Causal relationship between oral/gut microbiota and lung cancer: a two-sample Mendelian randomization study

Causal relationship between oral/gut microbiota and lung cancer: a two-sample 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 Causal relationship between oral/gut microbiota and lung cancer: a two-sample Mendelian randomization study Zi-Jian Huang, Lv Wu, Ying-Long Peng, Zhi-Hong Chen, Chong-Rui Xu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7222084/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Several studies have already proven a significant correlation between the microbiota and lung cancer. In this study, we explore the potential relative oral and gut microbiota which influence the risk of lung cancer. Methods We utilized genome-wide association study (GWAS) data from oral microbiota (2984 healthy individuals) and gut microbiota (2002 healthy individuals) and lung cancer with a two-sample Mendelian randomization (MR) analysis method. In this analysis, oral microbiota and gut microbiota were conducted as exposure. Lung cancer data obtained from GWAS including a total of 212453 individuals. Inverse-variance weighted (IVW) method was used as the primary method. Results IVW analysis identified that genus Pauljensenia, Capnocytophaga and Aggregatibacter in oral microbiota are potentially protective against lung cancer. On the contrary, higher abundances of bacteria within the genus Granulicatella , Streptococcus , TM7x , Neisseria in oral microbiota were associated with increased lung cancer risk. Among gut bacteria, species Enterococcus_faecium were positively associated with an increased risk of lung cancer. Conclusion The findings of this study suggest a potential causal relationship between distinct oral and gut microbial communities and lung cancer risk, offering valuable insights into microbial candidates that may serve as targets for future diagnostic innovations. lung cancer oral microbiota gut microbiota Mendelian randomization single nucleotide polymorphism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Globally, lung cancer constitutes a significant public health concern due to its high incidence and death rate, ranking among the primary causes of cancer-associated deaths [ 1 ]. Despite continuous improvement of therapy strategy such as radiotherapy,Operation chemotherapy, targeted therapy and immune checkpoint inhibitors (ICIs), patients still face challenges of drug resistance and recurrence with metastasis [ 2 ]. The advent of the Human Microbiome Project has facilitated a more comprehensive understanding of the human microbiome. The historical perception that the lungs are a sterile environment has been refuted by contemporary research. Utilizing molecular techniques such as Polymerase Chain Reaction (PCR) and Next-Generation Sequencing (NGS), numerous research have now confirmed a close relationship to the oral microbiomes, gut microbiomes and the pulmonary microbiome [ 3 , 4 ]. The oral microbiota, second to the gut microbiota in diversity, encompasses a broad range of bacterial genera and families [ 5 ]. Anatomically, the oral cavity is contiguous with the respiratory tract through the oropharynx, which serves as a passageway to both the upper and lower airways, ultimately reaching the lungs. Growing evidence suggests that oral microbiota can translocate to the lungs, where they may establish colonization and influence the composition and dynamics of the pulmonary microbiome [ 6 , 7 ]. Moreover, recent research has increasingly emphasized the importance of the pulmonary microbiota within the tumor microenvironment of lung cancer, highlighting its involvement in oncogenesis, tumor progression, and the development of drug resistance [ 8 – 10 ]. Mendelian Randomization (MR) is an epidemiological methodology that leverages naturally occurring and randomly assigned genetic variants as instrumental variables. This approach effectively mitigates confounding variables and reverse causality [ 11 ]. MR analyses have been widely utilized to investigate potential causal correlations between gut microbiota and the development of various types of cancer [ 12 , 13 ]. In this study by Feng et al., Aggregatibacter and Gemella were positively correlated with a higher risk of lung cancer, whereas Fusobacterium , Streptococcus , Campylobacter A , and members of the Saccharibacteria TM7x family were negatively associated with lung cancer risk. [ 14 ]. However, there remains a paucity of study elucidating the causal relationships between the more comprehensive oral microbiota and lung cancer. This research endeavored to explore the potential causal relationships between lung cancer and both oral and gut microbiota, with a particular focus on identifying pathogenic microbial clusters. To achieve this, we performed a MR analysis based on the data derived from Genome-Wide Association Studies (GWAS). By treating lung cancer as the outcome and oral and gut microbiota as exposures, we sought to determine whether specific microbial taxa play contributory or protective roles in lung cancer development. Methods and materials Study designs In this work, we conducted a two-sample MR analysis to research the potential causal connection between oral and gut microbiota which derived from the Shenzhen cohort and lung cancer risk in the Japanese cohort. Data were extracted from multiple publicly available repositories. To guarantee the stability and credibility of the outcomes, an array of sensitivity analyses were performed. An overview of the research design is illustrated in Fig. 1 . Source of oral/ gut microbiome data In our investigation of the oral microbiota, we drew upon data from an extensive GWAS, covering 2,017 samples from the dorsal tongue and 1,915 saliva samples obtained from 2,984 Chinese healthy subjects. [ 15 ]. We had access to extensive whole-genome sequencing data, which revealed 455 independent associations. These associations involved 340 distinct genetic loci and 385 microbial taxa, all reaching genome-wide significance [ 15 ]. The microbial community exhibited high coverage, with 99.7% in tongue dorsum samples and 98.7% in saliva samples, indicating comprehensive representation of the oral microbiota in the dataset. [ 15 ]. For the gut microbiome component, all Chinese adult participants included in this study were enrolled as part of a multi-omics investigation. The discovery cohort comprised 2,002 individuals recruited during routine physical examinations between March and May 2017 in Shenzhen. Among them, blood samples were captured from all volunteers, and fecal samples were available for 1,539 individuals. All participants underwent high-depth whole-genome and whole-metagenomic sequencing. For replication, 1,430 individuals were recruited across multiple cities in China (e.g., Wuhan, Qingdao), following the same study design but on a smaller scale. Of these, 1,006 provided both blood and fecal samples. Sample collection protocols for both blood and stool, as well as sequencing procedures, were consistent with those established in our previous studies [ 15 – 20 ]. Sources of lung cancer data GWAS summary statistics for lung cancer were obtained from a large genome-wide association study conducted in an East Asian population. The dataset featured a comprehensive genetic association map highlighting the pleiotropic landscape, including key loci within the major histocompatibility complex (MHC) region and fine-mapped human leukocyte antigen (HLA) targets. This non-European cohort comprised a total of 212,453 individuals and included 8,885,805 single-nucleotide polymorphisms (SNPs). Quality control procedures were applied to address imbalances, and adjustments were made for potential confounding factors. The annotated version was builted with HG19/GRCh37. Instrument selection Given the substantial number of SNPs achieving genome-wide significance (p < 5 × 10^-8) for traits related to oral and gut microbiome groups, we applied a more stringent significance threshold (p < 5 × 10^-9) to select robust genetic IVs. These IVs were identified by grouping them according to the reference panel of the Linkage Disequilibrium (LD) from the 1000 Genomes Project, with a threshold of R^2 < 0.001 within a distance of 1,000 kilobases (kb) [ 21 ]. To ensure the robustness and reliability of IVs, we retained only those with F-statistics over than 10, thereby identifying them as strong tools for following MR analysis. These selected IVs were then drawn from the GWAS summary statistics for lung cancer. To minimize potential bias from horizontal pleiotropy, SNPs exhibiting direct associations with lung cancer (p < 10^-5) were excluded. These are in accordance with established protocols in previous studies [ 22 ]. To maintain consistency in our analysis, the SNPs between the exposure and outcome datasets were synchronized to confirm uniform effect estimates for the same effect allele [ 21 ]. Statistical analysis In this research, a range of individual genetic variants were utilized as IVs, instead of depending solely on aggregated allele scores. This strategy was adopted to enable a more comprehensive assessment of the core assumptions underlying MR as well as to detect and account for horizontal pleiotropy thus enhancing the robustness of sensitivity analyses [ 23 ]. To evaluate the robustness of our findings under varying assumptions regarding heterogeneity and pleiotropy, we applied complementary MR methods: inverse variance weighted (IVW; random-effects model), weighted median, MR-Egger regression, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis. The IVW method, implemented under a random-effects model, was used as the main analytical approach across all four category of instrumental variables. Heterogeneity among the IVs was assessed using Cochran’s Q statistic to evaluate variability. To enhance the robustness of our findings, we also conducted analyses under more stringent conditions. While the IVW method presumes that all genetic variants are sound instruments, its estimates may be biased in the existence of substantial horizontal pleiotropy affecting a considerable number of SNPs [ 24 ]. Conversely, the weighted median method presumes that at least 50% of the overall weight comes from IVs, making it robust even when up to half of the variants are affected by horizontal pleiotropy [ 25 ]. When more than 50% of the variants were potentially affected by horizontal pleiotropy, we evaluated the robustness of the genetic instruments using F-statistics, with a mean F-statistic below 10 considered suggestive of weak instrument bias [ 26 ]. In addition, the MR-Egger method was employed to detect potential directional pleiotropy. A statistically significant intercept from the MR-Egger analysis would reveal a violation of the instrumental variable assumptions, suggesting the existence of unbalanced directional pleiotropy [ 27 ]. Besides, the MR-PRESSO method was implemented to reduce heterogeneity in causal effect assessments by identifying and excluding outlier SNPs that exerted disproportionate influence on the results (NbDistribution = 1,500) [ 28 ]. What’s more, steiger filtering was implemented to recognize and exclude genetic