Single-Cell and Mendelian Analyses Reveal Shared Mechanisms Between Head and Neck Neoplasms and Aging

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Methods: Using single-cell RNA sequencing data of peripheral blood from HNN patients, aging individuals, and healthy controls, we identified cross-group co-expressed, downregulated cell subpopulations as core targets. Integrated pseudotime trajectory analysis and intercellular communication modeling were employed to investigate the dynamic evolution and functional interaction patterns of these subpopulations. Differentially expressed genes were identified, followed by Mendelian randomization analysis to assess their causal associations with HNN. Co-localization analysis were performed using GWAS data for HNN and expression quantitative trait loci (eQTL) datasets. Key genes were further subjected to metabolic pathway enrichment analysis. Results: T cell subsets were found to be significantly represented in both HNN and aging individuals. Among them, naive CD4(+) T cells was down-regulated in both groups, leading to the identification of 24 differentially expressed genes. Mendelian randomization studies have shown that CCR , LEF1 , NOSIP and FHIT have causal relationships with HNN. In the validation phase, however, only FHIT was retained, for which co-localization analysis revealed limited evidence of a shared causal variant between the GWAS and eQTL signals (H4 = 0.01). The metabolic enrichment highlighted metabolic pathways associated with these genes. Conclusions: This study identified naive CD4(+) T cells downregulation as a shared feature of HNN and aging and highlighted: CCR , LEF1 , NOSIP and particularly FHIT as potential molecular links. These findings provide novel insights into the intersection of aging and tumorigenesis, offering potential targets for combined therapeutic strategies. head and neck squamous cell carcinoma(HNSCC) head and neck neoplasm(HNN) aging mendelian randomization single-cell Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Head and neck neoplasm (HNN) is one of the most common malignant tumors worldwide[ 1 ], primarily occurring in the lips, oral cavity, pharynx, larynx, paranasal sinuses, as well as salivary gland tumors and mucosal melanoma[ 2 ]. Notably, head and neck cancer (HNC) ranks as the sixth most common cancer globally[ 3 , 4 ]. In 2021, the International Agency for Research on Cancer (IARC) reported 840,000 new cases of HNC annually[ 5 ]. Smoking and alcohol consumption are considered the main risk factors[ 6 ]. Besides, cancer incidence generally increases with age, making aging a potential major risk factor[ 7 , 8 ]. Thus, clarifying senescence-carcinogenesis molecular links is crucial for oncotherapy and prevention. Aging involves physiological/pathological changes causing tissue/organ function decline[ 9 ]. Experimental data show senescence-mediated chemokine storm and cytokine imbalance are key pathways linking age-related pathologies to immune surveillance impairment[ 10 , 11 ]. Research shows accelerated biological age increases cancer susceptibility (e.g. lung and colorectal) irrespective of chronological age, sex, or other risk factors[ 12 ]. In addition, mechanistic studies reveal Treg-derived cytokine networks critically regulate HNN tumorigenesis.These lymphocytes suppress immunity by secreting TGF-β, IL-10 and expressing CTLA-4, all linked to tumor progression [ 13 ]. Although preliminary studies have highlighted the involvement of immune cells in HNSCC pathological, the specific functions of different immune cell subsets —particularly T cells—and their association with clinical manifestations remain poorly defined. Recent advances in high-throughput sequencing technology provide unprecedented resolution to decode the clonal evolution of tumor-infiltrating lymphocytes during HNN progression and organismal aging processes. Through a multi-cohort integration framework, we harmonized peripheral blood-derived scRNA-seq datasets encompassing HNN patients and age-stratified controls to systematically analyze the transcriptional heterogeneity of immune cell subsets, screen for key genes shared between aging and HNN, and analyze enriched pathways to delineate conserved immunoregulatory axes in HNN and senescence-associated immune remodeling. Further more, employing two-sample Mendelian randomization (MR), we conducted instrumental variable analyses to interrogate the causal key genes on HNN pathogenesis. This study's characterization of pivotal molecular determinants and their regulatory networks offers critical mechanistic insights into HNN pathogenesis and aging-related biological processes. Material and methods Analytical datasets were sourced from established open-access repositories. Data source This investigation leveraged The Gene Expression Omnibus(GEO) to procure senescence-associated and HNN-linked gene expression signatures. Specifically, we searched using the keywords “neck malignant neoplasm”, “senescence” and “blood” to identify relevant data sets. Then, we selected two samples (GSM5017033 and GSM5017021) from HNSCC data set (GSE164690) for HNN analysis, and two samples (GSM4750298 and GSM4750299) from Aging data set (GSE157007) for senescence-related analysis. GSM4750303 and GSM4750304 were used as control samples from GSE157007 dataset. All HNSCC cases were confirmed by oncologists and met the established diagnostic criteria for HNSCC [14]. To complement our gene expression analysis, we further incorporated genome-wide association study (GWAS) data related to HNN from the GWAS Catalog. The training data set for HNN included 492 African American or Afro-Caribbean cases and 121,151 controls of the same ancestry, 4,578 European ancestry cases and 442,171 controls, as well as 233 Hispanic or Latin American cases and 59,498 controls (GCST90479818). Additionally, the validation data set for HNN specifically consisted of 4,671 European participants, including 2,342 confirmed cases (GCST012234). scRNA-seq analysis In this study, we integrated and analyzed scRNA-seq datasets derived from HNSCC tissues, peripheral blood of aged individuals, and samples from healthy controls. Cell type annotation was carried out using canonical marker genes in combination with the SingleR (Version 2.8.0) algorithm. We identified a specific cell subpopulation showing consistent downregulation in both HNSCC and senescent samples compared to controls, defined as the core cellular subpopulation (Log2FC < -0.5). Comprehensive data processing was performed using the Seurat R package (Version 4.4.0). Quality control involved the removal of low-quality cells with fewer than 500 or more than 4,000 detected genes, or with mitochondrial gene content exceeding 20%. Normalization, variance stabilization, and selection of highly variable genes were performed, providing robust input for downstream analysis. Dimensionality reduction was conducted through principal component analysis (PCA), retaining the top 5 components, followed by visualization using t-distributed stochastic neighbor embedding (t-SNE). Cell clustering was executed using the FindNeighbors and FindClusters functions. Differentially expressed genes within each cluster were identified via the Wilcoxon rank-sum test using the FindAllMarkers function. Annotation was validated by referencing curated databases including CellMarker. Trajectory inference was carried out using the Slingshot R package (Version 2.14.0) to reconstruct potential cellular developmental pathways and lineage relationships. Additionally, we evaluated intercellular communication to explore signaling interactions within and between clusters. To investigate the functional significance of the core cellular subpopulation, we performed functional enrichment analysis on its marker genes. Finally, we elucidated the putative roles of the core cellular subpopulation in both HNSCC progression and aging-related cellular reprogramming, highlighting its relevance to disease pathogenesis and age-associated immune or stromal changes. MR To explore causal gene links between senescence and HNN pathogenesis, we used two-sample Mendelian randomization (MR) frameworks. Univariable MR analyses were conducted via the TwoSampleMR R package (V0.6.14), with the inverse variance weighted (IVW) method as the main causal estimation approach. To enhance the accuracy and credibility of the instrumental variables (IVs) applied in this MR analysis, a