Dynamic Changes of Dental Plaque and Saliva Microbiota in OSCC Progression

Dynamic Changes of Dental Plaque and Saliva Microbiota in OSCC Progression | 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 Dynamic Changes of Dental Plaque and Saliva Microbiota in OSCC Progression Man Zhang, Hailin Zhang#, Ao Hong, Jing Huang, Lirong Yang, Zheng Yu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6143003/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 May, 2025 Read the published version in Clinical Oral Investigations → Version 1 posted 9 You are reading this latest preprint version Abstract Objectives To elucidate the microbial and genomic changes in saliva and dental plaque during Oral Squamous Cell Carcinoma (OSCC) progression, and to identify potential mechanisms and virulence factors involved in OSCC pathogenesis. Materials and Methods Using metagenomic sequencing, 64 saliva and dental plaque samples from OSCC patients at different stages of differentiation were examined. Results The results showed notable differences in the microbial composition and genomic profiles across ecological regions and differentiation degrees. Notably, the relative abundance of specific microbes, such as Porphyromonas gingivalis , Fusobacterium nucleatum , and Haemophilus parainfluenzae , increased in poorly differentiated OSCC. Microbial alpha diversity in dental plaque and saliva correlates with tumor T staging. Dental plaque microbiota shows higher specialization, especially in poorly differentiated tumors. Both microbiota types become more stable with advanced T staging. Genomic analysis reveals increased virulence factors in poorly differentiated stages. Subsequently, functional pathway analysis and tracing of pathogens reveal specific microbial mechanisms in oral cancer pathogenesis. Oral pathogens may promote tumorigenesis by secreting factors like GAPDH, GspG, and AllS, and drive tumor initiation and progression through microbial interactions. Conclusions OSCC progression is associated with altered microbial composition, diversity, and genomic profiles in saliva and dental plaque. Poorly differentiated stages show higher abundance of pathogens and virulence factors, implicating them in tumorigenesis. Clinical Relevance Understanding microbial and genomic changes in saliva and dental plaque during OSCC progression could help develop new diagnostic biomarkers and therapies targeting the oral microbiota, potentially improving early detection, treatment efficacy, and prognosis for patients. Maintaining oral microbiome homeostasis may also help prevent oral cancer. Oral squamous cell carcinoma dental plaque saliva oral microbiota cancer progression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Oral cancer is one of the most common head and neck cancer (HNC) types in the world, and 90% of these are oral squamous cell carcinoma (OSCC) [ 1 ]. Despite enhancements in surgical interventions, radiotherapy, and chemotherapeutic strategies, a concerning upward trend in OSCC incidence is observed on a global scale [ 2 ]. Projections from the Global Cancer Observatory (GCO) estimate a near 40% increase in OSCC incidence by 2040, paralleled by a rise in mortality rates [ 3 ]. Smoking, drinking, and chewing betel are the main risk factors for oral cancer [ 4 ]. Additional potential risk factors may encompass viral and fungal infections, as well as chronic periodontitis [ 5 – 7 ]. Nonetheless, approximately 15% of oral squamous carcinomas cannot be accounted for by these factors alone, necessitating further exploration of other potential risk factors. The intricate interplay between oral microbiota and oral health has become an increasingly prominent area of research [ 8 ]. Imbalances in the microbiota may lead to chronic inflammation, creating favorable conditions for the development and progression of oral cancer [ 9 ]. According to the World Health Organization (WHO), OSCC differentiation is usually recorded as histological grade and is classified into three types: well-differentiated (WD, grade I), moderately differentiated (MD, grade II), and poorly differentiated (PD, grade III) [ 10 ]. Well-differentiated tumors tend to grow more slowly and are less aggressive, whereas poorly differentiated tumors often exhibit rapid growth and a high propensity for metastasis [ 11 ]. At present, the treatment strategy and prognosis assessment of OSCC are mainly based on common clinical indicators, and tumor differentiation is an indispensable indicator for OSCC management [ 12 ]. Despite the increasing amount of research, there are still substantial gaps in our understanding of the relationship between tumor differentiation and the oral microbiome. Addressing these gaps is crucial as it may reveal new biomarkers for early detection and novel targets for therapeutic intervention, potentially improving patient outcomes [ 13 ]. However, there is a dearth of longitudinal studies on the bacterial communities and genomic composition in the oral cavity during the progression of OSCC, and the comprehensive profile of bacteria remains unclear. The human oral cavity is a complex ecosystem teeming with diverse microbial communities, playing critical roles in health and disease [ 14 ]. Among these microbial habitats, saliva and dental plaque represent two distinct ecological niches that harbor unique microbial populations [ 15 ]. Our previous study found that oral cancer patients exhibit significant differences in microbial composition, gene composition, and function between saliva and dental plaque [ 16 ]. Studies have demonstrated that functional genes within these microbial communities not only facilitate essential metabolic processes but also modulate the immune response, influencing oral disease states [ 17 , 18 ]. Additionally, accumulating evidence underscores the pivotal role of bacteria harboring virulence factors in the etiology and progression of oral cancer [ 19 ]. One of the key bacterial species implicated in OSCC is Porphyromonas gingivalis , a known periodontal pathogen. Porphyromonas gingivalis produces a variety of virulence factors to trigger an infection, including fimbriae (FimA), cysteine proteases (gingipains), lipopolysaccharide (LPS), and nucleoside diphosphate kinase (NDK), which facilitate its adherence to and invasion of host tissues [ 20 ]. Studies have demonstrated that the positive rate of Porphyromonas gingivalis was higher in patients with TNM (tumor, nodes, and metastasis) stage III-IV, poorly differentiated tissue, and lymph node metastasis [ 21 ]. Infection caused by Porphyromonas gingivalis may be regarded as a potential risk factor for oral cancer [ 22 ]. Multi-omics research unravels intricate functional genes and virulence factors, leading to breakthroughs in understanding disease mechanisms and advancing drug therapy. Despite numerous studies highlighting the significant role of pathogenic bacteria in the development of oral cancer, there is still a relative lack of comprehensive analysis regarding the differences and associations between functional genes and independent factors. This analytical gap hinders our comprehensive understanding of how specific pathogenic bacteria and their associated genes specifically impact the occurrence and progression of OSCC. This study utilized metagenomic sequencing technology to analyze 64 samples of saliva and dental plaque from patients with OSCC at various stages of differentiation. It investigated the changes in microbial communities and genomes during the development of OSCC, evaluated the association between oral microbiota diversity, richness, and cancer T staging, and identified specific microbial species and virulence factors associated with disease progression. The study provides evidence of alterations in the composition of oral bacterial communities and genomes during the progression of OSCC, offering potential novel biomarkers and intervention targets for the diagnosis and treatment of oral cancer. 2. Materials and methods 2.1 Experimental Design and Sample Collection To elucidate the characteristics of microbial composition, functional genes, and virulence factors in saliva and dental plaque of patients with different degrees of OSCC, a total of 32 saliva samples and 32 dental plaque samples were collected from patients hospitalized at Hunan Cancer Hospital in China. The patients were diagnosed with oral squamous cell carcinoma based on pathological examination and had complete medical records. The clinical differentiation stage of the patients was evaluated using the clinical TNM index and the pathological TNM index. The 64 samples were divided into six groups: dental plaque high differentiation group (DPI) and saliva high differentiation group (SI); the group with dental plaque between low differentiation and moderate differentiation was defined as DPI-II, corresponding to the saliva group SI-II; dental plaque low differentiation group (DPIII) and saliva low differentiation group (SIII). All participants in this sample collection provided informed consent and had complete clinical and pathological data. The inclusion criteria for participants were as follows: (1) No other malignant tumors were found in the systemic examination, excluding distant metastasis; (2) Avoid smoking, drinking, and eating at least 30 minutes before sample collection; (3) No bleeding occurred during sample collection; (4) No immunosuppressive drugs were taken within the past 6 months; (5) No severe periodontal disease, severe dental caries, or oral mucosal diseases within the past 3 months, no other systemic diseases, no history of oral surgery, and no history of antibiotic use; (6) Patients without oral infectious diseases, bleeding history, or a history of other malignant tumors. According to the Helsinki Declaration, the cases included in this study were collected and approved by the Ethics Committee of Hunan Cancer Hospital (Ethics Approval Number: KYJJ-2023-025). All participating patients were informed about the sample collection, experimental purpose, and voluntarily signed an informed consent form. Before sample collection, participants were disinfected and taken to a sterile laboratory. The area around the mouths of the participants was disinfected with alcohol. Sterile distilled water was used for mouth rinsing to remove any residual food debris. Then, a swab was gently rubbed back and forth three times on the upper and lower incisors, first molars, first premolars, and placed in a sterile collection tube. Subsequently, 1 mL of sterile PBS buffer was added to the sterile collection tube, ensuring that the swab was completely immersed in the elution buffer for complete dissolution of the sample. The sample tube was then centrifuged at 12,000 rpm for 15 minutes at 21°C (Glanlab, Changsha, China). This step was repeated three times to collect enough eluted buffer. Saliva was collected in sterile tubes with a minimum volume of 1 mL. After sample collection, the samples were immediately stored at -80°C (Eppendorf, Changsha, China) and sent for metagenomic sequencing. 2.2 Total DNA extraction and sequencing Bacterial genomic DNA was extracted from saliva and dental plaque samples using the E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, USA). The extracted DNA was eluted in elution buffer and stored at -20°C. DNA yield was quantified using a full-spectrum ultraviolet spectrophotometer (Amersham Biosciences, USA), and DNA purity was assessed by agarose gel electrophoresis for both metagenomic DNA and PCR products. Subsequently, the high-quality genomic DNA was randomly fragmented into 200–500 bp fragments using an ultrasonicator, and the fragment size distribution was confirmed by agarose gel electrophoresis (Bio-Rad, USA). The resulting fragments were then recovered using the QIAquick Gel Extraction Kit. Following successful library construction, the products were purified and sequenced on the Illumina NovaSeq 6000 PE250 platform (Shanghai Biozeron Biotech. Co., Ltd., China). Illumina PE libraries were constructed, and the obtained sequencing data underwent quality control. 2.3 Quality control and processing of metagenomic sequencing data The sequencing raw data contained sequences with short lengths, excessively ambiguous bases, and inserted adapters, which are low-quality sequences likely to interfere with subsequent analyses. Therefore, to ensure data accuracy, we performed quality control on the raw sequences obtained after sequencing. Specifically, using Fastp (version 0.23.2), we filtered out low-quality reads defined by an average quality score < 15 and lengths shorter than 15 bases. Additionally, we removed contaminating sequences from potential human host interference [ 23 ]. For removing duplicate sequences, especially PCR duplicates or those caused by amplification, we utilized FastUniq (version 1.1.0) [ 24 ]. Sequences were aligned against the human genome database (hg38) using Bowtie2 (version 2.5.1), and sequences originating from humans were filtered out [ 25 ]. After completing the quality control process, Kraken2 (version 2.1.2) was employed for species annotation of the genetic sequences, resulting in taxids (unique identifiers for NCBI taxonomic units) assigned to each gene [ 26 ]. Subsequently, the names and classification information associated with these taxids were translated into corresponding species information, spanning from kingdom to species level. MEGAHIT (version 1.2.9) was then used to reconstruct high-quality reads from the quality-controlled data [ 27 ]. Following assembly, Prodigal (version 2.6.3) predicted genes from the contigs generated by MEGAHIT [ 28 ]. EggNOG-mapper (version 2.0.1) was used to accomplish the functional annotation of the gene catalog [ 29 ]. Furthermore, CD-HIT (version 4.7) was employed to cluster and remove redundant genes with a global sequence similarity threshold of 90% [ 30 ]. Salmon (version 0.13.1) was used to determine the relative abundance of genes [ 31 ]. Additionally, the set B database from the VFDB (Virulence Factors of Pathogenic Bacteria Database), which includes predicted and experimentally confirmed virulence genes, was used to align virulence factors using DIAMOND (version 2.1.8) [ 32 ]. 