Oral Microbiota and Pharyngeal-Laryngeal Cancer Risk: Evidence from Mendelian Randomization in East Asian Populations
Kategoria artykułu: Original Paper
Data publikacji: 16 wrz 2025
Zakres stron: 338 - 346
Otrzymano: 30 cze 2025
Przyjęty: 05 sie 2025
DOI: https://doi.org/10.33073/pjm-2025-029
Słowa kluczowe
© 2025 JINGFENG FU, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Pharyngeal and laryngeal cancer (PLC), primarily squamous cell carcinoma, ranks among the common head and neck malignancies, impacting regions such as the nasopharynx, oropharynx, hypopharynx, and larynx (Igissin et al. 2023). It predominantly affects men over 40, with tobacco use elevating the risk by over tenfold and alcohol consumption further intensifying this susceptibility. Human papillomavirus (HPV) infection also plays a significant role in oropharyngeal cancer development. Typical symptoms include persistent hoarseness, sore throat, and chronic cough (La Vecchia et al. 2008; Liu et al. 2018). Current therapeutic approaches for PLC encompass surgical resection, radiotherapy, chemotherapy, targeted therapies, and immunotherapies (Obid et al. 2019). Despite advances, research continues to uncover the disease’s etiology, enhance early detection, refine treatment strategies, and improve patient quality of life. The potential role of oral microbiota in the initiation and progression of PLC remains underexplored, necessitating further investigation to identify specific microbial communities involved and to elucidate the underlying mechanisms of the development of PLC.
The human microbiome, present across multiple body sites, is integral to many physiological functions. It supports nutrient uptake, maintains epithelial barrier integrity, detoxifies harmful compounds, modulates inflammation and immune responses, and protect against pathogenic microbes (Xu et al. 2015). Advances in next-generation sequencing (NGS) technologies have significantly deepened insights into the microbiome, highlighting the roles of not only gut microbiota but also skin and oral microbial communities (Sędzikowska and Szablewski 2021; Peng et al. 2022; Yang et al. 2022). Despite the rapid growth in microbiome research, therapeutic strategies for microbiome-associated diseases remain in their infancy. For example, while fecal microbiota transplantation can correct antibiotic-induced dysbiosis, the distinct mechanisms of the proliferation of specific bacteria, such as
The oral microbiota is essential for sustaining oral homeostasis, shielding the oral cavity, and preventing disease (Tuganbaev et al. 2022). A balanced oral microbial community collaborates with the host immune system to block external pathogen invasion. It also contributes to diverse physiological functions, such as digestion and immune modulation. Metabolites generated by oral microbes are vital for preserving oral health. Factors such as diet, smoking, alcohol intake, lifestyle, and health conditions shape the composition and variability of the oral microbiota. Studies have linked the oral microbiota to various systemic conditions, including cardiovascular disorders, diabetes, respiratory diseases, autoimmune disorders, and cancer (Sedghi et al.2021). Recent research has revealed a connection between oral microbiota and oncogenesis (Li et al. 2022). Evidence suggests that microbial dysbiosis significantly influences the development of cancers, including oral, gastric, colorectal, liver, lung, and breast cancer (Lan et al. 2023). The interplay between oral microbiota and cancer, along with its underlying oncogenic mechanisms, remains unknown. Interactions among bacterial taxa, particularly
Mendelian randomization (MR) has elucidated causal relationships between the gut microbiome and periodontitis, providing novel insights for the prevention and management of periodontitis (Ye et al. 2023). Additionally, MR analyses have revealed a positive association between uric acid levels and the risk of venous thromboembolism in East Asian populations (Weng et al. 2023). Overall, MR is generating an increasing number of significant discoveries in the medical field, offering valuable clues for disease intervention and treatment strategies. MR provides a practical approach for examining the causal association between oral microbiota and PLC by utilizing genetic variants as instrumental tools to assess causal effects, mitigating biases from confounding through the random distribution of genotypes. Although MR has been extensively employed to explore microbiota-related diseases, such as cancer and metabolic conditions, previous MR studies on PLC have predominantly focused on the gut microbiome. Given the close anatomical proximity of the oral microbiota to PLC sites, it is postulated to exert a more direct impact on PLC development compared to the gut microbiome. This study leveraged genome-wide association study (GWAS) data on oral microbiota, derived from 2,017 tongue dorsum and 1,915 saliva samples from 2,984 healthy Chinese individuals, alongside PLC GWAS data, encompassing 300 cases and 178,426 controls of East Asian descent, to perform a two-sample MR analysis exploring the causal connection between oral microbiota and PLC.
