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. 2025 Jul 12;16:1323. doi: 10.1007/s12672-025-03181-z

Genetic determinants of head and neck cancer: exploring causality among immune cells and plasma metabolites through two-sample Mendelian randomization and mediation analysis

Yuting Lu 1, Xiangnan Du 1, Hanlin Liu 2, Qing He 2, Xiaolin Cao 2,✉
PMCID: PMC12255607  PMID: 40652067

Abstract

Background

Head and neck cancer represents a major health challenge worldwide, characterized by frequent late-stage diagnoses that adversely affect treatment success. The immune system’s cellular components significantly influence the initiation, advancement, and outcome of head and neck malignancies.

Methods

Our investigation employed Mendelian randomization techniques to analyze genome-wide association data encompassing 731 immune cell phenotypes, 1400 plasma metabolites, and 2281 head and neck cancer patients. We utilized mediation analysis to assess how plasma metabolites might serve as intermediaries between immune cells and head and neck cancer development. We evaluated heterogeneity using IVW and MR-Egger approaches, examined pleiotropy through MR-Egger intercepts, and conducted sensitivity testing via the leave-one-out methodology.

Results

Our analysis revealed potential causal connections linking three immune cell phenotypes and twelve plasma metabolites to head and neck cancer. Through mediation Mendelian randomization, we identified that 3-hydroxypyridine glucuronide concentrations function as a mediator between CD3 on CD39 + secreting Treg cells and head and neck cancer. The measured mediating effect was − 0.011, 95%CI (-0.02, -0.002), representing a mediating proportion of 9.27%. Additionally, we also found that CD3 on CD39 + secreting Treg mediated Cortolone glucuronide (1) levels and CD80 on granulocyte mediated 3-amino-2-piperidone levels exhibit the masking effect on head and neck cancer.

Conclusions

These discoveries provide fresh perspectives on the intricate interactions occurring within the tumor microenvironment and may highlight potential targets for reducing head and neck cancer risk.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03181-z.

Introduction

Head and neck cancer (HNC) originates from malignant tumors on the mucosal surfaces of the oral cavity, nasal sinuses, pharynx, and larynx, making it one of the most prevalent cancers globally [1, 2]. In 2020, approximately 930,000 new cases and 450,000 deaths were reported worldwide, with a projected 30% increase in new cases by 2030 [3]. Due to the insidious growth of tumors, more than 40% of patients are diagnosed at an advanced stage, often accompanied by extensive lymph vascular invasion or distant metastasis [4].

The tumor microenvironment (TME) is a complex network that includes cancer-associated stromal fibroblasts, T cells, B cells, neutrophils, macrophages, myeloid-derived suppressor cells (MDSCs), natural killer (NK) cells, and mast cells. It plays a crucial role in driving tumor progression, invasion, metastasis, and response to treatment [5, 6]. Specific immune cell populations within the TME significantly influence the progression of HNC [7]. The interaction between the surrounding tissue stroma and immune cells in the TME may serve as a key driver of HNC progression. Additionally, while the immune system is capable of recognizing and eliminating tumor cells, the process of immune editing allows tumor cells to evade immune surveillance and even harness the immune system to accelerate metastasis [8]. Notably, HNC not only evades recognition by immune cells but also exerts immunosuppressive effects. This phenomenon may be achieved by downregulating the expression of human leukocyte antigen (HLA), thereby impairing the ability of T cells to recognize cancer cells [9].

Recent studies have demonstrated that abnormal metabolites generated during cancer metabolism may compete for metabolic energy through unique metabolic pathways, playing a critical role in regulating the proliferation, differentiation, activation, and function of immune cells. This, in turn, influences immune cell metabolism and suppresses their antitumor activity [10, 11]. For example, in oesophageal squamous cell carcinoma, Gao et al. found that creatine accumulation and HK3 deficiency synergistically drive M2-like TAM polarization. This metabolic shift fostered an immunosuppressive microenvironment that accelerated tumour progression [12]. In recurrent metastatic nasopharyngeal carcinoma, studies have shown that asparaginase reshapes CD8⁺ T cell metabolism by depleting asparagine, enhancing mitochondrial activity and ROS-mediated activation of the NFAT signaling pathway. Combined with PD-1 inhibitors, this significantly inhibits tumor progression and prolongs patient survival [13]. In the context of head and neck cancer, Zhang et al. revealed that kynurenine induces Siglec-15 expression via the aryl hydrocarbon receptor pathway, leading to functional exhaustion of CD8⁺ T cells and suppression of antitumor immune activity [14]. Therefore, understanding the interactions between immune cells and plasma metabolites is crucial to unravel the pathophysiology of HNC and advance personalized treatment and prevention strategies.

