Oral squamous cell carcinoma is associated with altered salivary microbiome structure and reduced community evenness

Oral squamous cell carcinoma is associated with altered salivary microbiome structure and reduced community evenness | 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 Article Oral squamous cell carcinoma is associated with altered salivary microbiome structure and reduced community evenness Miguel Ferriz-Jordán, David Hervás, Leticia Bagan, Concepción Gimeno, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9257747/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Oral squamous cell carcinoma (OSCC) remains a major global health burden, highlighting the need for improved non-invasive tools for early detection and disease monitoring. Because saliva is easily accessible and reflects the oral microenvironment, the salivary microbiome has emerged as a promising source of candidate biomarkers. In this study, we characterized the salivary microbiota of 113 individuals, including 52 patients with OSCC and 61 healthy controls, using 16S rRNA gene sequencing targeting the V3-V4 region. Microbial diversity and composition were analyzed using complementary bioinformatic and statistical approaches, including four differential abundance methods (MaAsLin2, ANCOM-BC2, LinDA, and ALDEx2). OSCC was associated with significant differences in overall microbial community structure, as shown by beta diversity analyses, together with reduced community evenness but no major loss of richness. Differential abundance analyses identified several taxa overrepresented in OSCC, including the genera Tannerella , Solobacterium , Dialister , and Bergeyella , as well as species such as Solobacterium moorei , Tannerella forsythia , and Prevotella nigrescens . In contrast, Leptotrichia was underrepresented in OSCC. These findings support the existence of an OSCC-associated salivary dysbiosis characterized by ecological restructuring rather than global diversity loss. The identified taxa represent candidate microbial biomarkers for OSCC that warrant validation in larger, independent, and longitudinal cohorts. Health sciences/Biomarkers Biological sciences/Microbiology Oral squamous cell carcinoma salivary microbiome 16S rRNA sequencing dysbiosis biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Oral cancer (OC) represents a major global health burden, with oral squamous cell carcinoma (OSCC) accounting for approximately 90% of all cases. Worldwide, OC ranks among the most common malignancies, with an estimated annual incidence exceeding 389,000 new cases and 188,000 deaths 1 . Over the past decade, both the incidence and mortality of OC have continued to increase 2 . The etiology of OC is multifactorial, with well-established risk factors including tobacco use, alcohol consumption, and betel quid chewing 3 , 4 . In addition, infection with oncogenic pathogens, such as high-risk human papillomavirus (HPV) has emerged as an additional etiological factor 3 , 4 . The human oral cavity, together with the aerodigestive tract, harbors one of the most complex and densely populated microbial ecosystems in the body. Current repositories such as the expanded Human Oral Microbiome Database (eHOMD) catalog 836 bacterial species as of March 2026, approximately 77% of which are culturable under laboratory conditions 5 . The resident oral microbiota interacts dynamically with the host, influencing local and systemic physiology through metabolic activity, immune modulation, and maintenance of epithelial homeostasis 6 . Consequently, disruption of this balanced community, known as dysbiosis, has been implicated in a wide range of systemic and local diseases, including atherosclerosis 7 , autoimmune disorders 8 , cancer 9 , bacteremia associated with endocarditis 10 , and diabetes 11 . Increasing evidence suggests that dysbiosis may contribute to the development of chronic periodontal disease 12 , 13 , proliferative verrucous leukoplakia (PVL) 14 , 15 , and OSCC 16 , 17 . Chronic exposure to proinflammatory bacteria can promote a persistent inflammatory microenvironment, characterized by oxidative stress, DNA damage, and activation of oncogenic signaling pathways 18 . Several bacterial taxa, including Fusobacterium nucleatum 19 , Porphyromonas gingivalis 20 , Prevotella intermedia 21 , and Capnocytophaga spp. 22 , have been repeatedly associated with OSCC and precancerous lesions, suggesting a potential etiological role or possible use as diagnostic biomarkers. Our group has established a research line investigating the role of the oral microbiome in oral potentially malignant disorders (OPMDs) and OC. In an initial study, we identified reduced microbial diversity and increased abundance of potentially oncogenic bacteria, such as Campylobacter jejuni , in tissue biopsies from patients with PVL 23 . Building on these findings, we subsequently compared the oral microbiome of tissue biopsies from patients with homogeneous leucoplakia, PVL, OSCC, and OSCC arising from PVL (PVL-OSCC). These analyses revealed distinct patterns of dysbiosis associated with disease progression, with cancer samples showing reduced microbial diversity and a microbiome enriched in members of the phylum Fusobacteriota 23 . We next sought to determine whether the dysbiosis detected in tissue biopsies could also be identified in saliva, a promising non-invasive biofluid for OC detection. Unlike tissue biopsies, saliva collection is simple, repeatable, and suitable for screening. Saliva contains exfoliated epithelial cells, extracellular vesicles, and microbial DNA that reflects the oral microenvironment 24 . Although no saliva-based diagnostic test for oral cancer has yet received FDA approval, largely due to the need for further clinical validation, saliva remains an attractive biospecimen for biomarker discovery because it can be collected easily, repeatedly, and non-invasively 25 . Recently, we conducted a pilot study analyzing saliva samples from 10 patients with oral leukoplakia, 28 patients with PVL, and 28 patients with OSCC, which suggested progressive microbial dysbiosis during malignant transformation 26 . However, this study did not include a healthy control group. In the present study, we analyze saliva samples of a cohort of 113 individuals, including 61 healthy controls and 52 patients with OSCC, using 16S rRNA gene sequencing. By applying multiple complementary statistical approaches, we aim to identify robust microbial signatures associated with OSCC. Our findings may support the development of saliva-based, non-invasive diagnostic strategies and help identify bacterial taxa potentially involved in oral carcinogenesis, thereby providing a foundation for future mechanistic studies. Results Participant characteristics and sequencing data summary This study included 113 participants, comprising 61 healthy controls and 52 patients diagnosed with OSCC. The clinicopathological characteristics of patients with OSCC are summarized in Table 1 . The mean age of patients was 67.93 years (range 37.00–92.45), 55.8% were males, and 67.3% were diagnosed at an early stage of the disease (stages I and II). Table 1 Clinicopathological characteristics of patients with OSCC. Mean age (Standard deviation) 67.93 (12.43) Number of cases Percentage Gender Male 29 55.8 Female 23 44.2 Location Buccal Mucosa 4 7.7 Floor mouth 6 11.5 Gingiva 17 32.7 Palate 7 13.5 Tongue 18 34.6 Clinical form Erythroleukoplakia 7 13.5 Exophytic 8 15.4 Mixed 1 1.9 Ulcerate 36 69.2 TNM T1N0M0 27 51.9 T2N1M0 7 13.5 T2N0M0 8 15.4 T3N1M0 1 1.9 T3N2M0 0 0.0 T3N0M0 1 1.9 T4aN1M0 8 15.4 Stage I 29 55.8 II 6 11.5 III 9 17.3 IV 8 15.4 Early-Advanced stage Advanced 17 32.7 Early 35 67.3 A total of 7,088,020 paired-end reads were obtained, with a mean of 62,726 reads per sample (range 31,566 − 115,113). Rarefaction curves relating sequencing depth to taxonomic richness showed a tendency to plateau in most samples, suggesting that sequencing depth was sufficient to capture the majority of bacterial diversity (Fig. 1 ). All detected taxa belonged to the domain Bacteria. In total, 12 phyla, 17 classes, 35 orders, 51 families, and 94 genera were identified. Additionally, 172 putative species-level taxa were detected. Taxonomic composition of the oral microbiota The composition of the oral microbiota was characterized in both OSCC patients and healthy controls. The relative abundances of bacterial taxa at the phylum, family, and genus levels are shown in Fig. 2 . A high degree of inter-individual variability in microbial composition was observed across samples. At the phylum level, the microbial community was dominated by Firmicutes (mean relative abundance 34.1%), followed by Bacteroidota (28.1%), Pseudomonadota (22.6%), Fusobacteriota (10.6%), and Actinomycetota (3.0%). All other phyla accounted for less than 1% of the total relative abundance. At the family level, the most abundant taxa were Prevotellaceae (22.6%), Streptococcaceae (15.4%), Veillonellaceae (14.2%), Neisseriaceae (12.0%), Pasteurellaceae (8.2%), and Fusobacteriaceae (7.8%), followed by Porphyromonadaceae (4.4%), Leptotrichiaceae (2.8%), Micrococcaceae (2.3%), Carnobacteriaceae (1.4%), and Lachnospiraceae (1.3%). Remaining families were present at relative abundances below 1%. At the genus level, the most abundant genera were Prevotella (20.5%), Streptococcus (15.3%), Veillonella (13.5%), Neisseria (11.9%), Fusobacterium (7.8%), Haemophilus (6.9%), and Porphyromonas (4.4%), followed by Rothia (2.3%), Leptotrichia (2.1%), and Granulicatella (1.4%). All remaining genera individually accounted for less than 1% of total abundance. On average, 4.5% of reads could not be assigned at the genus level. Comparison of alpha and beta diversity between OSCC patients and controls Intra-sample microbial diversity (alpha diversity) was assessed using multiple indices and is shown in Fig. 3 , stratified by clinical group. Across the entire cohort, the mean values were 3,578.2 for Breakaway richness, 7.33 for the Shannon index, 0.9987 for the Simpson index, and 0.904 for Pielou’s evenness. No statistically significant differences between clinical groups were observed for Breakaway richness, Shannon, or Simpson indices. However, Breakaway richness showed greater variability in OSCC patients, with a tendency toward higher values in some samples. OSCC patients