2.4 Data analysis and visualization The data and visualization analyses in this study were conducted using R (v.4.2.3). Microbial community composition analysis was performed using the microeco package (version 1.1.0) [ 33 ]. The Wilcoxon test was used to assess differences in the relative abundance of microbial or functional genes between two groups, while the Kruskal-Wallis test was employed for comparisons among multiple groups. Visualization of these differences was carried out using ggboxplot from the ggpubr package. A significance level of P < 0.05 was considered statistically significant. To investigate the variations in microbial composition of saliva, dental plaque, and at different differentiation stages, the MicrobiotaProcess package (version 1.12.4) was used to compute α-diversity indices of the microbiota [ 34 ]. Alpha diversity metrics, including the ACE, Chao1, Pielou, Shannon-Weaver, Gini-Simpson, and Richness index were calculated to assess microbial community diversity and evenness within the habitat. Linear regression using the lm function was performed to model the relationship between α-diversity indices and the T index [ 35 ]. To assess similarities between microbial communities within different groups, the Bray-Curtis dissimilarity-based "vegdist" function and Principal co-ordinates analysis (PCoA) dimensional reduction analysis was employed to determine β-diversity indices across different groupings. ANOSIM was applied to test the significance of differences between two groups [ 36 ]. Species specificity and occupancy were calculated within different groups, defining Operational Taxonomic Units (OUT) with both indices ≥ 0.7 as specialized species for that group. The distribution of microbial species across different differentiation stages was projected onto a specificity-occupancy (SPEC-OCUS) plot [ 37 ]. The AVD index was computed to evaluate the stability of microbial communities within different groups [ 38 ]. From a genomic perspective, non-metric multidimensional scaling (NMDS) ordination analysis was performed using the metaMDS function from the vegan package, with a stress value 2 and P < 0.05 [ 16 ]. Additionally, differences in KEGG functional pathways between two and multiple groups were examined using Wilcoxon and Kruskal-Wallis tests. Subsequently, the DESeq2 package was utilized to identify virulence factors with significantly different abundances between groups of varying differentiation levels (log 2 FoldChange > 1, P < 0.05). The Sankey diagrams were employed to illustrate the involvement of virulence factors from specific pathogens in differentially enriched functional pathways among groups. Lastly, the Pearson correlation coefficients were computed to assess correlations between selected pathogens, and the correlation matrix was visualized using the corrplot package (version 0.92). 3. Result 3.1 Microbial Composition Characteristics In this study, 64 samples were collected from all eligible subjects with OSCC, comprising 32 dental plaque samples and 32 saliva samples of varying pathological classifications. Metagenomic sequencing was conducted to investigate the microbial composition differences in various ecological niches (saliva and dental plaque) associated with different degrees of OSCC differentiation. At the phylum level, the dominant microorganisms in both saliva and dental plaque included Firmicutes , Proteobacteria , Bacteroidetes , Fusobacteria , and Actinobacteria . Firmicutes demonstrated a higher relative abundance in saliva compared to dental plaque, but the relative abundance of Bacteroidetes is higher in dental plaque. As the degree of differentiation in OSCC decreased, the abundance of Firmicutes in both saliva and dental plaque also decreased, whereas Proteobacteria exhibited an increased abundance in both groups with decreasing differentiation ( Fig. 1 A ). At the species level, Staphylococcus epidermidis and Klebsiella pneumoniae were the most abundant species in both saliva and dental plaque, with these three species showing higher relative abundance in saliva. ( Fig. 1 B ) . With decreasing differentiation, the abundance of Staphylococcus epidermidis in both saliva and dental plaque initially increased and then decreased. In dental plaque, Klebsiella pneumoniae showed lower relative abundance in DPIII compared to DPI and DPI-II. Across different differentiation stages, Streptococcus mitis had a higher abundance in saliva than in dental plaque ( Fig. 1 C ). Furthermore, differential abundance testing revealed that among the top 10 abundant species, Lautropia mirabilis , Corynebacterium matruchotii , Veillonella parvula , and Capnocytophaga sputigena exhibited significant differences in relative abundance across different differentiation stages ( P < 0.05, Kruskal-Walli’s test) (Figure S1 A) . Additionally, within the top 150 abundant species, significant differences were observed in the relative abundance of Haemophilus parainfluenzae , Porphyromonas gingivalis , Fusobacterium nucleatum , Streptococcus gordonii , and Capnocytophaga gingivalis between different groups ( Fig. 1 D ) . Wilcoxon test results indicated that these five species were enriched in the dental plaque group. Except for Porphyromonas gingivalis , the other four species showed significant differences in relative abundance across different groups. Moreover, in dental plaque, the relative abundance of Capnocytophaga gingivalis, Porphyromonas gingivalis , and Fusobacterium nucleatum was higher in poorly differentiated stages compared to well-differentiated and moderately differentiated stages (Figure S1 B) . To better describe the microbial composition characteristics of different groups, we conducted a screening of differential biomarkers. The top 30 species with the highest LDA scores were displayed, and the results of the differential bar plot were consistent with the species composition results. Firmicutes was found to be more abundant in the SI group, while Capnocytophaga was enriched in the DPIII group. (Figure S2A) . Furthermore, for the top 100 abundant species, at the phylum level, Firmicutes were significantly enriched in saliva, while Proteobacteria , Bacteroidetes , Fusobacteria , and Actinobacteria were significantly enriched in dental plaque ( P 2) (Figure S2B). At the genus level, the relative abundance of Streptococcus was significantly higher in saliva compared to dental plaque, while Neisseria , Actinomyces , Capnocytophaga , and Leptotrichia showed significantly higher abundance in dental plaque compared to saliva ( P 2) (Figure S2C) . 3.2 Changes in Microbial Diversity Alpha diversity is critical for understanding microbial communities. We calculated the Chao1, Richness, Pielou, and Simpson indices to compare the richness, evenness, and diversity of microbial communities in saliva and dental plaque of OSCC patients. The results indicated significant differences between groups for Chao1, Richness, and Pielou ( P < 0.05). Specifically, Chao1 and Richness were significantly higher in dental plaque than in saliva, while Pielou was significantly higher in saliva, suggesting greater microbial richness but lower evenness in dental plaque. The Gini-Simpson index did not show a significant difference between dental plaque and saliva ( P = 0.436), although microbial diversity was higher in dental plaque ( Fig. 2 A ) . As tumor size increased (T index), the richness and evenness of microbes in dental plaque significantly increased, and microbial diversity in saliva also significantly increased ( Fig. 2 B ) . Linear regression analysis showed that with increasing T stage, the Shannon-Weaver index ( P = 0.0699, R² = 0.105) and Pielou index ( P = 0.00315, R² = 0.251) in dental plaque increased, indicating an upward trend in microbial diversity and evenness. In saliva, both Shannon-Weaver ( P = 0.0206, R² = 0.166) and Pielou indices ( P = 0.00503, R² = 0.234) increased significantly with increasing T index, indicating significant upregulation in microbial diversity and evenness ( Fig. 2 C ) . Subsequently, we conducted dimensionality reduction analysis of the microbial community composition across different groups. Principal Coordinates Analysis (PCoA) revealed significant differences in microbial community composition between saliva and dental plaque ( Fig. 2 D ) . The Adonis test (R = 0.102, P = 0.006) further confirmed that the differences between the two ecological niches were greater than within each niche (Figure S3) . Additionally, significant differences were observed in microbial communities among different stages of differentiation in both saliva and dental plaque. Specifically, significant variations were noted during the poorly differentiated phase in both saliva and dental plaque compared to other stages ( Fig. 2 D ) . 3.3 Identification of Key Species and Community Stability Differences To examine species distribution within saliva and dental plaque, as well as changes and specificity patterns across different differentiation periods, the specificity and occupancy of each species were calculated. These distributions were then plotted on a specificity-occupancy (SPEC-OCUS) graph, revealing significant variability in microbial occupancy in both saliva and dental plaque. Specialist species for each habitat and differentiation period were identified by selecting those with specificity and occupancy values ≥ 0.7, indicating their particular association with a habitat and presence across most differentiation stages within that habitat. The results indicated differences in species distribution and specialization rates between saliva and dental plaque, with dental plaque showing a higher rate of species specialization (evidenced by more points within the dashed box in the SPEC-OCUS plot) (Figure S4) . Among all species (Total: 4,789), 89 specialist species were identified in dental plaque, originating from Candidatus saccharibacteria , Spirochaetes , Actinobacteria , Firmicutes , Fusobacteria , Bacteroidetes , and Proteobacteria . In contrast, only 10 specialist species were identified in saliva, originating from Actinobacteria , Firmicutes , and Uroviricota ( Fig. 3 A ) . Additionally, the specialization rates varied across different differentiation periods. In dental plaque, the specialization rate of microbes increased as the level of differentiation decreased. Conversely, in saliva, the specialization rate decreased as the level of differentiation decreased ( Fig. 3 B ) . Next, the stability differences of microbial communities in saliva and dental plaque were compared. The results indicated that microbial stability in saliva was higher than in dental plaque (Wilcox test, P < 0.05). Furthermore, with an increase in the T index, microbial stability in both dental plaque and saliva increased, with significant differences observed among the T1, T2, and T3 groups (Wilcox test, P < 0.01). In saliva, a similar trend was observed, with significant differences noted among the T2, T3, and T4 groups (Wilcox test, P < 0.05) ( Fig. 3 C ) . 3.4 Differential Composition of Genes and Virulence Factors To further explore the differences in saliva and dental plaque during various differentiation phases, an analysis of the composition of genes and virulence factors across different groups was conducted using the KEGG and VFDB databases. The Non-metric Multidimensional Scaling (NMDS) analysis effectively simulated the actual composition of genes in different groups (non-metric fit, R² = 1, Stress = 0.02) (Figure S5) . The confidence ellipses of dental plaque and saliva samples from patients with oral squamous cell carcinoma could be separated, indicating a difference in gene and virulence factor composition ( P < 0.001) ( Fig. 4 A ) . Based on the KEGG database, a total of 6,789 genes were obtained. A veen diagram displayed the distribution of genes in saliva and dental plaque, with 6,681 genes common to both, 72 genes unique to saliva, and 36 unique to dental plaque. UpsetR diagrams and circular charts illustrated the gene composition of microorganisms in saliva and dental plaque at different differentiation phases. A total of 5,434 genes were co-expressed across the six groups. There were 11 unique genes identified in DPI and 38 in SI, while DPI-II had 14 unique genes and SI-II had 9. In the low differentiation phase, no unique genes were found in dental plaque, and only one unique gene was present in saliva. As the degree of differentiation decreased, the number of unique genes in both dental plaque and saliva declined ( Fig. 4 B ) . Next, the composition of virulence factors between saliva and dental plaque were compared, revealing a certain degree of similarity (Figure S6) . However, variations were observed across different differentiation stages ( Fig. 4 C ) . Among all virulence factors, VFG007914 (Mycobacterium vanbaalenii PYR-1), VFG043093 (Escherichia coli O157:H7 str. EDL933), and VFG002176 (Enterococcus faecalis str. MMH594) exhibited the highest abundances. Notably, VFG007914 and VFG002176 were more abundant in poorly differentiated stages compared to well-differentiated and moderately well-differentiated stages. Additionally, volcano plots were used to depict the differences in various genes and virulence factors between saliva and dental plaque. After excluding genes with low abundance in most samples, 370 genes and 362 virulence factors were significantly upregulated, while 241 genes and 222 virulence factors were significantly downregulated in dental plaque. Functional pathway analysis of all genes, using the Wilcoxon test, revealed significant differences ( P < 0.05) in eight pathways between groups. For different differentiation stages, these eight pathways showed variability, with Cellular Processes displaying significant differences in six groups (Kruskal-Wallis test, P = 0.011). 