Mendelian Randomization (MR) is an epidemiological method based on genome-wide association study (GWAS) data that uses single nucleotide polymorphisms (SNPs) as instrumental variables to reveal causal relationships. Germline genetic variants are randomly inherited from parents to offspring and, as a result, should not be related to potential confounding factors that influence exposure–outcome associations. The genetic variant can therefore be used as a tool to link the risk factor and outcome, and to estimate this effect with less confounding and bias than conventional epidemiological approaches [15]. MR has proven valuable in identifying causal relationships and is increasingly used to study the causal links between immune cells and cancer [16]. This study employs a two-sample bidirectional MR approach to explore causal relationships between 731 immune cell phenotypes and HNC. Additionally, we investigate the mediating role of 1,400 plasma metabolite levels in these relationships through mediation MR. Our research aims to deepen the understanding of the interactions between immune cells, plasma metabolites, and HNC, offering new insights into the complex mechanisms of cancer immunoregulation and metabolic regulation.

Materials and methods

Study design

This study employed MR to investigate the causal relationships between immune cells and HNC. Additionally, we performed mediation MR to investigate the mediating role of plasma metabolites in the causal relationship between immune cells and HNC (Fig. 1). The instrumental variables (IVs) employed in this investigation must fulfill three core assumptions [17]: (1) Relevance Assumption: There must be a significant association between the genetic instrumental variables and the exposure; (2) Independence Assumption: Genetic instrumental variables should be independent of confounding factors; (3) Exclusivity Assumption: Genetic instrumental variables affect the outcome only through the exposure.

Fig. 1.

Fig. 1

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Study Flowchart

Data sources

Genome-Wide Association Study (GWAS) data for HNC were obtained from the FinnGen Consortium (https://www.finngen.fi/), which includes 2,281 cases and 314,193 controls of European ancestry, with the dataset identifier C3_HEAD_AND_NECK_EXALLC(R10). GWAS data for 731 immune cell phenotypes were sourced from a 2020 study by Orrù et al. [18], involving 3,757 individuals of European ancestry, and were retrieved from the GWAS Catalog (https://www.ebi.ac.uk/gwas, GCST90001391 to GCST90002121). GWAS data for 1,400 plasma metabolites were derived from the Canadian Longitudinal Study on Aging (CLSA) [19], encompassing 8,299 participants of European ancestry and including 1,091 metabolites and 309 metabolite ratios, accessed through the GWAS Catalog (GCST90199621 to GCST90201020). Ethical approval and participant consent were obtained in the original studies.

Selection of instrumental variables

To reduce the influence of confounding factors on the included instrumental variables (IVs), we incorporated the following restrictions based on multiple previous studies. First, the threshold for the association between IVs and exposure variables was set at P < 1 × 10−5, a criterion consistent with recent studies [20, 21]. Second, to ensure the independence of instrumental variables and minimize the impact of linkage disequilibrium (LD) on results, we set the threshold for the LD parameter (r2) at 0.001 and restricted the genetic distance to 10,000 kb. Additionally, to avoid biases caused by weak instrumental variables, we used the F-statistic to evaluate the strength of the association between each SNP and the exposure. The formula is: F = R2/(1- R2) * (N- K- 1)/K. F-values less than 10 indicate weak instrumental variables, which are therefore excluded [22, 23].

Mendelian randomization analysis

Bidirectional MR analysis between immune cells and HNC was performed using five methods: inverse variance weighting (IVW), MR-Egger, weighted median, simple mode, and weighted mode, with IVW serving as the primary method. P < 0.05 is considered to be statistically significant, and P < 0.01 is considered to have stronger significance. The P-values obtained from the IVW method were corrected using the Benjamini - Hochberg method to control the false discovery rate (FDR) due to multiple testing [24]. Following this, a two-step Mendelian randomization [25] was employed to estimate the total effect of immune cells on HNC, the effect of immune cells on metabolites (β1), and the effect of metabolites on HNC (β2). The mediation effect was calculated as (β1*β2), and the total effect was partitioned into mediation and direct effects, with the direct effect representing the total effect minus the mediation effect.

Statistical testing and sensitivity analysis

Cochran’s Q test using IVW and MR-Egger methods was performed to assess heterogeneity among IVs, ensuring the robustness of results. P < 0.05 for the Q statistic indicated potential heterogeneity. Horizontal pleiotropy was evaluated via the MR-Egger intercept test, with P < 0.05 suggesting the presence of horizontal pleiotropy, necessitating the exclusion of relevant results. Additionally, leave-one-out analysis was conducted to exclude outlier SNPs preventing individual SNPs from unduly influencing the causal inference between exposure and outcome.

Software and tools

All MR analyses were conducted using R software (version 4.4.1) with the “TwoSampleMR” package (version 0.6.4). Data visualization and other statistical tests were performed using R software.