strict screening protocol was adopted. Initially, we extracted single nucleotide polymorphisms (SNPs) that showed strong associations with aging-related traits ( P < 5 × 10⁻⁸ ) from large-scale GWAS conducted in populations of European descent. Subsequently, we applied linkage disequilibrium (LD) pruning with a cutoff of R² < 0.01 to eliminate correlated variants and retain only mutually independent SNPs. To minimize the impact of weak instruments, we calculated the F-statistic for each SNP using the formula F = β² / SE², and only retained those with F > 10, thereby improving the strength and reliability of the instrumental variables[15]. The selected SNPs were further validated by comparison with external datasets to confirm their stability and relevance to the exposure. To mitigate horizontal pleiotropic effects, we conducted comprehensive sensitivity analyses comprising MR-Egger regression, weighted median estimation, and leave-one-out validation. To assess heterogeneity, we calculated Cochran’s Q statistic, and used the Pleiotropy Residual Sum and Outlier (MR-PRESSO) method to detect and correct for outliers[16]. After excluding these outliers, the causal estimates were recalculated. In order to further validate our results, we selected the GWAS data of another HNN as the validation set. Colocalization analysis and expression quantitative trait locus analyses We executed genomic colocalization assessments employing the coloc R package[17], aiming to identify potential shared association signals between GWAS data related to HNN and aging-related eQTL datasets. The H4 posterior probability, generated by the coloc.abf function, was used as the primary indicator of colocalization. In addition, we carried out regional association mapping to further investigate the relationships between SNP genotypes and the expression levels of key genes, in the context of the biological link between aging and head and neck tumorigenesis. Moreover, to examine potential colocalization between the exposure (eQTL) and outcome (GWAS) datasets, we utilized the locuscomparer R package (Version 1.0.0), which visualized the relationship between the datasets. Finally, the results and datasets were saved for further investigation. Metabolic enrichment analysis To gain deeper insights into the intrinsic link between HNN and aging, we conducted metabolic enrichment analysis focusing on identifying potential key pathways. For this purpose, we utilized the scMetabolism package (Version 0.2.1), which incorporates curated pathway sets from KEGG and REACTOME pathway database. Using this tool, we calculated metabolic activity scores across different cell types. The AUCell algorithm assessed single-cell metabolic states to detect subtle pathway activity shifts. Statistical analysis All statistical analyses were conducted using R version 4.1.3 (https://www.r-project.org/), with a two-sided P-value < 0.05 considered statistically significant. Results Single-cell RNA-seq analysis T-cell subsets constitute a significant proportion of the immune landscape in both HNSCC and aging, as shown in Fig1.a. A significant decrease in Naive CD4⁺ T cells was observed in the HNSCC and senescent groups relative to healthy controls(Fig1.b). Fig2.(a-c) depicts the trajectories of Naive CD4⁺ T cell relative to other cell subpopulations in HNSCC and senescence. Cell-cell communication profiling (Fig2.(d,e) reveals that Naive CD4(+) T cells interacts with multiple subpopulations and play key roles in both HNSCC and aging. We identified 181 potential marker genes by contrasting Naive CD4⁺ T cells with other T-cell subsets and 26 markers through core Naive CD4⁺ T subset with other T cell subsets, and the intersection yielded 24 consensus differentially expressed genes. MR Further MR Analysis of the 24 differentially expressed genes identified above revealed four genes with causal effects on HNN risk. Fig3.a identifies senescence-associated genes within naive CD4⁺ T cells that demonstrate causal relationships with HNN. Specifically, as shown in Fig3.b, the IVW methodology shown that CCR7 (nsnp=5, OR = 0.0001, 95%CI:0.000-0.0425, p = 0.002 ), LEF1 (nsnp=3, OR = 0.0001, 95%CI:0.000-0.0674, p = 0.005 ), NOSIP (nsnp=5, OR = 0.0089, 95%CI: 0.0003-0.2424, p = 0.005 ), FHIT (nsnp=13, OR = 114.76, 95%CI:1.3866-9495.4434, p = 0.035 ) were causally associated with HNN. Sensitivity analyses—including tests for heterogeneity and horizontal pleiotropy—supported the validity of our MR approach. Furthermore, MR-Egger regression revealed no evidence of horizontal pleiotropy, with all intercepts non-significant ( P > 0.05 ). To independently validate our findings, we applied the same analysis to an additional HNN GWAS dataset as the validation set, results are presented in Fig3.c. The IVW methodology shown that FHIT (nsnp=10, OR = 0.7326 , 95%CI:0.5970-0.8991, p = 0.0029 ) was causally associated with HNN, while CCR7 (nsnp=5, OR = 0.5732, 95%CI: 0.3057- 1.0746, p = 0.08 ), LEF1 (nsnp=3, OR = 0.5479 , 95%CI:0.2678-1.1212, p=0.0996 ), NOSIP (nsnp=4, OR = 0.7359, 95%CI: 0.5358-1.0107, p = 0.058 ) were not causally associated with HNN. Colocalization analysis The results are shown in Fig3.d, showing the results of colocalization analysis of HNN and FHIT : H0 = 4.97e-299, H1 = 2.90e-299, H2 = 0.63, H3 = 0.37, H4 = 0.01. The posterior probability (H4) of a shared causal variant between HNN and FHIT was estimated at 1%. Constructing cell communication and pseudo-time analyses By cell communication analysis, Fig4.a and b show FHIT +Naive CD4(+) T cells and FHIT -Naive CD4(+) T cells interact with other cell subpopulations. The major Naive CD4(+) T cells communication pathways were different with high and low FHIT expression. For instance, the primary gene pathways between FHIT +CD4-naive cells and monocytes are ANXA1-FPR1, IL16-CD4, and MIF-(CD74+CD44) , while those between FHIT -CD4-naive cells and monocytes are ANXA1-FPR1 and MIF-(CD74+CD44) . Pseudotime trajectory analysis revealed distinct temporal expression patterns of cell cycle-associated genes: FHIT exhibited prominent expression during the early phase (0-10), while NOSIP , CCR7 , and LEF1 were preferentially upregulated in the terminal phase (Fig4.c), indicating potential involvement in cell cycle exit or checkpoint transitions. These temporally segregated expression profiles highlight their putative regulatory roles in different stages of cell cycle progression. As shown in Fig4.d , the expression of the FHIT gene significantly decreased over pseudotime (Pearson's r = -0.13, p= 3.85e−19 ), suggesting its potential involvement in early stages of the cell trajectory as well. Enrichment metabolism pathway analysis of key genes Fig5.a shows that FHIT are predominantly expressed in Naive CD4(+) T cells. The major Naive CD4(+) T cells enrichment pathways were different with high and low FHIT expression. As shown in Fig Fig5.b: Naive CD4(+) T cells expressing higher FHIT was mainly enriched in the following pathways: thiamine metabolism, porphyrin and chlorophyll metabolism, glycosphingolipid biosynthesis-globo and isoglobo series, glycosphingolipid biosynthesis-ganglio series, glycosaminoglycan biosynthesis-keratan sulfate. Naive CD4(+) T cells with lower FHIT was mainly enriched in the following pathways: thiamine metabolism, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, one carbon pool by folate. As shown in Fig5.c, the FHIT (OR=114.75 (1.39, 9495.44), p=3.53e-02 ) gene demonstrated consistently high expression levels in both healthy and HNN, while NOSIP (OR=0.01 (0.00, 0.24), p=5.08e-03 ), CCR7 (OR=0.00 (0.00, 0.04), p=2.46e-03 ), and LEF1 (OR=0.00 (0.00, 0.07), p=5.04e-03 ), exhibited significantly elevated expression in healthy cohorts but were markedly downregulated in HNN samples. Discussion HNN are a heterogeneous group of cancers, with HNSCC accounting for over 90% of cases, characterized by high global incidence and poor prognosis. To explore connections between HNN and aging, we focused on shared mechanisms and immunological mechanism. Initially, we integrated multiple single-cell RNA sequencing datasets using established methodologies. Guided by prior studies [ 18 – 20 ], we prioritized T-cell populations for analysis. Both the HNSCC and the aging cohorts displayed significant alterations in T-cell subset distribution compared to healthy controls, notably marked by a pronounced reduction in Naive CD4(+) T cells proportions. To further pinpoint