3.5 Screening of Differential Virulence Factors and Interactions Among Pathogenic Bacteria Focusing on the differential virulence factors identified from the aforementioned genes, we particularly examined the variations in virulence factors in saliva and dental plaque across different stages of differentiation. Among the top 300 virulence factors ranked by relative abundance, pairwise comparisons between groups were conducted, resulting in the selection of 17 differential virulence factors ( P 1). Notably, VFG030576 ( Mycobacterium vanbaalenii PYR-1 ), VFG044303 (Proteus mirabilis HI4320), VFG032250 ( Listeria monocytogenes J0161 ), VFG013178 (Haemophilus somnus 2336 ), and VFG013263 (Haemophilus ducreyi 35000HP ) were significantly enriched in dental plaque (Figure S7) . For dental plaque, five virulence factors, VFG051600, VFG044075, VFG051600, VFG044075, and VFG031404, were significantly enriched in the poorly differentiated group among the top 1000 virulence factors. In saliva, VFG012091 was significantly enriched in the SI-II stage compared to SI. Comparing SI and SI-II, a total of 19 virulence factors, including VFG005341, VFG049083, and VFG049114, were found to be significantly increased in abundance in stage SIII ( Fig. 5 A ) . Next, we utilized a Sankey diagram to analyze the functional pathways involving the selected differential virulence factors and traced their pathogenic sources. We focused on several bacterial species with high abundance at the species level, namely Escherichia coli , Streptococcus pyogenes , Burkholderia mallei , Burkholderia pseudomallei , and Klebsiella pneumoniae . It was found that VFG005341, VFG043118, VFG040923, VFG012509, VFG049114, and VFG049083 were significantly enriched in the SIII group. The Sankey diagram illustrated that the ATP-binding cassette transporter secreted by Escherichia coli CFT073 is involved in the intracellular iron ion absorption and metabolism process. VFG005341, originating from Streptococcus pyogenes M1 GAS, involves GAPDH, which plays a role in bacterial adhesion to host cells. Burkholderia pseudomallei K96243 secretes GspG, a major pseudopilin protein involved in the bacterial Type II secretion system (T2SS). Additionally, the DNA-binding transcriptional activator AllS produced by Klebsiella pneumoniae subsp. pneumoniae NTUH-K2044 is associated with allantoin utilization. Furthermore, Klebsiella pneumoniae subsp. pneumoniae MGH 78578 utilizes lipopolysaccharide (LPS) to play a significant role in the immune system ( Fig. 5 B ) . Finally, we conducted correlation analysis to understand the interactions between these pathogenic bacteria carrying differential virulence factors and Porphyromonas gingivalis and Fusobacterium nucleatum ( Fig. 5 C ) . Apart from Klebsiella pneumoniae, Burkholderia pseudomallei, and Escherichia coli, most pathogenic bacteria exhibit a significant positive correlation, demonstrating a synergistic effect. A significant positive correlation was observed between Porphyromonas gingivalis and Streptococcus pyogenes . Likewise, a significant positive correlation was found between Porphyromonas gingivalis and Fusobacterium nucleatum . These interactions could be related to the promotion of tumorigenesis. 4 Discussion Our study identified significant differences in the composition, richness, and evenness of the microbiota in saliva and dental plaque from patients with OSCC. Notably, the microbiota composition in both saliva and dental plaque varied according to the degree of tumor differentiation. A significant correlation was observed between microbial diversity and evenness in both saliva and dental plaque with the T stage of the tumor. As the T index increased, the richness and evenness of microorganisms in both saliva and dental plaque also significantly increased. Regarding the stability of microbial community structures, saliva displayed notably higher stability than dental plaque, and this stability increased with advancing tumor T stages. Additionally, from a genomic perspective, the gene composition of saliva and dental plaque differed at various stages of differentiation. A set of differential genes and virulence factors were identified, and further analysis was conducted on differential virulence factors across different stages of differentiation. Functional pathways involving high-abundance pathogenic bacteria in the oral cavity of OSCC patients were elucidated, and potential interactions among these pathogenic bacteria were analyzed. OSCC and its interaction with the oral microbiome have garnered increasing attention. In this study, we identified site-specific microbial ecotypes in OSCC patients (saliva vs. dental plaque) through metagenomic sequencing [ 40 ]. Furthermore, variations in oral microbial composition were observed across different pathological differentiation levels, suggesting a potential association between the composition and abundance of oral microbial communities and OSCC development [ 9 ]. These changes may be influenced by alterations in the tumor microenvironment, such as pH, oxygen availability, and nutritional status, which likely vary with tumor differentiation levels and affect microbial suitability and abundance [ 41 ]. Additionally, tumors of varying differentiation levels may provoke different degrees of host immune responses; certain microbes might evade or modulate host immune surveillance, thereby altering microbial community composition [ 42 ]. The degree of tumor differentiation correlates with its malignancy. Changes in gene expression and metabolic activity of tumor cells may impact their interactions with microbes, including nutrient supply and metabolic waste production [ 43 , 44 ]. At the species level, Haemophilus parainfluenzae , Porphyromonas gingivalis , Fusobacterium nucleatum , Streptococcus gordonii , and Capnocytophaga gingivalis were enriched in dental plaque, with higher relative abundance observed in low differentiation stages. Numerous studies suggest these bacteria are closely associated with the occurrence and progression of oral cancers; previous research has shown that Fusobacterium nucleatum , Porphyromonas gingivalis , and Haemophilus parainfluenzae increase as cancer progression [ 45 , 46 ]. Fusobacterium nucleatum , in particular, promotes tumor development through interactions with host cells; its high abundance in poorly differentiated tumors may relate to tumor invasiveness and metastasis [ 47 ]. Furthermore, Fusobacterium nucleatum enhances cancer invasiveness, survival rates, and epithelial-mesenchymal transition (EMT) within the oral tumor microenvironment [ 48 ]. Porphyromonas gingivalis ' outer membrane LPS induces pro-inflammatory cytokine production, promoting cancer development and progression [ 49 ]. It also increases PI3K/Akt signaling for epithelial cell survival and proliferation [ 50 ]. Capnocytophaga gingivalis may alsoCapnocytophaga gingivalis may also promote invasion and metastasis of OSCC by EMT [ 51 ]. The abundance and enrichment of microbes in tumor microenvironments likely align with their specific ecological niches and metabolic activities during differentiation stages. Thus, understanding microbe-host interactions is crucial for uncovering OSCC pathogenic mechanisms and developing new therapies. In our study, significant differences were revealed in microbial community composition between dental plaque and saliva, with higher microbial richness observed in dental plaque and greater evenness in saliva. Dental plaque forms a complex biofilm composed of bacteria, extracellular substances, and food residues on tooth surfaces, providing a habitat that potentially supports a greater diversity of microbial colonization [ 52 ]. In contrast, saliva is a relatively simpler and more fluid environment with a more balanced species proportion. Additionally, microbial interactions within dental plaque, including the mutual use of metabolic by-products, likely contribute to its higher microbial richness compared to saliva [ 53 ].We noted a marked rise in microbial diversity and evenness in dental plaque and saliva as the tumor T stage advanced, especially at T4 stage, suggesting intricate interactions among tumor cells, host immune responses, and microbial community structures [ 47 ]. In our investigation of microbial distribution in saliva and dental plaque, we observed significant differences in the specificity and occupancy rates of microorganisms between these two niches. Through SPEC-OCUS plot, we identified specialist species, with dental plaque exhibiting a higher rate of specialization compared to saliva. Dental plaque forms on tooth surfaces, creating a distinct environment that includes the hard surface of teeth and the gingival crevice microenvironment. This niche likely fosters the formation and maintenance of specialist species as they adapt to and exploit the specific resources available [ 54 ]. Microorganism interactions and their interactions with oral cells, along with niche specificity, likely promote the formation and maintenance of specialist species in dental plaque. Notably, the specialization rate of microorganisms in dental plaque increases as the differentiation degree of OSCC decreases. Poorly differentiated tumors create a challenging microenvironment with hypoxia, high acidity, and limited nutrients, supporting the growth of species adapted to these extreme conditions [ 55 ]. In contrast, the specialization rate of microorganisms in saliva is lower and further decreases with the reduced differentiation of OSCC. This could be attributed to saliva being a relatively "open" system, where microbial communities are more susceptible to host physiological changes and external factors. Interestingly, the stability of the salivary microbial community was higher than that of dental plaque, and microbial community stability significantly increased with the progression of tumor T stages in both niches. This suggests a complex relationship between oral microbial communities and tumor progression. Saliva contains various antimicrobial substances and immunoglobulins that can inhibit the growth of specific microorganisms, maintaining community balance and stability [ 56 ]. Additionally, immune cells and factors in saliva may play a positive role in microbial community stability by reducing microbial variation and instability through effective immune surveillance [ 57 ]. Lastly, the differentiation degree of OSCC is closely related to changes in the stability of oral microbial communities. With the increase in tumor T stages, microbial communities may transition from lower to higher stability, possibly reflecting the increased selective pressures (such as hypoxia, nutrient deficiency, and immune attacks) imposed by the tumor microenvironment. However, the exact mechanisms remain unclear [ 58 ]. Understanding these dynamics is crucial for elucidating the role of microbial communities in OSCC development and could provide valuable insights for new diagnostic and therapeutic strategies. Genetically, significant differences in functional genes and virulence factors exist between saliva and dental plaque. While many genes are common to both, the presence of unique genes highlights the distinct nature of each niche. The decrease in unique genes with reduced tumor differentiation may indicate increased microbial community homogeneity in poorly differentiated tumors, reflecting adaptive changes to the aggressive tumor microenvironment [ 59 ].The relative abundance of Cellular Processes varies significantly across different groups, likely due to fundamental biological activities such as the cell cycle, cell death, and cell signaling. These processes are crucial for maintaining the dynamic balance of oral microbial communities. The observed differences in Cellular Processes between saliva and dental plaque at various stages of tumor differentiation may result from changes in the tumor microenvironment, prompting microbial communities to adapt their metabolic pathways and cell signaling to fluctuations in nutrients and oxygen associated with tumor progression. Furthermore, the host's immune response may further influence the composition of microbial communities, thereby affecting the expression patterns of Cellular Processes [ 60 ]. Further analysis of virulence factors reveals stage-specific distribution in the oral cavities of oral cancer patients. Specifically, virulence factors significantly enriched in poorly differentiated stages may indicate that certain virulence factors play crucial roles in promoting disease progression or influencing host responses during the early stages of the disease. For instance, VFG005341, derived from Streptococcus pyogenes M1 GAS, produces GAPDH, which participates in bacterial adhesion to host cells, potentially impacting disease invasiveness and transmissibility [ 61 ]. Poorly differentiated cancer cells usually exhibit higher metabolic activity and invasiveness; they may rely on the multifunctionality of GAPDH to support their rapid metabolic demands and enhanced invasiveness. GAPDH’s role in signal transduction might be activated in poorly differentiated cancer cells, promoting tumor progression and immune evasion [ 62 ]. Additionally, considering that poorly differentiated cancer cells may exist in more challenging microenvironments, GAPDH might help them adapt and survive under nutrient-deprived and hypoxic conditions. Thus, the high activity of GAPDH in poorly differentiated cancer cells could reflect its multifunctionality and adaptability in tumor development, providing a potential therapeutic target for interventions aimed at this pathway. The primary pseudopilin protein GspG of the Type II secretion system (T2SS) is more abundant in the saliva of poorly differentiated oral cancer patients compared to those with well-differentiated tumors. This could be related to the biological characteristics of poorly differentiated cancer cells, which typically have higher proliferation rates and invasiveness, possibly requiring more GspG to support their rapid metabolic needs and enhanced invasive capacity [ 63 ]. As a key component of T2SS, GspG may participate in the secretion of virulence factors that aid bacterial survival and dissemination within the host, potentially influencing tumor cell behavior. The high abundance of AllS in the saliva of poorly differentiated oral cancer cells may indicate the importance of this transcriptional activator in tumor cell metabolic reprogramming. Given the rapid proliferation and metabolic demands of poorly differentiated cells, AllS may activate the allantoin utilization pathway to provide essential nitrogen and carbon sources, supporting cell growth and survival [ 64 ]. However, no direct literature currently links AllS with cancer differentiation levels. Future research should involve laboratory studies and clinical sample analyses to determine the role and expression patterns of AllS in cancer cells with varying degrees of differentiation. Recent studies have indicated that bacteria harboring virulence factors, such as Porphyromonas gingivalis , contribute to tumor diversity and size, promoting tumor progression [ 65 ]. The positive correlations between Porphyromonas gingivalis , Streptococcus pyogenes , and Fusobacterium nucleatum may underscore their shared mechanisms in facilitating tumorigenesis [ 66 ]. These microbial interactions likely enhance inflammation, stimulate cell proliferation, or suppress immune surveillance, collectively driving oral cancer advancement. Understanding these interactions is crucial for developing targeted therapeutic strategies against the oral cancer microbiome. 