory cytokines identified 5 factors with potential causal relationships to OC. Mediation analysis further suggested that IL-6, CD6, and LIFR may mediate the relationships between specific gut microbiota and OC, with a proportion mediated of 6.06% to 16.63%. While this proportion is modest, indicating that they explain only a fraction of the total effect of the gut microbiota on OC, these factors nonetheless represent a plausible biological pathway in a chronic disease like OC, which is driven by multiple pathways. IL-6 has been considered to be involved in multiple developmental stages of OC [ 69 ]. As a well-established promoter of tumorigenesis, it can enhance cancer stem cell self-renewal and foster immune evasion by activating the STAT3 signaling pathway [ 70 ]. The association of Bacillaceae A with elevated IL-6 levels aligns with the paradigm where gut dysbiosis induces systemic inflammation, thereby creating a “tumor-permissive microenvironment” (a term referring to an immune and stromal milieu that supports tumor growth, invasion, and metastasis) [ 71 ]. The link between CD6, a T-cell co-stimulatory molecule, and Bacillaceae A suggests potential microbial modulation of T-cell function within the tumor microenvironment [ 72 , 73 ]. LIFR exerts context-dependent roles in cancer, and the partial mediation of the protective effects of the genus Gluconobacter by LIFR implies that the microbiota might enhance tumor suppression via this receptor [ 74 , 75 ]. It is crucial to emphasize that this genetic evidence does not confirm these factors as therapeutic targets; rather, they may serve as risk markers for specific biological processes such as systemic inflammation or T-cell dysfunction. Furthermore, the modest proportion mediated suggests the likely existence of other important mechanisms, requiring more research for verification. It is also noteworthy that two associations exhibited an inconsistent mediation pattern (negative mediation proportion), which may indicate the presence of potential suppressor effects or more complex regulatory mechanisms, reflecting intricate biological interactions that warrant further investigation [ 36 ]. Reverse MR analysis suggested that OC itself may causally influence the abundance of 18 gut microbial taxa and the levels of 3 inflammatory cytokines. The alterations in the gut microbiota associated with OC could stem from several causes. First, OC progression is accompanied by systemic inflammation and immunosuppression, which can disrupt intestinal barrier integrity and increase permeability, leading to the translocation of pathogenic bacteria and displacement of beneficial commensals [ 76 , 77 ]. Second, first-line OC treatments, such as platinum-based chemotherapy and surgery, may directly alter gut microbial composition [ 78 ]. Additionally, advanced cancer patients frequently experience cachexia, anorexia, or malabsorption, resulting in reduced dietary fiber intake, which is a key factor for maintaining gut microbial diversity [ 79 ]. Beyond these reasons, another critical question warrants discussion: whether the specific gut microbial alterations we identified are unique to ovarian cancer or its subtypes, or if they represent a common consequence shared across multiple cancer types. This distinction may need to be verified through future comparative studies. Several key limitations inherent to our MR design must be acknowledged. First, despite rigorous sensitivity analyses, the possibility of residual horizontal pleiotropy cannot be fully excluded, whereby genetic variants influence OC through pathways independent of the exposures under investigation. Second, the use of a relaxed significance threshold ( P < 5 × 10 − 6 ) to select instrumental variables, while necessary for improving statistical power, may have introduced weak instrument bias, although this was mitigated by requiring a strong F-statistic > 10. Third, the reliance on European-ancestry data restricts the generalizability of our findings to ethnically diverse groups. Fourth, the lack of subtype-specific data for OC prevented us from exploring whether these associations differ across histological subtypes, which is an important limitation given the known heterogeneity of OC. Hence, future studies with larger datasets stratified by subtype are warranted to investigate whether the association identified in the current study is generalizable across or specific to different OC subtypes. Finally, while MR provides evidence for potential causality, it cannot substitute for functional validation in experimental models to elucidate the precise molecular mechanisms. Conclusion In conclusion, our study provides genetic evidence suggestive of potential causal relationships between the gut microbiota, inflammatory cytokines, and ovarian cancer, and identifies specific inflammatory cytokines as potential mediators in these pathways. These findings enhance our understanding of ovarian cancer pathogenesis and lay a groundwork for future research into preventive and therapeutic strategies. However, given the inherent limitations of the MR approach, further functional validation in experimental models, alongside multi-omics analyses, is warranted to confirm these mechanisms and assess their translational potential. Supplementary Information 13048_2026_1963_MOESM1_ESM.zip (17.3MB, zip) Supplementary Material 1. Figure S1: MR leave-one-out sensitivity analysis for gut microbiota on ovarian cancer. Figure S2: Scatter plots for the effect of gut microbiota on ovarian cancer. Figure S3: Funnel plots for the effect of gut microbiota on ovarian cancer. Figure S4: MR leave-one-out sensitivity analysis for inflammatory cytokines on ovarian cancer. Figure S5: Scatter plots for the effect of inflammatory cytokines on ovarian cancer. Figure S6: Funnel plots for the effect of inflammatory cytokines on ovarian cancer. Figure S7: MR leave-one-out sensitivity analysis for gut microbiota on inflammatory cytokines. Figure S8: Scatter plots for the effect of gut microbiota on inflammatory cytokines. Figure S9: Funnel plots for the effect of gut microbiota on inflammatory cytokines. 