Saliva from Oral Squamous Cell Carcinoma Patients Promotes Tumor Progression via Inflammation, Stromal Remodeling, and Metabolic Reprogramming in a Mouse Model

Saliva from Oral Squamous Cell Carcinoma Patients Promotes Tumor Progression via Inflammation, Stromal Remodeling, and Metabolic Reprogramming in a Mouse Model | 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 Saliva from Oral Squamous Cell Carcinoma Patients Promotes Tumor Progression via Inflammation, Stromal Remodeling, and Metabolic Reprogramming in a Mouse Model Xiao-yan Zhang, Yuan-tao Li, Jie Guo, Ying Feng, Qian Han, Shi-han Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7610282/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted 11 You are reading this latest preprint version Abstract Background Oral squamous cell carcinoma (OSCC) is a prevalent and aggressive malignancy with increasing evidence implicating the oral microbiome and tumor microenvironment in its progression. However, the mechanistic impact of OSCC patient-derived saliva on tumor development remains poorly understood. Methods We established an orthotopic OSCC mouse model and topically applied saliva collected from OSCC patients to assess its effects on tumor progression. Multi-omics analyses, including 16S rRNA sequencing, tumor transcriptomics (RNA-seq), and metabolomics (LC-MS), were performed to explore changes in the oral microbiota, gene expression profiles, and metabolic pathways. Results Treatment with OSCC patient saliva significantly accelerated tumor growth compared to controls. Saliva application altered the oral microbiota, most notably causing a significant enrichment of the genus Staphylococcus. Tumor transcriptomics revealed upregulation of genes associated with chronic neutrophilic inflammation ( Mpo ), cancer-associated fibroblast (CAF) activation, and extracellular matrix (ECM) remodeling ( Angptl4, Col2a1 ). Metabolomic analysis demonstrated profound metabolic reprogramming within the tumors, including enhanced amino acid metabolism (tryptophan, glutamate), fatty acid oxidation, and accumulation of the oncometabolite succinate. Integrated analysis showed that Staphylococcus abundance was strongly correlated with these inflammatory and metabolic signatures. Conclusions This study demonstrates that saliva from OSCC patients promotes tumor progression in vivo through a multifactorial mechanism involving inflammation, stromal remodeling, and metabolic rewiring. These findings highlight the tumor-promoting potential of salivary and microbial components, suggesting new avenues for diagnostic and therapeutic strategies targeting the oral microenvironment in OSCC. Oral squamous cell carcinoma 16S rRNA RNA-seq metabolomics tumor microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Oral squamous cell carcinoma (OSCC) represents the most common malignancy of the head and neck region, accounting for over 90% of all oral cancers [ 1 ]. Despite advancements in diagnostic techniques and therapeutic modalities, the five-year survival rate for OSCC has remained stagnant at approximately 50–60% over the past several decades, highlighting a pressing need for more effective interventions [ 2 ]. The high incidence, frequent recurrence, and poor prognosis associated with OSCC constitute a substantial global public health burden [ 3 ]. While traditional risk factors—including tobacco use, alcohol consumption, and human papillomavirus (HPV) infection—have been extensively investigated, they do not fully account for the complex pathogenesis and progression of OSCC. Accordingly, a deeper understanding of the unique tumor microenvironment (TME) in OSCC and its contribution to disease advancement is imperative for the development of novel therapeutic strategies. The TME is a dynamic and multifaceted ecosystem composed of tumor cells, stromal cells, immune infiltrates, extracellular matrix components, and a variety of soluble signaling molecules. It plays a pivotal role in modulating tumor growth, invasion, and metastasis [ 4 ]. In the context of the oral cavity, the TME is uniquely influenced by continuous exposure to saliva—a dynamic fluid environment. Saliva not only maintains oral physiological homeostasis but also serves as a “bio-information reservoir,” harboring proteins, metabolites, nucleic acids, extracellular vesicles, and a diverse microbial community [ 5 ]. In recent years, saliva has gained prominence as a promising non-invasive “liquid biopsy” medium for the early detection and prognostic monitoring of OSCC, with numerous studies focused on identifying disease-specific biomarkers [ 6 ]. However, the current body of research predominantly regards saliva as a passive disease indicator, overlooking its potential as a biologically active medium that may directly contribute to OSCC progression. Notably, the salivary composition of OSCC patients differs markedly from that of healthy individuals, exhibiting microbial dysbiosis, altered metabolite profiles, elevated pro-inflammatory cytokines, and disruptions in microbial community homeostasis [ 7 – 9 ]. These compositional changes, rather than merely reflecting disease status, may actively drive tumor development by reshaping the metabolic and immune landscape of the TME. Through persistent interaction with tumor tissues, altered salivary components may facilitate malignant transformation and progression. Despite this compelling hypothesis, direct experimental evidence elucidating the in vivo functional role of OSCC patient-derived saliva and its underlying molecular mechanisms remains scarce. Therefore, the present study aimed to determine whether saliva from OSCC patients actively promotes tumor growth and to explore the associated molecular mechanisms. We established a subcutaneous xenograft OSCC mouse model and implemented daily intra-oral exposure to pooled OSCC patient saliva. Mice treated with patient saliva exhibited significantly increased tumor volumes, supporting our central hypothesis. To elucidate the underlying mechanisms, we employed an integrative multi-omics approach—including 16S rRNA gene sequencing, transcriptomic profiling, and untargeted metabolomics—to systematically characterize the oral microbiota, tumor gene expression profiles, and metabolic alterations across treatment groups. This study reveals a direct tumor-promoting effect of OSCC patient saliva and provides novel insights into the dynamic interaction between saliva and the TME, thereby offering a theoretical basis for innovative adjuvant strategies in OSCC management. Materials and Methods Animals Male C57BL/6N mice (4 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) [Animal Certification No.: SCXK (Jing) 2021-0006]. All animals were allowed to acclimatize for one week under standard housing conditions prior to any experimental procedures. Mice were housed in groups of five per cage with the ambient temperature maintained at 25 ± 1°C and relative humidity at 50–70%. Food and water were provided ad libitum throughout the acclimatization period. OSCC Mouse Model and Experimental Design Male C57/BL6N mice (4 weeks old) were acclimated under standard laboratory conditions for 7 days. To reduce the influence of endogenous oral microbiota, mice received kanamycin sulfate (in drinking water) for 4 consecutive days, followed by 3 days of normal water. SCC7 cells were cultured to logarithmic phase (~ 70% confluence), then mixed with an equal volume of growth factor-reduced basement membrane matrix, yielding a final concentration of 0.25 × 10⁶ cells/0.05 mL for subcutaneous injection. Tumor formation was confirmed 18 days post-injection, and mice were randomly assigned to two groups (n = 3). In the control group, mice received 300 µL of ultrapure water via oral gavage twice daily (morning and afternoon) for 14 days. In the saliva group, 100 µL of pooled saliva from six OSCC patients was administered intra-orally once every morning using a pipette, followed by a 30-minute fasting period. These mice received 300 µL of ultrapure water by oral gavage in the afternoon. All treatments were administered for 14 consecutive days. On day 14, all mice were first rendered unconscious via deep anesthesia induced by intraperitoneal injection of tribromoethanol at a dosage of 250 mg/kg. A surgical plane of anesthesia was verified by the absence of response to a noxious stimulus (toe pinch). Following confirmation of unconsciousness, euthanasia was performed by cervical dislocation. The use of tribromoethanol ensured that the procedure was conducted without causing pain or distress, in accordance with the Refinement principle of the 3Rs. Subsequently, tumor tissues and oral swabs were collected, and tumor dimensions were measured using digital calipers. 