Oral Microbiome and CPT1A Function in Fatty Acid Metabolism in Oral Cancer

Oral Microbiome and CPT1A Function in Fatty Acid Metabolism in Oral Cancer | 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 Microbiome and CPT1A Function in Fatty Acid Metabolism in Oral Cancer Mi Kyung Kim, Zeba Praveen, Sung Choi, Jong Ho Lee, Joo Park, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4300099/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The oral microbiome plays vital roles in the human microbiome and human health. Although oral-microbiome dysbiosis can cause oral diseases and contribute to oral cancer, these relationships remain unclear. In this case-control study, we aimed to elucidate the link between the oral microbiome and potential mechanisms that can promote oral cancer progression. This study involved 1,022 participants, with the discovery and validation datasets including 637 patients (104 with oral cancer cases and 533 controls) and 385 patients (53 with oral cancer cases and 332 controls), respectively. Machine learning was employed, and the LightGBM model revealed four bacterial genera that were significantly associated with oral cancer. Streptococcus and Parvimonas were more abundant in the oral cancer group, whereas Corynebacterium and Prevotella showed decreased abundances. The levels of enzymes associated with fatty acid oxidation, such as carnitine O-palmitoyltransferase 1 (CPT1A), long-chain acyl-CoA synthetase, acyl-CoA dehydrogenase, diacylglycerol choline phosphotransferase, and H+-transporting ATPase, were significantly higher in patients with oral cancer than in control subjects. Streptococcus and Parvimonas correlated positively with the cytokines interleukin-6, tumor necrosis factor-alpha, and CPT1A, whereas Corynebacterium and Prevotella showed negative correlations. Our findings suggest a potential association between the changes in four distinct microbiomes and alterations in specific cytokines and enzymes in the context of oral cancer carcinogenesis. These microbiomes may function as promising predictive markers for oral cancer and improve its diagnostic accuracy. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Oral cancer is common globally, with an estimated 377,713 cases reported in 2020 1 . Established risk factors for oral cancer include smoking, alcohol use, human papillomavirus infection, and sunlight exposure 2, 3 . Associations between the oral microbiome and oral cancer have increasingly attracted attention; however, numerous knowledge gaps persist. Understanding the intricate process of oral cancer progression, identifying accurate oncological biomarkers, and implementing targeted therapies at an early stage are essential for effective oral cancer management 4, 5 . The oral cavity comprises different compact tissues and mucosal structures and has a diversified microbial abundance; it is home to over 700 bacterial species, making it home to a significant microbial community second only to the gastrointestinal tract 6 . This vast microbial community is vital for oral and systemic health, and dysbiosis is associated with inflammation and diseases, including oral cancer 4, 7 . Bacteria such as Streptococcus and Fusobacterium nucleatum induce inflammation, suppress immunity, and alter cell signaling, thereby promoting oral cancer 8-10 . Conversely, Lactobacillus plantarum elicits anticancer effects via immune modulation and metabolite-driven tumor inhibition in oral cancer 11 . Therefore, oral-microbiome modulation represents a promising route for cancer prevention, therapeutic interventions, and overall oral health. Metabolites produced by the oral microbiome, such as short chain fatty acids (SCFAs) and long-chain fatty acids, significantly influence cancer. Some oral bacteria, e.g., Streptococcus and Fusobacterium may limit SCFAs and raise tumor necrosis factor-α (TNF-α) and interleukin (IL)-6 levels, possibly facilitating cancer cell growth 12 . Previous data suggested that some gut bacteria severely impact the production of carnitine palmitoyltransferase 1 (CPT1), a crucial enzyme in mitochondrial fatty acid oxidation (FAO), which promotes the production of adenosine triphosphate (ATP), the primary energy source of cells 13 . Imbalanced FAO may facilitate oral cancer progression 14, 15 . Certain oral bacteria, such as Porphyromonas gingivalis , are potential cancer-causing agents that induce oxidative stress via oxidative stress-responsive kinase 1 (OXSR1) and DNA damage, whereas other bacteria, such as Lactobacillus , act as antioxidants that potentially suppress colon cancer 16, 17 . The understanding of the association between bacteria and oral cancer is limited by variations in research methods and inconsistent findings across studies involving different cancer subtypes and stages. We investigated whether a synergistic interaction between the oral microbiome and underlying metabolic pathways, particularly involving enzymes such as carnitine O-palmitoyltransferase 1 (CPT1A), might play a key role in oral cancer initiation. Furthermore, we studied the plausible association of the oral microbiome with enzymes and cytokines related to fatty acid metabolism, oxidative stress, and immune responses, which are considered crucial for oral cancer initiation. Materials And Methods Participant characteristics In this case-control study, adult patients (age >19 years) newly diagnosed with oral squamous cell carcinoma were enrolled from the National Cancer Center, Korea, and Seoul National University Dental Hospital, covering various oral-cavity regions. Healthy controls were recruited from the cancer-screening cohort of the National Cancer Center of the Republic of Korea. This study consisted of 1022 participants. The discovery and validation datasets included 637 patients (104 with oral cancer and 533 controls) and 385 patients (53 with oral cancer and 332 controls), respectively. Ethical approval was obtained from the National Cancer Center, Korea (IRB approval numbers NCC2019-0050, NCC2019-0116, and CRI15017), and written informed consent was obtained from all participants. All participants were interviewed to assess their sociodemographic characteristics using a structured questionnaire, followed by physical examinations. Saliva and blood sample collection Baseline saliva samples from participants were collected after a 1 h fasting period and stored in 1.5 mL tubes at -80 °C. Blood samples were drawn from their antecubital veins into BD Vacutainer K2 EDTA tubes after a 12 h fast and centrifuged at 3,000 rpm for 20 min at 4 °C. The resulting plasma, buffy coat, and red blood cell samples were stored at -80 °C. Oral microbiome characterization based on 16S rRNA gene amplification and sequencing Microbial DNA was extracted from saliva samples using a Fast DNA Spin Kit (MP Biomedicals, CA, USA). DNA quality and quantity were checked using a Qubit dsDNA BR Kit and a fluorometer (Life Technologies, CA, USA). Polymerase chain reaction products were purified from 2% agarose gels and secondarily amplified with Illumina NexTera barcodes using primers from Bionics Cosmogenetech (Seoul, Korea; Table S9). Amplicons were pooled at ChunLab (South Korea), and DNA was isolated and sequenced using the Illumina iSeq100 platform (Illumina