Results

Causal relationship between immune cells and head and neck cancer

This study first conducted a two-sample MR analysis to investigate the causal relationship between 731 immune cell phenotypes and HNC. Using the IVW method, four immune cell phenotypes (CD3 on CD39 + secreting Treg, CD80 on granulocyte, CD45 on CD33br HLA DR + CD14dim, TD CD4+ %CD4+) were identified as having stronger statistically significant causal relationships with HNC. Detailed results of the MR analysis can be found in Additional file Table S1. To mitigate potential confounding effects of IVs on the outcome through pathways other than the exposure, and thus maintain the assumptions of independence and exclusivity, we used the MR-Egger intercept method to assess the presence of horizontal pleiotropy in the data. None of the four immune cell phenotypes exhibited statistically significant horizontal pleiotropy (P > 0.05), indicating no genetic pleiotropic bias. Heterogeneity was assessed using IVW and MR-Egger tests, with P < 0.05 indicating heterogeneity. TD CD4+ %CD4+ (P = 0.036) showed heterogeneity and was therefore excluded from subsequent analyses. The results of the heterogeneity analysis and pleiotropy analysis are presented in Additional file Table S2-S3. Additionally, sensitivity analysis via leave-one-out method further confirmed that no SNPs significantly influenced the overall causal inference results. The results of funnel plots, scatter plots, and leave-one-out analysis are presented in Additional file Fig. S1.

Finally, three immune cell phenotypes were determined to have a stronger statistically significant causal relationship with HNC. Specifically, CD45 on CD33br HLA DR + CD14dim (OR = 1.117, 95% CI: 1.027–1.215, P = 0.010) was positively associated with HNC. Conversely, CD80 on granulocyte (OR = 0.918, 95% CI: 0.864–0.974, P = 0.005), CD3 on CD39 + secreting Treg (OR = 0.889, 95% CI: 0.833–0.947, P < 0.001) were negatively associated with HNC (Fig. 2a).

Fig. 2.

Fig. 2

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(a) The MR results between immune cell phenotypes and HNC. (b) The reverse MR results between HNC and three immune cell phenotypes

The results of reverse mendelian randomization analysis showed that there was no significant reverse causal effect between immune cells and head and neck cancer (P > 0.05) (Fig. 2b).

Causal relationship between plasma metabolites and head and neck cancer

Results revealed twelve plasma metabolites with stronger statistically significant causal relationships with HNC. 1-(1-enyl-stearoyl)−2-linoleoyl-GPE (p-18:0/18:2) levels (OR = 0.802, 95% CI: 0.696–0.924, P = 0.002), 3-hydroxypyridine glucuronide levels (OR = 0.817, 95% CI:0.707–0.943, P = 0.006), Proline to trans-4-hydroxyproline ratio (OR = 0.778, 95% CI: 0.659–0.919, P = 0.003), and Alpha-ketoglutarate to pyruvate ratio (OR = 0.784, 95% CI: 0.653–0.940, P = 0.009) were negatively associated with HNC. Conversely, 1,2-dipalmitoyl-gpc (16:0/16:0) levels (OR = 1.204, 95% CI:1.061–1.367, P = 0.004), Stearidonate (18:4n3) levels (OR = 1.275, 95% CI: 1.081–1.504, P = 0.004), 7-alpha-hydroxy-3-oxo-4-cholestenoate (7-hoca) levels (OR = 1.243, 95% CI: 1.058–1.461, P = 0.008), 4-allylphenol sulfate levels (OR = 1.249, 95% CI: 1.081–1.443, P = 0.003), Cortolone glucuronide (1) levels (OR = 1.236, 95% CI: 1.068–1.432, P = 0.005), 3-amino-2-piperidone levels (OR = 1.289, 95% CI: 1.087–1.529, P = 0.004), Glutamate levels (OR = 1.299, 95% CI 1.074, 1.571, P = 0.007), and Glucose to maltose ratio (OR = 1.303, 95% CI: 1.078–1.574, P = 0.006) were positively associated with HNC (Fig. 3). Detailed results of the MR analysis can be found in Additional file Table S4.

Fig. 3.

Fig. 3

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The MR results between twelve plasma metabolites and HNC

Both the pleiotropy MR-Egger intercept test and the heterogeneity Cochran’s Q test showed no significance (P > 0.05). The results of the heterogeneity analysis and pleiotropy analysis are presented in Additional file Table S5-S6. The leave-one-out sensitivity analysis confirmed the robustness of the MR results. The results of funnel plots, scatter plots, and leave-one-out analysis are presented in Additional file Fig. S2-S4.

Mediation Mendelian randomization analysis

Based on the identified immune cells and plasma metabolites mentioned above, we further calculated the mediating effect by using the two-step Mendelian randomization method. Detailed results of the MR analysis can be found in Additional file Tables S7 −8. This revealed the causal relationship between two immune cell phenotypes, three plasma metabolites and HNC. We found that CD3 on CD39 + secreting Treg affects HNC through Cortolone glucuronide (1) levels and 3-hydroxypyridine glucuronide levels. CD80 on granulocyte affects HNC through 3-amino-2-piperidone levels.(Fig. 4a). Notably, the mediating effects of CD3 on CD39 + secreting Treg mediated by Cortolone glucuronide (1) levels and CD80 on granulocyte mediated by 3-amino-2-piperidone levels on HNC are opposite to the direct effects, which is considered to be a masking effect (Fig. 4b, Additional file Table S9). Eventually, we have clearly determined that 3 − hydroxypyridine glucuronide levels play a significant mediating role between CD3 on CD39 + secreting Treg and HNC. The mediating effect is −0.011, 95%CI: −0.02, −0.00192), the mediating proportion is 9.27%, 95% CI: 1.62 − 16.9%), P = 0.017 and its direct effect is −0.107 (Fig. 4b).