We implemented a two-sample MR framework to rigorously examine the causal relationship between oral microbiota and PLC risk. This approach is grounded in three core assumptions: i) selected genetic variants are strongly associated with their corresponding proteins (relevance); ii) these variants are unaffected by potential confounders (independence); and iii) they impact the outcome solely via the designated proteins (exclusion restriction) (Gill et al. 2021). The analytical design is depicted in Fig. 1, and the study adheres to the STROBE-MR guidelines (Table SI) (Skrivankova et al. 2021).

Schematic representation of the MR analysis.
This study utilized two distinct summary-level genetic datasets. For PLC, we examined a GWAS dataset comprising 300 cases and 178,426 controls of East Asian descent, sourced from a European repository (IEU OpenGWAS ID: ebi-a-GCST90018678) (Sakaue et al. 2021). Genetic instruments for the exposure were obtained from a comprehensive GWAS of oral microbiota, encompassing 2,017 tongue dorsum and 1,915 saliva samples collected from 2,984 healthy Chinese individuals (Liu et al. 2021).
To be selected as instrumental variables (IVs), single-nucleotide polymorphisms (SNPs) were required to demonstrate a genome-wide significant association (typically
We employed the inverse-variance weighted (IVW) approach to evaluate the causal impact of oral microbiota on PLC risk. To ensure the robustness of these findings, we performed sensitivity analyses using weighted median, weighted mode, simple mode, and MR-Egger regression techniques to account for potential pleiotropy. Heterogeneity assessments were conducted to determine the most suitable analytical method, with a
We conducted sensitivity analyses to assess heterogeneity and directional pleiotropy among the instrumental variables. Heterogeneity was evaluated using Cochran’s
To explore potential reverse causation, we performed a reverse MR analysis to evaluate the causal effect of PLC on the previously identified oral microbiota. Instrumental variables for PLC were chosen from SNPs demonstrating an association significance threshold of
All statistical analyses were executed using R (v4.4.1) (R Core Team 2024). Causal effects were calculated with the TwoSampleMR package (v0.6.6), while pleiotropy evaluations were carried out using the MR-PRESSO package (v1.0).
This study relied exclusively on publicly accessible, summary-level GWAS data. All data were anonymized and aggregated, containing no individual-level details. As a result, independent institutional review board approval was not necessary.
This study applied a two-sample MR approach to investigate the causal link between oral microbiota and PLC risk, utilizing summary-level genetic datasets. Following the IV selection criteria, 40,427 SNPs were selected for oral microbiota, each passing the Steiger test with an F-statistic exceeding 10 to mitigate weak instrument bias (Table SII). The forward MR analysis, performed using the IVW method and supported by sensitivity analyses (including weighted median, weighted mode, simple mode, and MR-Egger regression) to account for pleiotropy, revealed 14 proteins with significant causal associations with PLC (

IVW MR analysis of the causal association between oral microbiota and PLC.
Seven oral microbiota were linked to increased PLC risk: g__TM7x (OR = 3.23, 95% CI: 1.35–7.74,

Scatter plots depicting the MR analysis of the causal association between oral microbiota and PLC (risk factors).
Conversely, seven oral microbiota exhibited protective effects: s__Fusobacterium_periodonticum-2 (OR = 0.52, 95% CI: 0.32–0.86,

Scatter plots depicting the MR analysis of the causal association between oral microbiota and PLC (protective factors).
The validity of the primary findings was verified through comprehensive sensitivity analyses. Cochran’s
Sensitivity analysis results of the MR study for PLC.