central genes with potential causal roles in HNN pathogenesis, MR analysis was performed. Finally, metabolic enrichment analysis was conducted to investigate how these key genes interact with immune cells and metabolic pathways in both HNN and aging contexts. This integrated approach highlights shared molecular and immunological features between HNN and aging, offering insights into potential therapeutic targets for precision interventions. Naive CD4(+) T cells play key roles in both HNN and aging, exhibiting dysfunctions that promote immune evasion and inflammation. IL-6 and TGF-β drive differentiation toward Th17 cells[ 21 ], promoting inflammation and tumor progression in HNSCC and chronic inflammation in aging[ 22 , 23 ]. Additionally, IL-12 deficiency impairs Th1 differentiation, while Treg-mediated suppression weakens anti-tumor immunity and pathogen defense[ 24 , 25 ]. Both conditions involve the presence of immunosuppressive factors (e.g., PD-L1, TGF-β) that induce Treg differentiation or exhaustion[ 26 , 27 ], hindering CD8 + T and NK cell function[ 26 , 28 , 29 ]. The accumulation of Tregs correlates with advanced stages and poor outcomes[ 25 ]. In aging, thymic involution reduces naive T cell production, and senescent naive CD4(+) T cells exhibit impaired proliferation and cytokine production[ 30 ]. These cells express exhaustion markers like PD-1 and CTLA-4, contributing to chronic inflammation, impaired immune surveillance, and the accumulation of senescent cells[ 31 – 33 ]. Aging also skews naive CD4(+) T cells toward the Th17 lineage[ 34 ], exacerbating inflammation despite reduced immune efficacy[ 35 ]. Aging-related immune dysregulation involving naive CD4(+) T cells may promote HNN development[ 36 , 37 ], suggesting aging as a potential risk factor. The FHIT gene at chromosome 3p14.2 encodes a tumor suppressor regulating apoptosis, cell cycle, oxidative stress, and genomic stability. [ 38 – 40 ]. FHIT loss (50–70% in HNN via deletion/skipping/aberrant transcripts) correlates with poor prognosis, metastasis, and therapy resistance[ 41 – 44 ]. Mechanistically, FHIT deficiency promotes genomic instability, impairs DNA repair, disrupts p53 signaling, enhances oncogene-induced senescence escape[ 45 ], and accelerates cancer progression[ 40 , 46 ]. FHIT regulates mitochondrial ROS via FdxR/Hsp60 interactions, and its loss worsens oxidative damage and cellular aging[ 40 , 45 ]. Clinically, restoring FHIT induces apoptosis/inhibits tumor growth, serving as a therapeutic target and response predictor[ 45 – 48 ], especially in heavy tobacco users [ 49 ]. Additionally, FHIT methylation has been linked to aging, though its relationship with protein expression remains unclear [ 38 , 50 ]. While FHIT is not traditionally studied in naive CD4(+) T cells, its role in maintaining genomic integrity and oxidative balance warrants further investigation in the context of immune aging and cancer immunology. A deeper understanding of FHIT -mediated pathways may offer novel strategies for managing HNN and age-related immune dysfunction. This study employs several innovative analytical strategies. First, by comparing differential genes between HNN and aging cohorts, we used gene heatmaps to visualize age-related expression patterns. Integrated with single-cell transcriptomic data, these heatmaps identified co-dysregulated genes (e.g., FHIT, NOSIP, CCR7, LEF1 ) as key targets for further investigation. Second, time-course analyses across HNN progression and aging stages revealed stage-specific gene expression and declining intercellular communication, linking these trends to immunosenescence and chronic inflammation. This temporal profiling also highlighted prognostically relevant, time-dependent genes with potential therapeutic value. Finally, cell communication modeling uncovered regulatory networks centered on FHIT , including its influence on pathways such as the Akt-survivin axis, offering a systems-level view for identifying targets of combinatorial therapy. Despite these strengths of this study, there are limitations. Our conclusions are hypothesis-generating and based primarily on bioinformatic data, which require experimental validation. The primary dataset used was GEO, which focused on HNSCC, but the MR results were not replicated in an external HNN cohort, necessitating further study. While HNSCC accounts for over 90% of HNN, our findings may not fully apply to other HNN subtypes. Future research should explore non-HNSCC HNN subtypes, such as hematolymphoid malignancies, mesenchymal sarcomas, and neuroendocrine tumors. Furthermore, due to data limitations, reverse MR was not conducted to further verify the effects of HNN and aging on related genes. Conclusion This study leveraged single-cell transcriptomics and MR to explore the relationship between HNN and aging. By comparing differential gene expression between HNSCC and aging cohorts, we identified key co-regulated genes ( NOSIP, CCR7, LEF1 and especially FHIT ) and highlighted their potential roles in tumor progression and aging. Time-course analyses revealed stage-specific gene expression patterns and age-related declines in intercellular communication, linked to immunosenescence and chronic inflammation. Additionally, cell communication modeling emphasized FHIT regulatory role in tumor microenvironment signaling, suggesting new therapeutic targets. This research contributes to understanding the connection between aging and HNN, paving the way for precision interventions. Declarations Conflict of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT authorship contribution statement Chen Sun contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, software, supervision, validation, visualization, writing – original draft, writing –review, and editing. Xinlei Chen contributed to the conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing – original draft, writing –review, and editing. Jinzhao Li contributed to the conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing – original draft, writing –review, and editing. Lirong Hu contributed to the conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing – original draft, writing –review, and editing. Rui Shi contributed to the conceptualization, data curation, funding acquisition, methodology, project administration, resources, supervision, writing – review, and editing. Chunhui Li contributed to the conceptualization, methodology, supervision, writing – review, and editing. Canli Wang contributed to the conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing – original draft, writing – review, and editing. Acknowledgements We acknowledge the Department of Periodontics and Oral Mucosal Diseases, The Affiliated Stomatology Hospital/the School of Stomatology, Southwest Medical University, for providing the essential facilities, resources and fundings that made my experiments possible. We also acknowledge Luzhou Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatology Hospital, Southwest Medical University, for their collaboration, technical support, and enriching discussions. Funding This work was supported by projects of Stomatology Hospital Affiliated to Southwest Medical University (NO. 201905 and NO. 2023Y06). Data availability Data will be made available on request. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Liu ZL, Meng XY, Bao RJ, Shen MY, Sun JJ, Chen WD, et al. 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Zhu X, Chen Z, Shen W, Huang G, Sedivy JM, Wang H, et al. Inflammation, epigenetics, and metabolism converge to cell senescence and ageing: the regulation and intervention. Signal Transduct Target Ther. 2021;6(1):245. https://dx.doi.org/10.1038/s41392-021-00646-9. Haynes L, Eaton SM. The effect of age on the cognate function of CD4+ T cells. Immunol Rev. 2005;205:220-8. https://dx.doi.org/10.1111/j.0105-2896.2005.00255.x. Li K, Zeng X, Liu P, Zeng X, Lv J, Qiu S, et al. The Role of Inflammation-Associated Factors in Head and Neck Squamous Cell Carcinoma. J Inflamm Res. 2023;16:4301-15. https://dx.doi.org/10.2147/jir.S428358. Lee YC, Nam Y, Kim M, Kim SI, Lee JW, Eun YG, et al. Prognostic significance of senescence-related tumor microenvironment genes in head and neck squamous cell carcinoma. Aging (Albany NY). 