TNFSF8 Drives Oral Squamous Cell Carcinoma Progression via CD8+ T Cell Regulation: Insights from Multi-Omics Integration and Single-Cell eQTL Analysis

TNFSF8 Drives Oral Squamous Cell Carcinoma Progression via CD8+ T Cell Regulation: Insights from Multi-Omics Integration and Single-Cell eQTL Analysis | 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 TNFSF8 Drives Oral Squamous Cell Carcinoma Progression via CD8+ T Cell Regulation: Insights from Multi-Omics Integration and Single-Cell eQTL Analysis YONG ZHUANG, CHEN GAO This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7101139/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Oral squamous cell carcinoma (OSCC) is a highly aggressive malignancy with limited therapeutic options. This study aimed to investigate the causal role of TNFSF8 in the progression of OSCC through multi-omics integration. Methods Cis-pQTL data from deCODE and the UK Biobank Plasma Proteomics Project (UKB-PPP) were used to perform two-sample Mendelian randomization (MR) analysis to evaluate the association between TNFSF8 and OSCC. Bayesian colocalization was employed to validate causal relationships. Mediation analysis quantified the role of immune cell phenotypes in mediating the TNFSF8-OSCC relationship. Single-cell RNA sequencing (scRNA-seq) was used to analyze the high expression of TNFSF8 in T cells from OSCC tissue.Meanwhile, single-cell eQTL analysis was conducted to further verify the cell-type-specific causal association between TNFSF8 and OSCC. Results MR analysis identified TNFSF8 as a causal factor for OSCC risk. Colocalization analysis confirmed shared causal variants. Mediation analysis revealed that T cell phenotypes significantly mediated the TNFSF8-OSCC relationship. scRNA-seq demonstrated significantly elevated expression of TNFSF8 in T cell subsets from OSCC patients.Single-cell eQTL analysis further found that the expression of TNFSF8 in CD8 + T cells was positively correlated with OSCC risk, reinforcing its cell-type-specific role. Conclusion TNFSF8 drives OSCC progression potentially through the regulation of T cell function. These findings suggest that TNFSF8 is a promising therapeutic target, warranting further validation. TNFSF8 Oral Squamous Cell Carcinoma (OSCC) Mendelian Randomization Single-Cell Analysis Single-cell eQTL analysis Multi-Omics Integration Figures Figure 1 Figure 2 1 Background Oral squamous cell carcinoma (OSCC), accounting for 85% of head and neck malignancies, is characterized by aggressive invasiveness and high metastatic propensity. Epidemiologic data indicated that 35-40% of patients presented with locally advanced disease (T3/T4 stage) or lymph node metastasis at initial diagnosis. Although multimodal therapy involving radical surgery combined with adjuvant chemoradiotherapy improved locoregional control rates, the 5-year overall survival rate remained suboptimal at less than 65% [ 1] . While EGFR-targeted monoclonal antibodies (e.g., cetuximab) and immune checkpoint inhibitors (e.g., anti-PD-1/PD-L1 agents) prolonged survival in subsets of patients, treatment failure ultimately occurred in over 40% of cases due to acquired resistance driven by complex stromal-immune interplay within the tumor microenvironment (TME) and epigenetic reprogramming [ 2] . These clinical challenges underscored the urgent need to identify core cellular subpopulations and causal molecular events driving OSCC progression for developing precision therapeutic targets. Recent advancements in systems biology methodologies provided innovative frameworks integrating Mendelian randomization (MR) with single-cell multi-omics for causal inference. This paradigm established a two-tiered evidentiary chain: First, genome-wide association studies (GWAS) combined with plasma protein quantitative trait loci (pQTL) enabled identification of genetically influenced protein-disease associations at population scale [ 3] . Second, spatially resolved single-cell transcriptomics delineated cell type-specific expression patterns of candidate proteins within the tumor ecosystem [ 4] . For instance, this approach revealed CD276 (B7-H3) as a microenvironment-specific immunoregulatory target in breast cancer Treg subsets [ 5][6] . However, systematic investigations coupling genetic causality with single-cell functional validation remained lacking in OSCC research. In this study, we innovatively constructed a multi-level causal validation system to investigate the molecular mechanisms of OSCC. First, we integrated pQTL data from the deCODE, UK Biobank Plasma Proteomics Project (UKB-PPP), and FinnGen consortium OSCC GWAS data. Two-sample Mendelian randomization analyses were performed to screen plasma proteins associated with OSCC risk. Next, Bayesian colocalization and eQTL validation were applied to exclude pleiotropic effects, while mediation analyses further revealed the regulatory roles of key immune cell phenotypes. Finally, single-cell transcriptomic data were utilized to validate target specificity at the cellular subpopulation level. This study provided multidimensional evidence for the molecular mechanisms of OSCC and established a theoretical foundation for developing precise therapeutic targets. 2 Methods 2.1 Study Design A stepwise ‘discovery-validation-mechanism-localization’framework was devised to establish biologically consistent causal inference (Figure 1). This hierarchical causal validation system comprised five core modules: (1) genetic causal discovery, (2) multi-dimensional validation, (3) mechanistic investigation, (4) cellular spatialization, and (5) single-cell eQTL validation. First, cis-pQTLs from blood plasma proteins were utilized as exposures, while OSCC GWAS summary statistics served as outcomes in two-sample Mendelian randomization (MR) analyses to identify protein-OSCC causal relationships. Stringent inclusion criteria were applied for instrumental variable (IV) selection. Sensitivity analyses were systematically conducted to verify MR robustness. For proteins demonstrating significant MR associations, replication analyses, Bayesian colocalization, and cis-eQTL validation were performed to address pleiotropy and refine causal effects. Mediation analyses further elucidated immune cell phenotypes potentially mediating protein-OSCC effects. Additionally, we introduced single-cell eQTL analysis to validate the genetic association between the expression of target proteins in specific cell subsets and OSCC at single-cell resolution, further strengthening the verification of causal relationships at the cell-specific level. We focused on the specific expression of target proteins in cell subsets using public single-cell datasets and combined single-cell eQTL data to validate the impact of such cell-type-specific expression on OSCC risk at the genetic level. Through multiple validations, we aimed to identify potential therapeutic targets for oral squamous cell carcinoma. This study adhered to strict ethical standards, and all data used had undergone ethical approval and participant consent procedures in their original studies. 2.2 Data Sources Plasma proteome quantitative trait loci (pQTLs) were sourced from the deCODE database [ 7] (Icelandic population; https://www.decode.com/summarydata/) and the UK Biobank Pharma Proteomics Project [ 8] (UKB-PPP; https://www.synapse.org/Synapse:syn51364943/wiki/622119). Cis-pQTLs within ±1,000 kb of gene coding regions, specifically linked to protein expression, were prioritized to ensure physiological relevance to OSCC. Blood cis-eQTL data and immune trait GWAS summary statistics were obtained from the eQTLGen Consortium[ 9] and GWAS Catalog (https://www.ebi.ac.uk/gwas/), respectively. A comprehensive list of each immunological profile is provided in Table S1[ 10] .OSCC GWAS data included 832 cases and 314,193 controls from FinnGen. Single-cell RNA sequencing (scRNA-seq) data (GSE172577) comprising six OSCC patient samples were retrieved from the GEO database. 2.3 Instrumental Variable Selection Three MR assumptions[ 11] guided IV selection: (1) strong IV-exposure association (p < 5 × 10⁻⁸), (2) independence from confounders, and (3) absence of horizontal pleiotropy. SNPs within ±1,000 kb of protein-coding genes were clumped (r²< 0.1; 10,000 kb window) using 1,000 Genomes Project European reference data[ 12] . Ambiguous SNPs (e.g., strand mismatches) and those with F-statistics <10 were excluded to mitigate weak instrument bias[ 13] .Due to the limited number of SNPs in single-cell eQTL data, we adjusted the threshold to 5 × 10⁻⁵ and relaxed the clumping parameters (r²< 0.1; 500 kb window). This method, widely used in previous studies[ 14][15] 2.4 Mendelian Randomization and Sensitivity Analyses Causal relationships between cis-pQTLs and OSCC risk were assessed using inverse-variance weighted (IVW) regression as the primary method[ 16] , supplemented by MR-Egger, weighted median, simple mode, and weighted mode estimators. Heterogeneity was quantified via Cochran’s Q test[ 17] . Horizontal pleiotropy was evaluated via MR-Egger intercept tests[ 18] and MR-PRESSO outlier correction[ 19] . Leave-one-out sensitivity analyses confirmed result robustness. Statistical analyses were performed in R v4.3.1 using the MendelianRandomization, TwoSampleMR, and MR-PRESSO packages. 