Inc., CA, USA) at the National Cancer Center, SK. Primers 341F and 805R (Supplementary Table 9) (Bionics Cosmogenetech) were used to amplify the V4 region of the 16S rRNA gene. Bacteria were classified based on taxonomic data provided by EzBioCloud 18 . Poor-quality sequence reads of 2,000 bp were excluded. Taxonomic analysis was performed using the USEARCH tool. The UPARSE algorithm was used to classify the reads into operational taxonomic units (OTUs) with 97% similarity. Single-end reads were clustered into OTUs using UCLUST and the cut-off numbers. Functional homology inferences: predicting orthologs The functional profile of the oral microbiome was constructed using the PICRUSt algorithm with EzBioCloud’s 18 microbiome taxonomic profiling (MTP). Sequencing reads were obtained using the EzBioCloud 16S MTP pipeline and matched to reference database entries. Functional profiles were annotated by multiplying the gene counts/OTU by the OTU abundance per sample, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The accuracy of each functional profile was analyzed using the nearest-sequenced taxon index. Cell lines Oral cancer cell lines (CAL-27 and SCC-1) and a normal cell line (HGF-1) (HyClone Laboratories, UT, USA), were maintained in Dulbecco’s modified Eagle’s medium. The YD-10B oral cancer cell line (HyClone Laboratories) was maintained in RPMI 1640. Both media were supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 μg/mL streptomycin and stored in 5% carbon dioxide at 37 °C. Small-interfering RNA (siRNA) experiments A negative-control siRNA and an siRNA targeting CPT1A mRNA were purchased from Genolution, Inc. (Seoul, South Korea). The abovementioned siRNAs had the following sequences: siControl: 5′-CUCGUGCCGUUCCAUCAGGUAGUU-3′; siCPT1A: 5′- GACGUUAGAUGAAACUGAAUU-3′. 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) assay We performed MTT assays to determine the viabilities of HGF-1, YD-10B, CAL27, and SCC1 cells. The cells were plated in 96-well plates, grown for 24 h, and treated with dimethyl sulfoxide (vehicle control) or reverse-transfected with siCPT1A for 48 h. MTT solution (5 mg/mL) was then added to the cells, and the cells were incubated for 6 h at 37 °C. Formazan pellets were dissolved in 2-propanol, and absorbances were measured at 540 and 650 nm using a VersaMax Microplate Reader (Molecular Devices, CA, USA). Western blotting HGF-1, YD-10B, CAL27, and SCC1 cells were reverse-transfected with siCPT1A for 48 h. Subsequently, the cells were harvested in ice-cold RIPA lysis buffer (R0278; Sigma Aldrich, South Korea) containing protease and phosphatase inhibitors. Soluble lysate was isolated from each sample via centrifugation and quantified using the BCA Protein Assay Lit (Pierce, Thermo Fisher Scientific, MA, USA). Proteins were resolved using sodium dodecyl-sulfate polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride or polyvinylidene difluoride membranes. The membranes were blocked with 5% skim milk and probed with a primary antibody against CPT1A (ab128568, Abcam, Cambridge, England) and a secondary horseradish peroxidase-conjugated anti-mouse antibody (A90–116P; Bethyl Laboratories, TX, USA). Immunohistochemistry (IHC) analysis of CPT1A and CD4 + To assess CPT1A expression, 2 mm core biopsies from control and tumor paraffin blocks were sliced into 4 μm sections and dried at 56 °C for 1 h. IHC was performed using the Discovery XT platform (Ventana Micro Systems Inc., CA, USA), a Chromomap DAB Detection Kit (Roche Diagnostics, Basel, Switzerland), and a CPT1A antibody (diluted 1:200). The results were captured using a Vectra Polaris imaging system and quantified using inForm software. CPT1A expression was calculated as the average H-score, yielding a final score of 0–300 cells. CD4 + tissue samples were prepared as 4 μm-thick sections using a microtome, deparaffinized, and rehydrated. Antigens were retrieved using Tris-EDTA and sodium citrate buffer (pH 6.0). After blocking peroxidases with 3% hydrogen peroxide, the samples were stained and scanned by PrismCDX Co., Ltd. (Gyeonggi-do, Korea), as per clinical protocols (SI1). Measuring oral microbial signals, including cytokine levels (OXSR1, CPT1A, SCFAs, IL6, and TNF -α ) Plasma oxidative stress was evaluated by performing enzyme linked immunosorbent assays (ELISAs) using the OXSR1 ELISA Kit (abx382011; Abbexa Ltd., Cambridge, UK). Briefly, saliva samples were dispensed into 96-well plates and incubated at 37 °C. Detection reagents A and B were added to the plates, and the plates were further incubated for 1 h at 37 °C. TMB substrate (90 µl) was added to each plate, followed by 50 µL of stop solution. Optical densities were measured at 450 nm using a microplate reader (SPECTROstar, BMG LabTech, Ortenberg, Germany). Total human SCFAs in each saliva sample was measured using an SCFA ELISA Kit (MBS7269061; MyBioSource, CA, USA), which has a sensitivity of 0.92 pg/mL. Plasma CPT1A levels were measured using a CPT1A ELISA Kit (MBS724213; MyBioSource) and the minimum detectable concentration was 0.1 ng/ml. Plasma IL-6 (catalog number BMS213-2; Thermo Fisher Scientific) and TNF-α (catalog number BMS223-4) levels were quantified using an ELISA kit. The minimum detectable concentrations of the kit were 0.92 pg/mL for IL-6 and 2.3 pg/mL for TNF-α. Statistical analysis Python software (version 3.7.15) and the H 2 O Python module (version 3.38.0.2) (https://github.com/h2oai/h2o-3) was used for cancer diagnosis and biomarker identification. The gradient-based one-side sampling method was utilized within the light gradient-boosting machine (LightGBM) model, and optimization was performed based on various metrics (accuracy, area under the receiver operating characteristic [ROC] curve, F1, precision, and recall). R software (version 4.1.1) was used for analysis and visualization, and t-tests and chi-square tests were performed to compare the observed traits. Alpha diversity was determined by observing OTUs and Chao index. Beta diversity was determined using principal coordinate analysis, employing both weighted and unweighted UniFrac analyses. Statistical significance was assessed based on quartiles, Wilcoxon’s rank-sum test, fold-changes, and logistic regression. Linear-discriminant analysis effect size analysis was conducted to determine genus-level microbial differences. Results Demographic characteristics of patients and controls The characteristic parameters of sex, age, body–mass index, smoking, drinking, cancer stage, and cancer grade were considered the main clinical variables. The detailed demographic characteristics of the patients and control subjects in the discovery and validation datasets were tabulated (Supplementary Fig. 1, Supplementary Table 1). The patient and control groups displayed contrasting oral microbiome compositions We identified 48 phyla and 1704 genera in the participants’ samples. The dominant phyla were Proteobacteria , Bacteroidetes , and Firmicutes , which accounted for over 90% of the bacterial population (Supplementary Fig. 2A). Patients with oral cancer showed a significantly higher bacterial diversity than the controls, whereas beta diversity analysis did not reveal any significant differences between the groups (Supplementary Fig. 2B–D). Linear discriminant