2023;16(2):985-1001. https://dx.doi.org/10.18632/aging.205346. Sard L, Accornero P, Tornielli S, Delia D, Bunone G, Campiglio M, et al. The tumor-suppressor gene FHIT is involved in the regulation of apoptosis and in cell cycle control. Proc Natl Acad Sci U S A. 1999;96(15):8489-92. https://dx.doi.org/10.1073/pnas.96.15.8489. Huebner K, Saldivar JC, Sun J, Shibata H, Druck T. Hits, Fhits and Nits: beyond enzymatic function. Adv Enzyme Regul. 2011;51(1):208-17. https://dx.doi.org/10.1016/j.advenzreg.2010.09.003. Karras JR, Paisie CA, Huebner K. Replicative Stress and the FHIT Gene: Roles in Tumor Suppression, Genome Stability and Prevention of Carcinogenesis. Cancers (Basel). 2014;6(2):1208-19. https://dx.doi.org/10.3390/cancers6021208. Turaçlar N, Vural H, Elagöz Ş, Altuntaş EE, Polat F. Investigation of Mutations in Exon 7,8 and Exon 9 of FHIT Gene in Laryngeal Squamous Cell Carcinoma. Integrative Molecular Medicine. 2016;3. https://dx.doi.org/10.15761/IMM.1000252. Tanimoto K, Hayashi S, Tsuchiya E, Tokuchi Y, Kobayashi Y, Yoshiga K, et al. Abnormalities of the FHIT gene in human oral carcinogenesis. Br J Cancer. 2000;82(4):838-43. https://dx.doi.org/10.1054/bjoc.1999.1009. Toledo G, Sola JJ, Lozano MD, Soria E, Pardo J. Loss of FHIT protein expression is related to high proliferation, low apoptosis and worse prognosis in non-small-cell lung cancer. Mod Pathol. 2004;17(4):440-8. https://dx.doi.org/10.1038/modpathol.3800081. Federica G, Andrea S, Valentina M, Renato C, Giuseppe S, Giulia F, et al. Molecular Genetics and Biology of Head and Neck Squamous Cell Carcinoma: Implications for Diagnosis, Prognosis and Treatment. 2012. https://dx.doi.org/10.5772/31956. Waters CE, Saldivar JC, Hosseini SA, Huebner K. The FHIT gene product: tumor suppressor and genome "caretaker". Cell Mol Life Sci. 2014;71(23):4577-87. https://dx.doi.org/10.1007/s00018-014-1722-0. Miuma S, Saldivar JC, Karras JR, Waters CE, Paisie CA, Wang Y, et al. Fhit deficiency-induced global genome instability promotes mutation and clonal expansion. PLoS One. 2013;8(11):e80730. https://dx.doi.org/10.1371/journal.pone.0080730. Roz L, Gramegna M, Ishii H, Croce CM, Sozzi G. Restoration of fragile histidine triad (FHIT) expression induces apoptosis and suppresses tumorigenicity in lung and cervical cancer cell lines. Proc Natl Acad Sci U S A. 2002;99(6):3615-20. https://dx.doi.org/10.1073/pnas.062030799. Gaudio E, Paduano F, Croce CM, Trapasso F. The Fhit protein: an opportunity to overcome chemoresistance. Aging (Albany NY). 2016;8(11):3147-50. https://dx.doi.org/10.18632/aging.101123. Czarnecka KH, Migdalska-Sęk M, Domańska D, Pastuszak-Lewandoska D, Dutkowska A, Kordiak J, et al. FHIT promoter methylation status, low protein and high mRNA levels in patients with non-small cell lung cancer. Int J Oncol. 2016;49(3):1175-84. https://dx.doi.org/10.3892/ijo.2016.3610. Jeong YJ, Jeong HY, Lee SM, Bong JG, Park SH, Oh HK. Promoter methylation status of the FHIT gene and Fhit expression: association with HER2/neu status in breast cancer patients. Oncol Rep. 2013;30(5):2270-8. https://dx.doi.org/10.3892/or.2013.2668. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 03 Sep, 2025 Reviewers agreed at journal 25 Aug, 2025 Reviews received at journal 24 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 04 Aug, 2025 Editor invited by journal 28 Jul, 2025 Editor assigned by journal 22 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 13 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-7113655","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496801784,"identity":"6db96a8b-f384-4e7c-9371-5253eb97ce43","order_by":0,"name":"Chen Sun","email":"","orcid":"","institution":"The Affiliated Stomatology Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Sun","suffix":""},{"id":496801785,"identity":"f7527131-412a-43a9-8b3c-3f375b3448cd","order_by":1,"name":"Xinlei Chen","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinlei","middleName":"","lastName":"Chen","suffix":""},{"id":496801787,"identity":"6ed32c89-c69a-41ec-ba9f-a68d255f20b6","order_by":2,"name":"Jinzhao Li","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinzhao","middleName":"","lastName":"Li","suffix":""},{"id":496801788,"identity":"0fe9b563-bcb2-4be3-b856-9e2eb276dff5","order_by":3,"name":"Lirong Hu","email":"","orcid":"","institution":"Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Hu","suffix":""},{"id":496801791,"identity":"d061978d-b094-4158-9311-5cb1fc9836d7","order_by":4,"name":"Rui Shi","email":"","orcid":"","institution":"The Affiliated Stomatology Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Shi","suffix":""},{"id":496801793,"identity":"1efc04dd-e508-421c-846c-63199d58af8f","order_by":5,"name":"Chunhui Li","email":"","orcid":"","institution":"The Affiliated Stomatology Hospital of Southwest Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunhui","middleName":"","lastName":"Li","suffix":""},{"id":496801794,"identity":"62ad9b05-841c-4980-b8db-a7b6b5f362de","order_by":6,"name":"Canli Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNv7mAwYJPP/k7I83HyBOC5/EsYSCDzIHjBnOHEsgToscQ47Bxxk2BxIbbuQYEOkwhgOGm3ly7hgzNuR8vPGGwU5Ot4GQFuaGZGOeM8/kmBnObracw5BsbHaAsC3HjHl7mI3ZGHu3SfMwHEjcRlhLYvtv3n/MiT3MPM+I1ZLMYDiD53DiDDYeNiK1SBxjMPjAk2ZswMNmbDnHgAi/yPf3fwBGpY2cgfzjhzfeVNjJEdSCAiR4iIwaZC2k6hgFo2AUjIIRAQBh0EI+fL1jUQAAAABJRU5ErkJggg==","orcid":"","institution":"The Affiliated Stomatology Hospital of Southwest Medical University","correspondingAuthor":true,"prefix":"","firstName":"Canli","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-13 13:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7113655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7113655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88652493,"identity":"3d73213e-5783-4b21-ba70-1d577380dc01","added_by":"auto","created_at":"2025-08-08 17:55:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":123337,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of single-cell RNA-seq data for HNSCC, senescent and normal groups. Fig1.a shows the proportion of different immune cell types in each disease. Fig1.b shows bar graphs of the proportion of different T-cell subpopulations in HNSCC, senescent, and normal groups. CM (Central Memory T Cell), EM (Effector Memory T Cell), REG(Regulatory T cells).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7113655/v1/d6d11e7aed379d504b8f4a21.png"},{"id":88652491,"identity":"bf1dd6ec-bd3f-4d22-8859-7df943f1c03c","added_by":"auto","created_at":"2025-08-08 17:55:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":190581,"visible":true,"origin":"","legend":"\u003cp\u003eCommunication between Naive CD4(+) T cells and other subpopulations between HNSCC and senescent. Fig2.a shows cell trajectory analysis. Fig2.b and c show circle plots summarizing the trajectory analysis between Naive CD4(+) T cells and other cell subpopulations in HNSCC and senescence groups. Fig2.d shows communication between the Naive CD4(+) T cells and other subpopulations in HNSCC group. Fig2.e shows communication between Naive CD4(+) T cells and other subpopulations in senescent group.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7113655/v1/d8b00f9c35c761fcadc204da.png"},{"id":88652998,"identity":"91d1f328-f960-4f8f-854c-b1fa8b93d0dd","added_by":"auto","created_at":"2025-08-08 18:03:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":382010,"visible":true,"origin":"","legend":"\u003cp\u003ea. Genes associated with senescence in the Naive CD4(+) T cells subpopulations and causally related to HNN. Fig3.b. Forest plot showing MR of key genes with HNN. Fig3.c. Results of key genes in the validation set with HNN. Fig3.d. Results of the colocalization analysis, showing the regional association maps for FHIT.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7113655/v1/bade03555be7b5751015d460.png"},{"id":88652497,"identity":"5e3f87b0-cf5e-4ac7-a523-1128f54df9ff","added_by":"auto","created_at":"2025-08-08 17:55:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":221483,"visible":true,"origin":"","legend":"\u003cp\u003ea shows circle plots summarizing the trajectory analysis among FHIT+CD4_Naive cells , FHIT-CD4_Naive cells and other cell subpopulations . Fig4.b shows communication trajectories among them. Fig4.c and d show pseudotime trajectory analysis of FHIT, NOSIP, CCR7, and LEF1.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7113655/v1/9b36d4cf0d0357b9d6f20516.png"},{"id":88652494,"identity":"ac55544b-5d6d-4e29-bdc7-8f84f269e01c","added_by":"auto","created_at":"2025-08-08 17:55:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":269620,"visible":true,"origin":"","legend":"\u003cp\u003ea shows that \u003cem\u003eFHIT\u003c/em\u003e are predominantly expressed in Naive CD4(+) T cells. Fig4.b shows the major enrichment pathways of Naive CD4(+) T cells with high and low \u003cem\u003eFHIT\u003c/em\u003eexpression\u003cem\u003e. Fig5.