16S rRNA Gene Library Preparation, Sequencing, and Analysis Total genomic DNA was extracted using the Magen DNA Kit (Cat. No. D6356-02). The V3–V4 hypervariable regions of the bacterial 16S rRNA gene were amplified using primers 338F/806R and Phusion® High-Fidelity PCR Master Mix. PCR products were verified by 2% agarose gel electrophoresis, pooled, and purified using the Qiagen Gel Extraction Kit. Libraries were constructed with the TruSeq® DNA PCR-Free Kit, quantified with a Qubit 2.0 fluorometer, and sequenced on the Illumina MiSeq platform (2 × 300 bp paired-end reads). Raw FASTQ data were imported into QIIME2 [ 10 ] for analysis. Demultiplexed sequences underwent quality filtering, trimming, denoising, and chimera removal using the DADA2 plugin to generate amplicon sequence variants (ASVs) [ 11 ]. Taxonomic assignment was performed using the feature-classifier plugin and the Silva (V138) reference database. Alpha and beta diversity metrics were calculated via the core-diversity plugin. Differential microbiota analysis was conducted using linear discriminant analysis effect size (LEfSe) ( http://huttenhower.sph.harvard.edu/galaxy ). Transcriptome Library Preparation, Sequencing, and Analysis RNA libraries were quantified using the Qubit dsDNA HS Assay Kit. DNA nanoballs (DNBs) were prepared using the DNBSEQ DNB Rapid Preparation Kit and sequenced on the DNBSEQ-T7 platform (paired-end 150 bp reads). Quality control was performed using fastp software [ 12 ], and gene expression quantification followed a previously established protocol [ 13 ]. Differential expression analysis was conducted using the DESeq2 package in R [ 14 ], with thresholds of fold change (FC) > 1.5 and adjusted p-value (padj) < 0.05. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed using the clusterProfiler R package [ 15 ]. Immune infiltration analysis was performed using the R package immunedeconv ( https://github.com/omnideconv/immunedeconv ). Untargeted Metabolomics Analysis Tumor tissues were cryo-ground and extracted with 80% methanol (20× volume) at − 50°C for 10 cycles. After centrifugation (14,000 rpm, 10 min, 4°C), supernatants were vacuum-dried and reconstituted in 50% acetonitrile containing internal standards. Chromatographic separation was performed using a Thermo Vanquish UHPLC system with a BEH C18 column. Mass spectrometry was conducted on a Thermo QE HF-X system in both positive and negative ionization modes, scanning an m/z range of 150–2000 at 30,000 resolution. Raw MS data were processed using Compound Discoverer 3.8. Differential metabolites were identified via univariate t-tests, followed by orthogonal projections to latent structures discriminant analysis (OPLS-DA) using the ropls R package [ 16 ]. Metabolites with FC > 1.5, p 1 were considered significant. Quantitative Real-Time PCR (RT-qPCR) RPL13A was used as the endogenous reference gene. Each reaction was performed in technical duplicates. The mean ΔCt of the control group was used as the calibrator for each target gene. The ΔΔCt was calculated for each sample, and relative expression (RQ) was determined using the 2 ⁻ΔΔCt method. Statistical analysis was performed using a two-tailed t-test. Results Establishment of the OSCC Mouse Model and Comparison of Tumor Volume after Saliva Treatment An OSCC mouse model was successfully established by submucosal injection of SCC7 murine oral squamous cell carcinoma cells into the mandibular region. After tumor development, mice in the saliva treatment group (S group) received 100 µL of OSCC patient saliva daily via oral application, while the control group (C group) received an equivalent volume of ultrapure water. After two weeks, visible inspection revealed that tumors in the saliva-treated group were significantly larger than those in the control group (Fig. 1 ). Mice were euthanized for tumor collection, and tumor volumes were measured using calipers (Fig. 1 ). The mean tumor volume in the control group was 203.2 ± 97.8 mm³, whereas in the saliva-treated group it was 703.0 ± 70.0 mm³. Welch’s t-test showed a statistically significant difference between the two groups (P < 0.01), indicating that OSCC patient saliva significantly promotes tumor growth in mice. Composition and Differential Analysis of the Oral Microbiota in OSCC Mice 16S rRNA gene sequencing yielded an average of 47,263 clean tags per sample. A total of 151 amplicon sequence variants (ASVs) were identified in the control group compared to 181 ASVs in the saliva group, with only 41 ASVs shared between the two groups (Fig. 2 A). Alpha diversity analysis using the Shannon index showed a slightly higher diversity in the S group, though the difference was not statistically significant (P = 0.38) (Fig. 2 B). Principal Coordinates Analysis (PCoA) based on Bray-Curtis dissimilarity revealed partial separation between the groups (PC1: 76.87%, PC2: 3.65%). However, PERMANOVA analysis showed no significant intergroup differences (R = 0.2222, P = 0.300) (Fig. 2 C). LEfSe analysis identified significantly different taxa (LDA > 4, P < 0.05), with the class Bacilli showing the most pronounced differences (Fig. 2 D, E). The control group was enriched in Lactobacillales and Streptococcaceae , particularly the genus Streptococcus . Conversely, the saliva group showed significant enrichment of Erysipelotrichales and Staphylococcus . Differential Gene Expression and Immune Infiltration Analysis in OSCC Tumor Tissues RNA sequencing revealed an average of 8.4 Gb of clean data per sample and identified 18,149 expressed genes. DESeq2 analysis (threshold: fold change > 1.5, adjusted P < 0.05) identified 14 upregulated and 5 downregulated genes in the S group. These included genes related to chronic inflammation ( Mpo , Tnfaip3 ) and extracellular matrix (ECM) remodeling ( Angptl4 , Sfrp2 , Col2a1 , Dmp1 ) (Fig. 3 A, B). GO enrichment indicated significant involvement in apoptosis regulation and cell differentiation pathways (e.g., “negative regulation of extrinsic apoptotic signaling pathway,” “response to BMP”) (Fig. 3 C). KEGG pathway enrichment highlighted the ECM-receptor interaction, focal adhesion, cholesterol metabolism, and IL-17 signaling pathways (Fig. 3 D). Immune infiltration analysis via MCP-counter revealed a significant increase in cancer-associated fibroblasts (CAFs) in the S group (Fig. 4 ). Mast cell infiltration was slightly elevated but not statistically significant. Analysis of Metabolites in OSCC Tumor Tissues Untargeted metabolomics using LC-MS identified 1,477 metabolites. OPLS-DA modeling showed clear group separation (cumulative Q² = 0.471). A combination of t-tests and VIP scores (FC > 1.5, P 1) identified 84 differentially expressed metabolites—64 upregulated and 20 downregulated in the S group (Fig. 5 A–C). KEGG Level 2 classification revealed primary involvement in “Amino acid metabolism” and “Carbohydrate metabolism,” followed by “Signal transduction” (Fig. 5 D). Pathway enrichment showed significant enrichment in ferroptosis, central carbon metabolism in cancer, butanoate metabolism, and the cAMP signaling pathway (Fig. 5 E). Integrated Multi-Omics Data Analysis Integration of transcriptomic and metabolomic datasets suggests that saliva from OSCC patients may promote tumor growth through three potential mechanisms: (1) induction of chronic inflammation, (2) activation of CAFs and ECM remodeling, and (3) metabolic reprogramming (Fig. 6 A). Pearson correlation analysis between microbial genus abundance and gene/metabolite levels showed that Staphylococcus was strongly correlated with expression changes of key genes and metabolites (Fig. 6 B). RT-qPCR validation confirmed the expression trends of eight representative genes, consistent with transcriptome data (Fig. 6 C). Discussion In this study, using an OSCC mouse model, we demonstrated that topical application of saliva from OSCC patients significantly promoted