5 Conclusion This study, through the collection of saliva and dental plaque samples from patients with oral cancer at various stages of differentiation and subsequent metagenomic sequencing, has revealed significant differences in the composition of saliva and dental plaque microbiota across different differentiation periods. It also observed changes in microbial diversity, evenness, and stability with tumor T staging, aiding in understanding the link between microbial community structure changes and disease progression. Furthermore, the study highlighted significant differences in virulence factors across different stages of differentiation, which may indicate their potential role in disease development, offering potential targets for future diagnostics and therapeutics. To improve research accuracy and clinical applicability, future studies must address limitations in controlling environmental factors, conducting functional analysis, and monitoring disease dynamics. It is crucial to expand research into microbial interactions with the host immune system and tumor cells. A comprehensive approach is necessary to better understand the intricate relationship between microbial communities and oral squamous cell carcinoma, facilitating more precise medical interventions for patients. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions YL and ZY designed the experiments. HLZ and MZ carried out experiments. LRY, HLZ and AH participated in the collection of samples. MZ analyzed data prepared the figures and drafted the manuscript. JH, YL and ZY participation in discussion and revised the manuscript. All authors contributed to this manuscript, read, and approved the final manuscript. Funding This work was funded by the National Natural Science Foundation of China (32170071 and 82273466), the Hunan Provincial Science and Technology Department (2023ZJ1120) and the Natural Science Foundation of Hunan Province (2024JJ2039 and 2024JJ8117). Ethics statement The studies involving human participants were reviewed and approved by ethics committee of Hunan Cancer Hospital (2023-KYJJ-025) following the ethical guidelines of the Declaration of Helsinki (No. 038, 2015). The patients/participants provided their written informed consent to participate in this study. Data availability statement Data will be made available on request. References Saikia PJ et al (2023) The emerging role of oral microbiota in oral cancer initiation, progression and stemness. 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Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 27 May, 2025 Read the published version in Clinical Oral Investigations → Version 1 posted Editorial decision: Revision requested 01 May, 2025 Reviews received at journal 23 Mar, 2025 Reviews received at journal 12 Mar, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers invited by journal 10 Mar, 2025 Editor assigned by journal 05 Mar, 2025 Submission checks completed at journal 05 Mar, 2025 First submitted to journal 03 Mar, 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-6143003","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":424321779,"identity":"0e8f5b53-afb9-4757-b123-f1fdca4783c1","order_by":0,"name":"Man Zhang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Man","middleName":"","lastName":"Zhang","suffix":""},{"id":424321783,"identity":"ebca10af-a69c-4b78-8733-25786c458416","order_by":1,"name":"Hailin Zhang#","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Hailin","middleName":"","lastName":"Zhang#","suffix":""},{"id":424321785,"identity":"6ae43681-06cb-4e75-be24-742010e4e5ae","order_by":2,"name":"Ao Hong","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ao","middleName":"","lastName":"Hong","suffix":""},{"id":424321786,"identity":"82847994-01a6-4928-b07e-71495d0538f9","order_by":3,"name":"Jing Huang","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Huang","suffix":""},{"id":424321788,"identity":"f32b2efc-00d4-4ed8-84dc-aab84fe88adc","order_by":4,"name":"Lirong Yang","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Yang","suffix":""},{"id":424321789,"identity":"c8b9039d-f6a5-4d5c-96fe-18ba7bb392e0","order_by":5,"name":"Zheng Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYHACNhAhh8QmUosx6VoSG4jWYnAj/dmDjztq0/v7zxgwfCg7zMA/u4GQloR0w5lnjufOuJFjwDjj3GEGiTsHCGo5Js3bdix3gwSPATNv22EGA4kEQloS26T/th1LN+A/Y8D8lzgtyWzSjG01CQYMOQbMjMRokTzzjE2yt+2A4YwbaQUHe86l80jcIKCF73j6M4mfbXXy/P2HNz74UWYtxz+DgBaFA2DqMJgEsXnwqwcC+QYwVUdQ4SgYBaNgFIxgAADK3kRn/4TgyAAAAABJRU5ErkJggg==","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Yu","suffix":""},{"id":424321791,"identity":"12838541-1033-44d9-8d16-551da75586d6","order_by":6,"name":"Ying Long","email":"","orcid":"","institution":"The Affiliated Cancer Hospital of Xiangya School of Medicine, Hunan Cancer Hospital, Central South University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Long","suffix":""}],"badges":[],"createdAt":"2025-03-03 06:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6143003/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6143003/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00784-025-06391-5","type":"published","date":"2025-05-27T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78238216,"identity":"f189e967-0238-4e15-805a-7f6758a221b1","added_by":"auto","created_at":"2025-03-11 08:43:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3636146,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial composition of saliva and dental plaque in patients with OSCC at different stages of differentiation.\u003cstrong\u003e (A)\u003c/strong\u003e Pie charts illustrate the species composition at the phylum level in saliva and dental plaque of OSCC patients with different degrees of differentiation. Different colors represent different phyla, and the size of each pie chart reflects the abundance of the respective species.\u003cstrong\u003e (B)\u003c/strong\u003e Donut chart represents the species composition at the species level in saliva and dental plaque of OSCC patients.\u003cstrong\u003e (C) \u003c/strong\u003eAt the species level, the species composition in saliva and dental plaque varies across different stages of differentiation. \u003cstrong\u003e(D)\u003c/strong\u003e Wilcoxon test (for two groups) and Kruskal-Wallis rank sum test (for multiple groups) show the differences in relative abundance of important species between different groups (* \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01,*** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/9f5d298456ccef84571a05ae.png"},{"id":78236984,"identity":"7d0312ea-0d67-478b-9ad7-b2dae38885e5","added_by":"auto","created_at":"2025-03-11 08:35:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3862748,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between microbial diversity and clinical T staging. \u003cstrong\u003e(A)\u003c/strong\u003e The Observed, Chao1, Pielou, and Richness indices for dental plaque and saliva microbiota are shown (Wilcoxon test, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01). \u003cstrong\u003e(B) \u003c/strong\u003eChanges in the ACE and Pielou indices of dental plaque microbiota across different T stages and variations in the Shannon-Weaver and Gini-Simpson indices of saliva microbiota (T test, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) are illustrated.\u003cstrong\u003e (C)\u003c/strong\u003e Linear regression analysis of alpha diversity indices in saliva and dental plaque and clinical T indicators. \u003cstrong\u003e(D) \u003c/strong\u003eBeta diversity of saliva and dental plaque is presented, with PCoA analysis indicating significant differences (R = 0.103, \u003cem\u003eP\u003c/em\u003e = 0.006), where 'a' and 'b' denote different levels of significance. \u003cstrong\u003e(E) \u003c/strong\u003ePCoA shows the differences in community composition between saliva and dental plaque in cancer patients at various differentiation stages, with 'a' and 'b' indicating different significance levels.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/167a8cbe898b07c447d264ab.png"},{"id":78241779,"identity":"ed515100-d34d-44cf-bfd4-e1bb62f6a75c","added_by":"auto","created_at":"2025-03-11 09:07:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7066684,"visible":true,"origin":"","legend":"\u003cp\u003eCore species selection and changes in microbial stability in saliva and dental plaque across different T stages. \u003cstrong\u003e(A)\u003c/strong\u003e Composition of specialist species at the phylum level in saliva and dental plaque. \u003cstrong\u003e(B)\u003c/strong\u003e SPEC-OCCU plots for all species across different differentiation stages of oral cancer. The X-axis represents occupancy, and the Y-axis represents specificity. Species with both occupancy and specificity values greater than 0.7 were identified as specialist species, as indicated by the dashed lines. \u003cstrong\u003e(C) \u003c/strong\u003eDifferences in microbial stability between saliva and dental plaque, and variations in microbial stability across different environments with respect to T stages.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/f808f495b7a06aab055693a2.png"},{"id":78236976,"identity":"ccd3f92e-012b-4361-8196-75d8f635040e","added_by":"auto","created_at":"2025-03-11 08:35:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4236394,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of microbial gene composition in different groups. \u003cstrong\u003e(A)\u003c/strong\u003e Using the KEGG database, NMDS plots illustrate the genomic composition characteristics of different groups, with samples color-coded by group.\u003cstrong\u003e (B)\u003c/strong\u003e UpsetR diagrams depict the gene composition in saliva, dental plaque, and various differentiation stages, showcasing the number of unique genes in pie and donut charts.\u003cstrong\u003e (C) \u003c/strong\u003eComposition of virulence factors across six groups. \u003cstrong\u003e(D) \u003c/strong\u003eVolcano plots show differences in functional genes and virulence factors between saliva and dental plaque groups, with 'up' indicating significant upregulation in dental plaque and 'down' indicating significant downregulation (|log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 2, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). \u003cstrong\u003e(E)\u003c/strong\u003e Differential abundance testing of the top eight ranked functional pathways among different groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/26400c1b54240201a95f8426.png"},{"id":78236979,"identity":"62eeddf3-10c3-42d0-8cae-4e67a6623c3e","added_by":"auto","created_at":"2025-03-11 08:35:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":7485626,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of differential virulence genes among different groups and interactions among pathogenic bacteria.\u003cstrong\u003e (A) \u003c/strong\u003eDifferential analysis of virulence factors ranked in the top 1000 by relative abundance. Virulence factors with |log\u003csub\u003e2\u003c/sub\u003e FoldChange| \u0026gt; 1 and \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 were considered to have significant differences in abundance between groups. \u003cstrong\u003e(B)\u003c/strong\u003e Source analysis of differential virulence factors. The first column lists the selected virulence factors, the second column indicates the functional pathways these factors are involved in, and the third column displays the species origins of these virulence factors. \u003cstrong\u003e(C)\u003c/strong\u003e Correlation analysis among pathogenic bacteria carrying virulence factors.