variants that showed stronger correlations with the outcome than with the exposure [ 29 ]. All statistical analyses were conducted using R version 4.3.1 (R Foundation) and R packages (“TwoSampleMR” and “MR”) [ 30 , 31 ]. The TwoSampleMR package provided causal estimates from the four MR models (IVW, weighted median, MR-Egger, and MR-PRESSO). Result Following a series of quality control procedures, 85,043 SNPs were retained for analysis including 84,578 associated with the oral microbiome and 465 with the gut microbiome. All selected IVs demonstrated F-statistics exceeding 10 thus indicating no evidence of weak instrument bias. Eventually, we selected 42,353 SNPs from the oral microbiome and 121 SNPs from the gut microbiome for further analysis. In the oral microbiome, we chose 968 SNPs representing genera such as genus Aggregatibacter , Pseudopropionibacterium , and Capnocytophaga . In the gut microbiome, we selected 6 SNPs representing species Enterococcus faecalis and three metabolic pathways. Causal effect of oral microbiota on lung cancer Utilizing the IVW method in MR analysis, we identified that increased abundance of several bacterial taxa, as genetically predicted, was correlated with a reduced risk of lung cancer. Specifically, these included the genus Pauljensenia (OR: 0.777, 95% CI: 0.651–0.927, p = 0.0052), genus Capnocytophaga (OR: 0.748, 95% CI: 0.580–0.966, p = 0.0260), genus Centipeda (represented by specie unclassified mgs 2230 : OR: 0.809, 95% CI: 0.669–0.977, p = 0.0279), and genus Aggregatibacter which includes specie segnis mgs 2462 (OR: 0.818, 95% CI: 0.673–0.994, p = 0.0432), sp000466335 mgs 1474 (OR: 0.770, 95% CI: 0.651–0.910, p = 0.0021) and sp000466335 mgs 2558 (OR: 0.746, 95% CI: 0.602–0.926, p = 0.0078) and so on. These results are shown in Fig. 2 . In contrast, increased abundance of certain bacterial taxa was connected to an elevated risk of lung cancer. Specifically, these included genus Prevotella which includes specie baroniae mgs 143 (OR: 1.294, 95% CI: 1.048–1.598, p = 0.0012) and specie buccae mgs 3394 (OR: 1.479, 95% CI: 1.071–2.044, p = 0.0037), genus Granulicatella which includes specie elegans mgs 1090 (OR: 1.210, 95% CI: 1.019–1.436, p = 0.0064) and specie unclassified mgs 2338 (OR: 1.192, 95% CI: 1.010–1.406, p = 0.0160), genus Streptococcus which includes specie oralis C mgs 62 (OR: 1.298, 95% CI: 1.004–1.679, p = 0.0292), specie unclassified mgs 1416 (OR: 1.279, 95% CI: 1.072–1.526, p = 0.0446), specie unclassified mgs 2628 (OR: 1.179, 95% CI: 1.031–1.348, p = 0.0370), genus Veillonella (OR: 1.304, 95% CI: 1.016–1.673, p = 0.0140), and genus Fusobacterium (OR: 1.296, 95% CI: 1.023–1.641, p = 0.0043) and so on. These findings are shown in Fig. 3 . Notably, bacteria within the genus Pauljensenia, Streptococcus, Centipeda, TM7x and Haemophilus_D and other four genus exhibited species level heterogeneity on lung cancer risk. Specifically, different species within the same genus were associated with increased or decreased risks of lung cancer. To clearly illustrate this phenomenon, we summarized these species level heterogeneity bacteria in Fig. 4 . Causal effects of gut microbiota on lung cancer Compared with the oral microbiota, certain features of the gut microbiota were correlated with lung cancer risk. Specifically, the metabolic pathways xylose degradation (OR: 0.490, 95% CI: 0.268–0.893, p = 0.0199) and threonine degradation II (OR: 0.419, 95% CI: 0.233–0.755, p = 0.0038) were associated with a reduced risk of lung cancer. Conversely, genus Enterococcus species faecium (OR: 1.180, 95% CI: 1.030–1.353, p = 0.0173) and the Acetyl-CoA Synthetase Pathway (OR: 1.884, 95% CI: 1.121–3.168, p = 0.0169) were positively associated with a higher risk of lung cancer. These findings are shown in Fig. 5 . Sensitivity analysis A set of sensitivity analyses were performed to evaluate the presence of heterogeneity and horizontal pleiotropy among the selected IVs. Horizontal pleiotropy was specifically analyzed using the MR-Egger intercept test. All p values derived from the MR-Egger intercepts were greater than 0.05, suggesting no significant proof of directional horizontal pleiotropy. Cochran’s Q test was applied to evaluate heterogeneity among the selected SNPs. As presented in Table 1 , the Q_pval values for both the IVW and MR-Egger methods exceeded 0.05, indicating no significant heterogeneity and suggesting that the results were unlikely to be affected. Owing to the relatively limited number of SNPs available for the gut microbiome analysis, sensitivity analyses could not be reliably performed for that dataset. Detailed scatter plots for each MR method are presented in Figure S1 . These results demonstrated consistent directions and reinforced the reliability of our findings. Table 1 Pleiotropy and sensitivity analyses of the relationship between oral/ gut microbiota and the risk of lung cancer. Exposure Egger_intercept Se pval IVW_Q_pval Egger_Q_pval g__Pauljensenia 0.0854 0.3362 0.8037 0.8516 0.8516 g__Pseudopropionibacterium 0.9368 0.4484 0.0750 0.2357 0.2357 g__Capnocytophaga 0.3898 0.9687 0.6951 0.2086 0.2086 f__Weeksellaceae 0.7291 0.8218 0.3958 0.9161 0.9161 s__unclassified_mgs_1674 0.1351 0.4303 0.7590 0.8994 0.8994 s__unclassified_mgs_1815 0.1560 0.3314 0.6576 0.9583 0.9583 g__Eggerthia -0.0051 0.0594 0.9335 0.7757 0.7757 s__Solobacterium_extructa_mgs_68 0.0690 1.0977 0.9512 0.6102 0.6102 g__Solobacterium|s__unclassified_mgs_2929 | 0.0036 0.4451 0.9937 0.5820 0.5820 g__Streptococcus|s__Streptococcus_constellatus -0.0162 0.2383 0.9466 0.0991 0.0991 g__Streptococcus|s__Streptococcus_infantis 0.1498 0.1516 0.3412 0.9641 0.9641 g__Streptococcus|s__Streptococcus_mitis_AT_mgs_1239 0.0496 0.3020 0.8732 0.8978 0.8978 g__Streptococcus|s__Streptococcus_mitis_I_mgs_3086 0.1763 0.1780 0.3378 0.8886 0.8886 g__Streptococcus|s__Streptococcus_oralis_mgs_174 -0.0092 0.1587 0.9549 0.3953 0.3953 g__Streptococcus|s__Streptococcus_pseudopneumoniae_A_mgs_1809 -0.0098 0.1033 0.9264 0.3619 0.3619 g__Streptococcus|s__Streptococcus_sp000187745_mgs_2287 0.0169 0.1541 0.9149 0.8477 0.8477 g__Streptococcus|s__unclassified_mgs_2100 0.5101 0.2804 0.0939 0.8567 0.8567 g__Streptococcus|s__unclassified_mgs_3505 0.3145 0.6274 0.6315 0.6629 0.6629 g__RUG343 0.0442 0.2342 0.8533 0.3846 0.3846 g__Gemella|s__Gemella_haemolysans_B_mgs_2903 0.2085 0.0999 0.0665 0.6800 0.6800 g__Gemella|s__Gemella_morbillorum_mgs_3548 0.4074 0.4060 0.3339 0.5045 0.5045 g__Catonella|s__unclassified_mgs_1601 0.0069 0.4025 0.9867 0.6485 0.6485 s__Lachnoanaerobaculum_sp000287675_mgs_3023 0.4521 0.4157 0.3085 0.5070 0.5070 s__Lachnoanaerobaculum_sp000287675_mgs_978 0.0199 0.2062 0.9248 0.2415 0.2415 g__Shuttleworthia 0.0548 1.0435 0.9598 0.3819 0.3819 g__Filifactor 0.1229 0.1865 0.5234 0.5253 0.5253 g__Parvimonas 0.1190 0.2429 0.6323 0.8142 0.8142 g__Centipeda 0.1347 0.2444 0.5917 0.4042 0.4042 f__Veillonellaceae|g__F0422|s__unclassified_mgs_1719 0.0028 0.2803 0.9922 0.3017 0.3017 g__Streptobacillus 0.0509 0.1401 0.7220 0.9856 0.9856 g__TM7x|s__unclassified_mgs_1515 0.1734 0.1868 0.3703 0.5339 0.5339 g__TM7x|s__unclassified_mgs_356 0.2930 0.2100 0.1862 0.0850 0.0850 g__TM7x|s__unclassified_mgs_488 0.3814 0.5766 0.5209 0.7303 0.7303 g__TM7x|s__unclassified_mgs_572 0.3090 0.2090 0.1701 0.5452 0.5452 g__TM7x|s__unclassified_mgs_660 0.2512 0.3569 0.4923 0.9443 0.9443 g__TM7x|s__unclassified_mgs_664 0.2874 0.2367 0.2413 0.7361 0.7361 g__Kingella_B 0.1371 0.2661 0.6166 0.6546 0.6546 g__Aggregatibacter|s__Aggregatibacter_segnis_mgs_2462 0.2920 0.2646 0.2872 0.3801 0.3801 g__Aggregatibacter|s__Aggregatibacter_sp000466335_mgs_1474 0.1564 0.1979 0.4416 0.5787 0.5787 g__Aggregatibacter|s__Aggregatibacter_sp000466335_mgs_2558 -0.0066 0.1928 0.9735 0.6985 0.6985 g__Haemophilus_D|s__Haemophilus_D_pittmaniae_mgs_3301 0.9352 0.5350 0.1144 0.6478 0.6478 g__Haemophilus|s__unclassified_mgs_1198 0.1428 0.2035 0.4988 0.6692 0.6692 g__Haemophilus|s__unclassified_mgs_2176 0.2060 0.3141 0.5329 0.7681 0.7681 g__Pauljensenia|s__unclassified_mgs_1046 -0.3224 0.3261 0.3368 0.7102 0.7102 g__Pauljensenia|s__unclassified_mgs_1346 -3.7069 2.9687 0.3004 0.6012 0.6012 g__Pauljensenia|s__unclassified_mgs_3155 -0.4963 0.4814 0.3327 0.7129 0.7129 g__Lancefieldella|s__Lancefieldella_sp000564995_mgs_1171 -0.2764 0.2096 0.2140 0.9107 0.9107 g__Lancefieldella|s__Lancefieldella_sp000564995_mgs_1519 -0.2159 0.1784 0.2428 0.7874 0.7874 g__Lancefieldella|s__unclassified_mgs_1233 -0.1138 0.2333 0.6354 0.8474 0.8474 g__Lancefieldella|s__unclassified_mgs_2115 -0.2186 0.2799 0.4455 0.7938 0.7938 g__Prevotella|s__Prevotella_baroniae_mgs_143 -0.2970 0.3073 0.3620 0.4077 0.4077 g__Prevotella|s__Prevotella_buccae_mgs_3394 0.0012 0.4607 0.9980 0.6516 0.6516 f__Weeksellaceae|g__unclassified_mgs_1987 -0.2826 0.7527 0.7264 0.3089 0.3089 g__Solobacterium|s__unclassified_mgs_2820 -0.1118 0.2554 0.6682 0.6929 0.6929 g__Granulicatella|s__Granulicatella_elegans_mgs_1090 -0.0776 0.1722 0.6630 0.5148 0.5148 g__Granulicatella|s__unclassified_mgs_2338 -0.4660 0.4120 0.2747 0.8800 0.8800 g__Streptococcus|s__Streptococcus_oralis_C_mgs_62 -0.1455 0.4544 0.7571 0.6267 0.6267 g__Streptococcus|s__unclassified_mgs_1416 0.0011 0.2257 0.9963 0.6101 0.6101 g__Streptococcus|s__unclassified_mgs_2628 0.0014 0.1381 0.9922 0.5848 0.5848 g__Gemella|s__unclassified_mgs_3512 -0.1971 0.1740 0.2729 0.7350 0.7350 g__Oribacterium|s__unclassified_mgs_2356 0.0006 0.2196 0.9980 0.2706 0.2706 g__Centipeda|s__unclassified_mgs_1154 -0.0079 0.4475 0.9864 0.6884 0.6884 g__Veillonella|s__Veillonella_rogosae_mgs_1856 -0.2446 0.2272 0.3129 0.8999 0.8999 g__Fusobacterium|s__unclassified_mgs_2195 -0.5431 0.5056 0.3107 0.5194 0.5194 g__TM7x|s__unclassified_mgs_1548 -0.0675 0.2102 0.7527 0.8008 0.8008 g__TM7x|s__unclassified_mgs_3406 -0.0777 0.1192 0.5248 0.8277 0.8277 g__TM7x|s__unclassified_mgs_421 -0.3262 0.3525 0.3766 0.0532 0.0532 g__TM7x|s__unclassified_mgs_605 -0.1655 0.1888 0.4013 0.8418 0.8418 f__Saccharimonadaceae|g__unclassified_mgs_2610 -0.4546 0.3791 0.2581 0.5014 0.5014 s__Neisseria_sicca_A_mgs_3572 -0.1238 0.2062 0.5572 0.8544 0.8544 s__Neisseria_sp000186165_mgs_1395 -0.1022 0.3969 0.8012 0.5532 0.5532 g__Haemophilus_D|s__unclassified_mgs_1145 -0.5706 0.3311 0.1069 0.3090 0.3090 g__Haemophilus_D|s__unclassified_mgs_3426 0.0078 0.2039 0.9699 0.3137 0.3137 MF0021:xylose_degradation NA NA NA NA NA MF0050:threonine_degradation NA NA NA NA NA s_Enterococcus_faecium NA NA NA NA NA MF0075:acetate_to_acetyl-CoA NA NA NA NA NA Leave-one-out analysis revealed that sequential removal of individual SNPs did not result in substantial changes to the overall causal estimates. This suggests that no single SNP exerted a disproportionate influence and no influential outliers were identified. These results are illustrated in Figure S2 . Discussion To our understanding, this is the first study which utilized the two-sample MR approach to systematically evaluate the potential causal relationships between oral microbiota and lung cancer. Our findings identified several novel microbial taxa potentially increased or decreased risk of lung cancer. What’s more, we summarized several specific bacterial genera in which different species have different effects on lung cancer