\u003c/em\u003ec shows the \u003cem\u003eFHIT\u003c/em\u003e \u003cem\u003eNOSIP\u003c/em\u003e,\u003cem\u003e CCR7 \u003c/em\u003e, and \u003cem\u003eLEF1 \u003c/em\u003egene expression levels.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7113655/v1/4cadddb120f4dec279d2cb35.png"},{"id":88653002,"identity":"33983b31-d986-4f20-8c4c-9582e9d5976c","added_by":"auto","created_at":"2025-08-08 18:03:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1423534,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7113655/v1/50ffab35-802e-4cbe-b354-1ae799ee966b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-Cell and Mendelian Analyses Reveal Shared Mechanisms Between Head and Neck Neoplasms and Aging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck neoplasm (HNN) is one of the most common malignant tumors worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], primarily occurring in the lips, oral cavity, pharynx, larynx, paranasal sinuses, as well as salivary gland tumors and mucosal melanoma[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Notably, head and neck cancer (HNC) ranks as the sixth most common cancer globally[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2021, the International Agency for Research on Cancer (IARC) reported 840,000 new cases of HNC annually[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Smoking and alcohol consumption are considered the main risk factors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Besides, cancer incidence generally increases with age, making aging a potential major risk factor[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Thus, clarifying senescence-carcinogenesis molecular links is crucial for oncotherapy and prevention.\u003c/p\u003e\u003cp\u003eAging involves physiological/pathological changes causing tissue/organ function decline[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Experimental data show senescence-mediated chemokine storm and cytokine imbalance are key pathways linking age-related pathologies to immune surveillance impairment[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research shows accelerated biological age increases cancer susceptibility (e.g. lung and colorectal) irrespective of chronological age, sex, or other risk factors[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn addition, mechanistic studies reveal Treg-derived cytokine networks critically regulate HNN tumorigenesis.These lymphocytes suppress immunity by secreting TGF-β, IL-10 and expressing CTLA-4, all linked to tumor progression [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Although preliminary studies have highlighted the involvement of immune cells in HNSCC pathological, the specific functions of different immune cell subsets \u0026mdash;particularly T cells\u0026mdash;and their association with clinical manifestations remain poorly defined. Recent advances in high-throughput sequencing technology provide unprecedented resolution to decode the clonal evolution of tumor-infiltrating lymphocytes during HNN progression and organismal aging processes.\u003c/p\u003e\u003cp\u003eThrough a multi-cohort integration framework, we harmonized peripheral blood-derived scRNA-seq datasets encompassing HNN patients and age-stratified controls to systematically analyze the transcriptional heterogeneity of immune cell subsets, screen for key genes shared between aging and HNN, and analyze enriched pathways to delineate conserved immunoregulatory axes in HNN and senescence-associated immune remodeling. Further more, employing two-sample Mendelian randomization (MR), we conducted instrumental variable analyses to interrogate the causal key genes on HNN pathogenesis. This study's characterization of pivotal molecular determinants and their regulatory networks offers critical mechanistic insights into HNN pathogenesis and aging-related biological processes.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eAnalytical datasets were sourced from established open-access repositories.\u003c/p\u003e\n\u003cp\u003eData source\u003c/p\u003e\n\u003cp\u003eThis investigation leveraged The Gene Expression Omnibus(GEO) to procure senescence-associated and HNN-linked gene expression signatures. Specifically, we searched using the keywords \u0026ldquo;neck malignant neoplasm\u0026rdquo;, \u0026ldquo;senescence\u0026rdquo; and \u0026ldquo;blood\u0026rdquo; to identify relevant data sets. Then, we selected two samples (GSM5017033 and GSM5017021) from HNSCC data set (GSE164690) for HNN analysis, and two samples (GSM4750298 and GSM4750299) from Aging data set (GSE157007) for senescence-related analysis. GSM4750303 and GSM4750304 were used as control samples from GSE157007 dataset. All HNSCC cases were confirmed by oncologists and met the established diagnostic criteria for HNSCC [14].\u003c/p\u003e\n\u003cp\u003eTo complement our gene expression analysis, we further incorporated genome-wide association study (GWAS) data related to HNN from the GWAS Catalog. The training data set for HNN included 492 African American or Afro-Caribbean cases and 121,151 controls of the same ancestry, 4,578 European ancestry cases and 442,171 controls, as well as 233 Hispanic or Latin American cases and 59,498 controls (GCST90479818). Additionally, the validation data set for HNN specifically consisted of 4,671 European participants, including 2,342 confirmed cases (GCST012234). \u003c/p\u003e\n\u003cp\u003escRNA-seq analysis \u003c/p\u003e\n\u003cp\u003eIn this study, we integrated and analyzed scRNA-seq datasets derived from HNSCC tissues, peripheral blood of aged individuals, and samples from healthy controls. Cell type annotation was carried out using canonical marker genes in combination with the SingleR (Version 2.8.0) algorithm. We identified a specific cell subpopulation showing consistent downregulation in both HNSCC and senescent samples compared to controls, defined as the core cellular subpopulation (Log2FC \u0026lt; -0.5). Comprehensive data processing was performed using the Seurat R package (Version 4.4.0). Quality control involved the removal of low-quality cells with fewer than 500 or more than 4,000 detected genes, or with mitochondrial gene content exceeding 20%. Normalization, variance stabilization, and selection of highly variable genes were performed, providing robust input for downstream analysis. Dimensionality reduction was conducted through principal component analysis (PCA), retaining the top 5 components, followed by visualization using t-distributed stochastic neighbor embedding (t-SNE). Cell clustering was executed using the FindNeighbors and FindClusters functions. Differentially expressed genes within each cluster were identified via the Wilcoxon rank-sum test using the FindAllMarkers function. Annotation was validated by referencing curated databases including CellMarker. Trajectory inference was carried out using the Slingshot R package (Version 2.14.0) to reconstruct potential cellular developmental pathways and lineage relationships. Additionally, we evaluated intercellular communication to explore signaling interactions within and between clusters. To investigate the functional significance of the core cellular subpopulation, we performed functional enrichment analysis on its marker genes. Finally, we elucidated the putative roles of the core cellular subpopulation in both HNSCC progression and aging-related cellular reprogramming, highlighting its relevance to disease pathogenesis and age-associated immune or stromal changes. \u003c/p\u003e\n\u003cp\u003eMR\u003c/p\u003e\n\u003cp\u003eTo explore causal gene links between senescence and HNN pathogenesis, we used two-sample Mendelian randomization (MR) frameworks. Univariable MR analyses were conducted via the TwoSampleMR R package (V0.6.14), with the inverse variance weighted (IVW) method as the main causal estimation approach.