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/18b8c110e7354cdccd63bcb6.png"},{"id":83783021,"identity":"9965c35b-d676-41be-9489-18f1b08242e3","added_by":"auto","created_at":"2025-06-02 16:10:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25722846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/b00e9b64-cccf-4db8-a31d-99b40a8c92b9.pdf"},{"id":78236964,"identity":"33ae8688-bf95-40b3-84b5-061cac037a02","added_by":"auto","created_at":"2025-03-11 08:35:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1938321,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6143003/v1/ffe67edf584d2f442b2b6fb7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Changes of Dental Plaque and Saliva Microbiota in OSCC Progression","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOral cancer is one of the most common head and neck cancer (HNC) types in the world, and 90% of these are oral squamous cell carcinoma (OSCC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite enhancements in surgical interventions, radiotherapy, and chemotherapeutic strategies, a concerning upward trend in OSCC incidence is observed on a global scale [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Projections from the Global Cancer Observatory (GCO) estimate a near 40% increase in OSCC incidence by 2040, paralleled by a rise in mortality rates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Smoking, drinking, and chewing betel are the main risk factors for oral cancer [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Additional potential risk factors may encompass viral and fungal infections, as well as chronic periodontitis [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nonetheless, approximately 15% of oral squamous carcinomas cannot be accounted for by these factors alone, necessitating further exploration of other potential risk factors.\u003c/p\u003e \u003cp\u003eThe intricate interplay between oral microbiota and oral health has become an increasingly prominent area of research [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Imbalances in the microbiota may lead to chronic inflammation, creating favorable conditions for the development and progression of oral cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. According to the World Health Organization (WHO), OSCC differentiation is usually recorded as histological grade and is classified into three types: well-differentiated (WD, grade I), moderately differentiated (MD, grade II), and poorly differentiated (PD, grade III) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Well-differentiated tumors tend to grow more slowly and are less aggressive, whereas poorly differentiated tumors often exhibit rapid growth and a high propensity for metastasis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. At present, the treatment strategy and prognosis assessment of OSCC are mainly based on common clinical indicators, and tumor differentiation is an indispensable indicator for OSCC management [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Despite the increasing amount of research, there are still substantial gaps in our understanding of the relationship between tumor differentiation and the oral microbiome. Addressing these gaps is crucial as it may reveal new biomarkers for early detection and novel targets for therapeutic intervention, potentially improving patient outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, there is a dearth of longitudinal studies on the bacterial communities and genomic composition in the oral cavity during the progression of OSCC, and the comprehensive profile of bacteria remains unclear.\u003c/p\u003e \u003cp\u003eThe human oral cavity is a complex ecosystem teeming with diverse microbial communities, playing critical roles in health and disease [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Among these microbial habitats, saliva and dental plaque represent two distinct ecological niches that harbor unique microbial populations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Our previous study found that oral cancer patients exhibit significant differences in microbial composition, gene composition, and function between saliva and dental plaque [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Studies have demonstrated that functional genes within these microbial communities not only facilitate essential metabolic processes but also modulate the immune response, influencing oral disease states [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Additionally, accumulating evidence underscores the pivotal role of bacteria harboring virulence factors in the etiology and progression of oral cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. One of the key bacterial species implicated in OSCC is \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, a known periodontal pathogen. \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e produces a variety of virulence factors to trigger an infection, including fimbriae (FimA), cysteine proteases (gingipains), lipopolysaccharide (LPS), and nucleoside diphosphate kinase (NDK), which facilitate its adherence to and invasion of host tissues [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Studies have demonstrated that the positive rate of \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e was higher in patients with TNM (tumor, nodes, and metastasis) stage III-IV, poorly differentiated tissue, and lymph node metastasis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Infection caused by \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e may be regarded as a potential risk factor for oral cancer [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Multi-omics research unravels intricate functional genes and virulence factors, leading to breakthroughs in understanding disease mechanisms and advancing drug therapy. Despite numerous studies highlighting the significant role of pathogenic bacteria in the development of oral cancer, there is still a relative lack of comprehensive analysis regarding the differences and associations between functional genes and independent factors. This analytical gap hinders our comprehensive understanding of how specific pathogenic bacteria and their associated genes specifically impact the occurrence and progression of OSCC.\u003c/p\u003e \u003cp\u003eThis study utilized metagenomic sequencing technology to analyze 64 samples of saliva and dental plaque from patients with OSCC at various stages of differentiation. It investigated the changes in microbial communities and genomes during the development of OSCC, evaluated the association between oral microbiota diversity, richness, and cancer T staging, and identified specific microbial species and virulence factors associated with disease progression. The study provides evidence of alterations in the composition of oral bacterial communities and genomes during the progression of OSCC, offering potential novel biomarkers and intervention targets for the diagnosis and treatment of oral cancer.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Design and Sample Collection\u003c/h2\u003e \u003cp\u003eTo elucidate the characteristics of microbial composition, functional genes, and virulence factors in saliva and dental plaque of patients with different degrees of OSCC, a total of 32 saliva samples and 32 dental plaque samples were collected from patients hospitalized at Hunan Cancer Hospital in China. The patients were diagnosed with oral squamous cell carcinoma based on pathological examination and had complete medical records. The clinical differentiation stage of the patients was evaluated using the clinical TNM index and the pathological TNM index. The 64 samples were divided into six groups: dental plaque high differentiation group (DPI) and saliva high differentiation group (SI); the group with dental plaque between low differentiation and moderate differentiation was defined as DPI-II, corresponding to the saliva group SI-II; dental plaque low differentiation group (DPIII) and saliva low differentiation group (SIII). All participants in this sample collection provided informed consent and had complete clinical and pathological data. The inclusion criteria for participants were as follows: (1) No other malignant tumors were found in the systemic examination, excluding distant metastasis; (2) Avoid smoking, drinking, and eating at least 30 minutes before sample collection; (3) No bleeding occurred during sample collection; (4) No immunosuppressive drugs were taken within the past 6 months; (5) No severe periodontal disease, severe dental caries, or oral mucosal diseases within the past 3 months, no other systemic diseases, no history of oral surgery, and no history of antibiotic use; (6) Patients without oral infectious diseases, bleeding history, or a history of other malignant tumors. According to the Helsinki Declaration, the cases included in this study were collected and approved by the Ethics Committee of Hunan Cancer Hospital (Ethics Approval Number: KYJJ-2023-025). All participating patients were informed about the sample collection, experimental purpose, and voluntarily signed an informed consent form.\u003c/p\u003e \u003cp\u003eBefore sample collection, participants were disinfected and taken to a sterile laboratory. The area around the mouths of the participants was disinfected with alcohol. Sterile distilled water was used for mouth rinsing to remove any residual food debris. Then, a swab was gently rubbed back and forth three times on the upper and lower incisors, first molars, first premolars, and placed in a sterile collection tube. Subsequently, 1 mL of sterile PBS buffer was added to the sterile collection tube, ensuring that the swab was completely immersed in the elution buffer for complete dissolution of the sample. The sample tube was then centrifuged at 12,000 rpm for 15 minutes at 21\u0026deg;C (Glanlab, Changsha, China). This step was repeated three times to collect enough eluted buffer. Saliva was collected in sterile tubes with a minimum volume of 1 mL. After sample collection, the samples were immediately stored at -80\u0026deg;C (Eppendorf, Changsha, China) and sent for metagenomic sequencing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Total DNA extraction and sequencing\u003c/h2\u003e \u003cp\u003eBacterial genomic DNA was extracted from saliva and dental plaque samples using the E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, USA). The extracted DNA was eluted in elution buffer and stored at -20\u0026deg;C. DNA yield was quantified using a full-spectrum ultraviolet spectrophotometer (Amersham Biosciences, USA), and DNA purity was assessed by agarose gel electrophoresis for both metagenomic DNA and PCR products. Subsequently, the high-quality genomic DNA was randomly fragmented into 200\u0026ndash;500 bp fragments using an ultrasonicator, and the fragment size distribution was confirmed by agarose gel electrophoresis (Bio-Rad, USA). The resulting fragments were then recovered using the QIAquick Gel Extraction Kit. Following successful library construction, the products were purified and sequenced on the Illumina NovaSeq 6000 PE250 platform (Shanghai Biozeron Biotech. Co., Ltd., China). Illumina PE libraries were constructed, and the obtained sequencing data underwent quality control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Quality control and processing of metagenomic sequencing data\u003c/h2\u003e \u003cp\u003eThe sequencing raw data contained sequences with short lengths, excessively ambiguous bases, and inserted adapters, which are low-quality sequences likely to interfere with subsequent analyses. Therefore, to ensure data accuracy, we performed quality control on the raw sequences obtained after sequencing. Specifically, using Fastp (version 0.23.2), we filtered out low-quality reads defined by an average quality score\u0026thinsp;\u0026lt;\u0026thinsp;15 and lengths shorter than 15 bases. Additionally, we removed contaminating sequences from potential human host interference [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For removing duplicate sequences, especially PCR duplicates or those caused by amplification, we utilized FastUniq (version 1.1.0) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Sequences were aligned against the human genome database (hg38) using Bowtie2 (version 2.5.1), and sequences originating from humans were filtered out [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAfter completing the quality control process, Kraken2 (version 2.1.2) was employed for species annotation of the genetic sequences, resulting in taxids (unique identifiers for NCBI taxonomic units) assigned to each gene [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Subsequently, the names and classification information associated with these taxids were translated into corresponding species information, spanning from kingdom to species level. MEGAHIT (version 1.2.9) was then used to reconstruct high-quality reads from the quality-controlled data [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Following assembly, Prodigal (version 2.6.3) predicted genes from the contigs generated by MEGAHIT [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. EggNOG-mapper (version 2.0.1) was used to accomplish the functional annotation of the gene catalog [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Furthermore, CD-HIT (version 4.7) was employed to cluster and remove redundant genes with a global sequence similarity threshold of 90% [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Salmon (version 0.13.1) was used to determine the relative abundance of genes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Additionally, the set B database from the VFDB (Virulence Factors of Pathogenic Bacteria Database), which includes predicted and experimentally confirmed virulence genes, was used to align virulence factors using DIAMOND (version 2.1.8) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis and visualization\u003c/h2\u003e \u003cp\u003eThe data and visualization analyses in this study were conducted using R (v.4.2.3). Microbial community composition analysis was performed using the microeco package (version 1.1.0) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The Wilcoxon test was used to assess differences in the relative abundance of microbial or functional genes between two groups, while the Kruskal-Wallis test was employed for comparisons among multiple groups. Visualization of these differences was carried out using ggboxplot from the ggpubr package. A significance level of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. To investigate the variations in microbial composition of saliva, dental plaque, and at different differentiation stages, the MicrobiotaProcess package (version 1.12.4) was used to compute