Fig. 4.

Fig. 4

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Mediation MR results of the causal relationships between immune cells and HNC mediated by plasma metabolites

Discussion

In this study, we investigated the causal relationships between 731 immune cell phenotypes and 1400 plasma metabolites with HNC using MR. Our results identified significant associations between three immune cell phenotypes and twelve plasma metabolites with HNC. Reverse MR analysis revealed no significant reverse causality between these three immune cell phenotypes and HNC, ruling out the possibility that HNC influences these immune cell phenotypes. This approach thus avoids, to a certain extent, the interference of reverse causal bias on the study results and further strengthens the reliability of forward causal inference. Additionally, this supports the validity of our mediational effect model, rather than through other confounded or reverse causal pathways. Mediational MR analysis showed that CD3 on CD39 + secreting Tregs reduces HNC risk by mediating 3-hydroxypyridine glucuronide levels. Notably, CD3 on CD39 + secreting Treg mediated Cortolone glucuronide (1) levels and CD80 on granulocyte-mediated 3-amino-2-piperidone levels both exhibit a masking effect on HNC.

The TME is a highly structured ecosystem where cells and their secreted molecules play crucial roles in cancer progression [26]. Tumor-infiltrating lymphocytes (TILs) are key components of the TME that regulate antitumor immune responses and are significantly associated with patient survival. Our study found that elevated levels of CD3 on CD39 + secreting Treg are associated with the reduced risk of HNC. CD3 is the common marker of T lymphocytes [27] and an integral part of the T cell receptor (TCR) complex, playing a crucial role in T cell activation and function. When the TCR on the surface of T cells recognizes an antigenic peptide presented by the major histocompatibility complex (MHC), the CD3 molecule transmits signals into the cell, initiating T cell activation [28] and exerting antitumor effects. Studies have shown that HNC with CD3 + TILs infiltration have a better prognosis [29, 30]. Additionally, another study indicates that high levels of CD3+, CD20+, and CD4 + T cell infiltration are markers of favorable prognosis in laryngeal cancer and are associated with better survival outcomes [31]. CD39, an ectonucleoside triphosphate diphosphohydrolases, is widely expressed in various TME cells, including endothelial cells, fibroblasts, myeloid cells, regulatory T cells, tumor-specific T effector cells, and NK cells [32, 33]. It is a key marker of Foxp3 + Tregs [34] and plays a crucial role in Treg-mediated immunosuppressive functions through adenosine production [33].

Regulatory T cells (Tregs) are a subtype of CD4 + and CD25 + T lymphocytes with immunosuppressive functions that play a complex role in the tumor microenvironment. Treg cells promote tumor immune evasion by releasing immunosuppressive cytokines or inhibiting the functions of other immune cells [35]. The transcription factor Foxp3 is critical for maintaining the stability and function of Treg cells, endowing them with immunosuppressive activity [36]. Compared to healthy individuals, the levels of Treg cells in the peripheral blood of HNC patients are elevated [37, 38]. Most studies suggest that regulatory Tregs are closely associated with poor prognosis in patients with malignant tumors. Research has indicated that CD4 + CD25 + Foxp3 + + Tregs exert immunosuppressive effects by producing IL-10 and TGF-β1, thereby increasing the risk of HNC and reducing survival rates in patients with advanced HNC [39]. However, our study reveals a new finding: elevated CD3 levels on CD39⁺-secreting Tregs are associated with a reduced risk of HNC, a result that contradicts conventional wisdom. This is because the current research findings on Tregs in HNC are inconsistent. Notably, Treg infiltration has demonstrated positive effects in certain cancers, with evidence showing it can improve outcomes in some patients [40–42]. For example, Foxp3 + Tregs have been identified as independent favorable factors for T staging and survival in nasopharyngeal carcinoma [43]. Other studies found that Treg infiltration in HNC is positively correlated with local regional control [44], and high Treg cell levels in the stroma are significantly associated with improved survival in HNC patients [45]. Furthermore, tumor-infiltrating CD4 + CD25 + T lymphocytes have been linked to better prognosis in head and neck squamous cell carcinoma [46]. A meta-analysis indicated that although intratumoral Treg cells had no significant impact on prognosis, Treg cells in peripheral blood were significantly associated with favorable clinical outcomes in HNC patients, especially in those with oral cancer [47]. The complexity and diversity of the roles of Tregs in tumors may result from the combined effects of multiple factors such as the microenvironment and phenotypic characteristics. This phenomenon may be related to diverse molecular features, multiple markers of Treg heterogeneity, Treg distribution patterns (tumor nests or stroma), and interaction patterns between Tregs and the infiltration of certain immune cell subsets in tumors.