development. Collectively, these findings deepen our understanding of the lung cancer microbiota axis as well as providing potential evidence for future clinical translation and microbiome-targeted research. MR analysis reduce confounding factors by utilizing IVs, which are common limitations in observational research. Additional sensitivity further validated the consistency of our results to strengthen the credibility of our conclusions. This integrative MR framework allowed us to gain clearer insights into the potential roles of microbiota from distinct anatomical sites in the etiology of lung cancer. Our research identified that certain specific bacteria within genus Pauljensenia , Centipeda and Aggregatibacter , are significantly associated with reduced lung cancer risk. In contrast, higher abundances of bacteria within the genus Prevotella , Granulicatella , Streptococcus , Veillonella , Fusobacterium , TM7x , Neisseria , and Haemophilus_D were associated with increased lung cancer risk. Those discoveries have important implications for diagnostic biomarkers, as it suggests that these bacteria could potentially serve as indicators for lung cancer risk assessment. Our study findings are in accordance with some of the previous research. Former studies suggested that approximately 25% of all cancers are etiologically associated with chronic inflammation and infection [ 32 ]. The risk for cancer of the respiratory system is positively associated with inflammatory diseases [ 32 ]. Among the bacteria significantly linked to reduced lung cancer risk, the study by He et al. found that Pauljensenia and Capnocytophaga are associated with decreased incidence of bronchitis and tonsillitis, as well as the inhibition of pneumonia and bronchitis [ 33 ]. The abundance of Aggregatibacter has been shown to be negatively associated with inflammatory markers in sputum like interleukin-8 (IL-8) and interleukin-1β (IL-1β), and demonstrates anti-inflammatory properties in lower respiratory tract samples from individuals with chronic airway disease (CAD). [ 34 ]. Conversely, among the bacteria of increased lung cancer risk, Huang et al. found that bacteria of the genus Granulicatella are significantly enriched in the lung microbiota of patients with lung cancer [ 35 ]. Wang et al. discovered that increased abundance of Granulicatella is linked to the transition from neutrophilic to eosinophilic chronic obstructive pulmonary disease (COPD) [ 36 ], which is often considered as a common risk factor for lung cancer [ 37 ]. The genus Streptococcus has been shown to be enriched in NSCLC patient [ 38 ]. Mao et al. reported a significant rise in the abundance of TM7 phylum bacteria in patients with lung cancer particularly in those who diagnosed with lung squamous cell carcinoma and lung adenocarcinoma [ 39 ]. The phylum TM7 bacteria and its subgroup c:TM7-3 were significantly increased in bronchoalveolar lavage fluid (BALF) samples from persons with lung cancer and potentially serving as reliable biomarkers [ 40 ]. Hosts with higher abundance of genus Neisseria are more susceptible to environmental damage, resulting in an increased risk of respiratory cancer [ 41 ]. More importantly, we identified certain genus, such as Streptococcus , TM7x , and Haemophilus , where different species within the same genus has varying effects on lung cancer.Within the genus Streptococcus , Some speices of Streptococcus was found to mediated the anticancer effects through β-Galactosidase which activate oxidative phosphorylation and downregulate the Hippo pathway kinases [ 42 ]. Certain Streptococcus species have been shown to accelerate cell cycle while preventing apoptosis in lung cancer cells, thereby facilitating the occurrence of lung cancer. Studies have shown that increased abundance of Haemophilus is associated with exacerbated inflammatory responses [ 43 ]. However, the presence of Haemophilus is also correlated with increased abundance of the antimicrobial peptide secretory leukocyte protease inhibitor (SLPI), which is one of the key factors in maintaining respiratory homeostasis [ 44 ]. Our study results advocate microbiota research should be detailed to the species level and highlight the importance of microbial community diversity in maintaining normal oral and respiratory functions, as well as the necessity of dynamically monitoring the composition of the upper respiratory tract microbiota for its significance in the development of lung cancer. Compared with the oral microbiota, we identified fewer gut microbial taxa associated with altered lung cancer risk. From a physiological standpoint, the oral and respiratory microbiota directly constitute the pulmonary microbiome, thereby more directly influencing the physiological and immune functions of the lungs. However, several limitations should be acknowledged. Firstly, the study population was predominantly composed of Asian descent, which may restrict the generalizability of the results to other ethnic groups. Secondly, the analysis did not distinguish between specific subtypes of lung cancer. Given the heterogeneous nature of lung cancer, future studies are warranted to explore subtype-specific associations. Lastly, this study did not include experimental validation to confirm the biological mechanisms underlying the observed associations. Therefore, further research including studies involving diverse populations, subtypes, and experimental models is essential to elucidate the effect of microbiota in lung cancer pathogenesis. Conclusion In summary, this study is the first to systematically investigate the potential causal relationships between microbiota from distinct anatomical sites and lung cancer using a two-sample MR analysis. The findings suggested that specific oral and gut microbial taxa may contribute to lung cancer development, offering valuable insights for future clinical diagnostics and basic research. Further studies are advocated to validate these results in more diverse populations and to elucidate the underlying biological mechanisms by which microbiota from different body sites may influence lung carcinogenesis and strategies for prevention and early intervention. Declarations Acknowledgement None. Author contributions Zi-Jian Huang collected data, developed the methodology, processed the data, created data, visualizations. Lv Wu processed the data, created data visualizations, and wrote the original draft. Ying-Long Peng collected data, processed the data. Hong-Hong Yan provided professional statistical guidance. Zhi-Hong Chen, Chong-Rui Xu, Yu Deng guided the revision of the article. Qing Zhou and Chang Lu conceptualized and designed the study, reviewed and edited the manuscript, administered and supervised the project and acquired funding. All authors read and approved the final manuscript. Fundings This work was supported by Guangzhou Science and Technology Program (Grant No. 2025A03J4506)，the National Natural Science Foundation of China (Grant No. 82373349)， Guangdong Provincial Science and Technology Planning Project (Grant No. 2023B110009) , MOE Changjiang Distinguished Professor Supporting Project (Grant No. KY0120240205) and the National Natural Science Foundation of China (Grant No. 82303643 to Chang Lu) Data availability The datasets analyzed in this study are publicly available through the following online repositories. Genome-wide association study (GWAS) data for lung cancer were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas). Data for both gut and oral microbiota were accessed from the China National GeneBank (CNGB) project database (https://db.cngb.org/search/project/CNP0000794/). The R code used for data processing and analysis is not publicly available but can be obtained from the corresponding author upon reasonable request. Ethical approval and consent for participation All genome-wide association studies (GWAS) and microbiome datasets utilized in this study were obtained from publicly available repositories and were previously approved by the relevant institutional ethical review boards. Informed consent was obtained from all participants in the original studies. As only publicly available, de-identified summary statistics were analyzed, no additional ethical approval was required for the present study. Clinical trial number: not applicable. Consent to Publish declaration Not applicable Declaration of interests Q. Zhou reports honoraria from AstraZeneca, Boehringer Ingelheim, BMS, Eli Lilly, MSD, Pfizer, Roche, and Sanofi outside the submitted work. The other authors have no competing interests to declare. Competing interests None. References Filho, A.M., et al., The GLOBOCAN 2022 cancer estimates: Data sources, methods, and a snapshot of the cancer burden worldwide. Int J Cancer, 2025. 156 (7): p. 1336-1346. Meyer, M.-L., et al., New promises and challenges in the treatment of advanced non-small-cell lung cancer. The Lancet, 2024. 404 (10454): p. 803-822. Dickson, R.P., et al., Enrichment of the lung microbiome with gut bacteria in sepsis and the acute respiratory distress syndrome. Nature microbiology, 2016. 1 (10): p. 1-9. Natalini, J.G., S. Singh, and L.N. Segal, The dynamic lung microbiome in health and disease. Nature Reviews Microbiology, 2023. 21 (4): p. 222-235. Caselli, E., et al., Defining the oral microbiome by whole-genome sequencing and resistome analysis: the complexity of the healthy picture. BMC microbiology, 2020. 20 : p. 1-19. Scher, J.U., et al., The lung microbiota in early rheumatoid arthritis and autoimmunity. Microbiome, 2016. 4 : p. 1-10. Baker, J.M., et al., Whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota. Microbiome, 2021. 9 (1): p. 99. Tsay, J.-C.J., et al., Airway microbiota is associated with upregulation of the PI3K pathway in lung cancer. American journal of respiratory and critical care medicine, 2018. 198 (9): p. 1188-1198. Patnaik, S.K., et al., Lower airway bacterial microbiome may influence recurrence after resection of early-stage non–small cell lung cancer. The Journal of thoracic and cardiovascular surgery, 2021. 161 (2): p. 419-429. e16. Zitvogel, L. and G. Kroemer, Lower airway dysbiosis exacerbates lung cancer. Cancer Discovery, 2021. 11 (2): p. 224-226. Emdin, C.A., A.V. Khera, and S. Kathiresan, Mendelian randomization. Jama, 2017. 318 (19): p. 1925-1926. Ma, J., et al., Association of gut microbiome and primary liver cancer: A two ‐sample Mendelian randomization and case–control study. Liver International, 2023. 43 (1): p. 221-233. Zeng, J., et al., The causal effect of gut microbiota and plasma metabolome on lung cancer and the heterogeneity across subtypes: A mendelian randomization study. Journal of Personalized Medicine, 2024. 14 (5): p. 453. Feng, K., F. Ren, and X. Wang, Association between oral microbiome and seven types of cancers in East Asian population: a two-sample Mendelian randomization analysis. Frontiers in Molecular Biosciences, 2023. 10 : p. 1327893. Liu, X., et al., Metagenome-genome-wide association studies reveal human genetic impact on the oral microbiome. Cell Discovery, 2021. 7 (1): p. 117. Liu, X., et al., A genome-wide association study for gut metagenome in Chinese adults illuminates complex diseases. Cell discovery, 2021. 