α-diversity indices of the microbiota [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Alpha diversity metrics, including the ACE, Chao1, Pielou, Shannon-Weaver, Gini-Simpson, and Richness index were calculated to assess microbial community diversity and evenness within the habitat. Linear regression using the lm function was performed to model the relationship between α-diversity indices and the T index [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. To assess similarities between microbial communities within different groups, the Bray-Curtis dissimilarity-based \"vegdist\" function and Principal co-ordinates analysis (PCoA) dimensional reduction analysis was employed to determine β-diversity indices across different groupings. ANOSIM was applied to test the significance of differences between two groups [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Species specificity and occupancy were calculated within different groups, defining Operational Taxonomic Units (OUT) with both indices\u0026thinsp;\u0026ge;\u0026thinsp;0.7 as specialized species for that group. The distribution of microbial species across different differentiation stages was projected onto a specificity-occupancy (SPEC-OCUS) plot [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The AVD index was computed to evaluate the stability of microbial communities within different groups [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a genomic perspective, non-metric multidimensional scaling (NMDS) ordination analysis was performed using the metaMDS function from the vegan package, with a stress value\u0026thinsp;\u0026lt;\u0026thinsp;0.2 indicating a good model fit [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A volcano plot was generated using the DESeq2 package, highlighting functional genes and virulence factors meeting the criteria |log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Additionally, differences in KEGG functional pathways between two and multiple groups were examined using Wilcoxon and Kruskal-Wallis tests. Subsequently, the DESeq2 package was utilized to identify virulence factors with significantly different abundances between groups of varying differentiation levels (log\u003csub\u003e2\u003c/sub\u003eFoldChange\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Sankey diagrams were employed to illustrate the involvement of virulence factors from specific pathogens in differentially enriched functional pathways among groups. Lastly, the Pearson correlation coefficients were computed to assess correlations between selected pathogens, and the correlation matrix was visualized using the corrplot package (version 0.92).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Microbial Composition Characteristics\u003c/h2\u003e \u003cp\u003eIn this study, 64 samples were collected from all eligible subjects with OSCC, comprising 32 dental plaque samples and 32 saliva samples of varying pathological classifications. Metagenomic sequencing was conducted to investigate the microbial composition differences in various ecological niches (saliva and dental plaque) associated with different degrees of OSCC differentiation. At the phylum level, the dominant microorganisms in both saliva and dental plaque included \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and \u003cem\u003eActinobacteria\u003c/em\u003e. \u003cem\u003eFirmicutes\u003c/em\u003e demonstrated a higher relative abundance in saliva compared to dental plaque, but the relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e is higher in dental plaque. As the degree of differentiation in OSCC decreased, the abundance of \u003cem\u003eFirmicutes\u003c/em\u003e in both saliva and dental plaque also decreased, whereas \u003cem\u003eProteobacteria\u003c/em\u003e exhibited an increased abundance in both groups with decreasing differentiation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e).\u003c/b\u003e At the species level, \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e were the most abundant species in both saliva and dental plaque, with these three species showing higher relative abundance in saliva. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. With decreasing differentiation, the abundance of \u003cem\u003eStaphylococcus epidermidis\u003c/em\u003e in both saliva and dental plaque initially increased and then decreased. In dental plaque, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e showed lower relative abundance in DPIII compared to DPI and DPI-II. Across different differentiation stages, \u003cem\u003eStreptococcus mitis\u003c/em\u003e had a higher abundance in saliva than in dental plaque \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e).\u003c/b\u003e Furthermore, differential abundance testing revealed that among the top 10 abundant species, \u003cem\u003eLautropia mirabilis\u003c/em\u003e, \u003cem\u003eCorynebacterium matruchotii\u003c/em\u003e, \u003cem\u003eVeillonella parvula\u003c/em\u003e, and \u003cem\u003eCapnocytophaga sputigena\u003c/em\u003e exhibited significant differences in relative abundance across different differentiation stages (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Kruskal-Walli\u0026rsquo;s test) \u003cb\u003e(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA)\u003c/b\u003e. Additionally, within the top 150 abundant species, significant differences were observed in the relative abundance of \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e, \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, \u003cem\u003eStreptococcus gordonii\u003c/em\u003e, and \u003cem\u003eCapnocytophaga gingivalis\u003c/em\u003e between different groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Wilcoxon test results indicated that these five species were enriched in the dental plaque group. Except for \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, the other four species showed significant differences in relative abundance across different groups. Moreover, in dental plaque, the relative abundance of \u003cem\u003eCapnocytophaga gingivalis, Porphyromonas gingivalis\u003c/em\u003e, and \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e was higher in poorly differentiated stages compared to well-differentiated and moderately differentiated stages \u003cb\u003e(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo better describe the microbial composition characteristics of different groups, we conducted a screening of differential biomarkers. The top 30 species with the highest LDA scores were displayed, and the results of the differential bar plot were consistent with the species composition results. \u003cem\u003eFirmicutes\u003c/em\u003e was found to be more abundant in the SI group, while Capnocytophaga was enriched in the DPIII group. \u003cb\u003e(Figure S2A)\u003c/b\u003e. Furthermore, for the top 100 abundant species, at the phylum level, \u003cem\u003eFirmicutes\u003c/em\u003e were significantly enriched in saliva, while \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, and Actinobacteria were significantly enriched in dental plaque (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, LDA\u0026thinsp;\u0026gt;\u0026thinsp;2) \u003cb\u003e(Figure S2B).\u003c/b\u003e At the genus level, the relative abundance of Streptococcus was significantly higher in saliva compared to dental plaque, while \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eCapnocytophaga\u003c/em\u003e, and \u003cem\u003eLeptotrichia\u003c/em\u003e showed significantly higher abundance in dental plaque compared to saliva (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, LDA\u0026thinsp;\u0026gt;\u0026thinsp;2) \u003cb\u003e(Figure S2C)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Changes in Microbial Diversity\u003c/h2\u003e \u003cp\u003eAlpha diversity is critical for understanding microbial communities. We calculated the Chao1, Richness, Pielou, and Simpson indices to compare the richness, evenness, and diversity of microbial communities in saliva and dental plaque of OSCC patients. The results indicated significant differences between groups for Chao1, Richness, and Pielou (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, Chao1 and Richness were significantly higher in dental plaque than in saliva, while Pielou was significantly higher in saliva, suggesting greater microbial richness but lower evenness in dental plaque. The Gini-Simpson index did not show a significant difference between dental plaque and saliva (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.436), although microbial diversity was higher in dental plaque \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. As tumor size increased (T index), the richness and evenness of microbes in dental plaque significantly increased, and microbial diversity in saliva also significantly increased \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Linear regression analysis showed that with increasing T stage, the Shannon-Weaver index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0699, R\u0026sup2; = 0.105) and Pielou index (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00315, R\u0026sup2; = 0.251) in dental plaque increased, indicating an upward trend in microbial diversity and evenness. In saliva, both Shannon-Weaver (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0206, R\u0026sup2; = 0.166) and Pielou indices (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00503, R\u0026sup2; = 0.234) increased significantly with increasing T index, indicating significant upregulation in microbial diversity and evenness \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Subsequently, we conducted dimensionality reduction analysis of the microbial community composition across different groups. Principal Coordinates Analysis (PCoA) revealed significant differences in microbial community composition between saliva and dental plaque \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. The Adonis test (R\u0026thinsp;=\u0026thinsp;0.102, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) further confirmed that the differences between the two ecological niches were greater than within each niche \u003cb\u003e(Figure S3)\u003c/b\u003e. Additionally, significant differences were observed in microbial communities among different stages of differentiation in both saliva and dental plaque. Specifically, significant variations were noted during the poorly differentiated phase in both saliva and dental plaque compared to other stages \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Identification of Key Species and Community Stability Differences\u003c/h2\u003e \u003cp\u003eTo examine species distribution within saliva and dental plaque, as well as changes and specificity patterns across different differentiation periods, the specificity and occupancy of each species were calculated. These distributions were then plotted on a specificity-occupancy (SPEC-OCUS) graph, revealing significant variability in microbial occupancy in both saliva and dental plaque. Specialist species for each habitat and differentiation period were identified by selecting those with specificity and occupancy values\u0026thinsp;\u0026ge;\u0026thinsp;0.7, indicating their particular association with a habitat and presence across most differentiation stages within that habitat. The results indicated differences in species distribution and specialization rates between saliva and dental plaque, with dental plaque showing a higher rate of species specialization (evidenced by more points within the dashed box in the SPEC-OCUS plot) \u003cb\u003e(Figure S4)\u003c/b\u003e. Among all species (Total: 4,789), 89 specialist species were identified in dental plaque, originating from \u003cem\u003eCandidatus saccharibacteria\u003c/em\u003e, \u003cem\u003eSpirochaetes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eFusobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, and \u003cem\u003eProteobacteria\u003c/em\u003e. In contrast, only 10 specialist species were identified in saliva, originating from \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and \u003cem\u003eUroviricota\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Additionally, the specialization rates varied across different differentiation periods. In dental plaque, the specialization rate of microbes increased as the level of differentiation decreased. Conversely, in saliva, the specialization rate decreased as the level of differentiation decreased \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Next, the stability differences of microbial communities in saliva and dental plaque were compared. The results indicated that microbial stability in saliva was higher than in dental plaque (Wilcox test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Furthermore, with an increase in the T index, microbial stability in both dental plaque and saliva increased, with significant differences observed among the T1, T2, and T3 groups (Wilcox test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). In saliva, a similar trend was observed, with significant differences noted among the T2, T3, and T4 groups (Wilcox test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Differential Composition of Genes and Virulence Factors\u003c/h2\u003e \u003cp\u003eTo further explore the differences in saliva and dental plaque during various differentiation phases, an analysis of the composition of genes and virulence factors across different groups was conducted using the KEGG and VFDB databases. The Non-metric Multidimensional Scaling (NMDS) analysis effectively simulated the actual composition of genes in different groups (non-metric fit, R\u0026sup2; = 1, Stress\u0026thinsp;=\u0026thinsp;0.02) \u003cb\u003e(Figure S5)\u003c/b\u003e. The confidence ellipses of dental plaque and saliva samples from patients with oral squamous cell carcinoma could be separated, indicating a difference in gene and virulence factor composition (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Based