tumor growth compared to the control group. Subsequent comprehensive analyses of the oral microbiota, tumor transcriptome, and metabolome suggest that this tumor-promoting effect is mediated through a combination of chronic inflammation, activation of CAFs, ECM remodeling, and metabolic reprogramming. One of the key mechanisms identified is the establishment of a pro-tumorigenic chronic inflammatory microenvironment. The significantly elevated levels of muramic acid (log2FC = 3.67) in the saliva-treated tumors strongly suggest the translocation of bacterial components into the tumor microenvironment. As a component of peptidoglycan, muramic acid is capable of activating TLR2 receptors on tumor cells, thereby triggering NF-κB and STAT3 signaling pathways, which are known to promote cancer cell invasiveness [ 17 ]. This immune activation recruits neutrophils into the tumor microenvironment, which is supported by the significant upregulation of the Mpo gene encoding myeloperoxidase (MPO). MPO catalyzes the generation of hypochlorous acid (HOCl), a reactive species that can induce DNA damage and mutations, ultimately enhancing tumor cell heterogeneity and growth [ 18 ]. In parallel, the downregulation of the anti-inflammatory gene Tnfaip3 (A20) may impair the negative feedback regulation of NF-κB signaling, allowing persistent inflammatory responses. Loss of TNFAIP3 has been previously associated with enhanced tumorigenesis and immune evasion. Moreover, the enrichment of differentially expressed genes in the IL-17 signaling pathway, a known driver of neutrophilic infiltration and chronic inflammation [ 19 ], further underscores the inflammatory state induced by OSCC-derived saliva. Another important finding is the significant activation of CAFs and ECM remodeling, which collectively serve as drivers of tumor progression [ 20 , 21 ]. Immune infiltration analysis revealed a higher proportion of CAFs in the saliva group, consistent with the known role of chronic inflammation in CAF activation. These fibroblasts secrete large quantities of ECM proteins such as Col2a1, Bglap , and Dmp1 , resulting in increased stromal stiffness and fibrosis. Additionally, both CAFs and tumor cells highly express Angptl4 , a multifunctional protein that facilitates angiogenesis, cell migration, and metabolic reprogramming, and has been implicated in tumor progression across various cancers [ 22 ]. Elevated expression of Ctsk , which encodes cathepsin K—a protease capable of degrading ECM collagen—further suggests enhanced ECM turnover [ 23 ]. Enrichment of the ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways in the transcriptome supports the notion that the remodeled ECM activates integrin-mediated pro-survival signaling cascades. Specifically, integrin binding recruits focal adhesion kinase (FAK), which in turn activates PI3K/Akt signaling—a classical pathway that promotes cell proliferation, survival, and migration [ 24 ]. Thus, OSCC saliva-induced inflammation appears to facilitate CAF activation and ECM remodeling, creating a structural and biochemical environment that favors tumor progression. Metabolic reprogramming represents a third mechanism by which OSCC patient-derived saliva promotes tumor development. Tumors in the saliva-treated group exhibited classic hallmarks of cancer metabolism. For example, cholesterol metabolism was significantly upregulated, likely reflecting the tumor's increased demand for membrane biosynthesis [ 25 ]. The reduced levels of cholesterol sulfate may indicate accelerated utilization of free cholesterol. Amino acid metabolism was also altered, particularly the tryptophan metabolic pathway. Elevated levels of tryptophan and its derivatives, such as indole-3-pyruvic acid, suggest activation of the IDO/TDO and IL4I1 pathways, which are known to produce immunomodulatory metabolites acting via the aryl hydrocarbon receptor (AHR) [ 26 ]. Additionally, the accumulation of succinate—a tricarboxylic acid (TCA) cycle intermediate and recognized oncometabolite—can mimic hypoxia and stimulate angiogenesis and glycolysis [ 27 ]. Fatty acid oxidation (FAO) also appeared to be enhanced, as indicated by elevated levels of carnitine metabolites (e.g., O-propionylcarnitine, C18-carnitine). Increased FAO supports ATP production under nutrient-deprived or hypoxic conditions and has been associated with elevated expression of CPT1C in tumor cells [ 28 ]. Furthermore, glutamine/glutamate metabolism was reprogrammed, as evidenced by glutamate accumulation, highlighting tumor reliance on glutamine for anaplerosis, nucleotide synthesis, and lipid production [ 28 ]. Pathway enrichment analyses consistently identified significant changes in central carbon metabolism, alanine, aspartate, and glutamate metabolism, and butanoate metabolism. In response to elevated oxidative stress due to inflammation and metabolic activity, tumors also upregulated antioxidant systems, particularly through increased levels of reduced glutathione (GSH), a critical mediator of ferroptosis resistance [ 29 ]. Although the salivary microbiota in OSCC patients is diverse, Staphylococcus emerged as a potentially critical contributor to the observed tumor-promoting effects. 16S rRNA sequencing revealed a significant enrichment of Staphylococcus in the saliva group, with strong correlations between its relative abundance and the expression of key differential genes and metabolites. In clinically relevant studies, Staphylococcus was found to be significantly more abundant in OSCC groups compared to healthy individuals [ 30 , 31 ]. Furthermore, a study involving 186 OSCC patients reported that the oral cavity of patients undergoing chemotherapy and chemoradiotherapy was dominated by Gram-positive bacteria, including Staphylococcus aureus and Staphylococcus epidermidis [ 32 ]. Staphylococcus can secrete various virulence factors, which can trigger strong local inflammatory responses, contributing to the formation of the tumor microenvironment. Furthermore, S. aureus has been shown to induce overexpression of COX-2 and activate NF-κB and TNF signaling in oral epithelial cells, upregulating pro-proliferative genes such as Cyclin D1 and downregulating tumor suppressors like p16. In vitro experiments have confirmed that S. aureus infection can enhance proliferation and malignant transformation of oral keratinocytes [ 33 ]. These findings suggest that Staphylococcus colonization in the murine oral cavity following saliva application may contribute to tumorigenesis through pro-inflammatory and oncogenic pathways. Despite these findings, this study has several limitations..First, the sample size was relatively small;expanding the cohort could enhance the reliability of multi-omics analyses and statistical power. Second, some key findings require further validation in clinical patient samples. Moreover, the specific effective components in saliva have not been identified in this study.However, existing literature has reported that certain oral microorganisms or salivary exosomes may participate in regulating the progression of OSCC. For instance, P. gingivalis can promote tumor development by activating inflammatory pathways and accelerating cell cycle progression, while tumor-derived exosomes can suppress tumor suppressor gene expression and enhance cell proliferation and migration. Therefore, our next research step will focus on analyzing and identifying the key active factors in saliva, and systematically validating their precise mechanisms in OSCC initiation and progression through in vivo and in vitro experiments. Conclusion Our findings indicate that saliva from OSCC patients is not merely a passive byproduct of malignancy but an active contributor to tumor progression. It exerts pro-tumorigenic effects through a multifactorial mechanism involving chronic inflammation, stromal remodeling, and metabolic reprogramming. These results highlight the intricate and dynamic crosstalk between saliva, the oral microbiota, and the TME, offering new insights into the pathogenesis of OSCC. Importantly, our study suggests that targeting the altered oral microecosystem or intercepting the downstream signaling pathways activated by salivary components may represent a promising adjunctive therapeutic approach for OSCC management. Declarations Author