2.5 Colocalization Analysis Bayesian colocalization (R package coloc) was applied to cis-pQTLs with significant MR effects to evaluate shared causal variants between protein expression and OSCC risk. Default priors (P1 = 1 × 10⁻⁴, P2 = 1 × 10⁻⁴, P12 = 1 × 10⁻⁵) and a ±1,000 kb window around the lead SNP were used. A posterior probability for colocalization (PPH4) >0.75 defined conclusive evidence of shared causality[ 20] . 2.6 Replication Analysis, Validation Analysis, and Directionality Testing To validate the findings from preliminary analysis, replication analysis was performed using an independent cis-pQTL dataset obtained from the UKB-PPP database. Genes demonstrating positive signals in both primary and replication analyses with colocalization analysis (PPH4 > 0.75) were selected for subsequent validation analysis using blood cis-eQTL data from the eQTLGen Consortium to ensure reliability. A Steiger test was additionally conducted to assess potential biases, thereby enhancing the robustness of the study and minimizing confounding effects from reverse causation[ 21] . 2.7 Mediation Analysis A mediation analysis framework was implemented to investigate potential downstream mechanisms of target proteins. Initially, a two-sample Mendelian randomization (MR) approach was applied to estimate the effects of target proteins on immune cell phenotypes (β₁) and oral squamous cell carcinoma (OSCC) (α). Immune cell phenotypes significantly associated with target proteins and exhibiting effects on OSCC (β₂) were subsequently identified. The mediated proportion of the immune cell phenotype in the target protein-OSCC relationship was calculated as β₁×β₂/α. The percentage of mediation effect was derived by dividing the indirect effect by the total effect. The delta method was employed to compute 95% confidence intervals and statistical p-values for the mediation effects[ 22][23] . 2.8 Single-Cell Analysis Single-cell transcriptomic data from six OSCC specimens were processed using bioinformatics toolkits (Seurat, limma, dplyr) within the R platform. Raw sequencing data underwent quality filtering to retain cells with >50 detected genes and mitochondrial gene content <5%, followed by log-normalization with a scaling factor of 10,000. The variance stabilizing transformation (VST) method identified the top 1,500 highly variable genes (HVGs). Batch effects across samples were corrected using Seurat's anchor integration algorithm to generate a unified expression matrix.Dimensionality reduction involved principal component analysis (PCA) with the first 30 principal components determined by JackStraw testing and elbow plots. Cell clustering was performed via shared nearest neighbor (SNN) graph construction (resolution=0.5), complemented by nonlinear visualization using t-SNE. Marker genes for each cluster were identified (|log₂FC| > 0.5, adjusted p < 0.05) and annotated through SingleR package referencing the Human Primary Cell Atlas (HPCA). Subpopulation differential analysis was subsequently conducted using Seurat's built-in module (thresholds: |log₂FC| > 2, adjusted p < 0.05. 2.9 Single-Cell eQTL Analysis Based on single-cell resolution eQTL data[ 24] , we further validated the genetic regulatory patterns of target proteins in specific cell subsets. Single-cell eQTL data were obtained from public databases, and SNPs were selected as instrumental variables using the IV selection criteria specific to single-cell eQTLs mentioned above. Two-sample MR analysis was performed to evaluate the causal association between the expression of target genes in specific cell subsets and OSCC risk. 3 Results 3.1 Primary Mendelian Randomization and Replication Analysis We identified 1,614 cis-pQTLs by selecting SNPs located within ±1,000 kb of the coding sequences of their corresponding genes. Through stringent quality control for instrumental variables (IVs), SNPs with F-statistics > 10 were retained for subsequent Mendelian randomization (MR) analyses. Using the inverse-variance weighted (IVW) method, 157 plasma proteins demonstrated causal associations with OSCC risk (Table S2). Replication analysis with cis-pQTL data from the UK Biobank Plasma Proteomics Project (UKB-PPP) further validated 46 proteins with consistent causal effects across both phases (Table S3 and FigureS1). Sensitivity analyses revealed no significant heterogeneity (Cochran’s Q-test, p > 0.05), no horizontal pleiotropy (MR-Egger intercept, p > 0.05), and no reverse causation (Steiger test; Tables 1–2). 3.2 Colocalization Analysis For plasma proteins showing significant MR associations in both the discovery and replication analyses, colocalization analysis was performed to assess the probability of shared causal variants between cis-pQTLs and OSCC outcomes (Tables S4-S5). The results demonstrated that TNFSF8 might share causal variants with OSCC across both datasets (cis-pQTLs), with posterior probability (PP.H4) exceeding 0.75 (Figure 2). 3.3 eQTL Validation To validate the impact of TNFSF8 on OSCC, cis-expression quantitative trait loci (cis-eQTL) data were utilized to explore the association between TNFSF8 gene expression levels and OSCC. MR analyses based on these eQTLs confirmed a causal relationship (Tables 1-2). Table 1 Mendelian Randomization Analysis of TNFSF8 on OSCC: Preliminary, Replication, and Validation Results. Protein Mendelian randomization analysis method p beta OR OR(95%CI) deCODE cispQTL TNFSF8 IVW 0.0002 0.963 2.62 (1.58,4.34) UKB-PP cispQTL TNFSF8 IVW 5.71e-06 0.678 1.97 (1.47,2.64) eQTLGen Consortium ciseQTL TNFSF8 IVW 0.0002 0.437 1.549 (1.23,1.95) Table 2 Sensitivity Analysis and Directionality Test in TNFSF8-OSCC Association: Preliminary, Replication, and Validation Results. Protein SNP Steiger direction Steiger P value Heterogeneity Pleiotropy MR-PRESSO Global Test Pvalue deCODE cispQTL TNFSF8 rs1006026 TRUE 9.30e-50 0.443 0.546 0.572 UKB-PP cispQTL TNFSF8 rs10081728 TRUE 7.79e-180 0.157 0.371 0.22 eQTLGen Consortium ciseQTL TNFSF8 rs10817679 TRUE 5.98e-250 0.243 0.863 0.332 3.4 Mediation Analysis A two-step mediation analysis was conducted to quantify the proportion of the causal effect of plasma proteins on OSCC mediated by immune cell phenotypes. First, immune cell traits associated with OSCC were identified through MR (Table S6). Subsequently, MR was used to evaluate the relationship between TNFSF8 and these immune cell traits (Table S7). Combining these results with the plasma protein-OSCC associations, the mediated effects were calculated using the delta method to derive confidence intervals and p-values. Three statistically significant mediating effects were identified, all involving T-cell-related phenotypes (Table3). These findings suggest that T-cell traits mediate the causal pathway between TNFSF8 and OSCC, providing critical insights into the role of T-cells in OSCC pathogenesis. Table 3 Mediation Analysis of Immune Cells in the Association Between Plasma Proteins and OSCC. Exposure Mediator Proportion mediated P value α β1 β2 β1*β2/α TNFSF8 CD8br %leukocyte 0.963 0.493 0.977 50% 0.015 CD127- CD8br AC 0.381 0.819 32% 0.016 CD28- CD8br AC 0.392 0.715 29% 0.024 3.5 Single-Cell Analysis Single-cell RNA sequencing data were processed through quality control, highly variable gene selection, data integration, dimensionality reduction, and cluster analysis, revealing 21 clusters corresponding to 10 distinct cell types (FigureS2). Based on mediation analysis results, differentially expressed genes in T-cells were identified by filtering for absolute log2FC>2 and adjusted p-value <0.05, yielding 119 upregulated and 1,078 downregulated genes in T-cells(Table S8). Notably, TNFSF8 exhibited significant overexpression in T-cells, highlighting its potential as a therapeutic target for precision immunotherapy in OSCC. 3.6 Single-Cell eQTL Analysis To further validate the specific role of TNFSF8 at the cell subset level, we conducted targeted analyses using single-cell eQTL data. The results showed a significant positive correlation between TNFSF8 and OSCC risk in the CD8+ T cell subset (Table 4). This result suggests that the impact of TNFSF8 on OSCC may be primarily mediated through the CD8+ T cell subset, further supporting its critical role in cell-specific regulation. Table 4 Mendelian Randomization and Sensitivity Analysis Results of TNFSF8 Expression in CD8+ T Cells Associated with OSCC Risk Based on Single-Cell eQTL Data. Single-Cell eQTL Mendelian randomization analysis Heterogeneity Pleiotropy MR-PRESSO Global Test Pvalue method p beta 0.534 0.205 0.325 TNFSF8 IVW 0.02 0.552 4 Discussion This study systematically elucidated the causal role of TNFSF8 in oral squamous cell carcinoma (OSCC) and its potential immune regulatory mechanisms by integrating Mendelian randomization (MR) with single-cell sequencing technology and single-cell eQTL analysis. Through a series of rigorous repeat analyses, sensitivity analyses, and