showed significantly lower evenness, as measured by Pielou’s index (p = 0.018). These results suggest that OSCC is not associated with major changes in overall richness, but rather with alterations in community structure reflected by reduced evenness. Inter-sample microbial diversity (beta diversity) was assessed using four distance metrics based on genus-level data: Bray–Curtis, Jaccard, unweighted UniFrac, and weighted UniFrac. The corresponding principal coordinates analyses (PCoA) are shown in Fig. 4 . Significant differences in microbial community composition between healthy controls and OSCC patients were observed using PERMANOVA for the Bray–Curtis (p = 0.009), Jaccard (p = 0.007), and unweighted UniFrac (p < 0.001) distances. Although the weighted UniFrac analysis showed a tendency toward significance, the results were not considered robust, as variability across repeated analyses resulted in some p-values exceeding the significance threshold. The lack of robust differences in weighted UniFrac suggests that group differences are primarily driven by the presence or absence of taxa rather than shifts in dominant taxa. Overall, these findings indicate that the microbial community structure differs significantly between controls and OSCC patients. Differential abundance analysis of the oral microbiota in OSCC To identify specific taxa associated with the differences in overall microbiome composition between OSCC patients and healthy controls revealed by the PERMANOVA analyses, differential abundance analyses were performed using four complementary methods across multiple taxonomic levels: MaAsLin2, ANCOM-BC2, LinDA, and ALDEx2. Given the known methodological differences among these approaches, results from all methods were considered to provide a comprehensive overview of taxa associated with OSCC. The consistency of significant taxa across methods is summarized in Fig. 5 . The full results obtained by each method are provided in Suppl. File S1 , while taxa identified by at least one method are summarized in Table 2 . Table 2 Differentially abundant taxa between healthy controls and OSCC patients across taxonomic levels. Overrepresented in OSCC Phyla Families Genera Species Pseudomonadota Bacillota Tannerellaceae Weeksellaceae Erysipelotrichaceae Flavobacteriaceae Tannerella Dialister Bergeyella Solobacterium Prevotella nigrescens Fusobacterium simiae Haemophilus haemolyticus Tannerella forsythia Lachnoanaerobaculum umeaense Solobacterium moorei Haemophilus influenzae Streptococcus mitis Dialister pneumosintes Veillonella tobetsuensis Kingella denitrificans Underrepresented in OSCC - - Leptotrichia - At the phylum level, Pseudomonadota and Bacillota were enriched in OSCC patients. At lower taxonomic levels, several taxa previously associated with oral dysbiosis and periodontal disease were found to be overrepresented in OSCC, including members of the families Tannerellaceae, Erysipelotrichaceae, and Flavobacteriaceae, as well as the genera Tannerella, Dialister, Bergeyella , and Solobacterium . At the species level, enrichments included Tannerella forsythia, Fusobacterium simiae, Prevotella nigrescens, Solobacterium moorei, Dialister pneumosintes, Haemophilus haemolyticus, Haemophilus influenzae, Lachnoanaerobaculum umeaense, Streptococcus mitis, Veillonella tobetsuensis , and Kingella denitrificans . Leptotrichia was the only genus that consistently decreased in OSCC. Discussion This study evaluated the oral microbiome of 52 patients diagnosed with OSCC and 61 healthy controls. The OSCC cohort is clinically representative of a typical hospital-based oral cavity cancer series, with an older patient population, a slight male predominance, and a predominance of tumors arising in the tongue and gingiva, most frequently presenting as ulcerative lesions. Importantly, most cases were diagnosed at early stages (I–II, 67.3%), although a substantial fraction presented with advanced disease (III–IV, 32.7%), reflecting the heterogeneity of clinical presentation in OSCC. The taxonomic composition observed in this cohort is broadly consistent with the expected structure of the oral microbiota, being dominated by genera such as Prevotella, Streptococcus, Veillonella , and Neisseria , while also showing a high degree of inter-individual variability. This heterogeneity is a well-recognized feature of the oral ecosystem and highlights the importance of identifying disease-associated changes against a highly variable microbial background. Despite the absence of major differences in overall richness between OSCC patients and healthy controls, alpha diversity analyses revealed significantly lower evenness in OSCC, as measured by Pielou’s index, in agreement with previous reports 27 . This finding suggests that OSCC is not necessarily associated with a global loss of microbial diversity, but rather with an imbalance in community structure, in which certain taxa may become disproportionately represented. In this context, the trend toward greater variability in Breakaway richness among OSCC cases may further reflect the ecological instability of the tumor-associated oral microbiome. This interpretation is supported by beta diversity analyses, which consistently showed significant differences in overall microbial composition between OSCC patients and controls. The fact that these differences were detected using Bray-Curtis, Jaccard, and unweighted UniFrac distances indicates that the two groups differ not only in relative abundance patterns but also in the presence and absence of specific taxa. By contrast, the lack of robust significance for weighted UniFrac suggests that these differences are less strongly driven by dominant taxa and may instead be influenced by shifts in less abundant or less prevalent community members. We performed differential abundance analyses using complementary methods across multiple taxonomic levels, which revealed several taxa overrepresented in OSCC. At the species level, we identified Tannerella forsythia, Streptococcus mitis, Veillonella tobetsuensis, Prevotella nigrescens , Haemophilus haemolyticus , Haemophilus influenzae , and Solobacterium moorei as overrepresented in OSCC patients, among others. Tannerella forsythia is a well-known periodontal pathogen with strong pro-inflammatory potential, capable of promoting tissue degradation and creating a microenvironment conducive to tumor development 28 , 29 . Previous studies have also reported its association with early-stage head and neck squamous cell carcinoma 30 . Species belonging to the genera Streptococcus and Veillonella have also been associated with head and neck cancer, being proposed as discriminative taxa 27 . Enrichment of the genera Prevotella and Haemophilus in saliva from OSCC patients was reported in a pilot study a few years ago 31 . These authors 31 also reported that Solobacterium moorei proportion increases with cancer progression in tumoral tissue. This microorganism has been consistently reported in different oral conditions, including dysbiosis, halitosis, and the production of volatile sulfur compounds 32 . In addition, a previous study including six patients with OSCC and four patients with oral leukoplakia already reported the enrichment of the genus Solobacterium in the saliva of patients with OSCC 33 . Beyond oral cancer, S. moorei has also been linked to colorectal cancer, where increased abundance has been observed not only in the oral microbiota 34 but also in gut microbiota 35 . In both contexts, its levels appear to correlate with disease progression, and it has been proposed that elevated oral levels of S. moorei may contribute to cancer initiation, while also potentially influencing tumor progression. These findings suggest that S. moorei may play a broader role in cancer-associated microbial dysbiosis and could represent a promising target for future mechanistic and biomarker studies. Other genera identified in our analysis, including Dialister and Bergeyella , have also been associated with chronic inflammation and pathogenic biofilm formation 28 , 36 , further supporting the presence of a pro-inflammatory microbial environment in OSCC. Notably, Dialister has previously been proposed as a discriminative taxon for head and neck cancer in saliva 27 . Several less-characterized families identified in this study, including Weeksellaceae , Erysipelotrichaceae , and Flavobacteriaceae , have been only sparsely described in the context of OSCC. While some evidence links these taxa to cancer-related conditions or risk factors 37 , 38 their precise role remains unclear, and further studies are required to determine whether they contribute to disease progression or reflect secondary ecological changes. Leptotrichia was the only taxon found to be significantly underrepresented in OSCC. Previous studies have reported a higher abundance of Leptotrichia in early-stage disease and associated its presence with improved survival outcomes 30 , 39 , suggesting a potential protective or context-dependent role. However, its behavior appears to vary depending on clinical and molecular factors such as HPV status and treatment strategy 27 . Several limitations of this study should be acknowledged. First, despite adjustment for age and sex, residual confounding is likely because several major determinants of the oral microbiome were not fully accounted for, including smoking, alcohol consumption, periodontal status, caries burden, recent dental procedures, oral hygiene habits, diet, socioeconomic factors, and HPV status. Imbalances in these variables may confound microbial differences attributed to OSCC. In addition, heterogeneity within the OSCC group, including tumor location, ulceration, and stage, may also influence the oral microbiota. Because the present analyses focused primarily on OSCC versus control comparisons, site- or stage-specific microbial signals may have been diluted. Second, the cross-sectional design precludes any inference about causality or temporality. Therefore, it remains unclear whether the observed dysbiosis precedes OSCC development, arises as a consequence of the tumor, or reflects a bidirectional interaction. This limitation is particularly relevant when interpreting candidate taxa as potential drivers rather than correlates with disease. Third, saliva offers clear advantages as a non-invasive and