7 (1): p. 9. Jie, Z., et al., A transomic cohort as a reference point for promoting a healthy human gut microbiome. Medicine in Microecology, 2021. 8 : p. 100039. Zhu, J., et al., Over 50,000 metagenomically assembled draft genomes for the human oral microbiome reveal new taxa. Genomics, proteomics & bioinformatics, 2022. 20 (2): p. 246-259. Yang, J., et al., GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics, 2011. 88 (1): p. 76-82. Fang, C., et al., Assessment of the cPAS-based BGISEQ-500 platform for metagenomic sequencing. Gigascience, 2018. 7 (3): p. gix133. Sidore, C., et al., Genome sequencing elucidates Sardinian genetic architecture and augments association analyses for lipid and blood inflammatory markers. Nature genetics, 2015. 47 (11): p. 1272-1281. Sun, Y., J. Zhou, and K. Ye, White blood cells and severe COVID-19: a Mendelian randomization study. Journal of personalized medicine, 2021. 11 (3): p. 195. Davies, N.M., M.V. Holmes, and G.D. Smith, Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. bmj, 2018. 362 . Hartwig, F.P., G. Davey Smith, and J. Bowden, Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. International journal of epidemiology, 2017. 46 (6): p. 1985-1998. Bowden, J., et al., Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genetic epidemiology, 2016. 40 (4): p. 304-314. Allen, R.J., et al., Genetic variants associated with susceptibility to idiopathic pulmonary fibrosis in people of European ancestry: a genome-wide association study. The Lancet respiratory medicine, 2017. 5 (11): p. 869-880. Burgess, S., et al., Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants. Epidemiology, 2017. 28 (1): p. 30-42. Verbanck, M., et al., Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nature genetics, 2018. 50 (5): p. 693-698. Hemani, G., K. Tilling, and G. Davey Smith, Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS genetics, 2017. 13 (11): p. e1007081. Hemani, G., et al., The MR-Base platform supports systematic causal inference across the human phenome. elife, 2018. 7 : p. e34408. Yavorska, O.O. and S. Burgess, MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. International journal of epidemiology, 2017. 46 (6): p. 1734-1739. Samadi, A.K., et al., A multi-targeted approach to suppress tumor-promoting inflammation. Semin Cancer Biol, 2015. 35 Suppl : p. S151-s184. He, J., et al., Association between oral microbiome and five types of respiratory infections: a two-sample Mendelian randomization study in east Asian population. Frontiers in Microbiology, 2024. 15 : p. 1392473. Goeteyn, E., et al., Aggregatibacter is inversely associated with inflammatory mediators in sputa of patients with chronic airway diseases and reduces inflammation in vitro. Respiratory research, 2024. 25 (1): p. 368. Huang, D.H., et al., The airway microbiota of non ‐small cell lung cancer patients and its relationship to tumor stage and EGFR gene mutation. Thoracic Cancer, 2022. 13 (6): p. 858-869. Wang, Z., et al., Inflammatory endotype Associated airway microbiome in COPD clinical stability and exacerbations-a multi-cohort longitudinal analysis. American Journal of Respiratory and Critical Care Medicine, 2021. Liao, S., et al., Associations between chronic obstructive pulmonary disease and ten common cancers: novel insights from Mendelian randomization analyses. BMC Cancer, 2024. 24 (1): p. 601. Zeng, W., et al., Alterations of lung microbiota in patients with non-small cell lung cancer. Bioengineered, 2022. 13 (3): p. 6665-6677. Mao, Q., et al., Interplay between the lung microbiome and lung cancer. Cancer letters, 2018. 415 : p. 40-48. Souza, V.G., et al., The complex role of the microbiome in non-small cell lung cancer development and progression. Cells, 2023. 12 (24): p. 2801. Lin, L., et al., The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans. Nature Medicine, 2023. 29 (7): p. 1750-1759. Li, Q., et al., Streptococcus thermophilus Inhibits Colorectal Tumorigenesis Through Secreting β-Galactosidase. Gastroenterology, 2021. 160 (4): p. 1179-1193.e14. Wypych, T.P., L.C. Wickramasinghe, and B.J. Marsland, The influence of the microbiome on respiratory health. Nature immunology, 2019. 20 (10): p. 1279-1290. Jaeger, N., et al., Airway microbiota-host interactions regulate secretory leukocyte protease inhibitor levels and influence allergic airway inflammation. Cell reports, 2020. 33 (5). Additional Declarations No competing interests reported. Supplementary Files figS1.pdf figS2.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Aug, 2025 Reviews received at journal 25 Aug, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviews received at journal 09 Aug, 2025 Reviewers agreed at journal 09 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 26 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7222084","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498741921,"identity":"a1edd5ef-9068-420c-bf08-aafad22a2eb4","order_by":0,"name":"Zi-Jian Huang","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zi-Jian","middleName":"","lastName":"Huang","suffix":""},{"id":498741922,"identity":"4bdb94d6-4d1c-4b84-a173-13321c1f504d","order_by":1,"name":"Lv Wu","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lv","middleName":"","lastName":"Wu","suffix":""},{"id":498741924,"identity":"9b54ed5b-9ffe-4c8c-b288-4d4ab27e8b45","order_by":2,"name":"Ying-Long Peng","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying-Long","middleName":"","lastName":"Peng","suffix":""},{"id":498741926,"identity":"6dcd7fc2-acdd-4e9b-b638-61113d1ab57e","order_by":3,"name":"Zhi-Hong Chen","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhi-Hong","middleName":"","lastName":"Chen","suffix":""},{"id":498741927,"identity":"0518f7d4-d376-4b40-b410-d6b591d6f12c","order_by":4,"name":"Chong-Rui Xu","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chong-Rui","middleName":"","lastName":"Xu","suffix":""},{"id":498741928,"identity":"014e2647-4610-452d-843b-132619ad9afe","order_by":5,"name":"Yu Deng","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Deng","suffix":""},{"id":498741929,"identity":"e63ba98a-5cec-41df-ad4b-3f61564fb524","order_by":6,"name":"Hong-Hong Yan","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong-Hong","middleName":"","lastName":"Yan","suffix":""},{"id":498741930,"identity":"aa272b9e-e659-4ab5-849e-25c178accedb","order_by":7,"name":"Chang Lu","email":"","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Lu","suffix":""},{"id":498741931,"identity":"fc1a5de4-6098-4dcc-b3ec-acda198f063f","order_by":8,"name":"Qing Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYHACNhDBw8DAfODAhwrStLAlHpxxhgQtIF3Gh3lbiFAv73/82YOPbdYy5vxrPhzgbWCQ5xc7gF+L4YED6YYz29J5LGe83XBAcgeD4czZCQS0NDYck+ZtO8xjcOPshgOGZxgSDG4T0tLM2AbVcubBgcQ2IrTIszGzQbSc72E4cJAYLQY8bGySM86lA21hMzjYcEaCsF/k+48/k/hQZm1vcP7w489/Kmzk+aUJ2XIATDEzMEiAVUrgVw62pQGmhf8AYdWjYBSMglEwMgEAmpBH0on+9ycAAAAASUVORK5CYII=","orcid":"","institution":"Guangdong Lung Cancer Institute, Southern Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-07-26 15:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7222084/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7222084/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88881808,"identity":"70b9f8ed-ec6b-40de-817c-5f204ec3cefe","added_by":"auto","created_at":"2025-08-12 11:13:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":488562,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Mendelian Randomization Hypothesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure1:A two-sample MR analysis investigating oral and gut microbiome as causal factors for lung cancer. (Dashed lines represent potential causal effects between variables that may contradict the MR hypothesis.) Abbreviations: IV, instrumental variable; MR, Mendelian randomization\u003c/p\u003e\n\u003cp\u003eAssumption 1: Relevance assumption, IVs are closely associated with the exposures.\u003c/p\u003e\n\u003cp\u003eAssumption 2: Independence assumption, IVs are independent of confounders.\u003c/p\u003e\n\u003cp\u003eAssumption 3: Exclusivity assumption, IVs affect the outcome only through exposure rather than other ways.\u003c/p\u003e\n\u003cp\u003eMR analysis: Including two-sample MR analysis and sensitivity analyses.\u003c/p\u003e","description":"","filename":"Binder11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/864e63b7d82f8d9222f40a0a.jpg"},{"id":88881809,"identity":"97525b6b-d801-4461-a0cb-e17a92ecdd1d","added_by":"auto","created_at":"2025-08-12 11:13:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295260,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of the causal relationship between oral protective microbiota and the risk of lung cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure2:OR: Odds Ratio, g__: genus, f__: family, s__: specie.\u003c/p\u003e","description":"","filename":"Binder12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/60e9a33a7a5fdd0af1fccd32.jpg"},{"id":88881811,"identity":"66612410-2d13-4801-8651-a14cf305b25d","added_by":"auto","created_at":"2025-08-12 11:13:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":196410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3 Forest plot of the causal relationship between oral harmful microbiota and the risk of lung cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure3:OR: Odds Ratio, \u0026nbsp;g__: genus, f__: family, s__: specie.\u003c/p\u003e","description":"","filename":"Binder13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/8daea4a8b5d758d2efb4941c.jpg"},{"id":88882242,"identity":"45c79d9f-5768-4d95-bdc1-78bbae0d8317","added_by":"auto","created_at":"2025-08-12 11:21:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":270321,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of the causal relationship between species level heterogeneity of oral microbiota and the risk of lung cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure4:OR: Odds Ratio, g__: genus, f__: family, s__: specie.\u003c/p\u003e","description":"","filename":"Binder14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/c0fb8189e02d40f7d29adb06.jpg"},{"id":88881810,"identity":"9f5c24b6-99af-498d-881d-470b1337b5c3","added_by":"auto","created_at":"2025-08-12 11:13:19","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39045,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of the causal relationship between gut microbiota and the risk of lung cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure5:OR: Odds Ratio, \u0026nbsp;g__: genus, f__: family, s__: specie.\u003c/p\u003e","description":"","filename":"Binder15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/150a84cf8573116ae4ed349a.jpg"},{"id":89063840,"identity":"5afca6ca-6750-4b9c-9d62-b01b83e4ce96","added_by":"auto","created_at":"2025-08-14 10:08:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2495524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/e9580ddb-8f30-4ffa-ad63-2dc746db0891.pdf"},{"id":88882243,"identity":"c8f45212-3740-46b8-8d5a-aab972fe53d4","added_by":"auto","created_at":"2025-08-12 11:21:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3682677,"visible":true,"origin":"","legend":"","description":"","filename":"figS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/75bcfa668cc3f1902205a587.pdf"},{"id":88881822,"identity":"22a32db8-4002-404b-b00b-638cfe9936e6","added_by":"auto","created_at":"2025-08-12 11:13:19","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2941203,"visible":true,"origin":"","legend":"","description":"","filename":"figS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7222084/v1/dca5f8d7e44896bdc3ddbc62.