validation analyses—including Cochran Q test, MR-Egger regression, repeated cis-pQTL and cis-eQTL analyses, and colocalization analysis—our findings provided robust evidence supporting TNFSF8 as a potential therapeutic target for OSCC. Importantly, by combining mediation analysis integrated with single-cell RNA sequencing data and single-cell eQTL analysis, we identified possible mechanisms through which TNFSF8 influences the development and progression of OSCC, and pinpointed key mediating immune cells. In summary, our results not only highlighted the potential of TNFSF8 as a therapeutic target but also revealed its underlying mechanisms and critical mediators. These findings offer new insights for precision treatment strategies in OSCC. TNFSF8 (TNF Superfamily Member 8), also known as CD30L or CD153, is a crucial member of the TNF superfamily, with significant roles in immune regulation, inflammatory responses, and cell survival through its interaction with the receptor CD30. Emerging evidence suggests that TNFSF8 is aberrantly expressed in various tumors and is closely associated with tumor progression and immune evasion. For instance, in Hodgkin’s lymphoma and other CD30-positive malignancies, the TNFSF8-CD30 signaling axis promotes tumor cell proliferation and survival[ 25][26] . However, despite its recognized role in several solid tumors[ 27] , the involvement of TNFSF8 in oral squamous cell carcinoma (OSCC) remains underexplored. In this study, we employed Mendelian randomization (MR) analysis to identify a causal relationship between TNFSF8 and OSCC risk. Furthermore, colocalization analysis corroborated that TNFSF8 and OSCC may share causal variants, thereby strengthening the evidence for a causal association. These findings suggest that TNFSF8 may act as a potential regulator in the development and progression of OSCC, possibly mediated through mechanisms involving immune microenvironment regulation, inflammatory responses, and cellular proliferation. The tumor microenvironment in OSCC is characterized by a complex interplay of immune cells, among which T cells play a pivotal role in orchestrating anti-tumor immune responses. Notably, the quantity and functional status of tumor-infiltrating lymphocytes (TILs) have been closely linked to OSCC prognosis[ 28] . For example, high levels of CD8+ T cell infiltration are generally associated with improved survival rates[ 29] , whereas an increase in regulatory T cells (Tregs) correlates with immune suppression and tumor progression[ 30] . Recent studies have identified additional immune cell subsets within the OSCC microenvironment, such as tumor-associated macrophages (TAMs)[ 31] and myeloid-derived suppressor cells (MDSCs)[ 32] ,which contribute to tumor immune evasion through the secretion of immunosuppressive cytokines like IL-10 and TGF-β[ 33][34] . In our study, mediation analysis integrated with single-cell RNA sequencing data and single-cell eQTL analysis revealed that TNFSF8 promotes OSCC progression by modulating specific T cell subsets, with TNFSF8 being highly expressed in T cells within OSCC tissues. These findings further underscore the critical role of T cells in OSCC pathogenesis. Building on our findings and existing literature, we propose that TNFSF8 drives OSCC progression through its regulation of T cell function. TNFSF8 is highly expressed in T cells and interacts with its receptor CD30 to modulate T cell activation and functionality. Our study demonstrated significant upregulation of TNFSF8 in T cell subsets from OSCC patients, and mediation analysis confirmed that TNFSF8 influences OSCC progression by regulating T cell function.TNFSF8 binding to CD30 activates the NF-κB and MAPK signaling pathways, thereby enhancing T cell activation and proliferation. The TNFSF8-CD30 axis has been shown to augment T cell effector functions, including cytokine production and tumor cell cytotoxicity. However, within the tumor microenvironment, this activation may have dual effects: while it may enhance anti-tumor immunity, it could also promote tumor-associated inflammation and immune suppression[ 35] .Furthermore, TNFSF8 may contribute to tumor immune evasion by altering the immunologic balance of the tumor microenvironment through its modulation of T cell function. For instance, TNFSF8 expression has been linked to the production of immunosuppressive cytokines such as IL-10 and TGF-β, which may drive tumor immune escape and progression[ 36] . Additionally, certain TNFSF8 isoforms have been shown to promote Treg induction and immunosuppressive cytokine production, further exacerbating immune evasion[ 37] .In summary, our study highlights the critical role of TNFSF8 in OSCC and elucidates its potential mechanisms in promoting tumor progression through T cell regulation. These findings not only deepen our understanding of OSCC pathogenesis but also provide a theoretical foundation for developing targeted therapies that exploit the TNFSF8 signaling axis. Future studies should further validate the mechanisms of TNFSF8 in OSCC and explore its clinical potential as a therapeutic target. Previous MR analyses on head and neck squamous cell carcinoma have explored factors such as cholesterol levels[ 38] , smoking, and alcohol consumption[ 39] . However, our study is the first to apply cis-pQTL and eQTL MR analyses specifically to OSCC, a subtype of head and neck squamous cell carcinoma. A major strength of our study lies in the use of two independent cis-pQTL databases, which enhanced the reliability of our results. The consistency of findings across these independent databases significantly bolstered the credibility of our conclusions. Such cross-validation is widely recognized in scientific research as an effective means to improve the stability and accuracy of results. Colocalization analysis further supported the causal relationship by identifying shared causal variants between the two cis-pQTL databases for TNFSF8 in OSCC. Additionally, the use of eQTL data for validation not only strengthened our confidence in the results but also provided supplementary evidence supporting our findings.Mediation analysis, coupled with single-cell sequencing data, indicated that TNFSF8 drives OSCC progression by modulating T cell activity. The specific enrichment of TNFSF8 in T cell subsets suggested that the TNFSF axis may promote OSCC development through T cell function. Single-cell eQTL analysis identified a positive correlation between TNFSF8 expression in CD8+ T cells and OSCC risk, further validating the mechanism by which TNFSF8 influences OSCC progression through T cell subsets at the cell-type-specific genetic level, and enhancing the persuasiveness of the evidence chain at the cellular resolution level.This finding reinforced our methodological approach, ensuring that our conclusions were not limited by specific databases or analytical methods. Furthermore, our study design adhered to stringent screening criteria to ensure the accuracy and validity of IV selection. By rigorously validating key assumptions, we minimized bias and obtained reliable results in multifactorial association analyses, thereby enhancing the scientific integrity of our study.The biomarkers identified in this study were statistically significant across all analyses, suggesting their potential critical roles in OSCC pathogenesis. These findings could serve as a foundation for identifying additional therapeutic targets in future research. Overall, our study provided valuable insights into OSCC through innovative methodologies and rigorous analyses. However, our study had several limitations that should be addressed in future research. First, although we identified an association between high TNFSF8 expression and OSCC through cis-pQTL analysis and suggested a potential role of the TNFSF axis in promoting OSCC via T cell function through mediation and single-cell analysis, these findings require further validation. While our study offers valuable insights into the molecular mechanisms of OSCC, translating these findings into clinical applications necessitates additional investigation. Second, our study did not fully explore the specific biological roles of TNFSF8 in OSCC or its complex interactions in tumorigenesis. Future studies should delve deeper into these mechanisms to inform more refined therapeutic approaches. Additionally, our reliance on blood-derived pQTL and eQTL data may limit a comprehensive understanding of the molecular pathology of OSCC. Future research should prioritize the collection and analysis of eQTL data from tumor tissues. Lastly, our study primarily included participants of European ancestry, which may limit the generalizability of our findings to other populations. Differences in genetic backgrounds and disease manifestations across diverse cohorts necessitate further validation to ensure broader applicability.Despite these limitations, our study provided a significant foundation for advancing understanding of OSCC pathogenesis and highlighted potential avenues for refining therapeutic approaches. These findings set the stage for subsequent research and the development of more tailored interventions for OSCC patients. 