Furthermore, our study further demonstrated that CD3 on CD39 + secreting Tregs can reduce the risk of HNC by elevating plasma 3-hydroxypyridine glucuronide levels. 3-Hydroxypyridines are produced by reaction of lipid-derived reactive carbonyls and ammonia-producing compounds [48]. 3-Hydroxypyridine glucuronide is a metabolic product formed by the covalent binding of 3-hydroxypyridine to glucuronic acid. 3-Hydroxypyridines and their derivatives have shown extensive potential applications in fields such as food industry, organic synthesis, pharmaceuticals, and dye production [49]. Biologically, 3-hydroxypyridine derivatives have demonstrated multiple bioactivities, including antitumor, antibacterial, and antioxidant effects [50–52]. For example, the study by Wu et al. found that 3-hydroxypyridine and its derivatives could serve as potential metabolic diagnostic markers for rectal cancer [53]; the study by Stevens et al. also pointed out that an increase in the level of 3-hydroxypyridine glucuronide levels may be associated with an increased risk of breast cancer [54]. However, the research on the association between 3 - hydroxypyridine glucuronide and HNC still requires further experimental confirmation.

We found that Cortolone glucuronide (1) levels mediated by CD3 on CD39 + secreting Treg, and 3-amino-2-piperidone levels mediated by CD80 on granulocyte showed a masking effect on HNC. The results of mediation analysis showed that an increase in CD3 on CD39 + secreting Treg would lead to an increase in Cortolone glucuronide (1) levels, and an increase in Cortolone glucuronide (1) levels would increase the risk of HNC. An increase in CD80 on granulocyte would lead to an increase in 3-amino-2-piperidone levels, and an increase in 3-amino-2-piperidone levels would also increase the risk of HNC. However, the direct effects of CD3 on CD39 + secreting Treg and CD80 on granulocyte on HNC had a protective effect. The above phenomenon indicates that the mediating effect is exactly the opposite of the direct effect, which is called the masking effect, thus concealing the true influence mechanism of the independent variable on the dependent variable. This phenomenon is relatively rare and may be related to the interaction between complex signaling pathways in cancer. A molecule may affect diseases or pathological processes through multiple pathways, and sometimes the effects of these pathways may even be opposite [55].

We found that the mediating effect of CD3 on CD39 + secreting Tregs in reducing the risk of HNC by decreasing 3-hydroxypyridine glucuronide levels accounted for 9.27% of the total effect, while the remaining effects may be jointly driven by multiple mechanisms. Given the limitations of the plasma metabolite detection range and the coverage of genetic instrumental variables, some potential mediating pathways have not been fully captured. In addition, although the other two pathways analyzed in this study showed a masking effect, they suggest that the interaction network between immune cells and metabolites may be more complex and may potentially influence the mediating effects. It is worth noting that CD3, as a key component of the T cell receptor complex, may also directly regulate HNC through a metabolite-independent pathway in the expression on CD39 + secreting Tregs [28]. Although the mediating proportion of the 3 - hydroxypyridine glucuronide mediating pathway is not high, it also indicates to a certain extent that this mediating pathway has an anti - tumor effect, which provides a potential auxiliary direction for the treatment of HNC.

Limitations

This study also has several limitations. First, when screening IVs, genome-wide significance is typically defined by a P-value threshold of less than 5 × 10−8. However, we found the number of SNPs available for inclusion to be limited. Therefore, this study adopted a relatively lenient inclusion criterion (P < 1 × 10−5). Although this adjustment expanded the pool of candidate SNPs, it may increase the risk of bias and the probability of false-positive results. Second, the sample size and population diversity are limited. This study only used data from European populations, which significantly restricts the generalizability of the research results. In subsequent studies, other racial populations can be included to improve the universality of the research results. In addition, it is difficult to completely remove confounding factors in Mendelian randomization. Although our study has conducted pleiotropy tests, heterogeneity tests, and sensitivity analyses, there may still be confounding factors that have not been considered, which may have a certain impact on the proportion of mediating effects and the results. Moreover, this study investigated HNC as a whole without distinguishing specific subtypes, which may obscure mechanistic differences between subtypes. Further validation in more stratified cohorts is required in the future. Finally, the specific biological mechanisms by which CD3 on CD39 + secreting Tregs lowers HNC risk by decreasing 3-hydroxypyridine glucuronide levels are not fully elucidated. Subsequent studies should further validate the mechanisms of action of immune cells and metabolites in HNC through cell-based and animal experiments. For example, using in vitro co-culture systems and humanized mouse models to investigate the specific mechanisms by which CD39 + secreting Tregs regulate 3-hydroxypyridine glucuronide via the CD3 molecule. Future research may also integrate multi-omics data (genomics, transcriptomics, metabolomics) to construct immune and metabolic regulatory networks and explore other potential pathways that may exist.