\u003c/p\u003e\n\u003cp\u003eTo enhance the accuracy and credibility of the instrumental variables (IVs) applied in this MR analysis, a strict screening protocol was adopted. Initially, we extracted single nucleotide polymorphisms (SNPs) that showed strong associations with aging-related traits (\u003cem\u003eP \u0026lt; 5 \u0026times; 10⁻⁸\u003c/em\u003e) from large-scale GWAS conducted in populations of European descent. Subsequently, we applied linkage disequilibrium (LD) pruning with a cutoff of R\u0026sup2; \u0026lt; 0.01 to eliminate correlated variants and retain only mutually independent SNPs. To minimize the impact of weak instruments, we calculated the F-statistic for each SNP using the formula F = \u0026beta;\u0026sup2; / SE\u0026sup2;, and only retained those with F \u0026gt; 10, thereby improving the strength and reliability of the instrumental variables[15]. The selected SNPs were further validated by comparison with external datasets to confirm their stability and relevance to the exposure. To mitigate horizontal pleiotropic effects, we conducted comprehensive sensitivity analyses comprising MR-Egger regression, weighted median estimation, and leave-one-out validation. To assess heterogeneity, we calculated Cochran\u0026rsquo;s Q statistic, and used the Pleiotropy Residual Sum and Outlier (MR-PRESSO) method to detect and correct for outliers[16]. After excluding these outliers, the causal estimates were recalculated. In order to further validate our results, we selected the GWAS data of another HNN as the validation set. \u003c/p\u003e\n\u003cp\u003eColocalization analysis and expression quantitative trait locus analyses\u003c/p\u003e\n\u003cp\u003eWe executed genomic colocalization assessments employing the coloc R package[17], aiming to identify potential shared association signals between GWAS data related to HNN and aging-related eQTL datasets. The H4 posterior probability, generated by the coloc.abf function, was used as the primary indicator of colocalization. In addition, we carried out regional association mapping to further investigate the relationships between SNP genotypes and the expression levels of key genes, in the context of the biological link between aging and head and neck tumorigenesis.\u003c/p\u003e\n\u003cp\u003eMoreover, to examine potential colocalization between the exposure (eQTL) and outcome (GWAS) datasets, we utilized the locuscomparer R package (Version 1.0.0), which visualized the relationship between the datasets. Finally, the results and datasets were saved for further investigation. \u003c/p\u003e\n\u003cp\u003eMetabolic enrichment analysis\u003c/p\u003e\n\u003cp\u003eTo gain deeper insights into the intrinsic link between HNN and aging, we conducted metabolic enrichment analysis focusing on identifying potential key pathways. For this purpose, we utilized the scMetabolism package (Version 0.2.1), which incorporates curated pathway sets from KEGG and REACTOME pathway database. Using this tool, we calculated metabolic activity scores across different cell types. The AUCell algorithm assessed single-cell metabolic states to detect subtle pathway activity shifts. \u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using R version 4.1.3 (https://www.r-project.org/), with a two-sided P-value \u0026lt; 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eSingle-cell RNA-seq analysis\u003c/p\u003e\n\u003cp\u003eT-cell subsets constitute a significant proportion of the immune landscape in both HNSCC and aging, as shown in Fig1.a. A significant decrease in Naive CD4⁺ T cells was observed in the HNSCC and senescent groups relative to healthy controls(Fig1.b). Fig2.(a-c) depicts the trajectories of Naive CD4⁺ T cell relative to other cell subpopulations in HNSCC and senescence. Cell-cell communication profiling (Fig2.(d,e) reveals that \u0026nbsp;Naive CD4(+) T cells \u0026nbsp;interacts with multiple subpopulations and play key roles in both HNSCC and aging. We identified 181 potential marker genes by contrasting Naive CD4⁺ T cells with other T-cell subsets and 26 markers through core Naive CD4⁺ T subset with other T cell subsets, and the intersection yielded 24 consensus differentially expressed genes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMR\u003c/p\u003e\n\u003cp\u003eFurther MR Analysis of the 24 differentially expressed genes identified above revealed four genes with causal effects on HNN risk. Fig3.a identifies senescence-associated genes within naive CD4⁺ T cells that demonstrate causal relationships with HNN. Specifically, as shown in Fig3.b, the IVW methodology shown that \u003cem\u003eCCR7\u0026nbsp;\u003c/em\u003e(nsnp=5, OR = 0.0001, 95%CI:0.000-0.0425,\u003cem\u003e\u0026nbsp;p = 0.002\u003c/em\u003e), \u003cem\u003eLEF1\u003c/em\u003e (nsnp=3, OR = 0.0001, 95%CI:0.000-0.0674, \u003cem\u003ep = 0.005\u003c/em\u003e), \u003cem\u003eNOSIP\u003c/em\u003e (nsnp=5, OR = 0.0089, 95%CI: 0.0003-0.2424, \u003cem\u003ep = 0.005\u003c/em\u003e), \u003cem\u003eFHIT\u003c/em\u003e (nsnp=13, OR = 114.76, 95%CI:1.3866-9495.4434, \u003cem\u003ep = 0.035\u003c/em\u003e) were causally associated with HNN. Sensitivity analyses\u0026mdash;including tests for heterogeneity and horizontal pleiotropy\u0026mdash;supported the validity of our MR approach. Furthermore, MR-Egger regression revealed no evidence of horizontal pleiotropy, with all intercepts non-significant (\u003cem\u003eP \u0026gt; 0.05\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003eTo independently validate our findings, we applied the same analysis to an additional HNN GWAS dataset as the validation set, results are presented in Fig3.c.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe IVW methodology shown that \u003cem\u003eFHIT\u003c/em\u003e (nsnp=10, OR = 0.7326 , 95%CI:0.5970-0.8991, \u003cem\u003ep = 0.0029\u003c/em\u003e) was causally associated with HNN, while \u003cem\u003eCCR7\u003c/em\u003e (nsnp=5, OR = 0.5732, 95%CI: 0.3057- 1.0746, \u003cem\u003ep = 0.08\u003c/em\u003e), \u003cem\u003eLEF1\u003c/em\u003e (nsnp=3, OR = 0.5479 , 95%CI:0.2678-1.1212, \u003cem\u003ep=0.0996\u003c/em\u003e), \u003cem\u003eNOSIP\u003c/em\u003e(nsnp=4, OR = 0.7359, 95%CI: 0.5358-1.0107, \u003cem\u003ep = 0.058\u003c/em\u003e) were not causally associated with HNN. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eColocalization analysis\u003c/p\u003e\n\u003cp\u003eThe results are shown in Fig3.d, showing the results of colocalization analysis of HNN and \u003cem\u003eFHIT\u003c/em\u003e: H0 = 4.97e-299, H1 = 2.90e-299, H2 = 0.63, H3 = 0.37, H4 = 0.01. The posterior probability (H4) of a shared causal variant between HNN and \u003cem\u003eFHIT\u003c/em\u003e was estimated at 1%.\u003c/p\u003e\n\u003cp\u003eConstructing cell communication and pseudo-time analyses\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy cell communication analysis, Fig4.a and b show \u003cem\u003eFHIT\u003c/em\u003e+Naive CD4(+) T cells and \u003cem\u003eFHIT\u003c/em\u003e-Naive CD4(+) T cells interact with other cell subpopulations. The major Naive CD4(+) T cells communication pathways were different with high and low \u003cem\u003eFHIT\u003c/em\u003e expression. For instance, the primary gene pathways between \u003cem\u003eFHIT\u003c/em\u003e+CD4-naive cells and monocytes are \u003cem\u003eANXA1-FPR1, IL16-CD4, and MIF-(CD74+CD44)\u003c/em\u003e, while those between\u003cem\u003e\u0026nbsp;FHIT\u003c/em\u003e-CD4-naive cells and monocytes are \u003cem\u003eANXA1-FPR1\u003c/em\u003e and \u003cem\u003eMIF-(CD74+CD44)\u003c/em\u003e .\u003c/p\u003e\n\u003cp\u003ePseudotime trajectory analysis revealed distinct temporal expression patterns of cell cycle-associated genes: \u003cem\u003eFHIT\u003c/em\u003e exhibited prominent expression during the early phase (0-10), while \u003cem\u003eNOSIP\u003c/em\u003e, \u003cem\u003eCCR7\u003c/em\u003e, and \u003cem\u003eLEF1\u003c/em\u003e were preferentially upregulated in the terminal phase (Fig4.c), indicating potential involvement in cell cycle exit or checkpoint transitions. These temporally segregated expression profiles highlight their putative regulatory roles in different stages of cell cycle progression. As shown in Fig4.d , the expression of the \u003cem\u003eFHIT\u0026nbsp;\u003c/em\u003egene significantly decreased over pseudotime (Pearson\u0026apos;s r = -0.13, \u003cem\u003ep= 3.85e\u0026minus;19\u003c/em\u003e), suggesting its potential involvement in early stages of the cell trajectory as well.