analysis at the genus level indicated that a higher risk for oral cancer was associated with Streptococcus and Parvimonas , whereas Corynebacterium and Prevotella were significantly associated with a reduced risk for oral cancer (Supplementary Fig. 2E). Furthermore, using Venn diagram analysis, we assessed oral microbiomes in patients with cancer and controls, which revealed 740 distinct overlapping bacterial genera (Supplementary Fig. 2F). Machine-learning analysis identified microbial biomarkers for oral cancer risk: LightGBM model accuracy and microbiome associations Machine-learning techniques can identify microbial taxa that may serve as oral cancer biomarkers. Utilizing the LightGBM model, we achieved a high accuracy level in predicting oral cancer, with an F1 score of 0.90, a sensitivity of 0.96, a precision of 0.98, an AUC of 0.98, and an overall accuracy of 0.97 (Supplementary Table 2). The performance was evaluated using a five-fold cross-validation approach. Top-feature importance analysis of the model revealed significant contributors to cancer prediction performance via ROC analysis (Fig. 1A–E, Supplementary Fig. 3). The analysis of 20 selected microbiomes revealed that higher abundances of Streptococcus (fold-change = 1.964, p = 2.01 × 10 -16 ) and Parvimonas (fold-change = 1.951, p = 2.01 × 10 -14 ) were linked with an increased cancer risk, whereas Corynebacterium (fold-change = 0.543, p = 2.91 × 10 -3 ) and Prevotella (fold-change = 0.590, p = 3.86 × 10 -11 ) were linked to a reduced cancer risk (Fig. 2A, Supplementary Table 3). An odds ratio plot was constructed to assess the relationships for each genus using logistic-regression analysis. Our results revealed higher abundances of Streptococcus and Parvimonas and lower abundance of Corynebacterium and Prevotella decreased in the patient group than in the control group (Fig. 2B, Supplementary Table 4). Integration of the microbiome and functional-pathway analysis revealed potential biomarkers and associations in oral cancer In this study, we identified 14,860 KEGG orthologs (KOs) and 446 pathways using PICRUSt and data from 1022 participants. This analysis revealed 24 significantly different KOs between the groups. CPT1A, acyl-CoA dehydrogenase, long-chain acyl-CoA synthetase, diacylglycerol choline phosphotransferase, and H + -transporting ATPase were associated with an elevated cancer risk (Fig. 2C, D, Supplementary Table 5, 6). A parallel analytical approach was used to predict outcomes associated with 15 specific pathways. Fatty acid metabolism (ko01212) and biosynthesis (ko00061) were significantly associated with oral cancer progression (Fig. 2E, F, Supplementary Table 7, 8). We explored the relationship between 20 specific microbiomes and 24 KOs using Spearman’s rank correlation coefficients. In patients with oral cancer, enzymes such as CPT1A (K08765), acyl-CoA synthetase (K01897), and diacylglycerol choline phosphotransferase (K00994) correlated positively with Streptococcus , whereas acyl-CoA dehydrogenase (K06445) and H + -transporting ATPase (K01535) correlated positively with Parvimonas (Fig. 3A, B; Supplementary Fig. 4A, B). Fatty acid degradation (ko00061) was significantly and positively correlated with Streptococcus and Parvimonas in the oral cancer group. In contrast, fatty acid metabolism (ko01212) showed greater correlations with Streptococcus , Parvimonas , and Corynebacterium in the oral cancer group than in the control group (Fig. 3C, D; Supplementary Fig. 4C, D). The oral microbiome may alter cytokine and immune pathways and contribute to the onset of cancer IHC and ELISA experiments were conducted to further analyze the significant pathways identified (K08765, K06445, K01897, K00994, and K01535). Oral cancer tissues showed significantly elevated levels of the CPT1A protein; an increased presence of T regulatory cells (CD4 + ); and higher levels of OXSR1, IL-6, and TNF-α. SCFAs production by oral microbial communities were lower in the oral cancer group than in the control group (Fig. 4A–H). MTT assays revealed that CPT1A knockdown did not affect the survival of either cancerous or normal cells. Further exploring the relationship between specific microbiomes and cytokines/enzymes revealed that Streptococcus correlated positively with IL-6, TNF-α, CPT1A, and OXSR1 levels; Parvimonas correlated positively with IL-6 alone; and Prevotella correlated negatively with IL-6, OXSR1, and TNF-α, suggesting that oral cancer may affect cytokine and CPT1A levels (Fig. 4I–K). Discussion This case-control study revealed a promising association between specific members of the oral microbiome and the underlying pathways in oral cancer. Patients with oral cancer showed prominent alterations in certain enzymes, such as CPT1A, which is significantly associated with fatty acid metabolism. Moreover, patients with oral cancer showed lower SCFAs levels and higher concentrations of key markers (such as CPT1A, IL-6, OXSR1, and TNF-α) than the control group. Reports on the association between the oral microbiome and oral cancer are limited 19 ; the role of CPT1A as a regulator of fatty acid metabolism with relevance to the oral microbiome and oral cancer risk has rarely been investigated. In this study, we elucidated associations between the oral microbiome, cellular enzymes, and cytokines in oral cancer in detail. We observed that the levels of Corynebacterium and Prevotella decreased significantly in patients with oral cancer , whereas the levels of Streptococcus and Parvimonas increased significantly. Our findings are consistent with those of earlier reports suggesting that bacterial imbalance may upset the complex equilibrium of oral microbiome–host relationships and cause oral cancer 20-24 . Our results suggest that oral cancer progression is intricately linked to altered fatty acid metabolism enzymes, as shown by the increased CPT1A levels driven by oral bacteria changes. Certain bacterial species, including Corynebacterium glutamicum and Prevotella intermedia , are linked to fatty acid metabolism via beta-oxidation and energy generation, as documented in the KEGG database 25, 26 . Extensive research has indicated that Streptococcus and Parvimonas are closely associated with the production of diverse SCFAs and bile acids 27, 28 , distinct from FA production metabolized by CPT1A in the mitochondria in different cancers 16, 29 . Our current findings demonstrated a correlation between changes in oral bacterial compositions, increased lipid metabolism, and oxidative stress in oral cancer, which are linked to variations in CPT1A enzyme levels and the production of vital metabolites such as fatty acids. These insights elucidate potential lipid-related mechanisms underlying oral cancer development and suggest that exploring imbalance in the oral microbiome is promising for the diagnosis and early prediction of oral cancer. Our findings exemplify the vital influence of the oral microbiome in governing the function of core metabolic enzymes such as CPT1A, OXSR1 and the generation of specific metabolites such as SCFAs and cytokines such as TNF-α, and IL-6 (Fig. 5). These molecules can trigger distinct immune responses and pathways linked to carcinogenesis 30 . Our results are consistent with those of