on the KEGG database, a total of 6,789 genes were obtained. A veen diagram displayed the distribution of genes in saliva and dental plaque, with 6,681 genes common to both, 72 genes unique to saliva, and 36 unique to dental plaque. UpsetR diagrams and circular charts illustrated the gene composition of microorganisms in saliva and dental plaque at different differentiation phases. A total of 5,434 genes were co-expressed across the six groups. There were 11 unique genes identified in DPI and 38 in SI, while DPI-II had 14 unique genes and SI-II had 9. In the low differentiation phase, no unique genes were found in dental plaque, and only one unique gene was present in saliva. As the degree of differentiation decreased, the number of unique genes in both dental plaque and saliva declined \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eNext, the composition of virulence factors between saliva and dental plaque were compared, revealing a certain degree of similarity \u003cb\u003e(Figure S6)\u003c/b\u003e. However, variations were observed across different differentiation stages \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Among all virulence factors, VFG007914 (Mycobacterium vanbaalenii PYR-1), VFG043093 (Escherichia coli O157:H7 str. EDL933), and VFG002176 (Enterococcus faecalis str. MMH594) exhibited the highest abundances. Notably, VFG007914 and VFG002176 were more abundant in poorly differentiated stages compared to well-differentiated and moderately well-differentiated stages. Additionally, volcano plots were used to depict the differences in various genes and virulence factors between saliva and dental plaque. After excluding genes with low abundance in most samples, 370 genes and 362 virulence factors were significantly upregulated, while 241 genes and 222 virulence factors were significantly downregulated in dental plaque. Functional pathway analysis of all genes, using the Wilcoxon test, revealed significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in eight pathways between groups. For different differentiation stages, these eight pathways showed variability, with Cellular Processes displaying significant differences in six groups (Kruskal-Wallis test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Screening of Differential Virulence Factors and Interactions Among Pathogenic Bacteria\u003c/h2\u003e \u003cp\u003eFocusing on the differential virulence factors identified from the aforementioned genes, we particularly examined the variations in virulence factors in saliva and dental plaque across different stages of differentiation. Among the top 300 virulence factors ranked by relative abundance, pairwise comparisons between groups were conducted, resulting in the selection of 17 differential virulence factors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFoldChange| \u0026gt; 1). Notably, VFG030576 (\u003cem\u003eMycobacterium vanbaalenii PYR-1\u003c/em\u003e), VFG044303 (Proteus mirabilis HI4320), VFG032250 (\u003cem\u003eListeria monocytogenes J0161\u003c/em\u003e), VFG013178 \u003cem\u003e(Haemophilus somnus 2336\u003c/em\u003e), and VFG013263 \u003cem\u003e(Haemophilus ducreyi 35000HP\u003c/em\u003e) were significantly enriched in dental plaque \u003cb\u003e(Figure S7)\u003c/b\u003e. For dental plaque, five virulence factors, VFG051600, VFG044075, VFG051600, VFG044075, and VFG031404, were significantly enriched in the poorly differentiated group among the top 1000 virulence factors. In saliva, VFG012091 was significantly enriched in the SI-II stage compared to SI. Comparing SI and SI-II, a total of 19 virulence factors, including VFG005341, VFG049083, and VFG049114, were found to be significantly increased in abundance in stage SIII \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eNext, we utilized a Sankey diagram to analyze the functional pathways involving the selected differential virulence factors and traced their pathogenic sources. We focused on several bacterial species with high abundance at the species level, namely \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e, \u003cem\u003eBurkholderia mallei\u003c/em\u003e, \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e, and \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e. It was found that VFG005341, VFG043118, VFG040923, VFG012509, VFG049114, and VFG049083 were significantly enriched in the SIII group. The Sankey diagram illustrated that the ATP-binding cassette transporter secreted by \u003cem\u003eEscherichia coli\u003c/em\u003e CFT073 is involved in the intracellular iron ion absorption and metabolism process. VFG005341, originating from \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e M1 GAS, involves GAPDH, which plays a role in bacterial adhesion to host cells. \u003cem\u003eBurkholderia pseudomallei\u003c/em\u003e K96243 secretes GspG, a major pseudopilin protein involved in the bacterial Type II secretion system (T2SS). Additionally, the DNA-binding transcriptional activator AllS produced by \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e NTUH-K2044 is associated with allantoin utilization. Furthermore, \u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e subsp. \u003cem\u003epneumoniae\u003c/em\u003e MGH 78578 utilizes lipopolysaccharide (LPS) to play a significant role in the immune system \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Finally, we conducted correlation analysis to understand the interactions between these pathogenic bacteria carrying differential virulence factors and Porphyromonas gingivalis and Fusobacterium nucleatum \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Apart from Klebsiella pneumoniae, Burkholderia pseudomallei, and Escherichia coli, most pathogenic bacteria exhibit a significant positive correlation, demonstrating a synergistic effect. A significant positive correlation was observed between \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e and \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e. Likewise, a significant positive correlation was found between \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e and \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e. These interactions could be related to the promotion of tumorigenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOur study identified significant differences in the composition, richness, and evenness of the microbiota in saliva and dental plaque from patients with OSCC. Notably, the microbiota composition in both saliva and dental plaque varied according to the degree of tumor differentiation. A significant correlation was observed between microbial diversity and evenness in both saliva and dental plaque with the T stage of the tumor. As the T index increased, the richness and evenness of microorganisms in both saliva and dental plaque also significantly increased. Regarding the stability of microbial community structures, saliva displayed notably higher stability than dental plaque, and this stability increased with advancing tumor T stages. Additionally, from a genomic perspective, the gene composition of saliva and dental plaque differed at various stages of differentiation. A set of differential genes and virulence factors were identified, and further analysis was conducted on differential virulence factors across different stages of differentiation. Functional pathways involving high-abundance pathogenic bacteria in the oral cavity of OSCC patients were elucidated, and potential interactions among these pathogenic bacteria were analyzed.\u003c/p\u003e \u003cp\u003eOSCC and its interaction with the oral microbiome have garnered increasing attention. In this study, we identified site-specific microbial ecotypes in OSCC patients (saliva vs. dental plaque) through metagenomic sequencing [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, variations in oral microbial composition were observed across different pathological differentiation levels, suggesting a potential association between the composition and abundance of oral microbial communities and OSCC development [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These changes may be influenced by alterations in the tumor microenvironment, such as pH, oxygen availability, and nutritional status, which likely vary with tumor differentiation levels and affect microbial suitability and abundance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, tumors of varying differentiation levels may provoke different degrees of host immune responses; certain microbes might evade or modulate host immune surveillance, thereby altering microbial community composition [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The degree of tumor differentiation correlates with its malignancy. Changes in gene expression and metabolic activity of tumor cells may impact their interactions with microbes, including nutrient supply and metabolic waste production [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. At the species level, \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e, \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, \u003cem\u003eStreptococcus gordonii\u003c/em\u003e, and \u003cem\u003eCapnocytophaga gingivalis\u003c/em\u003e were enriched in dental plaque, with higher relative abundance observed in low differentiation stages. Numerous studies suggest these bacteria are closely associated with the occurrence and progression of oral cancers; previous research has shown that \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, and \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e increase as cancer progression [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, in particular, promotes tumor development through interactions with host cells; its high abundance in poorly differentiated tumors may relate to tumor invasiveness and metastasis [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Furthermore, \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e enhances cancer invasiveness, survival rates, and epithelial-mesenchymal transition (EMT) within the oral tumor microenvironment [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e' outer membrane LPS induces pro-inflammatory cytokine production, promoting cancer development and progression [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. It also increases PI3K/Akt signaling for epithelial cell survival and proliferation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. \u003cem\u003eCapnocytophaga gingivalis\u003c/em\u003e may alsoCapnocytophaga gingivalis may also promote invasion and metastasis of OSCC by EMT [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The abundance and enrichment of microbes in tumor microenvironments likely align with their specific ecological niches and metabolic activities during differentiation stages. Thus, understanding microbe-host interactions is crucial for uncovering OSCC pathogenic mechanisms and developing new therapies.\u003c/p\u003e \u003cp\u003eIn our study, significant differences were revealed in microbial community composition between dental plaque and saliva, with higher microbial richness observed in dental plaque and greater evenness in saliva. Dental plaque forms a complex biofilm composed of bacteria, extracellular substances, and food residues on tooth surfaces, providing a habitat that potentially supports a greater diversity of microbial colonization [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In contrast, saliva is a relatively simpler and more fluid environment with a more balanced species proportion. Additionally, microbial interactions within dental plaque, including the mutual use of metabolic by-products, likely contribute to its higher microbial richness compared to saliva [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].We noted a marked rise in microbial diversity and evenness in dental plaque and saliva as the tumor T stage advanced, especially at T4 stage, suggesting intricate interactions among tumor cells, host immune responses, and microbial community structures [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our investigation of microbial distribution in saliva and dental plaque, we observed significant differences in the specificity and occupancy rates of microorganisms between these two niches. Through SPEC-OCUS plot, we identified specialist species, with dental plaque exhibiting a higher rate of specialization compared to saliva. Dental plaque forms on tooth surfaces, creating a distinct environment that includes the hard surface of teeth and the gingival crevice microenvironment. This niche likely fosters the formation and maintenance of specialist species as they adapt to and exploit the specific resources available [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Microorganism interactions and their interactions with oral cells, along with niche specificity, likely promote the formation and maintenance of specialist species in dental plaque. Notably, the specialization rate of microorganisms in dental plaque increases as the differentiation degree of OSCC decreases. Poorly differentiated tumors create a challenging microenvironment with hypoxia, high acidity, and limited nutrients, supporting the growth of species adapted to these extreme conditions [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In contrast, the specialization rate of microorganisms in saliva is lower and further decreases with the reduced differentiation of OSCC. This could be attributed to saliva being a relatively \"open\" system, where microbial communities are more susceptible to host physiological changes and external factors. Interestingly, the stability of the salivary microbial community was higher than that of dental plaque, and microbial community stability significantly increased with the progression of tumor T stages in both niches. This suggests a complex relationship between oral microbial communities and tumor progression. Saliva contains various antimicrobial substances and immunoglobulins that can inhibit