\u003c/p\u003e\n\u003cp\u003eEnrichment metabolism pathway analysis of key genes\u003c/p\u003e\n\u003cp\u003eFig5.a shows that \u003cem\u003eFHIT\u003c/em\u003e are predominantly expressed in Naive CD4(+) T cells. The major Naive CD4(+) T cells enrichment pathways were different with high and low \u003cem\u003eFHIT\u003c/em\u003e expression. As shown in Fig Fig5.b: Naive CD4(+) T cells expressing higher \u003cem\u003eFHIT\u003c/em\u003e was mainly enriched in the following pathways: thiamine metabolism, porphyrin and chlorophyll metabolism, glycosphingolipid biosynthesis-globo and isoglobo series, glycosphingolipid biosynthesis-ganglio series, glycosaminoglycan biosynthesis-keratan sulfate. Naive CD4(+) T cells with lower \u003cem\u003eFHIT\u003c/em\u003e was mainly enriched in the following pathways: thiamine metabolism, terpenoid backbone biosynthesis, porphyrin and chlorophyll metabolism, one carbon pool by folate.\u003c/p\u003e\n\u003cp\u003eAs shown in Fig5.c, the \u003cem\u003eFHIT\u003c/em\u003e (OR=114.75 (1.39, 9495.44), \u003cem\u003ep=3.53e-02\u003c/em\u003e) gene demonstrated consistently high expression levels in both healthy and HNN, while \u003cem\u003eNOSIP\u003c/em\u003e(OR=0.01 (0.00, 0.24), \u003cem\u003ep=5.08e-03\u003c/em\u003e),\u003cem\u003e\u0026nbsp;CCR7\u0026nbsp;\u003c/em\u003e(OR=0.00 (0.00, 0.04), \u003cem\u003ep=2.46e-03\u003c/em\u003e), and \u003cem\u003eLEF1\u003c/em\u003e(OR=0.00 (0.00, 0.07), \u003cem\u003ep=5.04e-03\u003c/em\u003e), exhibited significantly elevated expression in healthy cohorts but were markedly downregulated in HNN samples.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHNN are a heterogeneous group of cancers, with HNSCC accounting for over 90% of cases, characterized by high global incidence and poor prognosis. To explore connections between HNN and aging, we focused on shared mechanisms and immunological mechanism. Initially, we integrated multiple single-cell RNA sequencing datasets using established methodologies. Guided by prior studies [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], we prioritized T-cell populations for analysis. Both the HNSCC and the aging cohorts displayed significant alterations in T-cell subset distribution compared to healthy controls, notably marked by a pronounced reduction in Naive CD4(+) T cells proportions. To further pinpoint central genes with potential causal roles in HNN pathogenesis, MR analysis was performed. Finally, metabolic enrichment analysis was conducted to investigate how these key genes interact with immune cells and metabolic pathways in both HNN and aging contexts. This integrated approach highlights shared molecular and immunological features between HNN and aging, offering insights into potential therapeutic targets for precision interventions.\u003c/p\u003e\u003cp\u003eNaive CD4(+) T cells play key roles in both HNN and aging, exhibiting dysfunctions that promote immune evasion and inflammation. IL-6 and TGF-β drive differentiation toward Th17 cells[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], promoting inflammation and tumor progression in HNSCC and chronic inflammation in aging[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, IL-12 deficiency impairs Th1 differentiation, while Treg-mediated suppression weakens anti-tumor immunity and pathogen defense[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Both conditions involve the presence of immunosuppressive factors (e.g., PD-L1, TGF-β) that induce Treg differentiation or exhaustion[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], hindering CD8\u0026thinsp;+\u0026thinsp;T and NK cell function[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The accumulation of Tregs correlates with advanced stages and poor outcomes[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In aging, thymic involution reduces naive T cell production, and senescent naive CD4(+) T cells exhibit impaired proliferation and cytokine production[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. These cells express exhaustion markers like PD-1 and CTLA-4, contributing to chronic inflammation, impaired immune surveillance, and the accumulation of senescent cells[\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Aging also skews naive CD4(+) T cells toward the Th17 lineage[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], exacerbating inflammation despite reduced immune efficacy[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Aging-related immune dysregulation involving naive CD4(+) T cells may promote HNN development[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], suggesting aging as a potential risk factor.\u003c/p\u003e\u003cp\u003eThe FHIT gene at chromosome 3p14.2 encodes a tumor suppressor regulating apoptosis, cell cycle, oxidative stress, and genomic stability. [\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. FHIT loss (50\u0026ndash;70% in HNN via deletion/skipping/aberrant transcripts) correlates with poor prognosis, metastasis, and therapy resistance[\u003cspan additionalcitationids=\"CR42 CR43\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Mechanistically, \u003cem\u003eFHIT\u003c/em\u003e deficiency promotes genomic instability, impairs DNA repair, disrupts p53 signaling, enhances oncogene-induced senescence escape[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and accelerates cancer progression[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. FHIT regulates mitochondrial ROS via FdxR/Hsp60 interactions, and its loss worsens oxidative damage and cellular aging[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Clinically, restoring FHIT induces apoptosis/inhibits tumor growth, serving as a therapeutic target and response predictor[\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], especially in heavy tobacco users [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, \u003cem\u003eFHIT\u003c/em\u003e methylation has been linked to aging, though its relationship with protein expression remains unclear [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. While \u003cem\u003eFHIT\u003c/em\u003e is not traditionally studied in naive CD4(+) T cells, its role in maintaining genomic integrity and oxidative balance warrants further investigation in the context of immune aging and cancer immunology. A deeper understanding of \u003cem\u003eFHIT\u003c/em\u003e-mediated pathways may offer novel strategies for managing HNN and age-related immune dysfunction.\u003c/p\u003e\u003cp\u003eThis study employs several innovative analytical strategies. First, by comparing differential genes between HNN and aging cohorts, we used gene heatmaps to visualize age-related expression patterns. Integrated with single-cell transcriptomic data, these heatmaps identified co-dysregulated genes (e.g., \u003cem\u003eFHIT, NOSIP, CCR7, LEF1\u003c/em\u003e) as key targets for further investigation. Second, time-course analyses across HNN progression and aging stages revealed stage-specific gene expression and declining intercellular communication, linking these trends to immunosenescence and chronic inflammation. This temporal profiling also highlighted prognostically relevant, time-dependent genes with potential therapeutic value. Finally, cell communication modeling uncovered regulatory networks centered on \u003cem\u003eFHIT\u003c/em\u003e, including its influence on pathways such as the Akt-survivin axis, offering a systems-level view for identifying targets of combinatorial therapy. Despite these strengths of this study, there are limitations. Our conclusions are hypothesis-generating and based primarily on bioinformatic data, which require experimental validation. The primary dataset used was GEO, which focused on HNSCC, but the MR results were not replicated in an external HNN cohort, necessitating further study. While HNSCC accounts for over 90% of HNN, our findings may not fully apply to other HNN subtypes. Future research should explore non-HNSCC HNN subtypes, such as hematolymphoid malignancies, mesenchymal sarcomas, and neuroendocrine tumors. Furthermore, due to data limitations, reverse MR was not conducted to further verify the effects of HNN and aging on related genes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study leveraged single-cell transcriptomics and MR to explore the relationship between HNN and aging. By comparing differential gene expression between HNSCC and aging cohorts, we identified key co-regulated genes (\u003cem\u003eNOSIP, CCR7, LEF1\u003c/em\u003e and especially \u003cem\u003eFHIT\u003c/em\u003e) and highlighted their potential roles in tumor progression and aging. Time-course analyses revealed stage-specific gene expression patterns and age-related