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between oral/gut microbiota and lung cancer: a two-sample Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, lung cancer constitutes a significant public health concern due to its high incidence and death rate, ranking among the primary causes of cancer-associated deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite continuous improvement of therapy strategy such as radiotherapy,Operation chemotherapy, targeted therapy and immune checkpoint inhibitors (ICIs), patients still face challenges of drug resistance and recurrence with metastasis [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe advent of the Human Microbiome Project has facilitated a more comprehensive understanding of the human microbiome. The historical perception that the lungs are a sterile environment has been refuted by contemporary research. Utilizing molecular techniques such as Polymerase Chain Reaction (PCR) and Next-Generation Sequencing (NGS), numerous research have now confirmed a close relationship to the oral microbiomes, gut microbiomes and the pulmonary microbiome [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The oral microbiota, second to the gut microbiota in diversity, encompasses a broad range of bacterial genera and families [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Anatomically, the oral cavity is contiguous with the respiratory tract through the oropharynx, which serves as a passageway to both the upper and lower airways, ultimately reaching the lungs. Growing evidence suggests that oral microbiota can translocate to the lungs, where they may establish colonization and influence the composition and dynamics of the pulmonary microbiome [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Moreover, recent research has increasingly emphasized the importance of the pulmonary microbiota within the tumor microenvironment of lung cancer, highlighting its involvement in oncogenesis, tumor progression, and the development of drug resistance [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMendelian Randomization (MR) is an epidemiological methodology that leverages naturally occurring and randomly assigned genetic variants as instrumental variables. This approach effectively mitigates confounding variables and reverse causality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. MR analyses have been widely utilized to investigate potential causal correlations between gut microbiota and the development of various types of cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study by Feng et al., \u003cem\u003eAggregatibacter\u003c/em\u003e and \u003cem\u003eGemella\u003c/em\u003e were positively correlated with a higher risk of lung cancer, whereas \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eCampylobacter A\u003c/em\u003e, and members of the \u003cem\u003eSaccharibacteria\u003c/em\u003e TM7x family were negatively associated with lung cancer risk. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, there remains a paucity of study elucidating the causal relationships between the more comprehensive oral microbiota and lung cancer.\u003c/p\u003e\u003cp\u003eThis research endeavored to explore the potential causal relationships between lung cancer and both oral and gut microbiota, with a particular focus on identifying pathogenic microbial clusters. To achieve this, we performed a MR analysis based on the data derived from Genome-Wide Association Studies (GWAS). By treating lung cancer as the outcome and oral and gut microbiota as exposures, we sought to determine whether specific microbial taxa play contributory or protective roles in lung cancer development.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cp\u003e\u003cb\u003eStudy designs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn this work, we conducted a two-sample MR analysis to research the potential causal connection between oral and gut microbiota which derived from the Shenzhen cohort and lung cancer risk in the Japanese cohort. Data were extracted from multiple publicly available repositories. To guarantee the stability and credibility of the outcomes, an array of sensitivity analyses were performed. An overview of the research design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSource of oral/ gut microbiome data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn our investigation of the oral microbiota, we drew upon data from an extensive GWAS, covering 2,017 samples from the dorsal tongue and 1,915 saliva samples obtained from 2,984 Chinese healthy subjects. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. We had access to extensive whole-genome sequencing data, which revealed 455 independent associations. These associations involved 340 distinct genetic loci and 385 microbial taxa, all reaching genome-wide significance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The microbial community exhibited high coverage, with 99.7% in tongue dorsum samples and 98.7% in saliva samples, indicating comprehensive representation of the oral microbiota in the dataset. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the gut microbiome component, all Chinese adult participants included in this study were enrolled as part of a multi-omics investigation. The discovery cohort comprised 2,002 individuals recruited during routine physical examinations between March and May 2017 in Shenzhen. Among them, blood samples were captured from all volunteers, and fecal samples were available for 1,539 individuals. All participants underwent high-depth whole-genome and whole-metagenomic sequencing. For replication, 1,430 individuals were recruited across multiple cities in China (e.g., Wuhan, Qingdao), following the same study design but on a smaller scale. Of these, 1,006 provided both blood and fecal samples. Sample collection protocols for both blood and stool, as well as sequencing procedures, were consistent with those established in our previous studies [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSources of lung cancer data\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGWAS summary statistics for lung cancer were obtained from a large genome-wide association study conducted in an East Asian population. The dataset featured a comprehensive genetic association map highlighting the pleiotropic landscape, including key loci within the major histocompatibility complex (MHC) region and fine-mapped human leukocyte antigen (HLA) targets. This non-European cohort comprised a total of 212,453 individuals and included 8,885,805 single-nucleotide polymorphisms (SNPs). Quality control procedures were applied to address imbalances, and adjustments were made for potential confounding factors. The annotated version was builted with HG19/GRCh37.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInstrument selection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the substantial number of SNPs achieving genome-wide significance (p \u0026lt; 5 × 10^-8) for traits related to oral and gut microbiome groups, we applied a more stringent significance threshold (p \u0026lt; 5 × 10^-9) to select robust genetic IVs. These IVs were identified by grouping them according to the reference panel of the Linkage Disequilibrium (LD) from the 1000 Genomes Project, with a threshold of R^2 \u0026lt; 0.001 within a distance of 1,000 kilobases (kb) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To ensure the robustness and reliability of IVs, we retained only those with F-statistics over than 10, thereby identifying them as strong tools for following MR analysis. These selected IVs were then drawn from the GWAS summary statistics for lung cancer. To minimize potential bias from horizontal pleiotropy, SNPs exhibiting direct associations with lung cancer (p \u0026lt; 10^-5) were excluded. These are in accordance with established protocols in previous studies [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. To maintain consistency in our analysis, the SNPs between the exposure and outcome datasets were synchronized to confirm uniform effect estimates for the same effect allele [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eIn this research, a range of individual genetic variants were utilized as IVs, instead of depending solely on aggregated allele scores. This strategy was adopted to enable a more comprehensive assessment of the core assumptions underlying MR as well as to detect and account for horizontal pleiotropy thus enhancing the robustness of sensitivity analyses [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo evaluate the robustness of our findings under varying assumptions regarding heterogeneity and pleiotropy, we applied complementary MR methods: inverse variance weighted (IVW; random-effects model), weighted median, MR-Egger regression, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analysis. The IVW method, implemented under a random-effects model, was used as the main analytical approach across all four category of instrumental variables. Heterogeneity among the IVs was assessed using Cochran’s Q statistic to evaluate variability.\u003c/p\u003e\u003cp\u003eTo enhance the robustness of our findings, we also conducted analyses under more stringent conditions. While the IVW method presumes that all genetic variants are sound instruments, its estimates may be biased in the existence of substantial horizontal pleiotropy affecting a considerable number of SNPs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Conversely, the weighted median method presumes that at least 50% of the overall weight comes from IVs, making it robust even when up to half of the variants are affected by horizontal pleiotropy [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. When more than 50% of the variants were potentially affected by horizontal pleiotropy, we evaluated the robustness of the genetic instruments using F-statistics, with a mean F-statistic below 10 considered suggestive of weak instrument bias [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, the MR-Egger method was employed to detect potential directional pleiotropy. A statistically significant intercept from the MR-Egger analysis would reveal a violation of the instrumental variable assumptions, suggesting the existence of unbalanced directional pleiotropy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Besides, the MR-PRESSO method was implemented to reduce heterogeneity in causal effect assessments by identifying and excluding outlier SNPs that exerted disproportionate influence on the results (NbDistribution = 1,500) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. What’s more, steiger filtering was implemented to recognize and exclude genetic variants that showed stronger correlations with the outcome than with the exposure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted using R version 4.3.1 (R Foundation) and R packages (“TwoSampleMR” and “MR”) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The TwoSampleMR package provided causal estimates from the four MR models (IVW, weighted median, MR-Egger, and MR-PRESSO).