13048_2026_1963_MOESM2_ESM.zip (146.9KB, zip) Supplementary Material 2. Table S1: The potential causal effects of gut microbiota on ovarian cancer. Table S2: The potential casual effects of inflammatory cytokines on ovarian cancer. Table S3: The heterogeneity analysis of the association between gut microbiota on ovarian cancer. Table S4: The heterogeneity analysis of the association between inflammatory cytokines and ovarian cancer. Table S5: The pleiotropy analysis of the association between gut microbiota and ovarian cancer. Table S6: The pleiotropy analysis of the association between inflammatory cytokines and ovarian cancer. Table S7: The MR-PRESSO analysis of the association between gut microbiota and ovarian cancer. Table S8: The MR-PRESSO analysis of the association between inflammatory cytokines and ovarian cancer. Table S9: The potential causal effects of gut microbiota on inflammatory cytokines. Table S10: The reverse MR analysis of the potential causal association between gut microbiota and ovarian cancer. Table S11: The reverse MR analysis of the potential causal association between inflammatory cytokines and ovarian cancer. Acknowledgements The authors would like to thank all individuals who participated in this study, and appreciate all contributors for sharing the data involved in this study. Abbreviations OC Ovarian cancer MR Mendelian randomization GWAS Genome-wide association study SNP Single-nucleotide polymorphism IVs Instrumental variables LD Linkage disequilibrium IVW Inverse variance weighted OR Odds ratios CI Confidence intervals Authors’ contributions L.C. Writing - Original Draft, Formal Analysis, Visualization. B.Y. Conceptualization, Methodology, Investigation, Formal Analysis, Writing - Review & Editing, Visualization. H.Z. Visualization, Formal Analysis. S.L. Visualization, Conceptualization. G.L. Formal Analysis. Y.W. Data Curation. Y.R. Writing - Review & Editing, Supervision, Funding Acquisition, Project Administration, Validation, final approval of the version to be submitted. All authors read and approved the final manuscript. Funding This study was supported by the National Natural Science Foundation of China (Grant no. 82460305), Natural Science Foundation of Guizhou Province (Grant no. QKHJC-MS[2025]358), the United Fund of the Zunyi Science and Big Data Bureau and Zunyi Medical University (ZSKH-HZ-[2023]166), the Academic Promising Youth Cultivation and Innovation Exploration Project of Zunyi Medical University (QKPTRC[2021]1350-002), and the Graduate Scientific Research Foundation Project of Zunyi Medical University (YJSKYJJ2025034). Data availability All the data generated or analysed during this study are included in this published article. Declarations Ethics approval and consent to participate The data utilized in our study were derived from publicly available databases that had already received ethical approval, so supplementary ethical approval was deemed unnecessary. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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[ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials 13048_2026_1963_MOESM1_ESM.zip (17.3MB, zip) Supplementary Material 1. Figure S1: MR leave-one-out sensitivity analysis for gut microbiota on ovarian cancer. Figure S2: Scatter plots for the effect of gut microbiota on ovarian cancer. Figure S3: Funnel plots for the effect of gut microbiota on ovarian cancer. Figure S4: MR leave-one-out sensitivity analysis for inflammatory cytokines on ovarian cancer. Figure S5: Scatter plots for the effect of inflammatory cytokines on ovarian cancer. Figure S6: Funnel plots for the effect of inflammatory cytokines on ovarian cancer. Figure S7: MR leave-one-out sensitivity analysis for gut microbiota on inflammatory cytokines. Figure S8: Scatter plots for the effect of gut microbiota on inflammatory cytokines. Figure S9: Funnel plots for the effect of gut microbiota on inflammatory cytokines. 13048_2026_1963_MOESM2_ESM.zip (146.9KB, zip) Supplementary Material 2. Table S1: The potential causal effects of gut microbiota on ovarian cancer. Table S2: The potential casual effects of inflammatory cytokines on ovarian cancer. Table S3: The heterogeneity analysis of the association between gut microbiota on ovarian cancer. Table S4: The heterogeneity analysis of the association between inflammatory cytokines and ovarian cancer. Table S5: The pleiotropy analysis of the association between gut microbiota and ovarian cancer. Table S6: The pleiotropy analysis of the association between inflammatory cytokines and ovarian cancer. Table S7: The MR-PRESSO analysis of the association between gut microbiota and ovarian cancer. Table S8: The MR-PRESSO analysis of the association between inflammatory cytokines and ovarian cancer. Table S9: The potential causal effects of gut microbiota on inflammatory cytokines. Table S10: The reverse MR analysis of the potential causal association between gut microbiota and ovarian cancer. Table S11: The reverse MR analysis of the potential causal association between inflammatory cytokines and ovarian cancer. Data Availability Statement All the data generated or analysed during this study are included in this published article. 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