Contributions Xiao-yan Zhang, Yuan-tao Li, Bei-bei Liang, and Xiang-jun Li contributed to conception and designdata acquisition and analysis, drafted and critically revised the manuscript; Jie Guo, Ying Feng and Qian Han contributed to conception and design, data acquisition, analysis, and interpretation, drafted the manuscript; Shi-han Zhang, Yu Gao, Hao-tian Yin and Xiao-xu Ding contributed toconception, data analysis, drafted the manuscript; Bei-bei Liang and Xiang-jun Li contributed to conception and design,data acquisition, analysis, andinterpretation, drafted and critically revised the manuscript. All authors gavefinal approval and agreed to be accountable for all aspects of the work. Ethics Approval/Consent to Participate Our study adhered to the Declaration of Helsinki, specifies the approving ethics committee with its reference number, and describes the process of obtaining informed consent. The collection and use of these human tissue samples were strictly conductedin accordance with the guidelines approved by the Ethics Committee of the School and Hospital of Stomatology, Hebei Medical University(IRB-ID: [2024]066), and informedconsent was obtained from all patients prior to the study. All animal experiments were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) of Hebei Medical University (Ethics Approval Reference Number: [IACUC-Hebmu-P-2025393]). As the animals were commercially sourced specifically for biomedical research, informed consent from the owner was not applicable. All methods are reported in accordance with ARRIVE guidelines. Clinical trial number Not applicable Data Availability All raw data have been deposited in public repositories: 16S rRNA and RNA-seq data are available in the Sequence Read Archive (SRA) under accession numbers SRP603336 and SRP602255, respectively, while the untargeted metabolomics data are available in the MetaboLights database (MTBLS12805) at https://www.ebi.ac.uk/metabolights/MTBLS12805. Funding Funder name: Hebei Provincial Government-Funded Clinical Medicine Talent Cultivation Program, ID: ZF2024151 Funder name: Research projects under the Hebei Provincial Administration of Traditional Chinese Medicine, ID: 2025350 Funder name: Hebei Provincial Government-Funded Clinical Medicine Talent Cultivation Program, ID: ZF2025241 Funder name: Hebei Province Medical Science Research Project, ID: 20240101. Competing Interests The authors declare that there are no competing interests associated with the manuscript. Consent for publication Not applicable. 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Staphylococcus aureus induces COX-2-dependent proliferation and malignant transformation in oral keratinocytes. J Oral Microbiol. 2019;11(1):1643205. 10.1080/20002297.2019.1643205 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 02 Nov, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Editor invited by journal 10 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 Oct, 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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1","display":"","copyAsset":false,"role":"figure","size":812251,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of OSCC tumor volumes in mice from the control and saliva-treated groups. Statistical significance of the difference was calculated using Welch’s t-test.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7610282/v1/a2374f19385ab775051fc593.jpg"},{"id":95225074,"identity":"10c7f369-7876-4311-aba7-2c49f8acee76","added_by":"auto","created_at":"2025-11-05 16:24:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1044113,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of 16S rRNA sequencing results. (A) Venn diagram illustrating the distribution of ASVs among groups. (B) Boxplot of alpha diversity based on the Shannon index. (C) PCoA plot of beta diversity based on Bray-Curtis dissimilarity. (D) Bar plot showing the relative abundance at the genus level. (E) Differential analysis at various taxonomic levels based on LEfSe. The bar chart on the left represents the significance of differences, measured by the Linear Discriminant Analysis (LDA) score. The cladogram below represents the evolutionary branch. Colored nodes from the center to the periphery indicate significantly different taxa at the phylum (p), class (c), order (o), family (f), and genus (g) levels between the C and S groups.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7610282/v1/be327ee64da91bf09b5ab3fb.jpg"},{"id":95119009,"identity":"49538260-1730-43e5-b8ca-b3ef3facc344","added_by":"auto","created_at":"2025-11-04 13:39:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":947862,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of RNA-seq results. (A) Volcano plot of differentially expressed genes. The x-axis represents the log₂(fold change), and the y-axis represents -log₁₀(adjusted p-value). 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Statistical significance was determined using Welch’s t-test.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7610282/v1/b577082a9e6e043445a95cd4.jpg"},{"id":95119029,"identity":"ca752349-55b0-4aa4-8f5c-7b6b828c4d12","added_by":"auto","created_at":"2025-11-04 13:39:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":952400,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of untargeted metabolomics results. (A) Volcano plot of differential metabolites. The x-axis represents the log₂(fold change), and the y-axis represents -log₁₀(p-value). (B) OPLS-DA distribution plot. (C) OPLS-DA score plot. (D) Bar plot of KEGG Level 2 classification. The x-axis represents the number of metabolites. (E) Bubble plot of pathway enrichment analysis. The x-axis represents the Rich Factor. The size of the bubbles corresponds to the metabolite count, and a darker color indicates higher significance.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7610282/v1/7325d8933704ab14e690b7c0.jpg"},{"id":95119031,"identity":"cb17804c-9234-44f1-a7e0-ffd9fec64490","added_by":"auto","created_at":"2025-11-04 13:39:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1025004,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-omics correlation analysis and experimental validation. (A) Schematic diagram illustrating the proposed tumor-promoting mechanisms of OSCC saliva, with a corresponding heatmap of key genes and metabolites. (B) Heatmap of Pearson correlations between the relative abundance of oral microbial genera and the expression levels of genes and metabolites. * indicates p \u0026lt; 0.05. (C) Bar plot of RT-qPCR validation results. Statistical significance was determined using Welch’s t-test. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7610282/v1/37fb5f7bc39d0f3a64ddd513.jpg"},{"id":97723846,"identity":"b98d8415-88e4-44d2-b0af-98046f2e05cb","added_by":"auto","created_at":"2025-12-08 16:08:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6481884,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7610282/v1/e534d85b-31ef-4a4a-ab4b-778c19752aa6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Saliva from Oral Squamous Cell Carcinoma Patients Promotes Tumor Progression via Inflammation, Stromal Remodeling, and Metabolic Reprogramming in a Mouse Model","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral squamous cell carcinoma (OSCC) represents the most common malignancy of the head and neck region, accounting for over 90% of all oral cancers [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in diagnostic techniques and therapeutic modalities, the five-year survival rate for OSCC has remained stagnant at approximately 50\u0026ndash;60% over the past several decades, highlighting a pressing need for more effective interventions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The high incidence, frequent recurrence, and poor prognosis associated with OSCC constitute a substantial global public health burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While traditional risk factors\u0026mdash;including tobacco use, alcohol consumption, and human papillomavirus (HPV) infection\u0026mdash;have been extensively investigated, they do not fully account for the complex pathogenesis and progression of OSCC. Accordingly, a deeper understanding of the unique tumor microenvironment (TME) in OSCC and its contribution to disease advancement is imperative for the development of novel therapeutic strategies.