previous studies, in which oral bacteria were found to be associated with tumor metastasis through vascular inflammation and barrier disruption 31, 32 . Previously, FAO upregulation increased oxidative stress and potentially increased OXSR1 levels 33 . Here, we demonstrated that CPT1A knockdown did not significantly influence the survival of cancerous or normal cells. Previous data emphasized the significance of CPT1A in cancer cell proliferation, metastasis, and induced tumor senescence via FAO regulation 30, 32, 34-39 . Our findings affirm that CPT1A is a lipid-metabolism regulator related to oral cancer risk. This case-control study had several limitations, including a lack of available research data and the time of saliva-sample collection from the patients with cancer and the control participants. The 16S rRNA sequencing iSeq100 protocol had constraints such as short read lengths, a low throughput, and limited scalability, which can particularly impact large-scale projects. Nevertheless, this approach offered notable advantages, including affordability, rapid turnaround, and a user-friendly interface, making it well-suited for targeted applications within the budget and time constraints 40 . In conclusion, our findings suggest a potential association between oral microbiome dysbiosis and the plausible involvement of metabolic enzymes (such as CPT1A) and immunological pathways in oral cancer carcinogenesis. Furthermore, specific shifts in the bacterial composition may influence fundamental metabolic pathways, potentially contributing to cell growth, immune activation, and oxidative stress during oral cancer development. Our findings suggest the potential applicability of Streptococcus , Parvimonas , Corynebacterium , and Prevotella as oral cancer biomarkers. However, we did not investigate oral cancer progression; hence, additional research is required to elucidate the precise nature of these associations. Declarations Acknowledgements We would like to thank Dr. Pir Mohd Ishfaq and Dr. Yeon-hee Kim from the Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea, for their valuable insights in manuscript writing. Author’s contributions M.K. Kim, contributed to conceptualization, design, data analysis and interpretation, drafted and critically revised the manuscript; Z. Praveen, contributed to conceptualization, design, data analysis, drafted and critically revised the manuscript; S.W. Choi, contributed to conceptualization, design, data analysis, drafted and critically revised the manuscript; J.H. Lee, contributed to data analysis, critically revised the manuscript; J.Y. Park, contributed to data analysis, critically revised the manuscript; H.J. Oh, contributed to data analysis, critically revised the manuscript; I.J. Kwon, contributed to data analysis, critically revised the manuscript; J.H. Park, contributed to data analysis, critically revised the manuscript. All authors gave their final approval and agree to be accountable for all aspects of the work. Funding information This work was supported by the National Cancer Center (NCC), Republic of Korea (grant numbers 2211690 and 2210660), and a grant by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (grant number 2020R1A2C1009369). Institutional review board statement This research was conducted in compliance with the principles outlined in the Declaration of Helsinki for all institutions involved. The participating institutions were the National Cancer Center of Korea (IRB approval number NCC 2019-0050), including the Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, Department of Oral Cancer, Department of Cancer AI & Digital Health at the National Cancer Center Graduate School of Cancer Science and Policy (protocol code NCC2020-0214; date of approval: February 14, 2020), and Seoul National University Dental Hospital (IRB approval number CRI15017). Informed Consent Statement Informed consent was obtained from all participants involved in the study. Written informed consent was obtained from all patients (s) for the publication of this paper. References Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin . 71(3):209-249 (2021). 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The oral bacterium Streptococcus mutans promotes tumor metastasis by inducing vascular inflammation. Cancer Sci .113(11):3980-3994 (2022). Joshi M, Stoykova GE, Salzmann-Sullivan M, Dzieciatkowska M, Liebman LN, Deep G, Schlaepfer IR. CPT1A supports castration-resistant prostate cancer in androgen-deprived conditions. Cells . 8(10):1115 (2019). Li Y, Li L, Qin J, Wu J, Dai X, Xu J. OSR1 phosphorylates the Smad2/3 linker region and induces TGF-β1 autocrine to promote EMT and metastasis in breast cancer. Oncogene . 40(1):68-84 (2021). Guan L, Chen Y, Wang Y, Zhang H, Fan S, Gao Y, Jiao T, Fu K, Sun J, Yu A, Huang M, Bi H. Effects of carnitine palmitoyltransferases on cancer cellular senescence. J Cell Physiol . 234(2):1707-1719 (2019). Pacilli A, Calienni M, Margarucci S, D'Apolito M, Petillo O, Rocchi L, Pasquinelli G, Nicolai R, Koverech A, Calvani M, Peluso G, Montanaro L. Carnitine-acyltransferase system inhibition, cancer cell death, and prevention of myc-induced lymphomagenesis. J Natl Cancer Inst .105(7):489-498 (2013). Liang K. Mitochondrial CPT1A: Insights into structure, function, and basis for drug development. Front Pharmacol .14:1160440 (2023). Fingerhut R, Röschinger W, Muntau AC, Dame T, Kreischer J, Arnecke R, Superti-Furga A, Troxler H, Liebl B, Olgemöller B, Roscher AA. Hepatic carnitine palmitoyltransferase I deficiency: acylcarnitine profiles in blood spots are highly specific. Clin Chem . 47(10):1763-1768 (2001). Innes AM, Seargeant LE, Balachandra K, Roe CR, Wanders RJ, Ruiter JP, Casiro O, Grewar DA, Greenberg CR. Hepatic Carnitine Palmitoyltransferase I Deficiency Presenting as Maternal Illness in Pregnancy. Pediatr Res. 47(1):43-43 (2000). Collins SA, Sinclair G, McIntosh S, et al. Carnitine palmitoyltransferase 1A (CPT1A) P479L prevalence in live newborns in Yukon, Northwest Territories, and Nunavut. Molecular genetics and metabolism . 101(2-3):200-204 (2010). Pei XM, Yeung MHY, Wong ANN, Tsang HF, Yu ACS, Yim AKY, Wong SCC. Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases. Cells .12(3):493 (2023). Additional Declarations (Not answered) Supplementary Files Suppliresultnpj.