the growth of specific microorganisms, maintaining community balance and stability [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Additionally, immune cells and factors in saliva may play a positive role in microbial community stability by reducing microbial variation and instability through effective immune surveillance [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Lastly, the differentiation degree of OSCC is closely related to changes in the stability of oral microbial communities. With the increase in tumor T stages, microbial communities may transition from lower to higher stability, possibly reflecting the increased selective pressures (such as hypoxia, nutrient deficiency, and immune attacks) imposed by the tumor microenvironment. However, the exact mechanisms remain unclear [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Understanding these dynamics is crucial for elucidating the role of microbial communities in OSCC development and could provide valuable insights for new diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003eGenetically, significant differences in functional genes and virulence factors exist between saliva and dental plaque. While many genes are common to both, the presence of unique genes highlights the distinct nature of each niche. The decrease in unique genes with reduced tumor differentiation may indicate increased microbial community homogeneity in poorly differentiated tumors, reflecting adaptive changes to the aggressive tumor microenvironment [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].The relative abundance of Cellular Processes varies significantly across different groups, likely due to fundamental biological activities such as the cell cycle, cell death, and cell signaling. These processes are crucial for maintaining the dynamic balance of oral microbial communities. The observed differences in Cellular Processes between saliva and dental plaque at various stages of tumor differentiation may result from changes in the tumor microenvironment, prompting microbial communities to adapt their metabolic pathways and cell signaling to fluctuations in nutrients and oxygen associated with tumor progression. Furthermore, the host's immune response may further influence the composition of microbial communities, thereby affecting the expression patterns of Cellular Processes [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Further analysis of virulence factors reveals stage-specific distribution in the oral cavities of oral cancer patients. Specifically, virulence factors significantly enriched in poorly differentiated stages may indicate that certain virulence factors play crucial roles in promoting disease progression or influencing host responses during the early stages of the disease. For instance, VFG005341, derived from Streptococcus pyogenes M1 GAS, produces GAPDH, which participates in bacterial adhesion to host cells, potentially impacting disease invasiveness and transmissibility [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Poorly differentiated cancer cells usually exhibit higher metabolic activity and invasiveness; they may rely on the multifunctionality of GAPDH to support their rapid metabolic demands and enhanced invasiveness. GAPDH\u0026rsquo;s role in signal transduction might be activated in poorly differentiated cancer cells, promoting tumor progression and immune evasion [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Additionally, considering that poorly differentiated cancer cells may exist in more challenging microenvironments, GAPDH might help them adapt and survive under nutrient-deprived and hypoxic conditions. Thus, the high activity of GAPDH in poorly differentiated cancer cells could reflect its multifunctionality and adaptability in tumor development, providing a potential therapeutic target for interventions aimed at this pathway. The primary pseudopilin protein GspG of the Type II secretion system (T2SS) is more abundant in the saliva of poorly differentiated oral cancer patients compared to those with well-differentiated tumors. This could be related to the biological characteristics of poorly differentiated cancer cells, which typically have higher proliferation rates and invasiveness, possibly requiring more GspG to support their rapid metabolic needs and enhanced invasive capacity [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. As a key component of T2SS, GspG may participate in the secretion of virulence factors that aid bacterial survival and dissemination within the host, potentially influencing tumor cell behavior. The high abundance of AllS in the saliva of poorly differentiated oral cancer cells may indicate the importance of this transcriptional activator in tumor cell metabolic reprogramming. Given the rapid proliferation and metabolic demands of poorly differentiated cells, AllS may activate the allantoin utilization pathway to provide essential nitrogen and carbon sources, supporting cell growth and survival [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. However, no direct literature currently links AllS with cancer differentiation levels. Future research should involve laboratory studies and clinical sample analyses to determine the role and expression patterns of AllS in cancer cells with varying degrees of differentiation.\u003c/p\u003e \u003cp\u003eRecent studies have indicated that bacteria harboring virulence factors, such as \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, contribute to tumor diversity and size, promoting tumor progression [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The positive correlations between \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, \u003cem\u003eStreptococcus pyogenes\u003c/em\u003e, and \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e may underscore their shared mechanisms in facilitating tumorigenesis [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These microbial interactions likely enhance inflammation, stimulate cell proliferation, or suppress immune surveillance, collectively driving oral cancer advancement. Understanding these interactions is crucial for developing targeted therapeutic strategies against the oral cancer microbiome.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study, through the collection of saliva and dental plaque samples from patients with oral cancer at various stages of differentiation and subsequent metagenomic sequencing, has revealed significant differences in the composition of saliva and dental plaque microbiota across different differentiation periods. It also observed changes in microbial diversity, evenness, and stability with tumor T staging, aiding in understanding the link between microbial community structure changes and disease progression. Furthermore, the study highlighted significant differences in virulence factors across different stages of differentiation, which may indicate their potential role in disease development, offering potential targets for future diagnostics and therapeutics. To improve research accuracy and clinical applicability, future studies must address limitations in controlling environmental factors, conducting functional analysis, and monitoring disease dynamics. It is crucial to expand research into microbial interactions with the host immune system and tumor cells. A comprehensive approach is necessary to better understand the intricate relationship between microbial communities and oral squamous cell carcinoma, facilitating more precise medical interventions for patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL and ZY designed the experiments. HLZ and MZ carried out experiments. LRY, HLZ and AH participated in the collection of samples. MZ analyzed data prepared the figures and drafted the manuscript. JH, YL and ZY participation in discussion and revised the manuscript. All authors contributed to this manuscript, read, and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Natural Science Foundation of China (32170071 and 82273466), the Hunan Provincial Science and Technology Department (2023ZJ1120) and the Natural Science Foundation of Hunan Province (2024JJ2039 and 2024JJ8117).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by ethics committee of Hunan Cancer Hospital (2023-KYJJ-025) following the ethical guidelines of the Declaration of Helsinki (No. 038, 2015). The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Data availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSaikia PJ et al (2023) The emerging role of oral microbiota in oral cancer initiation, progression and stemness. Front Immunol 14:1198269\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScully C, Bagan J (2009) Oral squamous cell carcinoma: overview of current understanding of aetiopathogenesis and clinical implications. Oral Dis 15(6):388\u0026ndash;399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan Y et al (2023) Oral squamous cell carcinomas: state of the field and emerging directions. 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Microb Pathog 53(5\u0026ndash;6):234\u0026ndash;242\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":"clinical-oral-investigations","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cloi","sideBox":"Learn more about [Clinical Oral Investigations](http://link.springer.com/journal/784)","snPcode":"784","submissionUrl":"https://submission.nature.com/new-submission/784/3","title":"Clinical Oral Investigations","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Oral squamous cell carcinoma, dental plaque, saliva, oral microbiota, cancer progression","lastPublishedDoi":"10.21203/rs.3.rs-6143003/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6143003/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the microbial and genomic changes in saliva and dental plaque during Oral Squamous Cell Carcinoma (OSCC) progression, and to identify potential mechanisms and virulence factors involved in OSCC pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing metagenomic sequencing, 64 saliva and dental plaque samples from OSCC patients at different stages of differentiation were examined.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results showed notable differences in the microbial composition and genomic profiles across ecological regions and differentiation degrees. Notably, the relative abundance of specific microbes, such as \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e, and \u003cem\u003eHaemophilus parainfluenzae\u003c/em\u003e, increased in poorly differentiated OSCC. Microbial alpha diversity in dental plaque and saliva correlates with tumor T staging. Dental plaque microbiota shows higher specialization, especially in poorly differentiated tumors. Both microbiota types become more stable with advanced T staging. Genomic analysis reveals increased virulence factors in poorly differentiated stages. Subsequently, functional pathway analysis and tracing of pathogens reveal specific microbial mechanisms in oral cancer pathogenesis. Oral pathogens may promote tumorigenesis by secreting factors like GAPDH, GspG, and AllS, and drive tumor initiation and progression through microbial interactions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOSCC progression is associated with altered microbial composition, diversity, and genomic profiles in saliva and dental plaque. Poorly differentiated stages show higher abundance of pathogens and virulence factors, implicating them in tumorigenesis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Relevance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnderstanding microbial and genomic changes in saliva and dental plaque during OSCC progression could help develop new diagnostic biomarkers and therapies targeting the oral microbiota, potentially improving early detection, treatment efficacy, and prognosis for patients. Maintaining oral microbiome homeostasis may also help prevent oral cancer.\u003c/p\u003e","manuscriptTitle":"Dynamic Changes of Dental Plaque and Saliva Microbiota in OSCC Progression","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 08:34:49","doi":"10.21203/rs.3.rs-6143003/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-01T13:16:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-23T17:05:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-12T04:49:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88531141113321404743335901498091589316","date":"2025-03-12T04:13:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26800070984687848127781921350764530086","date":"2025-03-11T03:22:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-10T09:52:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-05T06:31:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-05T06:31:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Oral Investigations","date":"2025-03-03T05:53:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-oral-investigations","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cloi","sideBox":"Learn more about [Clinical Oral Investigations](http://link.springer.com/journal/784)","snPcode":"784","submissionUrl":"https://submission.nature.com/new-submission/784/3","title":"Clinical Oral Investigations","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c8fe8f35-32e8-4dcb-b267-77e88994e03d","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T16:04:16+00:00","versionOfRecord":{"articleIdentity":"rs-6143003","link":"https://doi.org/10.1007/s00784-025-06391-5","journal":{"identity":"clinical-oral-investigations","isVorOnly":false,"title":"Clinical Oral Investigations"},"publishedOn":"2025-05-27 15:57:11","publishedOnDateReadable":"May 27th, 2025"},"versionCreatedAt":"2025-03-11 08:34:49","video":"","vorDoi":"10.1007/s00784-025-06391-5","vorDoiUrl":"https://doi.org/10.1007/s00784-025-06391-5","workflowStages":[]},"version":"v1","identity":"rs-6143003","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6143003","identity":"rs-6143003","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}