clinically feasible sample type, but it is not tumor-specific. Rather than capturing only the tumor microenvironment, saliva reflects the ecology of the entire oral cavity. Moreover, only a single saliva sample was analyzed per participant, so intra-individual temporal variability related to diet, circadian patterns, oral hygiene, or short-term environmental exposures could not be assessed. Fourth, although this study has a considerable sample size, it is a single-center study, which may limit the generalizability of the findings to other populations and clinical settings. No independent external validation cohort was available, so the taxa identified here should be considered candidate biomarkers requiring confirmation in separate cohorts. Since most OSCC cases were diagnosed at early stages, the findings may also be less representative of advanced disease. Fifth, the use of 16S rRNA gene sequencing targeting the V3-V4 region imposes taxonomic and functional limitations. Species-level assignments should be interpreted cautiously, as this region does not always provide sufficient discriminatory power for confident species identification. Likewise, 16S sequencing does not allow direct assessment of functional pathways, virulence traits, or metabolite production. The study was therefore limited to taxonomic profiling, without metagenomic, transcriptomic, metabolomic, or experimental validation of the inferred biological relevance of the identified taxa. Finally, although compositionality and zero inflation were addressed by applying four complementary statistical tools, overlap across methods was only partial. Thus, some differential abundance findings were method-dependent and should be interpreted as exploratory until independently replicated. The main contribution of this study lies not only in confirming differences between OSCC patients and healthy controls, but in providing a robust and integrative characterization of these differences using a moderately sized saliva cohort, standardized sequencing depth, and a multi-method analytical framework. By combining four complementary approaches for differential abundance analysis, we aimed to prioritize reproducibility and methodological robustness, addressing a key limitation highlighted in previous studies, namely the inconsistency of reported microbial signatures across analytical pipelines. Within this framework, we identified a set of taxa repeatedly associated with OSCC across methods, including genera such as Tannerella, Solobacterium, Dialister , and Bergeyella . Notably, several of these taxa are linked to periodontal disease and pro-inflammatory oral environments, supporting the concept of OSCC-associated dysbiosis as an inflammation-driven ecological shift rather than a random compositional change. An additional relevant finding is the combination of reduced community evenness, in the absence of major changes in richness, together with consistent beta diversity differences driven primarily by presence/absence metrics. This pattern suggests that, in OSCC, the core oral microbiota is largely preserved but becomes imbalanced, with selective expansion of specific taxa and loss or reduction of others. Such ecological restructuring is consistent with a transition toward a dysbiotic, inflammation-associated microbial community and may have implications for the development of saliva-based diagnostic approaches. Taken together, our results reinforce the potential of saliva as a non-invasive matrix for capturing disease-associated microbial signatures. Longitudinal studies following patients with oral potentially malignant disorders through malignant transformation are needed to distinguish microbial risk markers from disease-associated changes. In addition, future analyses should incorporate detailed clinical stratification, including tumor stage, anatomical site, nodal status, ulceration, periodontal condition, and lifestyle factors such as smoking and alcohol consumption. Integrative multi-omics approaches, combining metagenomics or metatranscriptomics with salivary metabolomics, may help elucidate the functional mechanisms underlying the observed microbial shifts. From a translational perspective, the development and validation of saliva-based microbial classifiers in multicenter cohorts will be essential to assess their robustness and generalizability across populations and technical workflows. Methods Patients and samples This study included 113 individuals recruited at the Department of Stomatology and Maxillofacial Surgery of the General University Hospital of Valencia between 2023 and 2024. Participants were classified according to their oral status into two groups: Group I comprised 61 healthy controls, and Group II included 52 patients diagnosed with OSCC. Exclusion criteria were pregnancy, age under 18 years, history of previous cancer (primary tumor), presence of metastasis from another anatomical site, prior radiotherapy or chemotherapy to the head or neck region, and the presence of other oral conditions that could potentially alter the microbial composition of saliva. Participants were instructed to fast for at least two hours prior to sample collection. None of the participants had received medication in the three months preceding sampling. Unstimulated whole saliva was collected for five minutes into sterile 15-mL tubes using a funnel, following the protocol described by Navazesh 40 . To minimize blood contamination, participants were instructed not to brush their teeth within 45 minutes prior to sample collection. Samples visibly contaminated with blood were discarded and recollected. Saliva samples were immediately stored at − 80°C until further analysis. DNA extraction and 16S rRNA gene sequencing Genomic DNA was extracted from saliva sample using the GenElute™ Bacterial Genomic DNA Kit (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer’s instructions. Samples were treated with lysozyme, mutanolysin, and Proteinase K, followed by RNase treatment to remove residual RNA. Prior to extraction, saliva samples were homogenized to ensure representative recovery of microbial DNA. DNA concentration was measured by fluorometry using a Quantus™ Fluorometer (Promega, USA). The hypervariable V3-V4 region of the bacterial 16S rRNA gene was amplified using the universal primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GGACTACHVGGGTATCTAATCC-3′). Library preparation consisted of a first PCR to amplify the target region followed by a second PCR to attach sample-specific indices. The resulting libraries were purified and assessed using the QIAxcel Advanced System (Qiagen, Germany) to verify fragment size and library quality prior to sequencing. Sequencing was performed on an Illumina MiSeq platform using paired-end reads (2 × 250 bp) and sequencing-by-synthesis chemistry. Raw sequencing data were generated in FASTQ format for downstream bioinformatic analysis. Bioinformatic analysis and taxonomic annotation Raw sequencing reads were processed using the DADA2 package (v1.36.0) in R (v4.5.1). During quality filtering, forward and reverse reads were truncated at position 245, and sequences with a maximum expected error ≥ 3 were discarded. The DADA2 denoising algorithm was applied to infer amplicon sequence variants (ASVs), after which paired-end reads were merged. Chimeric sequences were removed using the removeBimeraDenovo function, setting the minFoldParentOverAbundance parameter to 4. Taxonomic assignment was performed up to the species level using the SILVA reference database (v138.2). Alpha diversity was assessed using the phyloseq (v1.52.0) and breakaway (v4.8.5) packages. Beta diversity analyses were performed using the vegan package (v2.7.1). Statistical analysis All statistical analyses were performed in R (v4.5.1). Age and sex were included as potential confounding variables in all analyses. Continuous variables were summarized as mean ± standard deviation, while categorical variables were summarized as absolute and relative frequencies. Group differences between healthy controls and OSCC patients were evaluated using PERMANOVA with 9,999 permutations. To improve robustness, the analysis was repeated 10 times, and the mean p-value was reported. Alpha diversity was evaluated using four indices reflecting microbial richness and evenness: Breakaway 41 , Shannon, Simpson, and Pielou indices. Differences in alpha diversity between groups were assessed using ordinal regression models implemented in the ordinal package (v2023.12.4.1). Beta diversity was assessed using principal coordinates analysis (PCoA) based on multiple distance metrics, including Bray-Curtis, Jaccard, unweighted UniFrac, and weighted UniFrac. Distance matrices were calculated using relative genus abundances. For UniFrac analyses, a phylogenetic tree was constructed using the DECIPHER (v3.6.0) and phangorn (v2.12.1) packages. The tree was inferred using the neighbor-joining method and optimized based on likelihood criteria using default parameters. Differential abundance analyses were performed to identify taxa associated with clinical groups at the phylum, family, genus, and species levels. Multiple complementary statistical approaches were applied, including MaAsLin2 42 (Maaslin2 package v1.22.0), ANCOM-BC2 43 (ANCOMBC package v2.12.0), LinDA 44 (MicrobiomeStat package v1.2), and ALDEx2 45 (ALDEx2 package v1.42.0). Particular emphasis was placed on taxa identified as significant across multiple methods, whereas taxa detected by only one or two methods were considered less robust findings. Statistical significance was set at α = 0.05 for both p-values and multiple-testing-adjusted q-values. Declarations Competing Interests The authors declare no competing interests. Ethic declarations This study was approved by the Ethical Research Committee (CEIM) of the General University Hospital of Valencia (approval number 10–2023, February 24, 2023). All procedures were conducted in accordance with applicable local legislation and institutional requirements. Written informed consent was obtained from all participants prior to their inclusion in the study. Funding declaration This research was funded by the Ministerio de Ciencia e Innovación (Spain) through the project “PID2022-138398OB-I00”. Author Contribution M.F.