\u003c/p\u003e\u003cp\u003eThe TME is a dynamic and multifaceted ecosystem composed of tumor cells, stromal cells, immune infiltrates, extracellular matrix components, and a variety of soluble signaling molecules. It plays a pivotal role in modulating tumor growth, invasion, and metastasis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the context of the oral cavity, the TME is uniquely influenced by continuous exposure to saliva\u0026mdash;a dynamic fluid environment. Saliva not only maintains oral physiological homeostasis but also serves as a \u0026ldquo;bio-information reservoir,\u0026rdquo; harboring proteins, metabolites, nucleic acids, extracellular vesicles, and a diverse microbial community [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In recent years, saliva has gained prominence as a promising non-invasive \u0026ldquo;liquid biopsy\u0026rdquo; medium for the early detection and prognostic monitoring of OSCC, with numerous studies focused on identifying disease-specific biomarkers [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the current body of research predominantly regards saliva as a passive disease indicator, overlooking its potential as a biologically active medium that may directly contribute to OSCC progression. Notably, the salivary composition of OSCC patients differs markedly from that of healthy individuals, exhibiting microbial dysbiosis, altered metabolite profiles, elevated pro-inflammatory cytokines, and disruptions in microbial community homeostasis [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These compositional changes, rather than merely reflecting disease status, may actively drive tumor development by reshaping the metabolic and immune landscape of the TME. Through persistent interaction with tumor tissues, altered salivary components may facilitate malignant transformation and progression.\u003c/p\u003e\u003cp\u003eDespite this compelling hypothesis, direct experimental evidence elucidating the in vivo functional role of OSCC patient-derived saliva and its underlying molecular mechanisms remains scarce. Therefore, the present study aimed to determine whether saliva from OSCC patients actively promotes tumor growth and to explore the associated molecular mechanisms. We established a subcutaneous xenograft OSCC mouse model and implemented daily intra-oral exposure to pooled OSCC patient saliva. Mice treated with patient saliva exhibited significantly increased tumor volumes, supporting our central hypothesis. To elucidate the underlying mechanisms, we employed an integrative multi-omics approach\u0026mdash;including 16S rRNA gene sequencing, transcriptomic profiling, and untargeted metabolomics\u0026mdash;to systematically characterize the oral microbiota, tumor gene expression profiles, and metabolic alterations across treatment groups. This study reveals a direct tumor-promoting effect of OSCC patient saliva and provides novel insights into the dynamic interaction between saliva and the TME, thereby offering a theoretical basis for innovative adjuvant strategies in OSCC management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eAnimals\u003c/h2\u003e\u003cp\u003eMale C57BL/6N mice (4 weeks old) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) [Animal Certification No.: SCXK (Jing) 2021-0006]. All animals were allowed to acclimatize for one week under standard housing conditions prior to any experimental procedures. Mice were housed in groups of five per cage with the ambient temperature maintained at 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C and relative humidity at 50\u0026ndash;70%. Food and water were provided ad libitum throughout the acclimatization period.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOSCC Mouse Model and Experimental Design\u003c/h3\u003e\n\u003cp\u003eMale C57/BL6N mice (4 weeks old) were acclimated under standard laboratory conditions for 7 days. To reduce the influence of endogenous oral microbiota, mice received kanamycin sulfate (in drinking water) for 4 consecutive days, followed by 3 days of normal water. SCC7 cells were cultured to logarithmic phase (~\u0026thinsp;70% confluence), then mixed with an equal volume of growth factor-reduced basement membrane matrix, yielding a final concentration of 0.25 \u0026times; 10⁶ cells/0.05 mL for subcutaneous injection. Tumor formation was confirmed 18 days post-injection, and mice were randomly assigned to two groups (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e\u003cp\u003eIn the control group, mice received 300 \u0026micro;L of ultrapure water via oral gavage twice daily (morning and afternoon) for 14 days. In the saliva group, 100 \u0026micro;L of pooled saliva from six OSCC patients was administered intra-orally once every morning using a pipette, followed by a 30-minute fasting period. These mice received 300 \u0026micro;L of ultrapure water by oral gavage in the afternoon. All treatments were administered for 14 consecutive days. On day 14, all mice were first rendered unconscious via deep anesthesia induced by intraperitoneal injection of tribromoethanol at a dosage of 250 mg/kg. A surgical plane of anesthesia was verified by the absence of response to a noxious stimulus (toe pinch). Following confirmation of unconsciousness, euthanasia was performed by cervical dislocation. The use of tribromoethanol ensured that the procedure was conducted without causing pain or distress, in accordance with the Refinement principle of the 3Rs. Subsequently, tumor tissues and oral swabs were collected, and tumor dimensions were measured using digital calipers.\u003c/p\u003e\u003cp\u003e\u003cb\u003e16S rRNA Gene Library Preparation, Sequencing, and Analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTotal genomic DNA was extracted using the Magen DNA Kit (Cat. No. D6356-02). The V3\u0026ndash;V4 hypervariable regions of the bacterial 16S rRNA gene were amplified using primers 338F/806R and Phusion\u0026reg; High-Fidelity PCR Master Mix. PCR products were verified by 2% agarose gel electrophoresis, pooled, and purified using the Qiagen Gel Extraction Kit. Libraries were constructed with the TruSeq\u0026reg; DNA PCR-Free Kit, quantified with a Qubit 2.0 fluorometer, and sequenced on the Illumina MiSeq platform (2 \u0026times; 300 bp paired-end reads).\u003c/p\u003e\u003cp\u003eRaw FASTQ data were imported into QIIME2 [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] for analysis. Demultiplexed sequences underwent quality filtering, trimming, denoising, and chimera removal using the DADA2 plugin to generate amplicon sequence variants (ASVs) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Taxonomic assignment was performed using the feature-classifier plugin and the Silva (V138) reference database. Alpha and beta diversity metrics were calculated via the core-diversity plugin. Differential microbiota analysis was conducted using linear discriminant analysis effect size (LEfSe) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://huttenhower.sph.harvard.edu/galaxy\u003c/span\u003e\u003cspan address=\"http://huttenhower.sph.harvard.edu/galaxy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eTranscriptome Library Preparation, Sequencing, and Analysis\u003c/h3\u003e\n\u003cp\u003eRNA libraries were quantified using the Qubit dsDNA HS Assay Kit. DNA nanoballs (DNBs) were prepared using the DNBSEQ DNB Rapid Preparation Kit and sequenced on the DNBSEQ-T7 platform (paired-end 150 bp reads). Quality control was performed using fastp software [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and gene expression quantification followed a previously established protocol [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Differential expression analysis was conducted using the DESeq2 package in R [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with thresholds of fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1.5 and adjusted p-value (padj)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed using the clusterProfiler R package [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Immune infiltration analysis was performed using the R package immunedeconv (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/omnideconv/immunedeconv\u003c/span\u003e\u003cspan address=\"https://github.com/omnideconv/immunedeconv\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eUntargeted Metabolomics Analysis\u003c/h3\u003e\n\u003cp\u003eTumor tissues were cryo-ground and extracted with 80% methanol (20\u0026times; volume) at \u0026minus;\u0026thinsp;50\u0026deg;C for 10 cycles. After centrifugation (14,000 rpm, 10 min, 4\u0026deg;C), supernatants were vacuum-dried and reconstituted in 50% acetonitrile containing internal standards. Chromatographic separation was performed using a Thermo Vanquish UHPLC system with a BEH C18 column. Mass spectrometry was conducted on a Thermo QE HF-X system in both positive and negative ionization modes, scanning an m/z range of 150\u0026ndash;2000 at 30,000 resolution.