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4300099","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":305549064,"identity":"4d5031e6-b681-462e-b39d-1ebeed671ba9","order_by":0,"name":"Mi Kyung Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYDACdiDmqYDxDhCjhRmk5QzJWnjbSNHC38z7TOLtvDvy/A28Dx8wnLlHWIvEYXYzybnbnhnOOMBubMBwo5gIaw6zsUnzbjucYMDAxibB8CGBsA55sJY5YC3sP4jSYgDW0gCxhYHhBhFaDA+zMVvOOXbYcAaQIZFwhggtcsfbGG+8qTksz9/exvjhwzEitCAAKIJI0jAKRsEoGAWjADcAANcvMMOwsukSAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-5279-4162","institution":"National Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Mi","middleName":"Kyung","lastName":"Kim","suffix":""},{"id":305549065,"identity":"a8853779-04d9-4fe2-8597-7cebb01d020b","order_by":1,"name":"Zeba Praveen","email":"","orcid":"","institution":"Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Zeba","middleName":"","lastName":"Praveen","suffix":""},{"id":305549066,"identity":"12c58611-f5bf-4a05-ad3f-3faad7dc8095","order_by":2,"name":"Sung Choi","email":"","orcid":"","institution":"Department of Oral Cancer","correspondingAuthor":false,"prefix":"","firstName":"Sung","middleName":"","lastName":"Choi","suffix":""},{"id":305549067,"identity":"fd1c0830-4242-4288-b95a-d8d9a87cd4bf","order_by":3,"name":"Jong Ho Lee","email":"","orcid":"","institution":"Department of Oral Cancer","correspondingAuthor":false,"prefix":"","firstName":"Jong","middleName":"Ho","lastName":"Lee","suffix":""},{"id":305549068,"identity":"a6b7b2b1-1628-4de6-9669-0c36d7b5a626","order_by":4,"name":"Joo Park","email":"","orcid":"","institution":"Department of Oral Cancer","correspondingAuthor":false,"prefix":"","firstName":"Joo","middleName":"","lastName":"Park","suffix":""},{"id":305549069,"identity":"c8754119-9f9f-4730-8ae7-9dafb26074ec","order_by":5,"name":"Hyun Jun Oh","email":"","orcid":"","institution":"National Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"Jun","lastName":"Oh","suffix":""},{"id":305549070,"identity":"a9fcff5f-1b05-44ed-a451-8a3921262667","order_by":6,"name":"Ik Jae Kwon","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Ik","middleName":"Jae","lastName":"Kwon","suffix":""},{"id":305549071,"identity":"da5063a0-0db1-4fd9-88f1-fc78b0072d68","order_by":7,"name":"Jin Hee Park","email":"","orcid":"","institution":"Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"Hee","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-04-21 09:15:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4300099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4300099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57942175,"identity":"cee7970e-4cf2-470c-a9ab-beb36725720f","added_by":"auto","created_at":"2024-06-07 19:00:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":650002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOral microbiota-based prediction of oral cancer (OC) using Light Gradient Boosting Machine learning (LightGBM) model.\u003c/strong\u003eMachine learning model training strategy: 5-fold cross-validation of the LightGBM model in predicting oral cancer (OC) dataset. \u003cstrong\u003e(A)\u003c/strong\u003e Feature importance analysis of the top 20 features contributing to the LightGBM model performance in discovery dataset (n=637). \u003cstrong\u003e(B)\u003c/strong\u003e Receiver Operating Characteristic (ROC) curve analysis showing the relationship between the true positive rate (TPR) and the false positive rate (FPR) of the LightGBM model. The area under the Receiver Operating Characteristic curve (ROC curve) represents the performance of the model in distinguishing between positive and negative samples in top 20 feature importance in validation dataset (n=385). \u003cstrong\u003e(C)\u003c/strong\u003eThe utilization of SHapley Additive exPlanations (SHAP) values has unveiled a comprehensive elucidation of the output generated by the machine learning model. This method offers a localized interpretation for individual predictions, accomplishing this by assigning proportional contributions of features to the ultimate prediction outcome, \u003cstrong\u003e(D)\u003c/strong\u003e The Probability of Detection Index (POD) in Light Gradient Boosting Machine Learning indicates the likelihood of classifying samples into cancer and control, with values ranging from 0 to 1. Higher values closer to 1 suggest a greater chance of classifying a sample as cancer, \u003cstrong\u003e(E)\u003c/strong\u003e fold’s confusion matrix evaluated LightGBM model performance with actual and predicted labels in discovery dataset.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/d2797ab17853c1754b63c8c1.jpg"},{"id":57942180,"identity":"f7b12174-d106-4055-97a8-e9ae59c1482e","added_by":"auto","created_at":"2024-06-07 19:00:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2591519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A, C, E) Volcano plot using the results of a fold change of in 20 microbiomes, orthologies, and pathways in OC compared to controls.\u003c/strong\u003e Red dots indicate significant microbiome, orthologies and pathways with |log2FC|\u0026gt;0.5; blue dot indicates non-significant microbiome, orthologies and pathways with |log2FC|\u0026gt;0.5; and gray dots indicate non-significant microbiome, orthologies and pathways with |log2FC|\u0026lt;0.5. \u003cstrong\u003e(B, D, F)\u003c/strong\u003e A graphical representation of a forest plot displaying odds ratios and their corresponding 95% confidence intervals (95% CI) reveals the results of a multivariate logistic regression analysis involving the continuous scale of microbiome, orthologies, and pathways. In odd ratio plot red line indicates significant, microbiome, orthologies, and pathways; blue lines indicate non-significant microbiome, orthologies, and pathways. OC, oral cancer; CI, confidence interval.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/59f9cd4c76e09717d1abe577.jpg"},{"id":57942179,"identity":"a50a6f08-3667-4454-a459-3ec3790bab62","added_by":"auto","created_at":"2024-06-07 19:00:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpearman correlation heatmap between microbiome and function data \u003c/strong\u003e(A, B) correlation between 4 genera and 5 orthologs (Carnitine O-palmitoyltransferase 1(K08765), Acyl-CoA dehydrogenase (K06445), Long-chain acyl-CoA synthetase (K01897), Diacylglycerol choline phosphotransferase (K00994), H+-transporting ATPase (K01535)) in oral cancer and control group, (C,D) correlation between 4 genera and 2 pathways (Fatty acid biosynthesis (ko00061), Fatty acid metabolism (ko01212)) in cancer and control, (The color chart range has been set from -1.00 to 1.00). OC, oral cancer.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/6f0011a4d009493f5173201f.jpg"},{"id":57942176,"identity":"081745ae-9612-417a-91b9-00b444335631","added_by":"auto","created_at":"2024-06-07 19:00:11","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1480656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpact of CPT1A siRNA treatment on cell viability, CPT1A depletion, immunohistochemical and biomarker analysis in oral cancer.\u003c/strong\u003e(A) Representative images of immunohistochemistry of CPT1A protein detected from oral tissues. Stained tissues are shown at 100× magnifications. Scale bar represent 100 μm (B) IHC staining of control and OC tissues, The staining is visualized using a yellow color (Opal 480 yellow), and DAPIin OC: oral cancer. (C) Quantified intensity of CPT1A in OC. Error bars indicate the mean ± SEM for three independent experiments, (D) Quantified CD4+ cell count, (E) Oxidative Stress Responsive 1 (OXRS1). (F) Human plasma interleukin-6 levels (IL6). (G) Tumor necrosis factor-alpha (TNFα). (J) Short chain fatty acids (SCFAs) concentrations in oral saliva. OC: oral cancer. (I) Western blot analysis showing the depletion of CPT1A by siRNA treatment in normal and oral cancer cells. The data are representative of at least three independent experiments. (J) HGF-1, YD-10B, CAL27 and SCC1 cells were reverse-transfected with either siCTL or siCPT1A, after 48 h, Cell viability of HGF-1, YD-10B, CAL27 and SCC1 cells were analyzed using MTT assay. (K) Spearman correlation heatmap between 4 specific microbiomes with cytokines and enzyme in oral cancer. (The color chart range has been set from -1.00 to 1.00). The symbol.