Exposure | Heterogeneity analysis | Pleiotropy analysis | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Inverse variance weighted | MR Egger | MR Egger | MR PRESSO global test | |||||||
Q | Q_df | Q_pval | Q | Q_df | Q_pval | Egger_ intercept | se | pval | pval | |
g__TM7x | 14.15 | 15.00 | 0.51 | 14.11 | 14.00 | 0.44 | –0.20 | 1.01 | 0.84 | 0.50 |
g__Streptococcus-1 | 13.55 | 14.00 | 0.48 | 13.54 | 13.00 | 0.41 | 0.05 | 0.58 | 0.93 | 0.56 |
s__Fusobacterium_periodonticum-1 | 12.79 | 21.00 | 0.92 | 12.27 | 20.00 | 0.91 | –0.62 | 0.86 | 0.48 | 0.93 |
s__Streptococcus_gordonii | 17.85 | 14.00 | 0.21 | 17.36 | 13.00 | 0.18 | –1.19 | 1.97 | 0.56 | 0.29 |
s__Streptococcus-2 | 11.94 | 17.00 | 0.80 | 11.65 | 16.00 | 0.77 | –0.41 | 0.78 | 0.60 | 0.83 |
g__Solobacterium | 14.12 | 15.00 | 0.52 | 14.05 | 14.00 | 0.45 | –0.22 | 0.83 | 0.80 | 0.56 |
s__Capnocytophaga_sputigena | 8.40 | 16.00 | 0.94 | 8.40 | 15.00 | 0.91 | –0.04 | 1.27 | 0.97 | 0.96 |
s__Fusobacterium_periodonticum-2 | 15.97 | 20.00 | 0.72 | 15.93 | 19.00 | 0.66 | 0.19 | 0.95 | 0.85 | 0.75 |
s__Streptococcus-3 | 17.36 | 20.00 | 0.63 | 16.08 | 19.00 | 0.65 | 1.51 | 1.34 | 0.27 | 0.65 |
s__Haemophilus | 8.52 | 13.00 | 0.81 | 7.46 | 12.00 | 0.83 | 0.60 | 0.58 | 0.32 | 0.83 |
g__Campylobacter | 18.33 | 16.00 | 0.30 | 15.32 | 15.00 | 0.43 | 0.76 | 0.44 | 0.11 | 0.36 |
f__Saccharimonadaceae-1 | 14.54 | 16.00 | 0.56 | 12.20 | 15.00 | 0.66 | 1.30 | 0.85 | 0.15 | 0.53 |
f__Saccharimonadaceae-2 | 31.32 | 22.00 | 0.09 | 31.32 | 21.00 | 0.07 | –0.01 | 0.98 | 0.99 | 0.10 |
f__Saccharimonadaceae-3 | 8.71 | 15.00 | 0.89 | 8.53 | 14.00 | 0.86 | 0.56 | 1.32 | 0.68 | 0.93 |
Reverse MR analyses showed no notable causal impact of genetic predisposition to PLC on the levels of the 14 previously identified oral microbiota (Table SVII). This absence of reverse causality supports a unidirectional relationship, indicating that these oral microbiotas are likely upstream drivers of PLC pathogenesis rather than downstream consequences of the disease. These results strengthen the validity of the primary causal findings and emphasize the potential biological importance of these microbiotas.
This research utilized a two-sample MR framework, leveraging summary statistics from oral microbiota data in East Asian populations and PLC data from the IEU OpenGWAS, to investigate their causal association. Our results identified seven oral microbial taxa conferring a protective effect against PLC, while seven others were linked to an elevated PLC risk.
Recent research has observed a marked elevation in
This study possesses several significant strengths. It is the first to utilize GWAS data from East Asian populations to examine the causal link between oral microbiota and PLC, marking a novel contribution to the field. Additionally, the reliance on a comprehensive, high-quality GWAS database bolstered the reliability of the established causal associations. Furthermore, our MR analysis pinpointed critical microbial taxa warranting further functional exploration, offering valuable insights into potential therapeutic and preventive approaches targeting specific oral microbiota for PLC management.
However, several limitations warrant consideration. The intricate host-microbiome interactions challenge GWAS-based microbiome studies, particularly in pinpointing causal microbial signatures. The small sample size (300 PLC cases) likely reduced statistical power. Given cancer’s complexity and microbiome variability, larger, diverse datasets are needed to validate findings and strengthen causal inferences. Including more genetic variants as IVs could enhance sensitivity analyses and better detect horizontal pleiotropy. The SNPs used did not meet the standard GWAS threshold (
This study explores the causal link between oral microbiota and PLC through a two-sample Mendelian randomization approach. It highlights seven microbial taxa with protective effects and seven associated with increased PLC risk, elucidating their contributions to disease development. Together, these findings deepen our knowledge on the relationship between oral microorganisms and throat health, and underscore the microbiome’s potential as a novel target for diagnostic and therapeutic advancements in PLC management.