\u003c/p\u003e\u003cp\u003eRaw MS data were processed using Compound Discoverer 3.8. Differential metabolites were identified via univariate t-tests, followed by orthogonal projections to latent structures discriminant analysis (OPLS-DA) using the ropls R package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Metabolites with FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and variable importance in projection (VIP)\u0026thinsp;\u0026gt;\u0026thinsp;1 were considered significant.\u003c/p\u003e\n\u003ch3\u003eQuantitative Real-Time PCR (RT-qPCR)\u003c/h3\u003e\n\u003cp\u003eRPL13A was used as the endogenous reference gene. Each reaction was performed in technical duplicates. The mean ΔCt of the control group was used as the calibrator for each target gene. The ΔΔCt was calculated for each sample, and relative expression (RQ) was determined using the 2\u003csup\u003e⁻ΔΔCt\u003c/sup\u003e method. Statistical analysis was performed using a two-tailed t-test.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eEstablishment of the OSCC Mouse Model and Comparison of Tumor Volume after Saliva Treatment\u003c/h2\u003e\u003cp\u003eAn OSCC mouse model was successfully established by submucosal injection of SCC7 murine oral squamous cell carcinoma cells into the mandibular region. After tumor development, mice in the saliva treatment group (S group) received 100 \u0026micro;L of OSCC patient saliva daily via oral application, while the control group (C group) received an equivalent volume of ultrapure water. After two weeks, visible inspection revealed that tumors in the saliva-treated group were significantly larger than those in the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Mice were euthanized for tumor collection, and tumor volumes were measured using calipers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean tumor volume in the control group was 203.2\u0026thinsp;\u0026plusmn;\u0026thinsp;97.8 mm\u0026sup3;, whereas in the saliva-treated group it was 703.0\u0026thinsp;\u0026plusmn;\u0026thinsp;70.0 mm\u0026sup3;. Welch\u0026rsquo;s t-test showed a statistically significant difference between the two groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that OSCC patient saliva significantly promotes tumor growth in mice.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eComposition and Differential Analysis of the Oral Microbiota in OSCC Mice\u003c/h3\u003e\n\u003cp\u003e16S rRNA gene sequencing yielded an average of 47,263 clean tags per sample. A total of 151 amplicon sequence variants (ASVs) were identified in the control group compared to 181 ASVs in the saliva group, with only 41 ASVs shared between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Alpha diversity analysis using the Shannon index showed a slightly higher diversity in the S group, though the difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.38) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Principal Coordinates Analysis (PCoA) based on Bray-Curtis dissimilarity revealed partial separation between the groups (PC1: 76.87%, PC2: 3.65%). However, PERMANOVA analysis showed no significant intergroup differences (R\u0026thinsp;=\u0026thinsp;0.2222, P\u0026thinsp;=\u0026thinsp;0.300) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). LEfSe analysis identified significantly different taxa (LDA\u0026thinsp;\u0026gt;\u0026thinsp;4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the class \u003cem\u003eBacilli\u003c/em\u003e showing the most pronounced differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, E). The control group was enriched in \u003cem\u003eLactobacillales\u003c/em\u003e and \u003cem\u003eStreptococcaceae\u003c/em\u003e, particularly the genus \u003cem\u003eStreptococcus\u003c/em\u003e. Conversely, the saliva group showed significant enrichment of \u003cem\u003eErysipelotrichales\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDifferential Gene Expression and Immune Infiltration Analysis in OSCC Tumor Tissues\u003c/h2\u003e\u003cp\u003eRNA sequencing revealed an average of 8.4 Gb of clean data per sample and identified 18,149 expressed genes. DESeq2 analysis (threshold: fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.5, adjusted P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) identified 14 upregulated and 5 downregulated genes in the S group. These included genes related to chronic inflammation (\u003cem\u003eMpo\u003c/em\u003e, \u003cem\u003eTnfaip3\u003c/em\u003e) and extracellular matrix (ECM) remodeling (\u003cem\u003eAngptl4\u003c/em\u003e, \u003cem\u003eSfrp2\u003c/em\u003e, \u003cem\u003eCol2a1\u003c/em\u003e, \u003cem\u003eDmp1\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). GO enrichment indicated significant involvement in apoptosis regulation and cell differentiation pathways (e.g., \u0026ldquo;negative regulation of extrinsic apoptotic signaling pathway,\u0026rdquo; \u0026ldquo;response to BMP\u0026rdquo;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). KEGG pathway enrichment highlighted the ECM-receptor interaction, focal adhesion, cholesterol metabolism, and IL-17 signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Immune infiltration analysis via MCP-counter revealed a significant increase in cancer-associated fibroblasts (CAFs) in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Mast cell infiltration was slightly elevated but not statistically significant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of Metabolites in OSCC Tumor Tissues\u003c/h2\u003e\u003cp\u003eUntargeted metabolomics using LC-MS identified 1,477 metabolites. OPLS-DA modeling showed clear group separation (cumulative Q\u0026sup2; = 0.471). A combination of t-tests and VIP scores (FC\u0026thinsp;\u0026gt;\u0026thinsp;1.5, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, VIP\u0026thinsp;\u0026gt;\u0026thinsp;1) identified 84 differentially expressed metabolites\u0026mdash;64 upregulated and 20 downregulated in the S group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;C). KEGG Level 2 classification revealed primary involvement in \u0026ldquo;Amino acid metabolism\u0026rdquo; and \u0026ldquo;Carbohydrate metabolism,\u0026rdquo; followed by \u0026ldquo;Signal transduction\u0026rdquo; (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Pathway enrichment showed significant enrichment in ferroptosis, central carbon metabolism in cancer, butanoate metabolism, and the cAMP signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIntegrated Multi-Omics Data Analysis\u003c/h2\u003e\u003cp\u003eIntegration of transcriptomic and metabolomic datasets suggests that saliva from OSCC patients may promote tumor growth through three potential mechanisms: (1) induction of chronic inflammation, (2) activation of CAFs and ECM remodeling, and (3) metabolic reprogramming (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Pearson correlation analysis between microbial genus abundance and gene/metabolite levels showed that \u003cem\u003eStaphylococcus\u003c/em\u003e was strongly correlated with expression changes of key genes and metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). RT-qPCR validation confirmed the expression trends of eight representative genes, consistent with transcriptome data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, using an OSCC mouse model, we demonstrated that topical application of saliva from OSCC patients significantly promoted tumor growth compared to the control group. Subsequent comprehensive analyses of the oral microbiota, tumor transcriptome, and metabolome suggest that this tumor-promoting effect is mediated through a combination of chronic inflammation, activation of CAFs, ECM remodeling, and metabolic reprogramming.