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/1bd993f01ed9e0fb32ab136b.jpg"},{"id":57942172,"identity":"11b7b58d-a5fc-4125-a216-9c1498ad83ec","added_by":"auto","created_at":"2024-06-07 19:00:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3705230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphic abstract: Exploring the impact of oral microbiota on carnitine O-palmitoyltransferase 1A (CPT1A) function in fatty acid metabolism and its potential immunomodulatory effects in oral cancer.\u003c/strong\u003eThe oral microbiome may modulate the upregulation of CPT1A, Oxidative Stress Responsive Kinase 1 (OXSR1), Interleukin-6 (IL-6), and Tumor Necrosis Factor-alpha (TNF-α) in oral cancer patients, while concurrently influencing the downregulation of short-chain fatty acids. red arrow: increased, blue arrow: decreased.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/578f65712beb99ae65d132dc.jpg"},{"id":60298634,"identity":"678f7dca-c6a5-4fa6-ae32-cbb467c8f02c","added_by":"auto","created_at":"2024-07-15 10:18:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9541227,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/76e3288b-dd21-43a4-bce8-3e188ada163e.pdf"},{"id":57942173,"identity":"e9862e58-267a-4267-872c-e913b8fcbaab","added_by":"auto","created_at":"2024-06-07 19:00:11","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":8469590,"visible":true,"origin":"","legend":"","description":"","filename":"Suppliresultnpj.docx","url":"https://assets-eu.researchsquare.com/files/rs-4300099/v1/e9cec444489dd80fc885a799.docx"}],"financialInterests":"(Not answered)","formattedTitle":"Oral Microbiome and CPT1A Function in Fatty Acid Metabolism in Oral Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral cancer is common globally,\u0026nbsp;with\u0026nbsp;an estimated 377,713 cases reported in 2020\u0026nbsp;\u003csup\u003e1\u003c/sup\u003e.\u0026nbsp;Established risk factors for oral cancer include smoking, alcohol use,\u0026nbsp;human papillomavirus infection, and sunlight exposure\u0026nbsp;\u003csup\u003e2, 3\u003c/sup\u003e.\u0026nbsp;Associations between\u0026nbsp;the oral\u0026nbsp;microbiome and oral cancer have increasingly attracted attention; however, numerous knowledge gaps persist.\u0026nbsp;Understanding the intricate process of\u0026nbsp;oral cancer\u0026nbsp;progression,\u0026nbsp;identifying accurate oncological biomarkers, and implementing targeted therapies at an early stage are essential for effective oral cancer management\u0026nbsp;\u003csup\u003e4, 5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe oral cavity comprises different compact tissues and mucosal structures and has a diversified microbial abundance; it is home to over 700 bacterial species, making it home to a significant microbial community second only to the gastrointestinal tract\u0026nbsp;\u003csup\u003e6\u003c/sup\u003e. This vast microbial community is vital for oral and systemic health, and dysbiosis is associated with inflammation and diseases, including oral cancer\u0026nbsp;\u003csup\u003e4, 7\u003c/sup\u003e. Bacteria such as \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e induce inflammation, suppress immunity, and alter cell signaling, thereby promoting oral cancer\u0026nbsp;\u003csup\u003e8-10\u003c/sup\u003e. Conversely, \u003cem\u003eLactobacillus plantarum\u003c/em\u003e elicits anticancer effects via immune modulation and metabolite-driven tumor inhibition in oral cancer\u0026nbsp;\u003csup\u003e11\u003c/sup\u003e. Therefore,\u0026nbsp;oral-microbiome modulation represents a promising route for cancer prevention, therapeutic interventions, and\u0026nbsp;overall oral health.\u003c/p\u003e\n\u003cp\u003eMetabolites produced by the oral microbiome, such as short chain fatty acids (SCFAs) and long-chain fatty acids, significantly influence cancer. Some oral bacteria, e.g., \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eFusobacterium\u003c/em\u003e may limit SCFAs and raise tumor necrosis factor-\u0026alpha; (TNF-\u0026alpha;)\u0026nbsp;and interleukin (IL)-6\u0026nbsp;levels,\u0026nbsp;possibly facilitating cancer cell growth\u0026nbsp;\u003csup\u003e12\u003c/sup\u003e. Previous data\u0026nbsp;suggested that\u0026nbsp;some gut bacteria\u0026nbsp;severely\u0026nbsp;impact the production of carnitine palmitoyltransferase 1 (CPT1), a crucial enzyme in mitochondrial fatty acid oxidation (FAO), which promotes the production of adenosine triphosphate (ATP),\u0026nbsp;the primary energy source of cells\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. Imbalanced FAO may facilitate oral cancer progression\u0026nbsp;\u003csup\u003e14, 15\u003c/sup\u003e. Certain oral bacteria, such as \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e, are potential cancer-causing agents that induce oxidative stress via oxidative stress-responsive kinase 1 (OXSR1) and DNA damage, whereas other bacteria, such as \u003cem\u003eLactobacillus\u003c/em\u003e, act as antioxidants that potentially suppress colon cancer\u0026nbsp;\u003csup\u003e16, 17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe understanding of the association between bacteria and oral cancer is limited by variations in research methods and inconsistent findings across studies involving different cancer subtypes and stages. We investigated whether a synergistic interaction between the oral microbiome and underlying metabolic pathways, particularly involving enzymes such as carnitine O-palmitoyltransferase 1 (CPT1A), might play a key role in oral cancer initiation. Furthermore, we studied the plausible association of the oral microbiome with enzymes and cytokines related to fatty acid metabolism, oxidative stress, and immune responses, which are considered crucial for oral cancer initiation.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Participant characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this case-control study, adult patients\u0026nbsp;(age \u0026gt;19 years)\u0026nbsp;newly diagnosed\u0026nbsp;with oral squamous cell carcinoma were enrolled from the National Cancer Center, Korea,\u0026nbsp;and Seoul National University Dental Hospital, covering various oral-cavity regions. Healthy controls were recruited from the cancer-screening cohort\u0026nbsp;of the National Cancer Center\u0026nbsp;of\u0026nbsp;the Republic of Korea.