-J. performed formal and statistical analysis and wrote the original draft of the manuscript. D.H. contributed to formal and statistical analysis. L.B. enrolled patients and reviewed clinical data annotations. C.G. contributed to data interpretation and provided microbiological expertise. A.H.-P. and J.B. conceived and designed the study, supervised the project, and acquired funding. All authors contributed to data interpretation, critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work. Acknowledgement The authors thank all enrolled patients and their families. Data Availability Raw data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with BioProject record PRJNA1445929 and BioSample records from SAMN56804886 to SAMN56804998. References Siegel, R. L., Kratzer, T. B., Wagle, N. S., Sung, H. & Jemal, A. Cancer statistics, 2026. CA Cancer J. Clin. 76 , e70043 (2026). Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74 , 229–263 (2024). Warnakulasuriya, S. Causes of oral cancer – an appraisal of controversies. Br. Dent. J. 207 , 471–475 (2009). Dunn, L. A., Ho, A. L. & Pfister, D. G. Head and Neck Cancer: A Review. JAMA 335 , 531–541 (2026). Escapa, F. Construction of habitat-specific training sets to achieve species-level assignment in 16S rRNA gene datasets. Microbiome 8 , 65 (2020). Lamont, R. J., Koo, H. & Hajishengallis, G. 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Kumar, P., Gupta, S. & Das, B. C. Saliva as a potential non-invasive liquid biopsy for early and easy diagnosis/prognosis of head and neck cancer. Transl Oncol. 40 , 101827 (2024). Intini, R. et al. Comparative analysis of oral microbiome in saliva samples of oral leukoplakia, proliferative leukoplakia and oral squamous cell carcinoma. Front. Oral Health . 6 , 1600090 (2025). Guerrero-Preston, R. et al. 16S rRNA amplicon sequencing identifies microbiota associated with oral cancer, human papilloma virus infection and surgical treatment. Oncotarget 7 , 51320–51334 (2016). Elebyary, O., Barbour, A., Fine, N., Tenenbaum, H. C. & Glogauer, M. The Crossroads of Periodontitis and Oral Squamous Cell Carcinoma: Immune Implications and Tumor Promoting Capacities. Front. Oral Health . 1 , 584705 (2020). Malinowski, B. et al. The role of Tannerella forsythia and Porphyromonas gingivalis in pathogenesis of esophageal cancer. Infect. Agent Cancer . 14 , 3 (2019). Yu, S. et al. Intratumoral Leptotrichia is a novel microbial marker for favorable clinical outcomes in head and neck cancer patients. MedComm (Beijing) . 4 , e344 (2023). Yang, K. et al. Oral Microbiota Analysis of Tissue Pairs and Saliva Samples From Patients With Oral Squamous Cell Carcinoma – A Pilot Study. Front. Microbiol. 12 , 719601 (2021). Haraszthy, V. I. et al. Characterization and prevalence of Solobacterium moorei associated with oral halitosis. J. Breath. Res. 2 , 017002 (2008). Hashimoto, K. et al. Changes in oral microbial profiles associated with oral squamous cell carcinoma vs leukoplakia. J. Investig Clin. Dent. 10 , e12445 (2019). Camañes-Gonzalvo, S. et al. Relationship between oral microbiota and colorectal cancer: A systematic review. J. Periodontal Res. 59 , 1071–1082 (2024). Uchino, Y. et al. Colorectal Cancer Patients Have Four Specific Bacterial Species in Oral and Gut Microbiota in Common - A Metagenomic Comparison with Healthy Subjects. Cancers 2021 . 13 , 3332 (2021). Ganly, I. et al. Periodontal pathogens are a risk factor of oral cavity squamous cell carcinoma, independent of tobacco and alcohol and human papillomavirus. Int. J. Cancer . 145 , 775–784 (2019). Zhong, X. et al. Oral microbiota alteration associated with oral cancer and areca chewing. Oral Dis. 27 , 226–239 (2021). Ohsawa, M. et al. Relationship Between the Oral Microbiome and Treatment Efficacy in Esophageal Squamous Cell Carcinoma. Ann. Surg. Oncol. 33 , 3203–3213 (2026). Hamada, M. et al. Potential Role of the Intratumoral Microbiota in Prognosis of Head and Neck Cancer. Int. J. Mol. Sci. 24 , 15456 (2023). Navazesh, M. Methods for Collecting Saliva. Ann. N Y Acad. Sci. 694 , 72–77 (1993). Willis, A., Bunge, J. & Estimating Divers. via Freq. Ratios Biometrics 71 , 1042–1049 (2015). Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17 , e1009442 (2021). Lin, H., Peddada, S. & Das Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat. Methods . 21 , 83–91 (2023). Zhou, H., He, K., Chen, J. & Zhang, X. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol. 23 , 95 (2022). Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B. ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq. PLoS One . 8 , e67019 (2013). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Editor invited by journal 06 Apr, 2026 Submission checks completed at journal 31 Mar, 2026 First submitted to journal 31 Mar, 2026 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-9257747","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619503426,"identity":"33038ef1-270f-4df0-8246-f43e42103791","order_by":0,"name":"Miguel Ferriz-Jordán","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Ferriz-Jordán","suffix":""},{"id":619503428,"identity":"2db955a8-d4ec-46d0-8968-11969ef544b3","order_by":1,"name":"David Hervás","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Hervás","suffix":""},{"id":619503430,"identity":"67ff1c8f-43d1-45c4-abf7-523287769392","order_by":2,"name":"Leticia Bagan","email":"","orcid":"","institution":"University of Valencia","correspondingAuthor":false,"prefix":"","firstName":"Leticia","middleName":"","lastName":"Bagan","suffix":""},{"id":619503432,"identity":"47c2e55a-9dd9-4b69-af27-86e50b29b319","order_by":3,"name":"Concepción Gimeno","email":"","orcid":"","institution":"Hospital General Universitario De Valencia","correspondingAuthor":false,"prefix":"","firstName":"Concepción","middleName":"","lastName":"Gimeno","suffix":""},{"id":619503434,"identity":"6463cdae-ec9e-4acf-915e-cc4c6a2eb856","order_by":4,"name":"Alejandro Herreros-Pomares","email":"","orcid":"","institution":"Universitat Politècnica de València","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"","lastName":"Herreros-Pomares","suffix":""},{"id":619503436,"identity":"1f7948ed-263e-405b-b769-8fabffa00a8b","order_by":5,"name":"José Bagán","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBAC9gYgwdjGUAfmfSBGCyNUSzKYM4MULYkgmpmHKC3t7Q8fMJxhSObvX/zssW3bYQZ+/gMEtPScMTYAaqmTuPHM3DgXqEVyRgIBLTNy2CRAtjDcOGAmnduWxmBwg5DDZqQ//8FQwZA4/8bxb9KWQC325wk4THBGghkDSMuG8z1m0oxtNgwGDAQcJs1zxlgiwUAi2fAGT5lkzzkbHokbBLTwsbc//PDBwKZO7vzxbRI/yiTk+PsJOAwMEhgkGBgkIIYTFTVQQCg6RsEoGAWjYOQCAHrxQMXjdJuHAAAAAElFTkSuQmCC","orcid":"","institution":"University of Valencia","correspondingAuthor":true,"prefix":"","firstName":"José","middleName":"","lastName":"Bagán","suffix":""}],"badges":[],"createdAt":"2026-03-29 09:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9257747/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9257747/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106635809,"identity":"4a0f103e-c039-4d10-b168-c72789c223fc","added_by":"auto","created_at":"2026-04-10 16:50:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1297467,"visible":true,"origin":"","legend":"\u003cp\u003eRarefaction curves showing the relationship between sequencing depth (log-transformed read counts) and genus richness for each sample. Curves are stratified by group (controls and OSCC patients). The plateau observed in most samples indicates sufficient sequencing depth to capture the majority of bacterial diversity.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/a25fd5210bd78641d6457d5a.png"},{"id":106727394,"identity":"0b1a5bfc-7230-48ae-a448-133ba6e0e0c5","added_by":"auto","created_at":"2026-04-12 18:38:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3465145,"visible":true,"origin":"","legend":"\u003cp\u003eStacked bar plots showing the relative abundance of the oral microbiota across samples from controls and OSCC patients. (A) Phylum-level composition including all identified phyla and unassigned reads. (B) Family-level composition showing the 10 most abundant families, together with unassigned reads and remaining low-abundance families grouped as “Other”. (C) Genus-level composition showing the 10 most abundant genera, along with unassigned reads and remaining genera grouped as “Other”.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/fd09317f8eef3ef2b6172440.png"},{"id":106726801,"identity":"da4c808b-96c8-4731-8f2a-3a9fe5f032f7","added_by":"auto","created_at":"2026-04-12 18:37:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":610899,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha diversity of salivary microbiota in healthy controls (blue) and OSCC patients (red). Boxplots represent (\u003cstrong\u003ea\u003c/strong\u003e) Breakaway richness, (\u003cstrong\u003eb\u003c/strong\u003e) Shannon index, (\u003cstrong\u003ec\u003c/strong\u003e) Simpson index, and (\u003cstrong\u003ed\u003c/strong\u003e) Pielou’s evenness. A significant decrease in evenness was observed in OSCC patients (p = 0.018).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/231551c4357965be58034f53.png"},{"id":107479260,"identity":"d7a652f0-a6bf-46b2-9897-3cf932f3e53f","added_by":"auto","created_at":"2026-04-22 01:21:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1385959,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinates analysis (PCoA) of beta diversity using (\u003cstrong\u003ea\u003c/strong\u003e) Bray–Curtis, (\u003cstrong\u003eb\u003c/strong\u003e) Jaccard, (\u003cstrong\u003ec\u003c/strong\u003e) unweighted UniFrac, and (\u003cstrong\u003ed\u003c/strong\u003e) weighted UniFrac distance metrics. Each point represents a sample, colored by clinical group (blue: controls; red: OSCC). Dashed ellipses indicate 95% confidence intervals. PERMANOVA p-values are shown in each panel.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/32988e9bc8213efedf9e9b70.png"},{"id":106993566,"identity":"8d7cb731-4f1b-4e89-abe1-9557107c5427","added_by":"auto","created_at":"2026-04-15 14:37:45","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1268278,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagrams showing the number of taxa identified as significantly different between groups by each of the four statistical methods (MaAsLin2, ANCOM-BC2, LinDA, and ALDEx2), as well as their overlaps. Taxa are stratified by taxonomic level: (\u003cstrong\u003ea\u003c/strong\u003e) phylum, (\u003cstrong\u003eb\u003c/strong\u003e) family, (\u003cstrong\u003ec\u003c/strong\u003e) genus, and (\u003cstrong\u003ed\u003c/strong\u003e) species. Overlapping regions indicate taxa identified by multiple methods, whereas non-overlapping regions represent method-specific findings.