Conclusion

This study revealed the causal associations between immune cell phenotypes and plasma metabolites in the development of head and neck cancer through two-sample Mendelian randomization and mediation analysis. This provides important evidence for analyzing the immune and metabolic mechanisms, and exploring preventive and therapeutic targets. Further experiments are needed to further clarify the relevant underlying mechanisms, and our study can lay the foundation for future research and provide direction.

Electronic supplementary material

Supplementary Material 1. (3.4MB, pdf)

Acknowledgements

The authors thank all participants for their contributions. We want to appreciate all genetics consortiums for providing GWAS data.

Author contributions

YL conceived and designed the study; XD, QH collected the data. YL, XD, HL analyzed the data. YL drafted the manuscript. XC supervised the study and revised the paper. All authors read and approved the final manuscript.

Funding

No financial support.

Data availability

All data used in this study are publicly available, which were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/), and the FinnGen consortium(https://www.finngen.fi/).

Declarations

Ethics approval and consent to participate

The study utilized publicly available GWAS data, and written informed consent and ethical approval had been obtained previously. As this study did not collect new data, no additional ethical approval was required.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

Not applicable.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Chow LQM. Head and neck Cancer. N Engl J Med. 2020;382(1):60–72. 10.1056/NEJMra1715715. [DOI] [PubMed] [Google Scholar]
  • 2.Pai SI, Westra WH. Molecular pathology of head and neck cancer: implications for diagnosis, prognosis, and treatment. Annu Rev Pathol. 2009;4:49–70. 10.1146/annurev.pathol.4.110807.092158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660. [DOI] [PubMed] [Google Scholar]
  • 4.Matar N, Haddad A. New trends in the management of head and neck cancers. J Med Liban. 2011;59(4):220–6. [PubMed] [Google Scholar]
  • 5.Economopoulou P, Kotsantis I, Psyrri A. Tumor microenvironment and immunotherapy response in head and neck Cancer. Cancers (Basel). 2020;12(11):3377. 10.3390/cancers12113377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Bejarano L, Jordāo MJC, Joyce JA. Therapeutic targeting of the tumor microenvironment. Cancer Discov. 2021;11(4):933–59. 10.1158/2159-8290.CD-20-1808. [DOI] [PubMed] [Google Scholar]
  • 7.Song J, Deng Z, Su J, Yuan D, Liu J, Zhu J. Patterns of immune infiltration in HNC and their clinical implications: A gene Expression-Based study. Front Oncol. 2019;9:1285. 10.3389/fonc.2019.01285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. 10.1016/j.cell.2011.02.013. [DOI] [PubMed] [Google Scholar]
  • 9.Chen SMY, Krinsky AL, Woolaver RA, Wang X, Chen Z, Wang JH. Tumor immune microenvironment in head and neck cancers. Mol Carcinog. 2020;59(7):766–74. 10.1002/mc.23162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Shyer JA, Flavell RA, Bailis W. Metabolic signaling in T cells. Cell Res. 2020;30(8):649–59. 10.1038/s41422-020-0379-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Xia L, Oyang L, Lin J, et al. The cancer metabolic reprogramming and immune response. Mol Cancer. 2021;20(1):28. 10.1186/s12943-021-01316-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Gao Y, He S, Meng X, et al. Multi-omics analysis reveals immunosuppression in oesophageal squamous cell carcinoma induced by creatine accumulation and HK3 deficiency. Genome Med. 2025;17(1):44. 10.1186/s13073-025-01465-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Chang HC, Tsai CY, Hsu CL, et al. Asparagine deprivation enhances T cell antitumour response in patients via ROS-mediated metabolic and signal adaptations. Nat Metab. 2025;7(5):918–27. 10.1038/s42255-025-01245-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Zhang XY, Shi JB, Jin SF, et al. Metabolic landscape of head and neck squamous cell carcinoma informs a novel kynurenine/Siglec-15 axis in immune escape. Cancer Commun (Lond). 2024;44(6):670–94. 10.1002/cac2.12545. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Richmond RC, Davey Smith G. Mendelian randomization: concepts and scope. Cold Spring Harb Perspect Med. 2022;12(1):a040501. 10.1101/cshperspect.a040501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wang X, Gao H, Zeng Y, Chen J. A Mendelian analysis of the relationships between immune cells and breast cancer. Front Oncol. 2024;14:1341292. 10.3389/fonc.2024.1341292. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Larsson SC, Butterworth AS, Burgess S. Mendelian randomization for cardiovascular diseases: principles and applications. Eur Heart J. 2023;44(47):4913–24. 10.1093/eurheartj/ehad736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Orrù V, Steri M, Sidore C, Marongiu M, Serra V, Olla S, et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat Genet. 