5 Conclusion By integrating multi-omics data, including cis-pQTL, cis-eQTL, mediation analysis, single-cell RNA sequencing, and single-cell eQTL analysis,we systematically revealed the potential role of TNFSF8 in the development and progression of oral squamous cell carcinoma (OSCC). Our findings demonstrated that TNFSF8 was significantly upregulated in T cell subsets of OSCC patients and may participate in OSCC progression by modulating T cell function. These results provide novel insights into the molecular mechanisms underlying OSCC and suggest that TNFSF8 could serve as a promising biomarker and therapeutic target for OSCC. However, further molecular experiments and clinical studies are required to validate the specific mechanisms of TNFSF8 in OSCC and explore its clinical applicability. Declarations Ethics, Consent to Participate, and Consent to Publish Declarations All data analyzed in this study were derived from publicly accessible genomic and protein quantitative trait loci databases (deCODE Genetics, UK Biobank Pharma Proteomics Project) and transcriptomic repositories (GEO: GSE172577). The original studies providing these datasets obtained necessary ethical approvals and participant consents in compliance with the Declaration of Helsinki. No human subjects were directly recruited or personally identifiable information accessed in the current study. Therefore, Ethics Approval, Consent to Participate, and Consent to Publish declarations are not applicable to this secondary analysis of de-identified, aggregated data. Consent to Publish Declaration This study exclusively analyzed de-identified, aggregate-level data from publicly available genetic and transcriptomic repositories (deCODE, UK Biobank Pharma Proteomics Project, GEO). No individual-level data, clinical images, or personally identifiable information were included. Therefore, Consent to Publish declaration is not applicable. Funding This work was supported by the Fujian Science and Technology Innovation Joint Funds Project (Grant No. 2023Y95010274). The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the work for publication. Conflict of Interests The authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author confirms full responsibility for the integrity of this declaration. Data Availability All data analyzed in this study are publicly accessible: Genetic association statistics were obtained from the deCODE Genetics repository (URL: https://www.decode.com/summarydata/) and the UK Biobank Pharma Proteomics Project (URL: https://www.ukbiobank.ac.uk/). Transcriptomic datasets (GSE172577) were sourced from the NCBI Gene Expression Omnibus (GEO, URL: https://www.ncbi.nlm.nih.gov/geo/). Immune cell-related data: Immune trait GWAS summary statistics were retrieved from GWAS Catalog (URL: https://www.ebi.ac.uk/gwas/). Immune cell signature metrics are provided in Supplementary Table S1 (accessible with this manuscript). No new datasets were generated in this work. Processed analytical results are available from the corresponding author upon reasonable request. Competing Interests The authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author (CHEN GAO) affirms full responsibility for the integrity of this declaration. Author Contributions YONG ZHUANG : Conceptualization, Methodology, Data Curation, Formal Analysis, Writing – Original Draft. CHEN GAO: Supervision, Funding Acquisition, Resources, Validation, Writing – Review & Editing. 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Immune checkpoint pathways in immunotherapy for head and neck squamous cell carcinoma. Int J Oral Sci. 2020;12 (1):16. doi:10.1038/s41368-020-0084-8 Xue Y,Song X,Fan S, et al. The role of tumor-associated macrophages in oral squamous cell carcinoma. Front Physiol. 2022;13:959747. doi:10.3389/fphys.2022.959747 Gabrilovich DI,Nagaraj S. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol. 2009;9 (3):162-74. doi:10.1038/nri2506 Gan M,Liu N,Li W, et al. Metabolic targeting of regulatory T cells in oral squamous cell carcinoma: new horizons in immunotherapy. Mol Cancer. 2024;23 (1):273. doi:10.1186/s12943-024-02193-7 Liu Z,Zhang Z,Zhang Y, et al. Spatial transcriptomics reveals that metabolic characteristics define the tumor immunosuppression microenvironment via iCAF transformation in oral squamous cell carcinoma. Int J Oral Sci. 2024;16 (1):9. doi:10.1038/s41368-023-00267-8 Simhadri VL,Hansen HP,Simhadri VR, et al. A novel role for reciprocal CD30-CD30L signaling in the cross-talk between natural killer and dendritic cells. Biol Chem. 2012;393 (1-2):101-6. doi:10.1515/BC-2011-213 Zhai WY,Duan FF,Wang YZ, et al. Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma. Am J Pathol. 2022;192 (10):1433-1447. doi:10.1016/j.ajpath.2022.06.015 Printsev I,Alalli E,Bilsborough J. The Opposite Functions of CD30 Ligand Isoforms. Curr Issues Mol Biol. 2024;46 (3):2741-2756. doi:10.3390/cimb46030172 Gormley M,Yarmolinsky J,Dudding T, et al. Using genetic variants to evaluate the causal effect of cholesterol lowering on head and neck cancer risk: A Mendelian randomization study. PLoS Genet. 2021;17 (4):e1009525. doi:10.1371/journal.pgen.1009525 Thakral A,Lee JJ,Hou T, et al. Smoking and alcohol by HPV status in head and neck cancer: a Mendelian randomization study. Nat Commun. 2024;15 (1):7835. doi:10.1038/s41467-024-51679-x Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg FigureS2.jpg TableS2.xlsx TableS1.xlsx TableS5.xlsx TableS6.xlsx TableS7.xlsx TableS3.xlsx TableS4.xlsx TableS8.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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-7101139","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495062481,"identity":"d36fb6d8-b83f-4e39-987c-9aa27ca88443","order_by":0,"name":"YONG ZHUANG","email":"","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"YONG","middleName":"","lastName":"ZHUANG","suffix":""},{"id":495062482,"identity":"b32e50cd-590e-4558-a578-022a540ddcbf","order_by":1,"name":"CHEN GAO","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYLACCYaEejb25oMPEipqiNLA2ADUksDHcyzZ4MGZY0RqYQBqkZPIMZN82MJMWL28++HjDyzb0vLYGBLMKhIb2Bj427sT8GoxPJOW2CDZllPMxnAg7UbiDhkGiTNnN+DX0pBj2CC5rYKxjbHh2I3EM2wMBhK5BLT0v4FqYWZsK0hsYyasRV4CbEtOYhsbMxsDUVoMJJ4lzpD8l2bMxsPGLJFw5hgPQb/I9ycf+CxxJllOfv77jx9/VNTI8bf3ErDlAAMDswSSAA9e5WBbGoBx+YGgslEwCkbBKBjRAAAzq0rQQD8CxgAAAABJRU5ErkJggg==","orcid":"","institution":"Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"CHEN","middleName":"","lastName":"GAO","suffix":""}],"badges":[],"createdAt":"2025-07-11 11:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7101139/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7101139/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88793263,"identity":"0abbdc95-4230-4799-bef6-26be98ac22a2","added_by":"auto","created_at":"2025-08-11 13:12:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1262165,"visible":true,"origin":"","legend":"\u003cp\u003eStudy workflow. MR, mendelian randomization; eQTL, expression quantitative trait locus; pQTL, protein quantitative trait locus;IVW,inverse variance-weighted.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7101139/v1/78a8cc6ec41c9e351d981d87.jpg"},{"id":88793261,"identity":"c40a4812-469d-4d2f-af52-ac85e6e344cd","added_by":"auto","created_at":"2025-08-11 13:12:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38185,"visible":true,"origin":"","legend":"\u003cp\u003eColocalization analysis of TNFSF8 in preliminary and replication analyses.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7101139/v1/e09952caecbd5bb66e18bee4.jpg"},{"id":91616722,"identity":"ba6b4d19-181d-4aac-a57a-1c4e08b81ccb","added_by":"auto","created_at":"2025-09-18 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13:20:12","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":103374,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7101139/v1/3f762b60ce0a7c1561e8683f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"TNFSF8 Drives Oral Squamous Cell Carcinoma Progression via CD8+ T Cell Regulation: Insights from Multi-Omics Integration and Single-Cell eQTL Analysis","fulltext":[{"header":"1 Background","content":"\u003cp\u003eOral squamous cell carcinoma (OSCC), accounting for 85% of head and neck malignancies, is characterized by aggressive invasiveness and high metastatic propensity. Epidemiologic data indicated that 35-40% of patients presented with locally advanced disease (T3/T4 stage) or lymph node metastasis at initial diagnosis. Although multimodal therapy involving radical surgery combined with adjuvant chemoradiotherapy improved locoregional control rates, the 5-year overall survival rate remained suboptimal at less than 65% [\u003csup\u003e1]\u003c/sup\u003e. While EGFR-targeted monoclonal antibodies (e.g., cetuximab) and immune checkpoint inhibitors (e.g., anti-PD-1/PD-L1 agents) prolonged survival in subsets of patients, treatment failure ultimately occurred in over 40% of cases due to acquired resistance driven by complex stromal-immune interplay within the tumor microenvironment (TME) and epigenetic reprogramming [\u003csup\u003e2]\u003c/sup\u003e. These clinical challenges underscored the urgent need to identify core cellular subpopulations and causal molecular events driving OSCC progression for developing precision therapeutic targets.