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/1f4cac0fb1060f9b7d04b66b.png"},{"id":107479266,"identity":"2fc5ed9e-0595-4f61-9b53-a4bea74c8bd4","added_by":"auto","created_at":"2026-04-22 01:21:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8364000,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/ecc0628c-2afc-4fbb-985b-e34e0e01010b.pdf"},{"id":106635811,"identity":"f08cbfac-e25e-4745-b8c2-cdbd8634753e","added_by":"auto","created_at":"2026-04-10 16:50:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16682,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9257747/v1/a671db4f25974c79305049a2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Oral squamous cell carcinoma is associated with altered salivary microbiome structure and reduced community evenness","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral cancer (OC) represents a major global health burden, with oral squamous cell carcinoma (OSCC) accounting for approximately 90% of all cases. Worldwide, OC ranks among the most common malignancies, with an estimated annual incidence exceeding 389,000 new cases and 188,000 deaths\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Over the past decade, both the incidence and mortality of OC have continued to increase\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The etiology of OC is multifactorial, with well-established risk factors including tobacco use, alcohol consumption, and betel quid chewing\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In addition, infection with oncogenic pathogens, such as high-risk human papillomavirus (HPV) has emerged as an additional etiological factor\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe human oral cavity, together with the aerodigestive tract, harbors one of the most complex and densely populated microbial ecosystems in the body. Current repositories such as the expanded Human Oral Microbiome Database (eHOMD) catalog 836 bacterial species as of March 2026, approximately 77% of which are culturable under laboratory conditions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The resident oral microbiota interacts dynamically with the host, influencing local and systemic physiology through metabolic activity, immune modulation, and maintenance of epithelial homeostasis\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Consequently, disruption of this balanced community, known as dysbiosis, has been implicated in a wide range of systemic and local diseases, including atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, autoimmune disorders\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, cancer\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, bacteremia associated with endocarditis\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and diabetes\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Increasing evidence suggests that dysbiosis may contribute to the development of chronic periodontal disease\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, proliferative verrucous leukoplakia (PVL)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, and OSCC\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Chronic exposure to proinflammatory bacteria can promote a persistent inflammatory microenvironment, characterized by oxidative stress, DNA damage, and activation of oncogenic signaling pathways\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Several bacterial taxa, including \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003ePrevotella intermedia\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e, and \u003cem\u003eCapnocytophaga\u003c/em\u003e spp.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, have been repeatedly associated with OSCC and precancerous lesions, suggesting a potential etiological role or possible use as diagnostic biomarkers.\u003c/p\u003e \u003cp\u003eOur group has established a research line investigating the role of the oral microbiome in oral potentially malignant disorders (OPMDs) and OC. In an initial study, we identified reduced microbial diversity and increased abundance of potentially oncogenic bacteria, such as \u003cem\u003eCampylobacter jejuni\u003c/em\u003e, in tissue biopsies from patients with PVL\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Building on these findings, we subsequently compared the oral microbiome of tissue biopsies from patients with homogeneous leucoplakia, PVL, OSCC, and OSCC arising from PVL (PVL-OSCC). These analyses revealed distinct patterns of dysbiosis associated with disease progression, with cancer samples showing reduced microbial diversity and a microbiome enriched in members of the phylum Fusobacteriota\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. We next sought to determine whether the dysbiosis detected in tissue biopsies could also be identified in saliva, a promising non-invasive biofluid for OC detection. Unlike tissue biopsies, saliva collection is simple, repeatable, and suitable for screening. Saliva contains exfoliated epithelial cells, extracellular vesicles, and microbial DNA that reflects the oral microenvironment\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Although no saliva-based diagnostic test for oral cancer has yet received FDA approval, largely due to the need for further clinical validation, saliva remains an attractive biospecimen for biomarker discovery because it can be collected easily, repeatedly, and non-invasively\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Recently, we conducted a pilot study analyzing saliva samples from 10 patients with oral leukoplakia, 28 patients with PVL, and 28 patients with OSCC, which suggested progressive microbial dysbiosis during malignant transformation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, this study did not include a healthy control group. In the present study, we analyze saliva samples of a cohort of 113 individuals, including 61 healthy controls and 52 patients with OSCC, using 16S rRNA gene sequencing. By applying multiple complementary statistical approaches, we aim to identify robust microbial signatures associated with OSCC. Our findings may support the development of saliva-based, non-invasive diagnostic strategies and help identify bacterial taxa potentially involved in oral carcinogenesis, thereby providing a foundation for future mechanistic studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipant characteristics and sequencing data summary\u003c/h2\u003e \u003cp\u003eThis study included 113 participants, comprising 61 healthy controls and 52 patients diagnosed with OSCC. The clinicopathological characteristics of patients with OSCC are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients was 67.93 years (range 37.00\u0026ndash;92.45), 55.8% were males, and 67.3% were diagnosed at an early stage of the disease (stages I and II).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinicopathological characteristics of patients with OSCC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean age \u003c/p\u003e \u003cp\u003e(Standard deviation)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e67.93 \u003c/p\u003e \u003cp\u003e(12.43)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuccal Mucosa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFloor mouth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGingiva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical form\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythroleukoplakia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExophytic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUlcerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTNM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1N0M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2N1M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2N0M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3N1M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3N2M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3N0M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4aN1M0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEarly-Advanced stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA total of 7,088,020 paired-end reads were obtained, with a mean of 62,726 reads per sample (range 31,566\u0026thinsp;\u0026minus;\u0026thinsp;115,113). Rarefaction curves relating sequencing depth to taxonomic richness showed a tendency to plateau in most samples, suggesting that sequencing depth was sufficient to capture the majority of bacterial diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All detected taxa belonged to the domain Bacteria. In total, 12 phyla, 17 classes, 35 orders, 51 families, and 94 genera were identified. Additionally, 172 putative species-level taxa were detected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTaxonomic composition of the oral microbiota\u003c/h3\u003e\n\u003cp\u003eThe composition of the oral microbiota was characterized in both OSCC patients and healthy controls. The relative abundances of bacterial taxa at the phylum, family, and genus levels are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA high degree of inter-individual variability in microbial composition was observed across samples. At the phylum level, the microbial community was dominated by Firmicutes (mean relative abundance 34.1%), followed by Bacteroidota (28.1%), Pseudomonadota (22.6%), Fusobacteriota (10.6%), and Actinomycetota (3.0%). All other phyla accounted for less than 1% of the total relative abundance. At the family level, the most abundant taxa were Prevotellaceae (22.6%), Streptococcaceae (15.4%), Veillonellaceae (14.2%), Neisseriaceae (12.0%), Pasteurellaceae (8.2%), and Fusobacteriaceae (7.8%), followed by Porphyromonadaceae (4.4%), Leptotrichiaceae (2.8%), Micrococcaceae (2.3%), Carnobacteriaceae (1.4%), and Lachnospiraceae (1.3%). Remaining families were present at relative abundances below 1%. At the genus level, the most abundant genera were \u003cem\u003ePrevotella\u003c/em\u003e (20.5%), \u003cem\u003eStreptococcus\u003c/em\u003e (15.3%), \u003cem\u003eVeillonella\u003c/em\u003e (13.5%), \u003cem\u003eNeisseria\u003c/em\u003e (11.9%), \u003cem\u003eFusobacterium\u003c/em\u003e (7.8%), \u003cem\u003eHaemophilus\u003c/em\u003e (6.9%), and \u003cem\u003ePorphyromonas\u003c/em\u003e (4.4%), followed by \u003cem\u003eRothia\u003c/em\u003e (2.3%), \u003cem\u003eLeptotrichia\u003c/em\u003e (2.1%), and \u003cem\u003eGranulicatella\u003c/em\u003e (1.4%). All remaining genera individually accounted for less than 1% of total abundance. On average, 4.5% of reads could not be assigned at the genus level.