2020;52(10):1036–45. 10.1038/s41588-020-0684-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet. 2023;55(1):44–53. 10.1038/s41588-022-01270-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ye Z, Deng X, Zhang J, et al. Causal relationship between immune cells and prostate cancer: a Mendelian randomization study. Front Cell Dev Biol. 2024;12:1381920. 10.3389/fcell.2024.1381920. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Lv H, Wang Z, Huang C, Yu X, Li X, Song X. Causal links between gut microbiota, blood metabolites, immune cells, inflammatory proteins, and myopia: A Mendelian randomization study. Ophthalmol Sci. 2025;5(4):100684. 10.1016/j.xops.2024.100684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Burgess S, Thompson SG, CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755–64. 10.1093/ije/dyr036. [DOI] [PubMed] [Google Scholar]
  • 23.Weng H, Li H, Zhang Z, et al. Association between uric acid and risk of venous thromboembolism in East Asian populations: a cohort and Mendelian randomization study. Lancet Reg Health West Pac. 2023;39:100848. 10.1016/j.lanwpc.2023.100848. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.22, Korthauer K, Kimes PK, Duvallet C, et al. A practical guide to methods controlling false discoveries in computational biology. Genome Biol. 2019;20(1):118. 10.1186/s13059-019-1716-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for Establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012;41(1):161–76. 10.1093/ije/dyr233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.de Visser KE, Joyce JA. The evolving tumor microenvironment: from cancer initiation to metastatic outgrowth. Cancer Cell. 2023;41(3):374–403. 10.1016/j.ccell.2023.02.016. [DOI] [PubMed] [Google Scholar]
  • 27.Gooden MJM, de Bock GH, Leffers N, Daemen T, Nijman HW. The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis. Br J Cancer. 2011;105(1):93–103. 10.1038/bjc.2011.189. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Zhao Q, Jiang Y, Xiang S, Kaboli PJ, Shen J, Zhao Y, et al. Engineered TCR-T cell immunotherapy in anticancer precision medicine: pros and cons. Front Immunol. 2021;12:658753. 10.3389/fimmu.2021.658753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.de Ruiter EJ, Ooft ML, Devriese LA, Willems SM. The prognostic role of tumor infiltrating T-lymphocytes in squamous cell carcinoma of the head and neck: A systematic review and meta-analysis. Oncoimmunology. 2017;6(11):e1356148. 10.1080/2162402X.2017.1356148. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Rodrigo JP, Sánchez-Canteli M, López F, Wolf GT, Hernández-Prera JC, Williams MD, et al. Tumor-Infiltrating lymphocytes in the tumor microenvironment of laryngeal squamous cell carcinoma: systematic review and Meta-Analysis. Biomedicines. 2021;9(5):486. 10.3390/biomedicines9050486. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Tevetoğlu F, Çomunoğlu N, Yener HM. The impact of the tumor immune microenvironment and tumor-infiltrating lymphocyte subgroups on laryngeal cancer prognosis. Sci Prog. 2024;107(3):368504241266087. 10.1177/00368504241266087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Deaglio S, Dwyer KM, Gao W, Friedman D, Usheva A, Erat A, et al. Adenosine generation catalyzed by CD39 and CD73 expressed on regulatory T cells mediates immune suppression. J Exp Med. 2007;204(6):1257–65. 10.1084/jem.20062512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Liu Y, Li Z, Zhao X, Xiao J, Bi J, Li XY, et al. Review immune response of targeting CD39 in cancer. Biomark Res. 2023;11(1):63. 10.1186/s40364-023-00500-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Borsellino G, Kleinewietfeld M, Di Mitri D, Sternjak A, Diamantini A, Giometto R, et al. Expression of ectonucleotidase CD39 by Foxp3 + Treg cells: hydrolysis of extracellular ATP and immune suppression. Blood. 2007;110(4):1225–32. 10.1182/blood-2006-12-064527. [DOI] [PubMed] [Google Scholar]
  • 35.Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109–18. 10.1038/cr.2016.151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Wing JB, Tanaka A, Sakaguchi S. Human FOXP3 + Regulatory T cell heterogeneity and function in autoimmunity and Cancer. Immunity. 2019;50(2):302–16. 10.1016/j.immuni.2019.01.020. [DOI] [PubMed] [Google Scholar]
  • 37.Strauss L, Bergmann C, Gooding W, Johnson JT, Whiteside TL. The frequency and suppressor function of CD4 + CD25highFoxp3 + T cells in the circulation of patients with squamous cell carcinoma of the head and neck. Clin Cancer Res. 2007;13(21):6301–11. 10.1158/1078-0432.CCR-07-1403. [DOI] [PubMed] [Google Scholar]