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eFollowing a series of quality control procedures, 85,043 SNPs were retained for analysis including 84,578 associated with the oral microbiome and 465 with the gut microbiome. All selected IVs demonstrated F-statistics exceeding 10 thus indicating no evidence of weak instrument bias. Eventually, we selected 42,353 SNPs from the oral microbiome and 121 SNPs from the gut microbiome for further analysis. In the oral microbiome, we chose 968 SNPs representing genera such as \u003cem\u003egenus Aggregatibacter\u003c/em\u003e, \u003cem\u003ePseudopropionibacterium\u003c/em\u003e, and \u003cem\u003eCapnocytophaga\u003c/em\u003e. In the gut microbiome, we selected 6 SNPs representing \u003cem\u003especies Enterococcus faecalis\u003c/em\u003e and three metabolic pathways.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCausal effect of oral microbiota on lung cancer\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUtilizing the IVW method in MR analysis, we identified that increased abundance of several bacterial taxa, as genetically predicted, was correlated with a reduced risk of lung cancer. Specifically, these included the \u003cem\u003egenus Pauljensenia\u003c/em\u003e (OR: 0.777, 95% CI: 0.651–0.927, \u003cem\u003ep\u003c/em\u003e = 0.0052), \u003cem\u003egenus Capnocytophaga\u003c/em\u003e (OR: 0.748, 95% CI: 0.580–0.966, \u003cem\u003ep\u003c/em\u003e = 0.0260), \u003cem\u003egenus Centipeda\u003c/em\u003e (represented by \u003cem\u003especie unclassified mgs 2230\u003c/em\u003e: OR: 0.809, 95% CI: 0.669–0.977, \u003cem\u003ep\u003c/em\u003e = 0.0279), and \u003cem\u003egenus Aggregatibacter\u003c/em\u003e which includes \u003cem\u003especie segnis mgs 2462\u003c/em\u003e (OR: 0.818, 95% CI: 0.673–0.994, \u003cem\u003ep\u003c/em\u003e = 0.0432), \u003cem\u003esp000466335 mgs 1474\u003c/em\u003e (OR: 0.770, 95% CI: 0.651–0.910, \u003cem\u003ep\u003c/em\u003e = 0.0021) and \u003cem\u003esp000466335 mgs 2558\u003c/em\u003e (OR: 0.746, 95% CI: 0.602–0.926, \u003cem\u003ep\u003c/em\u003e = 0.0078) and so on. These results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eIn contrast, increased abundance of certain bacterial taxa was connected to an elevated risk of lung cancer. Specifically, these included \u003cem\u003egenus Prevotella\u003c/em\u003e which includes \u003cem\u003especie baroniae mgs 143\u003c/em\u003e (OR: 1.294, 95% CI: 1.048–1.598, p = 0.0012) and \u003cem\u003especie buccae\u003c/em\u003e mgs 3394 (OR: 1.479, 95% CI: 1.071–2.044, p = 0.0037), \u003cem\u003egenus Granulicatella\u003c/em\u003e which includes \u003cem\u003especie elegans mgs 1090\u003c/em\u003e (OR: 1.210, 95% CI: 1.019–1.436, p = 0.0064) and \u003cem\u003especie unclassified mgs 2338\u003c/em\u003e (OR: 1.192, 95% CI: 1.010–1.406, p = 0.0160), \u003cem\u003egenus Streptococcus\u003c/em\u003e which includes \u003cem\u003especie oralis C mgs 62\u003c/em\u003e (OR: 1.298, 95% CI: 1.004–1.679, p = 0.0292), \u003cem\u003especie unclassified mgs 1416\u003c/em\u003e (OR: 1.279, 95% CI: 1.072–1.526, p = 0.0446), \u003cem\u003especie unclassified mgs 2628\u003c/em\u003e (OR: 1.179, 95% CI: 1.031–1.348, p = 0.0370), \u003cem\u003egenus Veillonella\u003c/em\u003e (OR: 1.304, 95% CI: 1.016–1.673, p = 0.0140), and \u003cem\u003egenus Fusobacterium\u003c/em\u003e (OR: 1.296, 95% CI: 1.023–1.641, p = 0.0043) and so on. These findings are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eNotably, bacteria within the \u003cem\u003egenus Pauljensenia, Streptococcus, Centipeda, TM7x\u003c/em\u003e and \u003cem\u003eHaemophilus_D\u003c/em\u003e and other four genus exhibited species level heterogeneity on lung cancer risk. Specifically, different species within the same genus were associated with increased or decreased risks of lung cancer. To clearly illustrate this phenomenon, we summarized these species level heterogeneity bacteria in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCausal effects of gut microbiota on lung cancer\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCompared with the oral microbiota, certain features of the gut microbiota were correlated with lung cancer risk. Specifically, the metabolic pathways xylose degradation (OR: 0.490, 95% CI: 0.268–0.893, \u003cem\u003ep\u003c/em\u003e = 0.0199) and threonine degradation II (OR: 0.419, 95% CI: 0.233–0.755, \u003cem\u003ep\u003c/em\u003e = 0.0038) were associated with a reduced risk of lung cancer. Conversely, \u003cem\u003egenus Enterococcus species faecium\u003c/em\u003e (OR: 1.180, 95% CI: 1.030–1.353, \u003cem\u003ep\u003c/em\u003e = 0.0173) and the \u003cem\u003eAcetyl-CoA Synthetase Pathway\u003c/em\u003e (OR: 1.884, 95% CI: 1.121–3.168, \u003cem\u003ep\u003c/em\u003e = 0.0169) were positively associated with a higher risk of lung cancer. These findings are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSensitivity analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA set of sensitivity analyses were performed to evaluate the presence of heterogeneity and horizontal pleiotropy among the selected IVs. Horizontal pleiotropy was specifically analyzed using the MR-Egger intercept test. All \u003cem\u003ep\u003c/em\u003e values derived from the MR-Egger intercepts were greater than 0.05, suggesting no significant proof of directional horizontal pleiotropy.\u003c/p\u003e\u003cp\u003eCochran’s Q test was applied to evaluate heterogeneity among the selected SNPs. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the \u003cem\u003eQ_pval\u003c/em\u003e values for both the IVW and MR-Egger methods exceeded 0.05, indicating no significant heterogeneity and suggesting that the results were unlikely to be affected. Owing to the relatively limited number of SNPs available for the gut microbiome analysis, sensitivity analyses could not be reliably performed for that dataset. Detailed scatter plots for each MR method are presented in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. These results demonstrated consistent directions and reinforced the reliability of our findings.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePleiotropy and sensitivity analyses of the relationship between oral/ gut microbiota and the risk of lung cancer.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEgger_intercept\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSe\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003epval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIVW_Q_pval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEgger_Q_pval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Pauljensenia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0854\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Pseudopropionibacterium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4484\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Capnocytophaga\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2086\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ef__Weeksellaceae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.7291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9583\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Eggerthia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0051\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7757\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003es__Solobacterium_extructa_mgs_68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0690\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Solobacterium|s__unclassified_mgs_2929 |\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5820\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_constellatus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0162\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0991\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0991\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_infantis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9641\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_mitis_AT_mgs_1239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8978\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_mitis_I_mgs_3086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8886\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_oralis_mgs_174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0092\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3953\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3953\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_pseudopneumoniae_A_mgs_1809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0098\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3619\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_sp000187745_mgs_2287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__unclassified_mgs_2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.5101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0939\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8567\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__unclassified_mgs_3505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6315\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6629\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__RUG343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.8533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Gemella|s__Gemella_haemolysans_B_mgs_2903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Gemella|s__Gemella_morbillorum_mgs_3548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4074\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Catonella|s__unclassified_mgs_1601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9867\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6485\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6485\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003es__Lachnoanaerobaculum_sp000287675_mgs_3023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5070\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003es__Lachnoanaerobaculum_sp000287675_mgs_978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0199\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2415\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Shuttleworthia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3819\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Filifactor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5253\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5253\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Parvimonas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6323\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Centipeda\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4042\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ef__Veillonellaceae|g__F0422|s__unclassified_mgs_1719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptobacillus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c6\"\u003e\u003cp\u003e0.5339\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.2930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0850\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7303\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_572\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.3090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1701\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd 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colname=\"c2\"\u003e\u003cp\u003e0.2920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3801\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Aggregatibacter|s__Aggregatibacter_sp000466335_mgs_1474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1564\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Aggregatibacter|s__Aggregatibacter_sp000466335_mgs_2558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0066\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6985\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6985\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Haemophilus_D|s__Haemophilus_D_pittmaniae_mgs_3301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.9352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6478\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6478\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Haemophilus|s__unclassified_mgs_1198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd 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colname=\"c4\"\u003e\u003cp\u003e0.3368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7102\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Pauljensenia|s__unclassified_mgs_1346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.7069\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6012\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Pauljensenia|s__unclassified_mgs_3155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4963\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Lancefieldella|s__Lancefieldella_sp000564995_mgs_1171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2764\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9107\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Lancefieldella|s__Lancefieldella_sp000564995_mgs_1519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2159\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Lancefieldella|s__unclassified_mgs_1233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1138\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Lancefieldella|s__unclassified_mgs_2115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2186\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2799\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4455\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7938\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Prevotella|s__Prevotella_baroniae_mgs_143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2970\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3073\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3620\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4077\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Prevotella|s__Prevotella_buccae_mgs_3394\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4607\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ef__Weeksellaceae|g__unclassified_mgs_1987\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2826\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3089\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Solobacterium|s__unclassified_mgs_2820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1118\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Granulicatella|s__Granulicatella_elegans_mgs_1090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0776\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1722\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Granulicatella|s__unclassified_mgs_2338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.4660\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8800\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__Streptococcus_oralis_C_mgs_62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1455\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6267\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__unclassified_mgs_1416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2257\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Streptococcus|s__unclassified_mgs_2628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5848\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Gemella|s__unclassified_mgs_3512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1971\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7350\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Oribacterium|s__unclassified_mgs_2356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2706\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Centipeda|s__unclassified_mgs_1154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0079\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.6884\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Veillonella|s__Veillonella_rogosae_mgs_1856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.2446\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2272\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Fusobacterium|s__unclassified_mgs_2195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.5431\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5194\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_1548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0675\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_3406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.0777\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8277\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.3262\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3766\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__TM7x|s__unclassified_mgs_605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1655\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8418\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.8418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd 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colname=\"c1\"\u003e\u003cp\u003eg__Haemophilus_D|s__unclassified_mgs_1145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.5706\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3090\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eg__Haemophilus_D|s__unclassified_mgs_3426\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3137\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF0021:xylose_degradation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF0050:threonine_degradation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003es_Enterococcus_faecium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMF0075:acetate_to_acetyl-CoA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eLeave-one-out analysis revealed that sequential removal of individual SNPs did not result in substantial changes to the overall causal estimates. This suggests that no single SNP exerted a disproportionate influence and no influential outliers were identified. These results are illustrated in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our understanding, this is the first study which utilized the two-sample MR approach to systematically evaluate the potential causal relationships between oral microbiota and lung cancer. Our findings identified several novel microbial taxa potentially increased or decreased risk of lung cancer. What\u0026rsquo;s more, we summarized several specific bacterial genera in which different species have different effects on lung cancer development. Collectively, these findings deepen our understanding of the lung cancer microbiota axis as well as providing potential evidence for future clinical translation and microbiome-targeted research.\u003c/p\u003e\u003cp\u003eMR analysis reduce confounding factors by utilizing IVs, which are common limitations in observational research. Additional sensitivity further validated the consistency of our results to strengthen the credibility of our conclusions. This integrative MR framework allowed us to gain clearer insights into the potential roles of microbiota from distinct anatomical sites in the etiology of lung cancer.\u003c/p\u003e\u003cp\u003eOur research identified that certain specific bacteria within \u003cem\u003egenus Pauljensenia\u003c/em\u003e, \u003cem\u003eCentipeda\u003c/em\u003e and \u003cem\u003eAggregatibacter\u003c/em\u003e, are significantly associated with reduced lung cancer risk. In contrast, higher abundances of bacteria within the \u003cem\u003egenus Prevotella\u003c/em\u003e, \u003cem\u003eGranulicatella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eTM7x\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, and \u003cem\u003eHaemophilus_D\u003c/em\u003e were associated with increased lung cancer risk. Those discoveries have important implications for diagnostic biomarkers, as it suggests that these bacteria could potentially serve as indicators for lung cancer risk assessment.\u003c/p\u003e\u003cp\u003eOur study findings are in accordance with some of the previous research. Former studies suggested that approximately 25% of all cancers are etiologically associated with chronic inflammation and infection [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The risk for cancer of the respiratory system is positively associated with inflammatory diseases [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Among the bacteria significantly linked to reduced lung cancer risk, the study by He et al. found that \u003cem\u003ePauljensenia\u003c/em\u003e and \u003cem\u003eCapnocytophaga\u003c/em\u003e are associated with decreased incidence of bronchitis and tonsillitis, as well as the inhibition of pneumonia and bronchitis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The abundance of \u003cem\u003eAggregatibacter\u003c/em\u003e has been shown to be negatively associated with inflammatory markers in sputum like interleukin-8 (IL-8) and interleukin-1β (IL-1β), and demonstrates anti-inflammatory properties in lower respiratory tract samples from individuals with chronic airway disease (CAD). [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConversely, among the bacteria of increased lung cancer risk, Huang et al. found that bacteria of the \u003cem\u003egenus Granulicatella\u003c/em\u003e are significantly enriched in the lung microbiota of patients with lung cancer [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Wang et al. discovered that increased abundance of \u003cem\u003eGranulicatella\u003c/em\u003e is linked to the transition from neutrophilic to eosinophilic chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which is often considered as a common risk factor for lung cancer [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The \u003cem\u003egenus Streptococcus\u003c/em\u003e has been shown to be enriched in NSCLC patient [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Mao et al. reported a significant rise in