\u0026nbsp;This study consisted of 1022 participants. The discovery and validation datasets included 637 patients (104 with oral cancer and 533 controls) and\u0026nbsp;385 patients (53 with oral cancer\u0026nbsp;and 332 controls), respectively. Ethical approval was obtained from the National Cancer Center, Korea (IRB approval numbers NCC2019-0050, NCC2019-0116, and CRI15017), and written informed consent was obtained from all participants. All participants were interviewed to assess their sociodemographic characteristics using a structured questionnaire, followed by physical examinations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSaliva and blood sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline saliva samples from participants were collected after a 1 h fasting period and stored in 1.5 mL tubes at -80 \u0026deg;C. Blood samples were drawn from their antecubital veins into BD Vacutainer K2 EDTA tubes after a 12 h fast and centrifuged at 3,000 rpm for 20 min at 4 \u0026deg;C. The resulting plasma, buffy coat, and red blood cell samples were stored at -80 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eOral microbiome characterization based on 16S rRNA gene amplification and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMicrobial DNA was extracted from saliva samples using a Fast DNA Spin Kit (MP Biomedicals, CA, USA). DNA quality and quantity were checked using a Qubit dsDNA BR Kit and a fluorometer (Life Technologies, CA, USA). Polymerase chain reaction products were purified from 2% agarose gels and secondarily amplified with Illumina NexTera barcodes using primers from Bionics Cosmogenetech (Seoul, Korea; Table S9). Amplicons were pooled at ChunLab (South Korea), and DNA was isolated and sequenced using the Illumina iSeq100 platform (Illumina Inc., CA, USA) at the National Cancer Center, SK. Primers 341F and 805R (Supplementary \u0026nbsp;Table 9) (Bionics Cosmogenetech) were used to amplify the V4 region of the 16S rRNA gene. Bacteria were classified based on taxonomic data provided by EzBioCloud \u003csup\u003e18\u003c/sup\u003e. Poor-quality sequence reads of \u0026lt;80 base pairs (bp) or \u0026gt;2,000 bp were excluded. Taxonomic analysis was performed using the USEARCH tool. The\u0026nbsp;UPARSE algorithm was used to classify the reads into\u0026nbsp;operational taxonomic units\u0026nbsp;(OTUs)\u0026nbsp;with 97% similarity.\u0026nbsp;Single-end reads were clustered into OTUs using UCLUST and\u0026nbsp;the\u0026nbsp;cut-off numbers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional homology inferences: predicting orthologs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe functional profile of the oral microbiome was constructed using the PICRUSt algorithm with EzBioCloud\u0026rsquo;s \u003csup\u003e18\u003c/sup\u003e microbiome taxonomic profiling (MTP). Sequencing reads were obtained using the EzBioCloud 16S MTP pipeline and matched to reference database entries. Functional profiles were annotated by multiplying the gene counts/OTU by the OTU abundance per sample, using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The accuracy of each functional profile was analyzed using the nearest-sequenced taxon index.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCell lines\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOral cancer cell lines (CAL-27 and SCC-1) and\u0026nbsp;a normal cell line (HGF-1) (HyClone Laboratories,\u0026nbsp;UT, USA), were maintained in Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium. The YD-10B oral cancer cell line (HyClone Laboratories) was maintained in RPMI 1640. Both media were supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 \u0026mu;g/mL streptomycin and stored in 5% carbon dioxide at 37 \u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSmall-interfering RNA (siRNA)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eexperiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA negative-control siRNA and an siRNA\u0026nbsp;targeting CPT1A mRNA were purchased from\u0026nbsp;Genolution,\u0026nbsp;Inc. (Seoul, South Korea). The abovementioned siRNAs had the\u0026nbsp;following sequences:\u0026nbsp;siControl: 5\u0026prime;-CUCGUGCCGUUCCAUCAGGUAGUU-3\u0026prime;; siCPT1A: 5\u0026prime;- GACGUUAGAUGAAACUGAAUU-3\u0026prime;.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed MTT assays to determine the viabilities of HGF-1, YD-10B, CAL27,\u0026nbsp;and SCC1 cells. The cells were plated in 96-well plates, grown for 24 h, and\u0026nbsp;treated with dimethyl sulfoxide (vehicle control) or reverse-transfected with siCPT1A for 48 h. MTT solution (5 mg/mL) was then added to the cells, and the cells were incubated for 6 h at 37 \u0026deg;C. Formazan pellets were dissolved in 2-propanol, and absorbances were measured at 540 and 650 nm using a VersaMax Microplate Reader (Molecular Devices, CA, USA).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eWestern blotting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHGF-1, YD-10B, CAL27,\u0026nbsp;and SCC1 cells were reverse-transfected with siCPT1A for 48 h.\u0026nbsp;Subsequently, the cells were harvested in ice-cold RIPA lysis buffer (R0278; Sigma Aldrich, South Korea) containing protease and phosphatase inhibitors. Soluble lysate was isolated from each sample via centrifugation and quantified using\u0026nbsp;the BCA Protein Assay Lit (Pierce, Thermo Fisher Scientific, MA, USA).\u0026nbsp;Proteins were resolved using sodium dodecyl-sulfate polyacrylamide gel electrophoresis and transferred to polyvinylidene fluoride\u0026nbsp;or polyvinylidene difluoride\u0026nbsp;membranes. The membranes were blocked with 5% skim milk and probed with a primary antibody against CPT1A (ab128568, Abcam, Cambridge, England) and a secondary horseradish peroxidase-conjugated anti-mouse antibody (A90\u0026ndash;116P; Bethyl Laboratories, TX, USA).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eImmunohistochemistry (IHC)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eanalysis of CPT1A and CD4\u003csup\u003e+\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess CPT1A expression, 2 mm core biopsies from control and tumor paraffin blocks were sliced into 4 \u0026mu;m sections and dried at 56 \u0026deg;C for 1 h. IHC was performed using the Discovery XT platform (Ventana Micro Systems Inc., CA, USA),\u0026nbsp;a\u0026nbsp;Chromomap DAB Detection Kit (Roche Diagnostics, Basel, Switzerland), and a CPT1A antibody (diluted 1:200). The results were captured using\u0026nbsp;a Vectra Polaris imaging system and quantified\u0026nbsp;using inForm software. CPT1A expression was calculated as\u0026nbsp;the average H-score, yielding a final score of 0\u0026ndash;300\u0026nbsp;cells.\u003c/p\u003e\n\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003e tissue samples were prepared as 4 \u0026mu;m-thick sections using a microtome, deparaffinized, and rehydrated. Antigens were retrieved using Tris-EDTA and sodium citrate buffer (pH 6.0). After blocking peroxidases with 3% hydrogen peroxide, the samples were stained and scanned by PrismCDX Co., Ltd. (Gyeonggi-do, Korea), as per clinical protocols (SI1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eMeasuring oral microbial signals, including cytokine levels (OXSR1, CPT1A, SCFAs, IL6, and TNF\u003c/strong\u003e\u003cstrong\u003e-\u0026alpha;\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma oxidative stress was evaluated by performing enzyme linked immunosorbent assays (ELISAs) using\u0026nbsp;the OXSR1 ELISA Kit (abx382011;\u0026nbsp;Abbexa Ltd., Cambridge, UK). Briefly, saliva samples were dispensed into 96-well plates and incubated at 37 \u0026deg;C. Detection reagents A and B were added to the plates, and the plates were further incubated for 1 h at 37 \u0026deg;C. TMB substrate (90 \u0026micro;l) was added to each plate, followed by 50 \u0026micro;L of stop solution. Optical densities were measured at 450 nm using\u0026nbsp;a microplate reader (SPECTROstar, BMG LabTech, Ortenberg, Germany). Total human SCFAs in each saliva sample was measured using an SCFA ELISA Kit (MBS7269061; MyBioSource, CA, USA), which has a sensitivity of 0.92 pg/mL.