  • 38.Schaefer C, Kim GG, Albers A, Hoermann K, Myers EN, Whiteside TL. Characteristics of CD4 + CD25 + regulatory T cells in the peripheral circulation of patients with head and neck cancer. Br J Cancer. 2005;92(5):913–20. 10.1038/sj.bjc.6602407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Strauss L, Bergmann C, Szczepanski M, Gooding W, Johnson JT, Whiteside TL. A unique subset of CD4 + CD25highFoxp3 + T cells secreting interleukin-10 and transforming growth factor-beta1 mediates suppression in the tumor microenvironment. Clin Cancer Res. 2007;13(15 Pt 1):4345–54. 10.1158/1078-0432.CCR-07-0472. [DOI] [PubMed] [Google Scholar]
  • 40.Whiteside TL. What are regulatory T cells (Treg) regulating in cancer and why? Semin Cancer Biol. 2012;22(4):327–34. 10.1016/j.semcancer.2012.03.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Yan Y, Huang L, Liu Y, Yi M, Chu Q, Jiao D, et al. Metabolic profiles of regulatory T cells and their adaptations to the tumor microenvironment: implications for antitumor immunity. J Hematol Oncol. 2022;15(1):104. 10.1186/s13045-022-01322-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Shang B, Liu Y, Jiang S, juan, Liu Y. Prognostic value of tumor-infiltrating FoxP3 + regulatory T cells in cancers: a systematic review and meta-analysis. Sci Rep. 2015;5:15179. 10.1038/srep15179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Zhang YL, Li J, Mo HY, Qiu F, Zheng LM, Qian CN, et al. Different subsets of tumor infiltrating lymphocytes correlate with NPC progression in different ways. Mol Cancer. 2010;9:4. 10.1186/1476-4598-9-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Badoual C, Hans S, Rodriguez J, Peyrard S, Klein C, Agueznay NEH, et al. Prognostic value of tumor-infiltrating CD4 + T-cell subpopulations in head and neck cancers. Clin Cancer Res. 2006;12(2):465–72. 10.1158/1078-0432.CCR-05-1886. [DOI] [PubMed] [Google Scholar]
  • 45.Echarti A, Hecht M, Büttner-Herold M, Haderlein M, Hartmann A, Fietkau R, et al. CD8 + and regulatory T cells differentiate tumor immune phenotypes and predict survival in locally advanced head and neck Cancer. Cancers (Basel). 2019;11(9):1398. 10.3390/cancers11091398. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Loose D, Signore A, Bonanno E, Vermeersch H, Dierckx R, Deron P, et al. Prognostic value of CD25 expression on lymphocytes and tumor cells in squamous-cell carcinoma of the head and neck. Cancer Biother Radiopharm. 2008;23(1):25–33. 10.1089/cbr.2007.0373. [DOI] [PubMed] [Google Scholar]
  • 47.Cho JH, Lim YC. Prognostic impact of regulatory T cell in head and neck squamous cell carcinoma: A systematic review and meta-analysis. Oral Oncol. 2021;112:105084. 10.1016/j.oraloncology.2020.105084. [DOI] [PubMed] [Google Scholar]
  • 48.Hidalgo FJ, Lavado-Tena CM, Zamora R. Formation of 3-hydroxypyridines by lipid oxidation products in the presence of ammonia and ammonia-producing compounds. Food Chem. 2020;328:127100. 10.1016/j.foodchem.2020.127100. [DOI] [PubMed] [Google Scholar]
  • 49.Wang H, Wang X, Ren H, Wang X, Lu Z. 3-Hydroxypyridine dehydrogenase HpdA is encoded by a novel Four-Component gene cluster and catalyzes the first step of 3-Hydroxypyridine catabolism in Ensifer adhaerens HP1. Appl Environ Microbiol. 2020;86(19):e01313–20. 10.1128/AEM.01313-20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Iksanova AG, Gabbasova RR, Kupriyanova TV, Akhunzyanov AA, Pugachev MV, Vafiva RM, et al. In-vitro antitumor activity of new quaternary phosphonium salts, derivatives of 3-hydroxypyridine. Anticancer Drugs. 2018;29(7):682–90. 10.1097/CAD.0000000000000642. [DOI] [PubMed] [Google Scholar]
  • 51.Sabet R, Fassihi A. QSAR study of antimicrobial 3-hydroxypyridine-4-one and 3-hydroxypyran-4-one derivatives using different chemometric tools. Int J Mol Sci. 2008;9(12):2407–23. 10.3390/ijms9122407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Dehkordi MM, Asgarshamsi MH, Fassihi A, Zborowski KK. A comparative DFT study on the antioxidant activity of some novel 3-Hydroxypyridine-4-One derivatives. Chem Biodivers. 2022;19(3):e202100703. 10.1002/cbdv.202100703. [DOI] [PubMed] [Google Scholar]
  • 53.Wu J, Wu M, Wu Q. Identification of potential metabolite markers for colon cancer and rectal cancer using serum metabolomics. J Clin Lab Anal. 2020;34(8):e23333. 10.1002/jcla.23333. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Stevens VL, Carter BD, Jacobs EJ, McCullough ML, Teras LR, Wang Y. A prospective case-cohort analysis of plasma metabolites and breast cancer risk. Breast Cancer Res. 2023;25(1):5. 10.1186/s13058-023-01602-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Dong F, Sun J, Zhang Y. The role of inflammatory proteins in regulating the impact of lipid specifications on deep venous thrombosis: a two sample and mediated Mendelian randomization study. Front Cardiovasc Med. 2024;11:1434600. 10.3389/fcvm.2024.1434600. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1. (3.4MB, pdf)

Data Availability Statement

All data used in this study are publicly available, which were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/), and the FinnGen consortium(https://www.finngen.fi/).

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