the abundance of \u003cem\u003eTM7\u003c/em\u003e phylum bacteria in patients with lung cancer particularly in those who diagnosed with lung squamous cell carcinoma and lung adenocarcinoma [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The \u003cem\u003ephylum TM7\u003c/em\u003e bacteria and its subgroup c:TM7-3 were significantly increased in bronchoalveolar lavage fluid (BALF) samples from persons with lung cancer and potentially serving as reliable biomarkers [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Hosts with higher abundance of \u003cem\u003egenus Neisseria\u003c/em\u003e are more susceptible to environmental damage, resulting in an increased risk of respiratory cancer [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMore importantly, we identified certain genus, such as \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eTM7x\u003c/em\u003e, and \u003cem\u003eHaemophilus\u003c/em\u003e, where different species within the same genus has varying effects on lung cancer.Within the genus \u003cem\u003eStreptococcus\u003c/em\u003e, Some speices of \u003cem\u003eStreptococcus\u003c/em\u003e was found to mediated the anticancer effects through β-Galactosidase which activate oxidative phosphorylation and downregulate the Hippo pathway kinases [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Certain \u003cem\u003eStreptococcus\u003c/em\u003e species have been shown to accelerate cell cycle while preventing apoptosis in lung cancer cells, thereby facilitating the occurrence of lung cancer. Studies have shown that increased abundance of \u003cem\u003eHaemophilus\u003c/em\u003e is associated with exacerbated inflammatory responses [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. However, the presence of \u003cem\u003eHaemophilus\u003c/em\u003e is also correlated with increased abundance of the antimicrobial peptide secretory leukocyte protease inhibitor (SLPI), which is one of the key factors in maintaining respiratory homeostasis [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our study results advocate microbiota research should be detailed to the species level and highlight the importance of microbial community diversity in maintaining normal oral and respiratory functions, as well as the necessity of dynamically monitoring the composition of the upper respiratory tract microbiota for its significance in the development of lung cancer.\u003c/p\u003e\u003cp\u003eCompared with the oral microbiota, we identified fewer gut microbial taxa associated with altered lung cancer risk. From a physiological standpoint, the oral and respiratory microbiota directly constitute the pulmonary microbiome, thereby more directly influencing the physiological and immune functions of the lungs.\u003c/p\u003e\u003cp\u003eHowever, several limitations should be acknowledged. Firstly, the study population was predominantly composed of Asian descent, which may restrict the generalizability of the results to other ethnic groups. Secondly, the analysis did not distinguish between specific subtypes of lung cancer. Given the heterogeneous nature of lung cancer, future studies are warranted to explore subtype-specific associations. Lastly, this study did not include experimental validation to confirm the biological mechanisms underlying the observed associations. Therefore, further research including studies involving diverse populations, subtypes, and experimental models is essential to elucidate the effect of microbiota in lung cancer pathogenesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study is the first to systematically investigate the potential causal relationships between microbiota from distinct anatomical sites and lung cancer using a two-sample MR analysis. The findings suggested that specific oral and gut microbial taxa may contribute to lung cancer development, offering valuable insights for future clinical diagnostics and basic research. Further studies are advocated to validate these results in more diverse populations and to elucidate the underlying biological mechanisms by which microbiota from different body sites may influence lung carcinogenesis and strategies for prevention and early intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eZi-Jian Huang collected data, developed the methodology, processed the data, created data, visualizations. Lv Wu processed the data, created data visualizations, and wrote the original draft. Ying-Long Peng collected data, processed the data. Hong-Hong Yan provided professional statistical guidance. Zhi-Hong Chen, Chong-Rui Xu, Yu Deng guided the revision of the article. Qing Zhou and Chang Lu conceptualized and designed the study, reviewed and edited the manuscript, administered and supervised the project and acquired funding. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u0026nbsp;\u003c/strong\u003eThis work was supported by Guangzhou Science and Technology Program (Grant No. 2025A03J4506)，the National Natural Science Foundation of China (Grant No. 82373349)，\u0026nbsp;Guangdong Provincial Science and Technology Planning Project (Grant No. 2023B110009) , MOE Changjiang Distinguished Professor Supporting Project (Grant No. KY0120240205) and the National Natural Science Foundation of China (Grant No. 82303643 to Chang Lu)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003eThe datasets analyzed in this study are publicly available through the following online repositories. Genome-wide association study (GWAS) data for lung cancer were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas). Data for both gut and oral microbiota were accessed from the China National GeneBank (CNGB) project database (https://db.cngb.org/search/project/CNP0000794/). The R code used for data processing and analysis is not publicly available but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent for participation\u003c/strong\u003eAll genome-wide association studies (GWAS) and microbiome datasets utilized in this study were obtained from publicly available repositories and were previously approved by the relevant institutional ethical review boards. Informed consent was obtained from all participants in the original studies. As only publicly available, de-identified summary statistics were analyzed, no additional ethical approval was required for the present study.\u0026nbsp;Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003eQ. Zhou reports honoraria from AstraZeneca, Boehringer Ingelheim, BMS, Eli Lilly, MSD, Pfizer, Roche, and Sanofi outside the submitted work. The other authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFilho, A.M., et al., \u003cem\u003eThe GLOBOCAN 2022 cancer estimates: Data sources, methods, and a snapshot of the cancer burden worldwide.\u003c/em\u003e Int J Cancer, 2025. \u003cstrong\u003e156\u003c/strong\u003e(7): p. 1336-1346.\u003c/li\u003e\n\u003cli\u003eMeyer, M.-L., et al., \u003cem\u003eNew promises and challenges in the treatment of advanced non-small-cell lung cancer.\u003c/em\u003e The Lancet, 2024. \u003cstrong\u003e404\u003c/strong\u003e(10454): p. 803-822.\u003c/li\u003e\n\u003cli\u003eDickson, R.P., et al., \u003cem\u003eEnrichment of the lung microbiome with gut bacteria in sepsis and the acute respiratory distress syndrome.\u003c/em\u003e Nature microbiology, 2016. \u003cstrong\u003e1\u003c/strong\u003e(10): p. 1-9.\u003c/li\u003e\n\u003cli\u003eNatalini, J.G., S. 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Wickramasinghe, and B.J. Marsland, \u003cem\u003eThe influence of the microbiome on respiratory health.\u003c/em\u003e Nature immunology, 2019. \u003cstrong\u003e20\u003c/strong\u003e(10): p. 1279-1290.\u003c/li\u003e\n\u003cli\u003eJaeger, N., et al., \u003cem\u003eAirway microbiota-host interactions regulate secretory leukocyte protease inhibitor levels and influence allergic airway inflammation.\u003c/em\u003e Cell reports, 2020. \u003cstrong\u003e33\u003c/strong\u003e(5).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lung cancer, oral microbiota, gut microbiota, Mendelian randomization, single nucleotide polymorphism","lastPublishedDoi":"10.21203/rs.3.rs-7222084/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7222084/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eSeveral studies have already proven a significant correlation between the microbiota and lung cancer. In this study, we explore the potential relative oral and gut microbiota which influence the risk of lung cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe utilized genome-wide association study (GWAS) data from oral microbiota (2984 healthy individuals) and gut microbiota (2002 healthy individuals) and lung cancer with a two-sample Mendelian randomization (MR) analysis method. In this analysis, oral microbiota and gut microbiota were conducted as exposure. Lung cancer data obtained from GWAS including a total of 212453 individuals. Inverse-variance weighted (IVW) method was used as the primary method.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIVW analysis identified that genus \u003cem\u003ePauljensenia, Capnocytophaga\u003c/em\u003e and \u003cem\u003eAggregatibacter\u003c/em\u003e in oral microbiota are potentially protective against lung cancer. On the contrary, higher abundances of bacteria within the \u003cem\u003egenus Granulicatella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003eTM7x\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e in oral microbiota were associated with increased lung cancer risk. Among gut bacteria, \u003cem\u003especies Enterococcus_faecium\u003c/em\u003e were positively associated with an increased risk of lung cancer.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe findings of this study suggest a potential causal relationship between distinct oral and gut microbial communities and lung cancer risk, offering valuable insights into microbial candidates that may serve as targets for future diagnostic innovations.\u003c/p\u003e","manuscriptTitle":"Causal relationship between oral/gut microbiota and lung cancer: a two-sample Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 11:13:14","doi":"10.21203/rs.3.rs-7222084/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-26T08:01:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T21:51:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135410942504930902408953783092810148894","date":"2025-08-25T06:27:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T16:36:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143698694434870360652260012008414405304","date":"2025-08-09T16:15:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T12:55:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T13:28:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T13:27:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-26T15:18:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e35ede51-45f1-49db-97d5-a77e6c3c4275","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-10T11:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 11:13:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7222084","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7222084","identity":"rs-7222084","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}