\u003c/p\u003e\u003cp\u003eOne of the key mechanisms identified is the establishment of a pro-tumorigenic chronic inflammatory microenvironment. The significantly elevated levels of muramic acid (log2FC\u0026thinsp;=\u0026thinsp;3.67) in the saliva-treated tumors strongly suggest the translocation of bacterial components into the tumor microenvironment. As a component of peptidoglycan, muramic acid is capable of activating TLR2 receptors on tumor cells, thereby triggering NF-κB and STAT3 signaling pathways, which are known to promote cancer cell invasiveness [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This immune activation recruits neutrophils into the tumor microenvironment, which is supported by the significant upregulation of the \u003cem\u003eMpo\u003c/em\u003e gene encoding myeloperoxidase (MPO). MPO catalyzes the generation of hypochlorous acid (HOCl), a reactive species that can induce DNA damage and mutations, ultimately enhancing tumor cell heterogeneity and growth [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In parallel, the downregulation of the anti-inflammatory gene \u003cem\u003eTnfaip3\u003c/em\u003e (A20) may impair the negative feedback regulation of NF-κB signaling, allowing persistent inflammatory responses. Loss of TNFAIP3 has been previously associated with enhanced tumorigenesis and immune evasion. Moreover, the enrichment of differentially expressed genes in the IL-17 signaling pathway, a known driver of neutrophilic infiltration and chronic inflammation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], further underscores the inflammatory state induced by OSCC-derived saliva.\u003c/p\u003e\u003cp\u003eAnother important finding is the significant activation of CAFs and ECM remodeling, which collectively serve as drivers of tumor progression [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Immune infiltration analysis revealed a higher proportion of CAFs in the saliva group, consistent with the known role of chronic inflammation in CAF activation. These fibroblasts secrete large quantities of ECM proteins such as \u003cem\u003eCol2a1, Bglap\u003c/em\u003e, and \u003cem\u003eDmp1\u003c/em\u003e, resulting in increased stromal stiffness and fibrosis. Additionally, both CAFs and tumor cells highly express \u003cem\u003eAngptl4\u003c/em\u003e, a multifunctional protein that facilitates angiogenesis, cell migration, and metabolic reprogramming, and has been implicated in tumor progression across various cancers [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Elevated expression of \u003cem\u003eCtsk\u003c/em\u003e, which encodes cathepsin K\u0026mdash;a protease capable of degrading ECM collagen\u0026mdash;further suggests enhanced ECM turnover [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Enrichment of the ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathways in the transcriptome supports the notion that the remodeled ECM activates integrin-mediated pro-survival signaling cascades. Specifically, integrin binding recruits focal adhesion kinase (FAK), which in turn activates PI3K/Akt signaling\u0026mdash;a classical pathway that promotes cell proliferation, survival, and migration [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, OSCC saliva-induced inflammation appears to facilitate CAF activation and ECM remodeling, creating a structural and biochemical environment that favors tumor progression.\u003c/p\u003e\u003cp\u003eMetabolic reprogramming represents a third mechanism by which OSCC patient-derived saliva promotes tumor development. Tumors in the saliva-treated group exhibited classic hallmarks of cancer metabolism. For example, cholesterol metabolism was significantly upregulated, likely reflecting the tumor's increased demand for membrane biosynthesis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The reduced levels of cholesterol sulfate may indicate accelerated utilization of free cholesterol. Amino acid metabolism was also altered, particularly the tryptophan metabolic pathway. Elevated levels of tryptophan and its derivatives, such as indole-3-pyruvic acid, suggest activation of the IDO/TDO and IL4I1 pathways, which are known to produce immunomodulatory metabolites acting via the aryl hydrocarbon receptor (AHR) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Additionally, the accumulation of succinate\u0026mdash;a tricarboxylic acid (TCA) cycle intermediate and recognized oncometabolite\u0026mdash;can mimic hypoxia and stimulate angiogenesis and glycolysis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFatty acid oxidation (FAO) also appeared to be enhanced, as indicated by elevated levels of carnitine metabolites (e.g., O-propionylcarnitine, C18-carnitine). Increased FAO supports ATP production under nutrient-deprived or hypoxic conditions and has been associated with elevated expression of CPT1C in tumor cells [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, glutamine/glutamate metabolism was reprogrammed, as evidenced by glutamate accumulation, highlighting tumor reliance on glutamine for anaplerosis, nucleotide synthesis, and lipid production [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Pathway enrichment analyses consistently identified significant changes in central carbon metabolism, alanine, aspartate, and glutamate metabolism, and butanoate metabolism. In response to elevated oxidative stress due to inflammation and metabolic activity, tumors also upregulated antioxidant systems, particularly through increased levels of reduced glutathione (GSH), a critical mediator of ferroptosis resistance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough the salivary microbiota in OSCC patients is diverse, \u003cem\u003eStaphylococcus\u003c/em\u003e emerged as a potentially critical contributor to the observed tumor-promoting effects. 16S rRNA sequencing revealed a significant enrichment of \u003cem\u003eStaphylococcus\u003c/em\u003e in the saliva group, with strong correlations between its relative abundance and the expression of key differential genes and metabolites. In clinically relevant studies, \u003cem\u003eStaphylococcus\u003c/em\u003e was found to be significantly more abundant in OSCC groups compared to healthy individuals [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, a study involving 186 OSCC patients reported that the oral cavity of patients undergoing chemotherapy and chemoradiotherapy was dominated by Gram-positive bacteria, including \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. \u003cem\u003eStaphylococcus\u003c/em\u003e can secrete various virulence factors, which can trigger strong local inflammatory responses, contributing to the formation of the tumor microenvironment. Furthermore, \u003cem\u003eS. aureus\u003c/em\u003e has been shown to induce overexpression of COX-2 and activate NF-κB and TNF signaling in oral epithelial cells, upregulating pro-proliferative genes such as Cyclin D1 and downregulating tumor suppressors like p16. In vitro experiments have confirmed that S. aureus infection can enhance proliferation and malignant transformation of oral keratinocytes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These findings suggest that \u003cem\u003eStaphylococcus\u003c/em\u003e colonization in the murine oral cavity following saliva application may contribute to tumorigenesis through pro-inflammatory and oncogenic pathways.\u003c/p\u003e\u003cp\u003eDespite these findings, this study has several limitations..First, the sample size was relatively small;expanding the cohort could enhance the reliability of multi-omics analyses and statistical power. Second, some key findings require further validation in clinical patient samples.