\u003c/p\u003e\n\u003cp\u003eRecent advancements in systems biology methodologies provided innovative frameworks integrating Mendelian randomization (MR) with single-cell multi-omics for causal inference. This paradigm established a two-tiered evidentiary chain: First, genome-wide association studies (GWAS) combined with plasma protein quantitative trait loci (pQTL) enabled identification of genetically influenced protein-disease associations at population scale [\u003csup\u003e3]\u003c/sup\u003e. Second, spatially resolved single-cell transcriptomics delineated cell type-specific expression patterns of candidate proteins within the tumor ecosystem [\u003csup\u003e4]\u003c/sup\u003e. For instance, this approach revealed CD276 (B7-H3) as a microenvironment-specific immunoregulatory target in breast cancer Treg subsets [\u003csup\u003e5][6]\u003c/sup\u003e. However, systematic investigations coupling genetic causality with single-cell functional validation remained lacking in OSCC research.\u003c/p\u003e\n\u003cp\u003eIn this study, we innovatively constructed a multi-level causal validation system to investigate the molecular mechanisms of OSCC. First, we integrated pQTL data from the deCODE, UK Biobank Plasma Proteomics Project (UKB-PPP), and FinnGen consortium OSCC GWAS data. Two-sample Mendelian randomization analyses were performed to screen plasma proteins associated with OSCC risk. Next, Bayesian colocalization and eQTL validation were applied to exclude pleiotropic effects, while mediation analyses further revealed the regulatory roles of key immune cell phenotypes. Finally, single-cell transcriptomic data were utilized to validate target specificity at the cellular subpopulation level. This study provided multidimensional evidence for the molecular mechanisms of OSCC and established a theoretical foundation for developing precise therapeutic targets.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e2.1 Study Design\u003c/p\u003e\n\u003cp\u003eA stepwise \u0026lsquo;discovery-validation-mechanism-localization\u0026rsquo;framework was devised to establish biologically consistent causal inference (Figure 1). This hierarchical causal validation system comprised five core modules: (1) genetic causal discovery, (2) multi-dimensional validation, (3) mechanistic investigation, (4) cellular spatialization, and (5) single-cell eQTL validation. First, cis-pQTLs from blood plasma proteins were utilized as exposures, while OSCC GWAS summary statistics served as outcomes in two-sample Mendelian randomization (MR) analyses to identify protein-OSCC causal relationships. Stringent inclusion criteria were applied for instrumental variable (IV) selection. Sensitivity analyses were systematically conducted to verify MR robustness. For proteins demonstrating significant MR associations, replication analyses, Bayesian colocalization, and cis-eQTL validation were performed to address pleiotropy and refine causal effects. Mediation analyses further elucidated immune cell phenotypes potentially mediating protein-OSCC effects. Additionally, we introduced single-cell eQTL analysis to validate the genetic association between the expression of target proteins in specific cell subsets and OSCC at single-cell resolution, further strengthening the verification of causal relationships at the cell-specific level. We focused on the specific expression of target proteins in cell subsets using public single-cell datasets and combined single-cell eQTL data to validate the impact of such cell-type-specific expression on OSCC risk at the genetic level. Through multiple validations, we aimed to identify potential therapeutic targets for oral squamous cell carcinoma. This study adhered to strict ethical standards, and all data used had undergone ethical approval and participant consent procedures in their original studies.\u003c/p\u003e\n\u003cp\u003e2.2 Data Sources\u003c/p\u003e\n\u003cp\u003ePlasma proteome quantitative trait loci (pQTLs) were sourced from the deCODE database [\u003csup\u003e7]\u003c/sup\u003e (Icelandic population; https://www.decode.com/summarydata/) and the UK Biobank Pharma Proteomics Project [\u003csup\u003e8]\u003c/sup\u003e(UKB-PPP; https://www.synapse.org/Synapse:syn51364943/wiki/622119). Cis-pQTLs within \u0026plusmn;1,000 kb of gene coding regions, specifically linked to protein expression, were prioritized to ensure physiological relevance to OSCC. Blood cis-eQTL data and immune trait GWAS summary statistics were obtained from the eQTLGen Consortium[\u003csup\u003e9]\u003c/sup\u003e and GWAS Catalog (https://www.ebi.ac.uk/gwas/), respectively. A comprehensive list of each immunological profile is provided in Table S1[\u003csup\u003e10]\u003c/sup\u003e.OSCC GWAS data included 832 cases and 314,193 controls from FinnGen. Single-cell RNA sequencing (scRNA-seq) data (GSE172577) comprising six OSCC patient samples were retrieved from the GEO database.\u003c/p\u003e\n\u003cp\u003e2.3 Instrumental Variable Selection\u003c/p\u003e\n\u003cp\u003eThree MR assumptions[\u003csup\u003e11]\u003c/sup\u003e guided IV selection: (1) strong IV-exposure association (p \u0026lt; 5 \u0026times; 10⁻⁸), (2) independence from confounders, and (3) absence of horizontal pleiotropy. SNPs within \u0026plusmn;1,000 kb of protein-coding genes were clumped (r\u0026sup2;\u0026lt; 0.1; 10,000 kb window) using 1,000 Genomes Project European reference data[\u003csup\u003e12]\u003c/sup\u003e. Ambiguous SNPs (e.g., strand mismatches) and those with F-statistics \u0026lt;10 were excluded to mitigate weak instrument bias[\u003csup\u003e13]\u003c/sup\u003e.Due to the limited number of SNPs in single-cell eQTL data, we adjusted the threshold to 5 \u0026times; 10⁻⁵ and relaxed the clumping parameters (r\u0026sup2;\u0026lt; 0.1; 500 kb window). This method, widely used in previous studies[\u003csup\u003e14][15]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e2.4 Mendelian Randomization and Sensitivity Analyses\u003c/p\u003e\n\u003cp\u003eCausal relationships between cis-pQTLs and OSCC risk were assessed using inverse-variance weighted (IVW) regression as the primary method[\u003csup\u003e16]\u003c/sup\u003e, supplemented by MR-Egger, weighted median, simple mode, and weighted mode estimators. Heterogeneity was quantified via Cochran\u0026rsquo;s Q test[\u003csup\u003e17]\u003c/sup\u003e. Horizontal pleiotropy was evaluated via MR-Egger intercept tests[\u003csup\u003e18]\u003c/sup\u003e and MR-PRESSO outlier correction[\u003csup\u003e19]\u003c/sup\u003e. Leave-one-out sensitivity analyses confirmed result robustness. Statistical analyses were performed in R v4.3.1 using the MendelianRandomization, TwoSampleMR, and MR-PRESSO packages.\u003c/p\u003e\n\u003cp\u003e2.5 Colocalization Analysis\u003c/p\u003e\n\u003cp\u003eBayesian colocalization (R package coloc) was applied to cis-pQTLs with significant MR effects to evaluate shared causal variants between protein expression and OSCC risk. Default priors (P1 = 1 \u0026times; 10⁻⁴, P2 = 1 \u0026times; 10⁻⁴, P12 = 1 \u0026times; 10⁻⁵) and a \u0026plusmn;1,000 kb window around the lead SNP were used. A posterior probability for colocalization (PPH4) \u0026gt;0.75 defined conclusive evidence of shared causality[\u003csup\u003e20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.6 Replication Analysis, Validation Analysis, and Directionality Testing\u003c/p\u003e\n\u003cp\u003eTo validate the findings from preliminary analysis, replication analysis was performed using an independent cis-pQTL dataset obtained from the UKB-PPP database. Genes demonstrating positive signals in both primary and replication analyses with colocalization analysis (PPH4 \u0026gt; 0.75) were selected for subsequent validation analysis using blood cis-eQTL data from the eQTLGen Consortium to ensure reliability. A Steiger test was additionally conducted to assess potential biases, thereby enhancing the robustness of the study and minimizing confounding effects from reverse causation[\u003csup\u003e21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.7 Mediation Analysis\u003c/p\u003e\n\u003cp\u003eA mediation analysis framework was implemented to investigate potential downstream mechanisms of target proteins. Initially, a two-sample Mendelian randomization (MR) approach was applied to estimate the effects of target proteins on immune cell phenotypes (\u0026beta;₁) and oral squamous cell carcinoma (OSCC) (\u0026alpha;). Immune cell phenotypes significantly associated with target proteins and exhibiting effects on OSCC (\u0026beta;₂) were subsequently identified. The mediated proportion of the immune cell phenotype in the target protein-OSCC relationship was calculated as \u0026beta;₁\u0026times;\u0026beta;₂/\u0026alpha;. The percentage of mediation effect was derived by dividing the indirect effect by the total