\u003c/p\u003e\n\u003ch3\u003eComparison of alpha and beta diversity between OSCC patients and controls\u003c/h3\u003e\n\u003cp\u003eIntra-sample microbial diversity (alpha diversity) was assessed using multiple indices and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, stratified by clinical group. Across the entire cohort, the mean values were 3,578.2 for Breakaway richness, 7.33 for the Shannon index, 0.9987 for the Simpson index, and 0.904 for Pielou\u0026rsquo;s evenness. No statistically significant differences between clinical groups were observed for Breakaway richness, Shannon, or Simpson indices. However, Breakaway richness showed greater variability in OSCC patients, with a tendency toward higher values in some samples. OSCC patients showed significantly lower evenness, as measured by Pielou\u0026rsquo;s index (p\u0026thinsp;=\u0026thinsp;0.018). These results suggest that OSCC is not associated with major changes in overall richness, but rather with alterations in community structure reflected by reduced evenness.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInter-sample microbial diversity (beta diversity) was assessed using four distance metrics based on genus-level data: Bray\u0026ndash;Curtis, Jaccard, unweighted UniFrac, and weighted UniFrac. The corresponding principal coordinates analyses (PCoA) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Significant differences in microbial community composition between healthy controls and OSCC patients were observed using PERMANOVA for the Bray\u0026ndash;Curtis (p\u0026thinsp;=\u0026thinsp;0.009), Jaccard (p\u0026thinsp;=\u0026thinsp;0.007), and unweighted UniFrac (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) distances. Although the weighted UniFrac analysis showed a tendency toward significance, the results were not considered robust, as variability across repeated analyses resulted in some p-values exceeding the significance threshold. The lack of robust differences in weighted UniFrac suggests that group differences are primarily driven by the presence or absence of taxa rather than shifts in dominant taxa. Overall, these findings indicate that the microbial community structure differs significantly between controls and OSCC patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDifferential abundance analysis of the oral microbiota in OSCC\u003c/h3\u003e\n\u003cp\u003eTo identify specific taxa associated with the differences in overall microbiome composition between OSCC patients and healthy controls revealed by the PERMANOVA analyses, differential abundance analyses were performed using four complementary methods across multiple taxonomic levels: MaAsLin2, ANCOM-BC2, LinDA, and ALDEx2. Given the known methodological differences among these approaches, results from all methods were considered to provide a comprehensive overview of taxa associated with OSCC. The consistency of significant taxa across methods is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The full results obtained by each method are provided in \u003cb\u003eSuppl. File S1\u003c/b\u003e, while taxa identified by at least one method are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferentially abundant taxa between healthy controls and OSCC patients across taxonomic levels.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eOverrepresented\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003ein OSCC\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhyla\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamilies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenera\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePseudomonadota\u003c/p\u003e \u003cp\u003eBacillota\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTannerellaceae\u003c/p\u003e \u003cp\u003eWeeksellaceae\u003c/p\u003e \u003cp\u003eErysipelotrichaceae\u003c/p\u003e \u003cp\u003eFlavobacteriaceae\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTannerella\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDialister\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBergeyella\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSolobacterium\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePrevotella nigrescens\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFusobacterium simiae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHaemophilus haemolyticus\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eTannerella forsythia\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eLachnoanaerobaculum umeaense\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eSolobacterium moorei\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eHaemophilus influenzae\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eStreptococcus mitis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eDialister pneumosintes\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVeillonella tobetsuensis\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eKingella denitrificans\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUnderrepresented\u003c/b\u003e \u003c/p\u003e \u003cp\u003e\u003cb\u003ein OSCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLeptotrichia\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt the phylum level, Pseudomonadota and Bacillota were enriched in OSCC patients. At lower taxonomic levels, several taxa previously associated with oral dysbiosis and periodontal disease were found to be overrepresented in OSCC, including members of the families Tannerellaceae, Erysipelotrichaceae, and Flavobacteriaceae, as well as the genera \u003cem\u003eTannerella, Dialister, Bergeyella\u003c/em\u003e, and \u003cem\u003eSolobacterium\u003c/em\u003e. At the species level, enrichments included \u003cem\u003eTannerella forsythia, Fusobacterium simiae, Prevotella nigrescens, Solobacterium moorei, Dialister pneumosintes, Haemophilus haemolyticus, Haemophilus influenzae, Lachnoanaerobaculum umeaense, Streptococcus mitis, Veillonella tobetsuensis\u003c/em\u003e, and \u003cem\u003eKingella denitrificans\u003c/em\u003e. \u003cem\u003eLeptotrichia\u003c/em\u003e was the only genus that consistently decreased in OSCC.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the oral microbiome of 52 patients diagnosed with OSCC and 61 healthy controls. The OSCC cohort is clinically representative of a typical hospital-based oral cavity cancer series, with an older patient population, a slight male predominance, and a predominance of tumors arising in the tongue and gingiva, most frequently presenting as ulcerative lesions. Importantly, most cases were diagnosed at early stages (I–II, 67.3%), although a substantial fraction presented with advanced disease (III–IV, 32.7%), reflecting the heterogeneity of clinical presentation in OSCC. The taxonomic composition observed in this cohort is broadly consistent with the expected structure of the oral microbiota, being dominated by genera such as \u003cem\u003ePrevotella, Streptococcus, Veillonella\u003c/em\u003e, and \u003cem\u003eNeisseria\u003c/em\u003e, while also showing a high degree of inter-individual variability. This heterogeneity is a well-recognized feature of the oral ecosystem and highlights the importance of identifying disease-associated changes against a highly variable microbial background.\u003c/p\u003e \u003cp\u003eDespite the absence of major differences in overall richness between OSCC patients and healthy controls, alpha diversity analyses revealed significantly lower evenness in OSCC, as measured by Pielou’s index, in agreement with previous reports\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This finding suggests that OSCC is not necessarily associated with a global loss of microbial diversity, but rather with an imbalance in community structure, in which certain taxa may become disproportionately represented. In this context, the trend toward greater variability in Breakaway richness among OSCC cases may further reflect the ecological instability of the tumor-associated oral microbiome. This interpretation is supported by beta diversity analyses, which consistently showed significant differences in overall microbial composition between OSCC patients and controls. The fact that these differences were detected using Bray-Curtis, Jaccard, and unweighted UniFrac distances indicates that the two groups differ not only in relative abundance patterns but also in the presence and absence of specific taxa. By contrast, the lack of robust significance for weighted UniFrac suggests that these differences are less strongly driven by dominant taxa and may instead be influenced by shifts in less abundant or less prevalent community members.