declines in intercellular communication, linked to immunosenescence and chronic inflammation. Additionally, cell communication modeling emphasized \u003cem\u003eFHIT\u003c/em\u003e regulatory role in tumor microenvironment signaling, suggesting new therapeutic targets. This research contributes to understanding the connection between aging and HNN, paving the way for precision interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChen Sun contributed to the conceptualization, data curation, formal analysis, investigation, methodology, resources, software, supervision, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash;review, and editing. Xinlei Chen contributed to the conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash;review, and editing. Jinzhao Li contributed to the conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash;review, and editing. Lirong Hu contributed to the conceptualization, formal analysis, investigation, methodology, software, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash;review, and editing. Rui Shi contributed to the conceptualization, data curation, funding acquisition, methodology, project administration, resources, supervision, writing \u0026ndash; review, and editing. Chunhui Li contributed to the conceptualization, methodology, supervision, writing \u0026ndash; review, and editing. Canli Wang contributed to the conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing \u0026ndash; original draft, writing \u0026ndash; review, and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Department of Periodontics and Oral Mucosal Diseases, The Affiliated Stomatology Hospital/the School of Stomatology, Southwest Medical University, for providing the essential facilities, \u0026nbsp;resources and fundings that made my experiments possible. We also acknowledge Luzhou Key Laboratory of Oral \u0026amp; Maxillofacial Reconstruction and Regeneration, The Affiliated Stomatology Hospital, Southwest Medical University, for their collaboration, technical support, and enriching discussions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by projects of Stomatology Hospital Affiliated to Southwest Medical University (NO. 201905 and NO. 2023Y06).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLiu ZL, Meng XY, Bao RJ, Shen MY, Sun JJ, Chen WD, et al. 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J Inflamm Res. 2023;16:4301-15. https://dx.doi.org/10.2147/jir.S428358.\u003c/li\u003e\n\u003cli\u003eLee YC, Nam Y, Kim M, Kim SI, Lee JW, Eun YG, et al. Prognostic significance of senescence-related tumor microenvironment genes in head and neck squamous cell carcinoma. Aging (Albany NY). 2023;16(2):985-1001. https://dx.doi.org/10.18632/aging.205346.\u003c/li\u003e\n\u003cli\u003eSard L, Accornero P, Tornielli S, Delia D, Bunone G, Campiglio M, et al. The tumor-suppressor gene FHIT is involved in the regulation of apoptosis and in cell cycle control. Proc Natl Acad Sci U S A. 1999;96(15):8489-92. https://dx.doi.org/10.1073/pnas.96.15.8489.\u003c/li\u003e\n\u003cli\u003eHuebner K, Saldivar JC, Sun J, Shibata H, Druck T. Hits, Fhits and Nits: beyond enzymatic function. Adv Enzyme Regul. 2011;51(1):208-17. https://dx.doi.org/10.1016/j.advenzreg.2010.09.003.\u003c/li\u003e\n\u003cli\u003eKarras JR, Paisie CA, Huebner K. 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Restoration of fragile histidine triad (FHIT) expression induces apoptosis and suppresses tumorigenicity in lung and cervical cancer cell lines. Proc Natl Acad Sci U S A. 2002;99(6):3615-20. https://dx.doi.org/10.1073/pnas.062030799.\u003c/li\u003e\n\u003cli\u003eGaudio E, Paduano F, Croce CM, Trapasso F. The Fhit protein: an opportunity to overcome chemoresistance. Aging (Albany NY). 2016;8(11):3147-50. https://dx.doi.org/10.18632/aging.101123.\u003c/li\u003e\n\u003cli\u003eCzarnecka KH, Migdalska-Sęk M, Domańska D, Pastuszak-Lewandoska D, Dutkowska A, Kordiak J, et al. FHIT promoter methylation status, low protein and high mRNA levels in patients with non-small cell lung cancer. Int J Oncol. 2016;49(3):1175-84. https://dx.doi.org/10.3892/ijo.2016.3610.\u003c/li\u003e\n\u003cli\u003eJeong YJ, Jeong HY, Lee SM, Bong JG, Park SH, Oh HK. Promoter methylation status of the FHIT gene and Fhit expression: association with HER2/neu status in breast cancer patients. Oncol Rep. 2013;30(5):2270-8. https://dx.doi.org/10.3892/or.2013.2668.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"head and neck squamous cell carcinoma(HNSCC), head and neck neoplasm(HNN), aging, mendelian randomization, single-cell","lastPublishedDoi":"10.21203/rs.3.rs-7113655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7113655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aims to explore shared key genes between head and neck neoplasm (HNN) and aging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003eUsing single-cell RNA sequencing data of peripheral blood from HNN patients, aging individuals, and healthy controls, we identified cross-group co-expressed, downregulated cell subpopulations as core targets. Integrated pseudotime trajectory analysis and intercellular communication modeling were employed to investigate the dynamic evolution and functional interaction patterns of these subpopulations. Differentially expressed genes were identified, followed by Mendelian randomization analysis to assess their causal associations with HNN. Co-localization analysis were performed using GWAS data for HNN and expression quantitative trait loci (eQTL) datasets. Key genes were further subjected to metabolic pathway enrichment analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003eT cell subsets were found to be significantly represented in both HNN and aging individuals. Among them, naive CD4(+) T cells was down-regulated in both groups, leading to the identification of 24 differentially expressed genes. Mendelian randomization studies have shown that \u003cem\u003eCCR\u003c/em\u003e, \u003cem\u003eLEF1\u003c/em\u003e, \u003cem\u003eNOSIP\u003c/em\u003eand \u003cem\u003eFHIT\u003c/em\u003e have causal relationships with HNN. In the validation phase, however, only \u003cem\u003eFHIT\u003c/em\u003e was retained, for which co-localization analysis revealed limited evidence of a shared causal variant between the GWAS and eQTL signals (H4 = 0.01). The metabolic enrichment highlighted metabolic pathways associated with these genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003eThis study identified naive CD4(+) T cells downregulation as a shared feature of HNN and aging and highlighted: \u003cem\u003eCCR\u003c/em\u003e, \u003cem\u003eLEF1\u003c/em\u003e, \u003cem\u003eNOSIP\u003c/em\u003e and particularly \u003cem\u003eFHIT\u003c/em\u003e as potential molecular links. These findings provide novel insights into the intersection of aging and tumorigenesis, offering potential targets for combined therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Single-Cell and Mendelian Analyses Reveal Shared Mechanisms Between Head and Neck Neoplasms and Aging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 17:55:43","doi":"10.21203/rs.3.rs-7113655/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-11T06:21:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T18:18:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151104228081310825833281777831263681725","date":"2025-08-25T18:27:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-25T01:41:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T04:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140754829679502999093745972534832036405","date":"2025-08-17T00:01:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305578219123904867995521598186430992071","date":"2025-08-07T12:46:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-04T17:50:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-28T11:24:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T03:35:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T03:35:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-07-13T13:39:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e5af54c7-6e5c-494a-96b7-7288c716c986","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-27T10:22:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 17:55:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7113655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7113655","identity":"rs-7113655","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}