effect. The delta method was employed to compute 95% confidence intervals and statistical p-values for the mediation effects[\u003csup\u003e22][23]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.8 Single-Cell Analysis\u003c/p\u003e\n\u003cp\u003eSingle-cell transcriptomic data from six OSCC specimens were processed using bioinformatics toolkits (Seurat, limma, dplyr) within the R platform. Raw sequencing data underwent quality filtering to retain cells with \u0026gt;50 detected genes and mitochondrial gene content \u0026lt;5%, followed by log-normalization with a scaling factor of 10,000. The variance stabilizing transformation (VST) method identified the top 1,500 highly variable genes (HVGs). Batch effects across samples were corrected using Seurat\u0026apos;s anchor integration algorithm to generate a unified expression matrix.Dimensionality reduction involved principal component analysis (PCA) with the first 30 principal components determined by JackStraw testing and elbow plots. Cell clustering was performed via shared nearest neighbor (SNN) graph construction (resolution=0.5), complemented by nonlinear visualization using t-SNE. Marker genes for each cluster were identified (|log₂FC| \u0026gt; 0.5, adjusted p \u0026lt; 0.05) and annotated through SingleR package referencing the Human Primary Cell Atlas (HPCA). Subpopulation differential analysis was subsequently conducted using Seurat\u0026apos;s built-in module (thresholds: |log₂FC| \u0026gt; 2, adjusted p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e2.9 Single-Cell eQTL Analysis\u003c/p\u003e\n\u003cp\u003eBased on single-cell resolution eQTL data[\u003csup\u003e24]\u003c/sup\u003e, we further validated the genetic regulatory patterns of target proteins in specific cell subsets. Single-cell eQTL data were obtained from public databases, and SNPs were selected as instrumental variables using the IV selection criteria specific to single-cell eQTLs mentioned above. Two-sample MR analysis was performed to evaluate the causal association between the expression of target genes in specific cell subsets and OSCC risk.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 Primary Mendelian Randomization and Replication Analysis\u003c/p\u003e\n\u003cp\u003eWe identified 1,614 cis-pQTLs by selecting SNPs located within \u0026plusmn;1,000 kb of the coding sequences of their corresponding genes. Through stringent quality control for instrumental variables (IVs), SNPs with F-statistics \u0026gt; 10 were retained for subsequent Mendelian randomization (MR) analyses. Using the inverse-variance weighted (IVW) method, 157 plasma proteins demonstrated causal associations with OSCC risk (Table S2). Replication analysis with cis-pQTL data from the UK Biobank Plasma Proteomics Project (UKB-PPP) further validated 46 proteins with consistent causal effects across both phases (Table S3 and FigureS1). Sensitivity analyses revealed no significant heterogeneity (Cochran\u0026rsquo;s Q-test, p \u0026gt; 0.05), no horizontal pleiotropy (MR-Egger intercept, p \u0026gt; 0.05), and no reverse causation (Steiger test; Tables 1\u0026ndash;2).\u003c/p\u003e\n\u003cp\u003e3.2 Colocalization Analysis\u003c/p\u003e\n\u003cp\u003eFor plasma proteins showing significant MR associations in both the discovery and replication analyses, colocalization analysis was performed to assess the probability of shared causal variants between cis-pQTLs and OSCC outcomes (Tables S4-S5). The results demonstrated that TNFSF8 might share causal variants with OSCC across both datasets (cis-pQTLs), with posterior probability (PP.H4) exceeding 0.75 (Figure 2).\u003c/p\u003e\n\u003cp\u003e3.3 eQTL Validation\u003c/p\u003e\n\u003cp\u003eTo validate the impact of TNFSF8 on OSCC, cis-expression quantitative trait loci (cis-eQTL) data were utilized to explore the association between TNFSF8 gene expression levels and OSCC. MR analyses based on these eQTLs confirmed a causal relationship (Tables 1-2).\u003c/p\u003e\n\u003cp\u003eTable 1 Mendelian Randomization Analysis of TNFSF8 on OSCC: Preliminary, Replication, and Validation Results.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"564\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 204px;\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 360px;\"\u003e\n \u003cp\u003eMendelian randomization \u0026nbsp;analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003ebeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 564px;\"\u003e\n \u003cp\u003edeCODE cispQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e(1.58,4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 564px;\"\u003e\n \u003cp\u003eUKB-PP cispQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e5.71e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e(1.47,2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" style=\"width: 564px;\"\u003e\n \u003cp\u003eeQTLGen Consortium ciseQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 204px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 46px;\"\u003e\n \u003cp\u003e1.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 106px;\"\u003e\n \u003cp\u003e(1.23,1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 Sensitivity Analysis and Directionality Test in TNFSF8-OSCC Association: Preliminary, Replication, and Validation Results.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"668\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eSteiger direction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003eSteiger P value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003ePleiotropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003cp\u003eGlobal Test Pvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 668px;\"\u003e\n \u003cp\u003edeCODE cispQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ers1006026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e9.30e-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 668px;\"\u003e\n \u003cp\u003eUKB-PP cispQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ers10081728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e7.79e-180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 668px;\"\u003e\n \u003cp\u003eeQTLGen Consortium ciseQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003ers10817679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eTRUE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e5.98e-250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.4 Mediation Analysis\u003c/p\u003e\n\u003cp\u003eA two-step mediation analysis was conducted to quantify the proportion of the causal effect of plasma proteins on OSCC mediated by immune cell phenotypes. First, immune cell traits associated with OSCC were identified through MR (Table S6). Subsequently, MR was used to evaluate the relationship between TNFSF8 and these immune cell traits (Table S7). Combining these results with the plasma protein-OSCC associations, the mediated effects were calculated using the delta method to derive confidence intervals and p-values. Three statistically significant mediating effects were identified, all involving T-cell-related phenotypes (Table3). These findings suggest that T-cell traits mediate the causal pathway between TNFSF8 and OSCC, providing critical insights into the role of T-cells in OSCC pathogenesis.\u003c/p\u003e\n\u003cp\u003eTable 3 Mediation Analysis of Immune Cells in the Association Between Plasma Proteins and OSCC.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eMediator\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eProportion \u0026nbsp;mediated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026beta;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026beta;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026beta;1*\u0026beta;2/\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 77px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eCD8br %leukocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.963\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eCD127- CD8br AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 156px;\"\u003e\n \u003cp\u003eCD28- CD8br AC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.5 Single-Cell Analysis\u003c/p\u003e\n\u003cp\u003eSingle-cell RNA sequencing data were processed through quality control, highly variable gene selection, data integration, dimensionality reduction, and cluster analysis, revealing 21 clusters corresponding to 10 distinct cell types (FigureS2). Based on mediation analysis results, differentially expressed genes in T-cells were identified by filtering for absolute log2FC\u0026gt;2 and adjusted p-value \u0026lt;0.05, yielding 119 upregulated and 1,078 downregulated genes in T-cells(Table S8). Notably, TNFSF8 exhibited significant overexpression in T-cells, highlighting its potential as a therapeutic target for precision immunotherapy in OSCC.