\u003c/p\u003e \u003cp\u003eWe performed differential abundance analyses using complementary methods across multiple taxonomic levels, which revealed several taxa overrepresented in OSCC. At the species level, we identified \u003cem\u003eTannerella forsythia, Streptococcus mitis, Veillonella tobetsuensis, Prevotella nigrescens\u003c/em\u003e, \u003cem\u003eHaemophilus haemolyticus\u003c/em\u003e, \u003cem\u003eHaemophilus influenzae\u003c/em\u003e, and \u003cem\u003eSolobacterium moorei\u003c/em\u003e as overrepresented in OSCC patients, among others. \u003cem\u003eTannerella forsythia\u003c/em\u003e is a well-known periodontal pathogen with strong pro-inflammatory potential, capable of promoting tissue degradation and creating a microenvironment conducive to tumor development\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Previous studies have also reported its association with early-stage head and neck squamous cell carcinoma\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Species belonging to the genera \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eVeillonella\u003c/em\u003e have also been associated with head and neck cancer, being proposed as discriminative taxa\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Enrichment of the genera \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eHaemophilus\u003c/em\u003e in saliva from OSCC patients was reported in a pilot study a few years ago\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. These authors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e also reported that \u003cem\u003eSolobacterium moorei\u003c/em\u003e proportion increases with cancer progression in tumoral tissue. This microorganism has been consistently reported in different oral conditions, including dysbiosis, halitosis, and the production of volatile sulfur compounds\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In addition, a previous study including six patients with OSCC and four patients with oral leukoplakia already reported the enrichment of the genus \u003cem\u003eSolobacterium\u003c/em\u003e in the saliva of patients with OSCC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Beyond oral cancer, \u003cem\u003eS. moorei\u003c/em\u003e has also been linked to colorectal cancer, where increased abundance has been observed not only in the oral microbiota\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e but also in gut microbiota\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In both contexts, its levels appear to correlate with disease progression, and it has been proposed that elevated oral levels of \u003cem\u003eS. moorei\u003c/em\u003e may contribute to cancer initiation, while also potentially influencing tumor progression. These findings suggest that \u003cem\u003eS. moorei\u003c/em\u003e may play a broader role in cancer-associated microbial dysbiosis and could represent a promising target for future mechanistic and biomarker studies. Other genera identified in our analysis, including \u003cem\u003eDialister\u003c/em\u003e and \u003cem\u003eBergeyella\u003c/em\u003e, have also been associated with chronic inflammation and pathogenic biofilm formation\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, further supporting the presence of a pro-inflammatory microbial environment in OSCC. Notably, \u003cem\u003eDialister\u003c/em\u003e has previously been proposed as a discriminative taxon for head and neck cancer in saliva\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Several less-characterized families identified in this study, including \u003cem\u003eWeeksellaceae\u003c/em\u003e, \u003cem\u003eErysipelotrichaceae\u003c/em\u003e, and \u003cem\u003eFlavobacteriaceae\u003c/em\u003e, have been only sparsely described in the context of OSCC. While some evidence links these taxa to cancer-related conditions or risk factors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e their precise role remains unclear, and further studies are required to determine whether they contribute to disease progression or reflect secondary ecological changes. \u003cem\u003eLeptotrichia\u003c/em\u003e was the only taxon found to be significantly underrepresented in OSCC. Previous studies have reported a higher abundance of \u003cem\u003eLeptotrichia\u003c/em\u003e in early-stage disease and associated its presence with improved survival outcomes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, suggesting a potential protective or context-dependent role. However, its behavior appears to vary depending on clinical and molecular factors such as HPV status and treatment strategy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, despite adjustment for age and sex, residual confounding is likely because several major determinants of the oral microbiome were not fully accounted for, including smoking, alcohol consumption, periodontal status, caries burden, recent dental procedures, oral hygiene habits, diet, socioeconomic factors, and HPV status. Imbalances in these variables may confound microbial differences attributed to OSCC. In addition, heterogeneity within the OSCC group, including tumor location, ulceration, and stage, may also influence the oral microbiota. Because the present analyses focused primarily on OSCC versus control comparisons, site- or stage-specific microbial signals may have been diluted. Second, the cross-sectional design precludes any inference about causality or temporality. Therefore, it remains unclear whether the observed dysbiosis precedes OSCC development, arises as a consequence of the tumor, or reflects a bidirectional interaction. This limitation is particularly relevant when interpreting candidate taxa as potential drivers rather than correlates with disease. Third, saliva offers clear advantages as a non-invasive and clinically feasible sample type, but it is not tumor-specific. Rather than capturing only the tumor microenvironment, saliva reflects the ecology of the entire oral cavity. Moreover, only a single saliva sample was analyzed per participant, so intra-individual temporal variability related to diet, circadian patterns, oral hygiene, or short-term environmental exposures could not be assessed. Fourth, although this study has a considerable sample size, it is a single-center study, which may limit the generalizability of the findings to other populations and clinical settings. No independent external validation cohort was available, so the taxa identified here should be considered candidate biomarkers requiring confirmation in separate cohorts. Since most OSCC cases were diagnosed at early stages, the findings may also be less representative of advanced disease. Fifth, the use of 16S rRNA gene sequencing targeting the V3-V4 region imposes taxonomic and functional limitations. Species-level assignments should be interpreted cautiously, as this region does not always provide sufficient discriminatory power for confident species identification. Likewise, 16S sequencing does not allow direct assessment of functional pathways, virulence traits, or metabolite production. The study was therefore limited to taxonomic profiling, without metagenomic, transcriptomic, metabolomic, or experimental validation of the inferred biological relevance of the identified taxa. Finally, although compositionality and zero inflation were addressed by applying four complementary statistical tools, overlap across methods was only partial. Thus, some differential abundance findings were method-dependent and should be interpreted as exploratory until independently replicated.\u003c/p\u003e \u003cp\u003eThe main contribution of this study lies not only in confirming differences between OSCC patients and healthy controls, but in providing a robust and integrative characterization of these differences using a moderately sized saliva cohort, standardized sequencing depth, and a multi-method analytical framework. By combining four complementary approaches for differential abundance analysis, we aimed to prioritize reproducibility and methodological robustness, addressing a key limitation highlighted in previous studies, namely the inconsistency of reported microbial signatures across analytical pipelines. Within this framework, we identified a set of taxa repeatedly associated with OSCC across methods, including genera such as \u003cem\u003eTannerella, Solobacterium, Dialister\u003c/em\u003e, and \u003cem\u003eBergeyella\u003c/em\u003e. Notably, several of these taxa are linked to periodontal disease and pro-inflammatory oral environments, supporting the concept of OSCC-associated dysbiosis as an inflammation-driven ecological shift rather than a random compositional change. An additional relevant finding is the combination of reduced community evenness, in the absence of major changes in richness, together with consistent beta diversity differences driven primarily by presence/absence metrics. This pattern suggests that, in OSCC, the core oral microbiota is largely preserved but becomes imbalanced, with selective expansion of specific taxa and loss or reduction of others. Such ecological restructuring is consistent with a transition toward a dysbiotic, inflammation-associated microbial community and may have implications for the development of saliva-based diagnostic approaches.\u003c/p\u003e \u003cp\u003eTaken together, our results reinforce the potential of saliva as a non-invasive matrix for capturing disease-associated microbial signatures. Longitudinal studies following patients with oral potentially malignant disorders through malignant transformation are needed to distinguish microbial risk markers from disease-associated changes. In addition, future analyses should incorporate detailed clinical stratification, including tumor stage, anatomical site, nodal status, ulceration, periodontal condition, and lifestyle factors such as smoking and alcohol consumption. Integrative multi-omics approaches, combining metagenomics or metatranscriptomics with salivary metabolomics, may help elucidate the functional mechanisms underlying the observed microbial shifts. From a translational perspective, the development and validation of saliva-based microbial classifiers in multicenter cohorts will be essential to assess their robustness and generalizability across populations and technical workflows.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003c/div\u003e \u003c/div\u003e\n\n "},{"header":"Methods","content":"\u003ch2\u003ePatients and samples\u003c/h2\u003e\u003cp\u003eThis study included 113 individuals recruited at the Department of Stomatology and Maxillofacial Surgery of the General University Hospital of Valencia between 2023 and 2024. Participants were classified according to their oral status into two groups: Group I comprised 61 healthy controls, and Group II included 52 patients diagnosed with OSCC. Exclusion criteria were pregnancy, age under 18 years, history of previous cancer (primary tumor), presence of metastasis from another anatomical site, prior radiotherapy or chemotherapy to the head or neck region, and the presence of other oral conditions that could potentially alter the microbial composition of saliva.