\u003c/p\u003e\u003cp\u003eMoreover, the specific effective components in saliva have not been identified in this study.However, existing literature has reported that certain oral microorganisms or salivary exosomes may participate in regulating the progression of OSCC. For instance, P. gingivalis can promote tumor development by activating inflammatory pathways and accelerating cell cycle progression, while tumor-derived exosomes can suppress tumor suppressor gene expression and enhance cell proliferation and migration. Therefore, our next research step will focus on analyzing and identifying the key active factors in saliva, and systematically validating their precise mechanisms in OSCC initiation and progression through in vivo and in vitro experiments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings indicate that saliva from OSCC patients is not merely a passive byproduct of malignancy but an active contributor to tumor progression. It exerts pro-tumorigenic effects through a multifactorial mechanism involving chronic inflammation, stromal remodeling, and metabolic reprogramming. These results highlight the intricate and dynamic crosstalk between saliva, the oral microbiota, and the TME, offering new insights into the pathogenesis of OSCC. Importantly, our study suggests that targeting the altered oral microecosystem or intercepting the downstream signaling pathways activated by salivary components may represent a promising adjunctive therapeutic approach for OSCC management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao-yan Zhang, Yuan-tao Li, Bei-bei Liang, and Xiang-jun Li contributed to conception and designdata acquisition and analysis, drafted and critically revised the manuscript; \u0026nbsp;Jie Guo, Ying Feng and Qian Han contributed to conception and design, data acquisition, analysis, and interpretation, drafted the manuscript; Shi-han Zhang, Yu Gao, Hao-tian Yin and Xiao-xu Ding contributed toconception, data analysis, drafted the manuscript; Bei-bei Liang and Xiang-jun Li contributed to conception and design,data acquisition, analysis, andinterpretation, drafted and critically revised the manuscript. All authors gavefinal approval and agreed to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval/Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study adhered to the Declaration of Helsinki, specifies the approving ethics committee with its reference number, and describes the process of obtaining informed consent. The collection and use of these human tissue samples were strictly conductedin accordance with the guidelines approved by the Ethics Committee of the School and Hospital of Stomatology, Hebei Medical University(IRB-ID: [2024]066), and informedconsent was obtained from all patients prior to the study. All animal experiments were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) of Hebei Medical University (Ethics Approval Reference Number: [IACUC-Hebmu-P-2025393]). As the animals were commercially sourced specifically for biomedical research, informed consent from the owner was not applicable. All methods are reported in accordance with ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data have been deposited in public repositories: 16S rRNA and RNA-seq data are available in the Sequence Read Archive (SRA) under accession numbers SRP603336 and SRP602255, respectively, while the untargeted metabolomics data are available in the MetaboLights database (MTBLS12805) at https://www.ebi.ac.uk/metabolights/MTBLS12805.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunder name: Hebei Provincial Government-Funded Clinical Medicine Talent Cultivation Program, ID: ZF2024151\u003c/p\u003e\n\u003cp\u003eFunder name: Research projects under the Hebei Provincial Administration of Traditional Chinese Medicine, ID: 2025350\u003c/p\u003e\n\u003cp\u003eFunder name: Hebei Provincial Government-Funded Clinical Medicine Talent Cultivation Program, ID: ZF2025241\u003c/p\u003e\n\u003cp\u003eFunder name: Hebei Province Medical Science Research Project, ID: 20240101.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests associated with the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. \u0026nbsp;This manuscript does not contain any individual person\u0026apos;s data in any form (including any individual details, images, or videos).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. 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J Oral Microbiol. 2019;11(1):1643205. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/20002297.2019.1643205\u003c/span\u003e\u003cspan address=\"10.1080/20002297.2019.1643205\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oral squamous cell carcinoma, 16S rRNA, RNA-seq, metabolomics, tumor microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-7610282/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7610282/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 prevalent and aggressive malignancy with increasing evidence implicating the oral microbiome and tumor microenvironment in its progression. However, the mechanistic impact of OSCC patient-derived saliva on tumor development remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe established an orthotopic OSCC mouse model and topically applied saliva collected from OSCC patients to assess its effects on tumor progression. Multi-omics analyses, including 16S rRNA sequencing, tumor transcriptomics (RNA-seq), and metabolomics (LC-MS), were performed to explore changes in the oral microbiota, gene expression profiles, and metabolic pathways.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eTreatment with OSCC patient saliva significantly accelerated tumor growth compared to controls. Saliva application altered the oral microbiota, most notably causing a significant enrichment of the genus Staphylococcus. Tumor transcriptomics revealed upregulation of genes associated with chronic neutrophilic inflammation (\u003cem\u003eMpo\u003c/em\u003e), cancer-associated fibroblast (CAF) activation, and extracellular matrix (ECM) remodeling (\u003cem\u003eAngptl4, Col2a1\u003c/em\u003e). Metabolomic analysis demonstrated profound metabolic reprogramming within the tumors, including enhanced amino acid metabolism (tryptophan, glutamate), fatty acid oxidation, and accumulation of the oncometabolite succinate. Integrated analysis showed that Staphylococcus abundance was strongly correlated with these inflammatory and metabolic signatures.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study demonstrates that saliva from OSCC patients promotes tumor progression in vivo through a multifactorial mechanism involving inflammation, stromal remodeling, and metabolic rewiring. These findings highlight the tumor-promoting potential of salivary and microbial components, suggesting new avenues for diagnostic and therapeutic strategies targeting the oral microenvironment in OSCC.\u003c/p\u003e","manuscriptTitle":"Saliva from Oral Squamous Cell Carcinoma Patients Promotes Tumor Progression via Inflammation, Stromal Remodeling, and Metabolic Reprogramming in a Mouse Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 13:39:37","doi":"10.21203/rs.3.rs-7610282/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-11T08:19:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-02T21:25:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T14:59:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273487394027401864943887921386513135089","date":"2025-10-23T13:46:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60739742129323063822535266909043774146","date":"2025-10-23T11:32:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165048559133136293720683816566799879818","date":"2025-10-23T11:18:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T11:11:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T10:51:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-10T10:37:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T03:21:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-10-10T03:18:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f998f975-ce30-4cc4-b956-73201c37b771","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:01:45+00:00","versionOfRecord":{"articleIdentity":"rs-7610282","link":"https://doi.org/10.1186/s12903-025-07413-0","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2025-12-01 15:57:43","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-11-04 13:39:37","video":"","vorDoi":"10.1186/s12903-025-07413-0","vorDoiUrl":"https://doi.org/10.1186/s12903-025-07413-0","workflowStages":[]},"version":"v1","identity":"rs-7610282","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7610282","identity":"rs-7610282","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}