\u003c/p\u003e\n\u003cp\u003e3.6 Single-Cell eQTL Analysis\u003c/p\u003e\n\u003cp\u003eTo further validate the specific role of TNFSF8 at the cell subset level, we conducted targeted analyses using single-cell eQTL data. The results showed a significant positive correlation between TNFSF8 and OSCC risk in the CD8+ T cell subset (Table 4). This result suggests that the impact of TNFSF8 on OSCC may be primarily mediated through the CD8+ T cell subset, further supporting its critical role in cell-specific regulation.\u003c/p\u003e\n\u003cp\u003eTable 4 Mendelian Randomization and Sensitivity Analysis Results of TNFSF8 Expression in CD8+ T Cells Associated with OSCC Risk Based on Single-Cell eQTL Data.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 118px;\"\u003e\n \u003cp\u003eSingle-Cell eQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 172px;\"\u003e\n \u003cp\u003eMendelian randomization \u0026nbsp;analysis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003ePleiotropy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 126px;\"\u003e\n \u003cp\u003eMR-PRESSO\u003c/p\u003e\n \u003cp\u003eGlobal Test Pvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003emethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003ebeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 126px;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003eTNFSF8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study systematically elucidated the causal role of TNFSF8 in oral squamous cell carcinoma (OSCC) and its potential immune regulatory mechanisms by integrating Mendelian randomization (MR) with single-cell sequencing technology and single-cell eQTL analysis. Through a series of rigorous repeat analyses, sensitivity analyses, and validation analyses\u0026mdash;including Cochran Q test, MR-Egger regression, repeated cis-pQTL and cis-eQTL analyses, and colocalization analysis\u0026mdash;our findings provided robust evidence supporting TNFSF8 as a potential therapeutic target for OSCC. Importantly, by combining\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emediation analysis integrated with single-cell RNA sequencing data and single-cell eQTL analysis, we identified possible mechanisms through which TNFSF8 influences the development and progression of OSCC, and pinpointed key mediating immune cells. In summary, our results not only highlighted the potential of TNFSF8 as a therapeutic target but also revealed its underlying mechanisms and critical mediators. These findings offer new insights for precision treatment strategies in OSCC.\u003c/p\u003e\n\u003cp\u003eTNFSF8 (TNF Superfamily Member 8), also known as CD30L or CD153, is a crucial member of the TNF superfamily, with significant roles in immune regulation, inflammatory responses, and cell survival through its interaction with the receptor CD30. Emerging evidence suggests that TNFSF8 is aberrantly expressed in various tumors and is closely associated with tumor progression and immune evasion. For instance, in Hodgkin\u0026rsquo;s lymphoma and other CD30-positive malignancies, the TNFSF8-CD30 signaling axis promotes tumor cell proliferation and survival[\u003csup\u003e25][26]\u003c/sup\u003e. However, despite its recognized role in several solid tumors[\u003csup\u003e27]\u003c/sup\u003e, the involvement of TNFSF8 in oral squamous cell carcinoma (OSCC) remains underexplored. In this study, we employed Mendelian randomization (MR) analysis to identify a causal relationship between TNFSF8 and OSCC risk. Furthermore, colocalization analysis corroborated that TNFSF8 and OSCC may share causal variants, thereby strengthening the evidence for a causal association. These findings suggest that TNFSF8 may act as a potential regulator in the development and progression of OSCC, possibly mediated through mechanisms involving immune microenvironment regulation, inflammatory responses, and cellular proliferation.\u003c/p\u003e\n\u003cp\u003eThe tumor microenvironment in OSCC is characterized by a complex interplay of immune cells, among which T cells play a pivotal role in orchestrating anti-tumor immune responses. Notably, the quantity and functional status of tumor-infiltrating lymphocytes (TILs) have been closely linked to OSCC prognosis[\u003csup\u003e28]\u003c/sup\u003e. For example, high levels of CD8+ T cell infiltration are generally associated with improved survival rates[\u003csup\u003e29]\u003c/sup\u003e, whereas an increase in regulatory T cells (Tregs) correlates with immune suppression and tumor progression[\u003csup\u003e30]\u003c/sup\u003e. Recent studies have identified additional immune cell subsets within the OSCC microenvironment, such as tumor-associated macrophages (TAMs)[\u003csup\u003e31]\u003c/sup\u003e and myeloid-derived suppressor cells (MDSCs)[\u003csup\u003e32]\u003c/sup\u003e,which contribute to tumor immune evasion through the secretion of immunosuppressive cytokines like IL-10 and TGF-\u0026beta;[\u003csup\u003e33][34]\u003c/sup\u003e. In our study, mediation analysis integrated with single-cell RNA sequencing data and single-cell eQTL analysis revealed that TNFSF8 promotes OSCC progression by modulating specific T cell subsets, with TNFSF8 being highly expressed in T cells within OSCC tissues. These findings further underscore the critical role of T cells in OSCC pathogenesis.\u003c/p\u003e\n\u003cp\u003eBuilding on our findings and existing literature, we propose that TNFSF8 drives OSCC progression through its regulation of T cell function. TNFSF8 is highly expressed in T cells and interacts with its receptor CD30 to modulate T cell activation and functionality. Our study demonstrated significant upregulation of TNFSF8 in T cell subsets from OSCC patients, and mediation analysis confirmed that TNFSF8 influences OSCC progression by regulating T cell function.TNFSF8 binding to CD30 activates the NF-\u0026kappa;B and MAPK signaling pathways, thereby enhancing T cell activation and proliferation. The TNFSF8-CD30 axis has been shown to augment T cell effector functions, including cytokine production and tumor cell cytotoxicity. However, within the tumor microenvironment, this activation may have dual effects: while it may enhance anti-tumor immunity, it could also promote tumor-associated inflammation and immune suppression[\u003csup\u003e35]\u003c/sup\u003e.Furthermore, TNFSF8 may contribute to tumor immune evasion by altering the immunologic balance of the tumor microenvironment through its modulation of T cell function. For instance, TNFSF8 expression has been linked to the production of immunosuppressive cytokines such as IL-10 and TGF-\u0026beta;, which may drive tumor immune escape and progression[\u003csup\u003e36]\u003c/sup\u003e. Additionally, certain TNFSF8 isoforms have been shown to promote Treg induction and immunosuppressive cytokine production, further exacerbating immune evasion[\u003csup\u003e37]\u003c/sup\u003e.In summary, our study highlights the critical role of TNFSF8 in OSCC and elucidates its potential mechanisms in promoting tumor progression through T cell regulation. These findings not only deepen our understanding of OSCC pathogenesis but also provide a theoretical foundation for developing targeted therapies that exploit the TNFSF8 signaling axis. Future studies should further validate the mechanisms of TNFSF8 in OSCC and explore its clinical potential as a therapeutic target.\u003c/p\u003e\n\u003cp\u003ePrevious MR analyses on head and neck squamous cell carcinoma have explored factors such as cholesterol levels[\u003csup\u003e38]\u003c/sup\u003e, smoking, and alcohol consumption[\u003csup\u003e39]\u003c/sup\u003e. However, our study is the first to apply cis-pQTL and eQTL MR analyses specifically to OSCC, a subtype of head and neck squamous cell carcinoma. A major strength of our study lies in the use of two independent cis-pQTL databases, which enhanced the reliability of our results. The consistency of findings across these independent databases significantly bolstered the credibility of our conclusions. Such cross-validation is widely recognized in scientific research as an effective means to improve the stability and accuracy of results. Colocalization analysis further supported the causal relationship by identifying shared causal variants between the two cis-pQTL databases for TNFSF8 in OSCC. Additionally, the use of eQTL data for validation not only strengthened our confidence in the results but also provided supplementary evidence supporting our findings.Mediation analysis, coupled with single-cell sequencing data, indicated that TNFSF8 drives OSCC progression by modulating T cell activity. The specific enrichment of TNFSF8 in T cell subsets suggested that the TNFSF axis may promote OSCC development through T cell function. Single-cell eQTL analysis identified a positive correlation between TNFSF8 expression in CD8+ T cells and OSCC risk, further validating the mechanism by which TNFSF8 influences OSCC progression through T cell subsets at the cell-type-specific genetic level, and enhancing the persuasiveness of the evidence chain at the cellular resolution level.This finding reinforced our methodological approach, ensuring that our conclusions were not limited by specific databases or analytical methods. Furthermore, our study design adhered to stringent screening criteria to ensure the accuracy and validity of IV selection. By rigorously validating key assumptions, we minimized bias and obtained reliable results in multifactorial association analyses, thereby enhancing the scientific integrity of our study.The biomarkers identified in this study were statistically significant across all analyses, suggesting their potential critical roles in OSCC pathogenesis. These findings could serve as a foundation for identifying additional therapeutic targets in future research. Overall, our study provided valuable insights into OSCC through innovative methodologies and rigorous analyses.