\u003c/p\u003e\u003cp\u003eParticipants were instructed to fast for at least two hours prior to sample collection. None of the participants had received medication in the three months preceding sampling. Unstimulated whole saliva was collected for five minutes into sterile 15-mL tubes using a funnel, following the protocol described by Navazesh\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. To minimize blood contamination, participants were instructed not to brush their teeth within 45 minutes prior to sample collection. Samples visibly contaminated with blood were discarded and recollected. Saliva samples were immediately stored at − 80°C until further analysis.\u003c/p\u003e\u003ch3\u003eDNA extraction and 16S rRNA gene sequencing\u003c/h3\u003e\u003cp\u003eGenomic DNA was extracted from saliva sample using the GenElute™ Bacterial Genomic DNA Kit (Sigma-Aldrich, St. Louis, MO, USA) following the manufacturer’s instructions. Samples were treated with lysozyme, mutanolysin, and Proteinase K, followed by RNase treatment to remove residual RNA. Prior to extraction, saliva samples were homogenized to ensure representative recovery of microbial DNA. DNA concentration was measured by fluorometry using a Quantus™ Fluorometer (Promega, USA).\u003c/p\u003e\u003cp\u003eThe hypervariable V3-V4 region of the bacterial 16S rRNA gene was amplified using the universal primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GGACTACHVGGGTATCTAATCC-3′). Library preparation consisted of a first PCR to amplify the target region followed by a second PCR to attach sample-specific indices. The resulting libraries were purified and assessed using the QIAxcel Advanced System (Qiagen, Germany) to verify fragment size and library quality prior to sequencing. Sequencing was performed on an Illumina MiSeq platform using paired-end reads (2 × 250 bp) and sequencing-by-synthesis chemistry. Raw sequencing data were generated in FASTQ format for downstream bioinformatic analysis.\u003c/p\u003e\u003ch2\u003eBioinformatic analysis and taxonomic annotation\u003c/h2\u003e\u003cp\u003eRaw sequencing reads were processed using the DADA2 package (v1.36.0) in R (v4.5.1). During quality filtering, forward and reverse reads were truncated at position 245, and sequences with a maximum expected error ≥ 3 were discarded. The DADA2 denoising algorithm was applied to infer amplicon sequence variants (ASVs), after which paired-end reads were merged.\u003c/p\u003e\u003cp\u003eChimeric sequences were removed using the \u003cem\u003eremoveBimeraDenovo\u003c/em\u003e function, setting the \u003cem\u003eminFoldParentOverAbundance\u003c/em\u003e parameter to 4. Taxonomic assignment was performed up to the species level using the SILVA reference database (v138.2). Alpha diversity was assessed using the phyloseq (v1.52.0) and breakaway (v4.8.5) packages. Beta diversity analyses were performed using the vegan package (v2.7.1).\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed in R (v4.5.1). Age and sex were included as potential confounding variables in all analyses.\u003c/p\u003e\u003cp\u003eContinuous variables were summarized as mean ± standard deviation, while categorical variables were summarized as absolute and relative frequencies. Group differences between healthy controls and OSCC patients were evaluated using PERMANOVA with 9,999 permutations. To improve robustness, the analysis was repeated 10 times, and the mean p-value was reported. Alpha diversity was evaluated using four indices reflecting microbial richness and evenness: Breakaway\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, Shannon, Simpson, and Pielou indices. Differences in alpha diversity between groups were assessed using ordinal regression models implemented in the ordinal package (v2023.12.4.1). Beta diversity was assessed using principal coordinates analysis (PCoA) based on multiple distance metrics, including Bray-Curtis, Jaccard, unweighted UniFrac, and weighted UniFrac. Distance matrices were calculated using relative genus abundances. For UniFrac analyses, a phylogenetic tree was constructed using the DECIPHER (v3.6.0) and phangorn (v2.12.1) packages. The tree was inferred using the neighbor-joining method and optimized based on likelihood criteria using default parameters.\u003c/p\u003e\u003cp\u003eDifferential abundance analyses were performed to identify taxa associated with clinical groups at the phylum, family, genus, and species levels. Multiple complementary statistical approaches were applied, including MaAsLin2\u003csup\u003e42\u003c/sup\u003e (Maaslin2 package v1.22.0), ANCOM-BC2\u003csup\u003e43\u003c/sup\u003e (ANCOMBC package v2.12.0), LinDA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e (MicrobiomeStat package v1.2), and ALDEx2\u003csup\u003e45\u003c/sup\u003e (ALDEx2 package v1.42.0). Particular emphasis was placed on taxa identified as significant across multiple methods, whereas taxa detected by only one or two methods were considered less robust findings. Statistical significance was set at α = 0.05 for both p-values and multiple-testing-adjusted q-values.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthic declarations\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethical Research Committee (CEIM) of the General University Hospital of Valencia (approval number 10\u0026ndash;2023, February 24, 2023). All procedures were conducted in accordance with applicable local legislation and institutional requirements. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003edeclaration\u003c/p\u003e \u003cp\u003eThis research was funded by the Ministerio de Ciencia e Innovaci\u0026oacute;n (Spain) through the project \u0026ldquo;PID2022-138398OB-I00\u0026rdquo;.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.F.-J. performed formal and statistical analysis and wrote the original draft of the manuscript. D.H. contributed to formal and statistical analysis. L.B. enrolled patients and reviewed clinical data annotations. C.G. contributed to data interpretation and provided microbiological expertise. A.H.-P. and J.B. conceived and designed the study, supervised the project, and acquired funding. All authors contributed to data interpretation, critically revised the manuscript, approved the final version, and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank all enrolled patients and their families.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eRaw data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) with BioProject record PRJNA1445929 and BioSample records from SAMN56804886 to SAMN56804998.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Kratzer, T. B., Wagle, N. S., Sung, H. \u0026amp; Jemal, A. Cancer statistics, 2026. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e, e70043 (2026).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray, F. et al. 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ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e67019 (2013).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Oral squamous cell carcinoma, salivary microbiome, 16S rRNA sequencing, dysbiosis, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9257747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9257747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOral squamous cell carcinoma (OSCC) remains a major global health burden, highlighting the need for improved non-invasive tools for early detection and disease monitoring. Because saliva is easily accessible and reflects the oral microenvironment, the salivary microbiome has emerged as a promising source of candidate biomarkers. In this study, we characterized the salivary microbiota of 113 individuals, including 52 patients with OSCC and 61 healthy controls, using 16S rRNA gene sequencing targeting the V3-V4 region. Microbial diversity and composition were analyzed using complementary bioinformatic and statistical approaches, including four differential abundance methods (MaAsLin2, ANCOM-BC2, LinDA, and ALDEx2). OSCC was associated with significant differences in overall microbial community structure, as shown by beta diversity analyses, together with reduced community evenness but no major loss of richness. Differential abundance analyses identified several taxa overrepresented in OSCC, including the genera \u003cem\u003eTannerella\u003c/em\u003e, \u003cem\u003eSolobacterium\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, and \u003cem\u003eBergeyella\u003c/em\u003e, as well as species such as \u003cem\u003eSolobacterium moorei\u003c/em\u003e, \u003cem\u003eTannerella forsythia\u003c/em\u003e, and \u003cem\u003ePrevotella nigrescens\u003c/em\u003e. In contrast, \u003cem\u003eLeptotrichia\u003c/em\u003e was underrepresented in OSCC. These findings support the existence of an OSCC-associated salivary dysbiosis characterized by ecological restructuring rather than global diversity loss. The identified taxa represent candidate microbial biomarkers for OSCC that warrant validation in larger, independent, and longitudinal cohorts.\u003c/p\u003e","manuscriptTitle":"Oral squamous cell carcinoma is associated with altered salivary microbiome structure and reduced community evenness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 16:50:22","doi":"10.21203/rs.3.rs-9257747/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T04:34:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T14:36:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T09:33:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179514186232917688315532890109684764570","date":"2026-04-08T08:43:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33798086795735251753627053691291532962","date":"2026-04-08T08:07:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"273487394027401864943887921386513135089","date":"2026-04-07T09:08:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T07:49:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T07:45:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-06T04:03:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-31T18:55:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-31T18:49:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4cfc4027-35eb-41d4-89e9-f1ef9e5660b3","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65920042,"name":"Health sciences/Biomarkers"},{"id":65920043,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-05-18T07:24:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 16:50:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9257747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9257747","identity":"rs-9257747","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}