\u003c/p\u003e\n\u003cp\u003eHowever, our study had several limitations that should be addressed in future research. First, although we identified an association between high TNFSF8 expression and OSCC through cis-pQTL analysis and suggested a potential role of the TNFSF axis in promoting OSCC via T cell function through mediation and single-cell analysis, these findings require further validation. While our study offers valuable insights into the molecular mechanisms of OSCC, translating these findings into clinical applications necessitates additional investigation. Second, our study did not fully explore the specific biological roles of TNFSF8 in OSCC or its complex interactions in tumorigenesis. Future studies should delve deeper into these mechanisms to inform more refined therapeutic approaches. Additionally, our reliance on blood-derived pQTL and eQTL data may limit a comprehensive understanding of the molecular pathology of OSCC. Future research should prioritize the collection and analysis of eQTL data from tumor tissues. Lastly, our study primarily included participants of European ancestry, which may limit the generalizability of our findings to other populations. Differences in genetic backgrounds and disease manifestations across diverse cohorts necessitate further validation to ensure broader applicability.Despite these limitations, our study provided a significant foundation for advancing understanding of OSCC pathogenesis and highlighted potential avenues for refining therapeutic approaches. These findings set the stage for subsequent research and the development of more tailored interventions for OSCC patients.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eBy integrating multi-omics data, including cis-pQTL, cis-eQTL, mediation analysis, single-cell RNA sequencing, and single-cell eQTL analysis,we systematically revealed the potential role of TNFSF8 in the development and progression of oral squamous cell carcinoma (OSCC). Our findings demonstrated that TNFSF8 was significantly upregulated in T cell subsets of OSCC patients and may participate in OSCC progression by modulating T cell function. These results provide novel insights into the molecular mechanisms underlying OSCC and suggest that TNFSF8 could serve as a promising biomarker and therapeutic target for OSCC. However, further molecular experiments and clinical studies are required to validate the specific mechanisms of TNFSF8 in OSCC and explore its clinical applicability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study were derived from publicly accessible genomic and protein quantitative trait loci databases (deCODE Genetics, UK Biobank Pharma Proteomics Project) and transcriptomic repositories (GEO: GSE172577). The original studies providing these datasets obtained necessary ethical approvals and participant consents in compliance with the Declaration of Helsinki. No human subjects were directly recruited or personally identifiable information accessed in the current study. Therefore, Ethics Approval, Consent to Participate, and Consent to Publish declarations are not applicable to this secondary analysis of de-identified, aggregated data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study exclusively analyzed de-identified, aggregate-level data from publicly available genetic and transcriptomic repositories (deCODE, UK Biobank Pharma Proteomics Project, GEO). No individual-level data, clinical images, or personally identifiable information were included. Therefore, Consent to Publish declaration is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Fujian Science and Technology Innovation Joint Funds Project (Grant No. 2023Y95010274). The funder had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to submit the work for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author confirms full responsibility for the integrity of this declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analyzed in this study are publicly accessible:\u003c/p\u003e\n\u003cp\u003eGenetic association statistics were obtained from the deCODE Genetics repository (URL: https://www.decode.com/summarydata/) and the UK Biobank Pharma Proteomics Project (URL: https://www.ukbiobank.ac.uk/).\u003c/p\u003e\n\u003cp\u003eTranscriptomic datasets (GSE172577) were sourced from the NCBI Gene Expression Omnibus (GEO, URL: https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e\n\u003cp\u003eImmune cell-related data:\u003c/p\u003e\n\u003cp\u003eImmune trait GWAS summary statistics were retrieved from GWAS Catalog (URL: https://www.ebi.ac.uk/gwas/).\u003c/p\u003e\n\u003cp\u003eImmune cell signature metrics are provided in Supplementary Table S1 (accessible with this manuscript).\u003c/p\u003e\n\u003cp\u003eNo new datasets were generated in this work. Processed analytical results are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial or non-financial interests directly or indirectly related to this work. The corresponding author (CHEN GAO) affirms full responsibility for the integrity of this declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYONG ZHUANG\u003c/em\u003e: Conceptualization, Methodology, Data Curation, Formal Analysis, Writing \u0026ndash; Original Draft.\u003c/p\u003e\n\u003cp\u003eCHEN GAO: Supervision, Funding Acquisition, Resources, Validation, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003eAll authors critically reviewed and approved the final manuscript, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable.\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Johnson DE,Burtness B,Leemans CR, et al. Head and neck squamous cell carcinoma. 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Nat Commun. 2024;15 (1):7835. doi:10.1038/s41467-024-51679-x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"TNFSF8, Oral Squamous Cell Carcinoma (OSCC), Mendelian Randomization, Single-Cell Analysis, Single-cell eQTL analysis, Multi-Omics Integration","lastPublishedDoi":"10.21203/rs.3.rs-7101139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7101139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOral squamous cell carcinoma (OSCC) is a highly aggressive malignancy with limited therapeutic options. This study aimed to investigate the causal role of TNFSF8 in the progression of OSCC through multi-omics integration.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eCis-pQTL data from deCODE and the UK Biobank Plasma Proteomics Project (UKB-PPP) were used to perform two-sample Mendelian randomization (MR) analysis to evaluate the association between TNFSF8 and OSCC. Bayesian colocalization was employed to validate causal relationships. Mediation analysis quantified the role of immune cell phenotypes in mediating the TNFSF8-OSCC relationship. Single-cell RNA sequencing (scRNA-seq) was used to analyze the high expression of TNFSF8 in T cells from OSCC tissue.Meanwhile, single-cell eQTL analysis was conducted to further verify the cell-type-specific causal association between TNFSF8 and OSCC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMR analysis identified TNFSF8 as a causal factor for OSCC risk. Colocalization analysis confirmed shared causal variants. Mediation analysis revealed that T cell phenotypes significantly mediated the TNFSF8-OSCC relationship. scRNA-seq demonstrated significantly elevated expression of TNFSF8 in T cell subsets from OSCC patients.Single-cell eQTL analysis further found that the expression of TNFSF8 in CD8\u0026thinsp;+\u0026thinsp;T cells was positively correlated with OSCC risk, reinforcing its cell-type-specific role.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eTNFSF8 drives OSCC progression potentially through the regulation of T cell function. These findings suggest that TNFSF8 is a promising therapeutic target, warranting further validation.\u003c/p\u003e","manuscriptTitle":"TNFSF8 Drives Oral Squamous Cell Carcinoma Progression via CD8+ T Cell Regulation: Insights from Multi-Omics Integration and Single-Cell eQTL Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-11 13:12:06","doi":"10.21203/rs.3.rs-7101139/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb383729-dff5-4c8c-aeaf-0a396c68e9c1","owner":[],"postedDate":"August 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:39:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-11 13:12:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7101139","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7101139","identity":"rs-7101139","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}