\u003c/p\u003e\n\u003cp\u003ePlasma CPT1A levels were measured using\u0026nbsp;a CPT1A ELISA Kit (MBS724213;\u0026nbsp;MyBioSource) and the minimum detectable concentration was 0.1 ng/ml. Plasma IL-6 (catalog number BMS213-2; Thermo Fisher\u0026nbsp;Scientific)\u0026nbsp;and TNF-\u0026alpha; (catalog number BMS223-4) levels were quantified using\u0026nbsp;an ELISA kit. The minimum detectable concentrations of the kit were 0.92 pg/mL for IL-6 and 2.3 pg/mL for TNF-\u0026alpha;.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePython software (version 3.7.15) and the H\u003csub\u003e2\u003c/sub\u003eO Python module (version 3.38.0.2) (https://github.com/h2oai/h2o-3) was used for cancer diagnosis and biomarker identification. The gradient-based one-side sampling method was utilized within the light gradient-boosting machine (LightGBM) model, and optimization was performed based on various metrics (accuracy, area under the receiver operating characteristic [ROC] curve, F1, precision, and recall). R software (version 4.1.1) was used for analysis and visualization, and t-tests and chi-square tests were performed to compare the observed traits. Alpha diversity was determined by observing OTUs and Chao index. Beta diversity was determined using principal coordinate analysis, employing both weighted and unweighted UniFrac analyses. Statistical significance was assessed based on quartiles, Wilcoxon\u0026rsquo;s rank-sum test, fold-changes, and logistic regression. Linear-discriminant analysis effect size analysis was conducted to determine genus-level microbial differences.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographic characteristics of patients and controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe characteristic parameters of sex, age, body\u0026ndash;mass index, smoking, drinking, cancer stage, and cancer grade were considered\u0026nbsp;the main clinical variables. The detailed demographic characteristics of the patients and control subjects in\u0026nbsp;the discovery and validation datasets\u0026nbsp;were tabulated (Supplementary Fig. 1, Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eThe patient and control groups displayed contrasting oral microbiome compositions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified 48 phyla and 1704 genera in the participants\u0026rsquo; samples. The dominant phyla were \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, and \u003cem\u003eFirmicutes\u003c/em\u003e, which accounted for over 90% of the bacterial population (Supplementary Fig. 2A). Patients with oral cancer showed a significantly higher bacterial diversity than the controls, whereas beta diversity analysis did not reveal any significant differences between the groups (Supplementary Fig. 2B\u0026ndash;D). Linear discriminant analysis at the genus level indicated that a higher risk for oral cancer was associated with \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eParvimonas\u003c/em\u003e, whereas \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e were significantly associated with a reduced risk for oral cancer (Supplementary Fig. 2E). Furthermore, using Venn diagram analysis, we assessed oral microbiomes in patients with cancer and controls, which revealed 740 distinct overlapping bacterial genera (Supplementary Fig. 2F).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eMachine-learning analysis identified microbial biomarkers for oral cancer risk: LightGBM model accuracy and microbiome associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine-learning techniques can identify microbial taxa that may serve as oral cancer biomarkers. Utilizing the LightGBM model, we achieved a high accuracy level in predicting oral cancer, with an F1 score of 0.90, a sensitivity of 0.96, a precision of 0.98, an AUC of 0.98, and an overall accuracy of 0.97 (Supplementary Table 2). The performance was evaluated using a five-fold cross-validation approach. Top-feature importance analysis of the model revealed significant contributors to cancer prediction performance via ROC analysis (Fig. 1A\u0026ndash;E, Supplementary Fig. 3). The analysis of 20 selected microbiomes revealed that higher abundances of \u003cem\u003eStreptococcus\u003c/em\u003e (fold-change = 1.964, p = 2.01 \u0026times; 10\u003csup\u003e-16\u003c/sup\u003e) and \u003cem\u003eParvimonas\u003c/em\u003e (fold-change = 1.951, p = 2.01 \u0026times; 10\u003csup\u003e-14\u003c/sup\u003e) were linked with an increased cancer risk, whereas \u003cem\u003eCorynebacterium\u003c/em\u003e (fold-change = 0.543, p = 2.91 \u0026times; 10\u003csup\u003e-3\u003c/sup\u003e) and \u003cem\u003ePrevotella\u003c/em\u003e (fold-change = 0.590, p = 3.86 \u0026times; 10\u003csup\u003e-11\u003c/sup\u003e) were linked to a reduced cancer risk (Fig. 2A, Supplementary Table 3). An odds ratio plot was constructed to assess the relationships for each genus using logistic-regression analysis. Our results revealed higher abundances of \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eParvimonas\u003c/em\u003e and lower abundance of \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e decreased in the patient group than in the control group (Fig. 2B, Supplementary Table 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eIntegration of the microbiome and functional-pathway analysis revealed potential biomarkers and associations in oral cancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we identified 14,860 KEGG orthologs (KOs) and 446 pathways using PICRUSt and data from 1022 participants. This analysis revealed 24 significantly different KOs between the\u0026nbsp;groups. CPT1A, acyl-CoA dehydrogenase, long-chain acyl-CoA synthetase, diacylglycerol choline phosphotransferase, and H\u003csup\u003e+\u003c/sup\u003e-transporting ATPase were associated with\u0026nbsp;an elevated cancer risk (Fig. 2C, D,\u0026nbsp;Supplementary Table\u0026nbsp;5, 6).\u0026nbsp;A parallel analytical approach was used to predict outcomes\u0026nbsp;associated with 15 specific pathways. Fatty acid metabolism (ko01212) and biosynthesis (ko00061) were significantly associated with oral cancer progression (Fig. 2E, F, Supplementary Table 7, 8).\u003c/p\u003e\n\u003cp\u003eWe explored the relationship between 20 specific microbiomes and 24 KOs using Spearman\u0026rsquo;s rank correlation coefficients. In patients with oral cancer, enzymes such as CPT1A (K08765), acyl-CoA synthetase (K01897), and diacylglycerol choline phosphotransferase (K00994) correlated positively with \u003cem\u003eStreptococcus\u003c/em\u003e, whereas acyl-CoA dehydrogenase (K06445) and H\u003csup\u003e+\u003c/sup\u003e-transporting ATPase (K01535) correlated positively with \u003cem\u003eParvimonas\u003c/em\u003e (Fig. 3A, B; Supplementary Fig. 4A, B). Fatty acid degradation (ko00061) was significantly and positively correlated with \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eParvimonas\u003c/em\u003e in the oral cancer group. In contrast, fatty acid metabolism (ko01212) showed greater correlations with \u003cem\u003eStreptococcus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Parvimonas\u003c/em\u003e, and \u003cem\u003eCorynebacterium\u003c/em\u003e in the oral cancer group than in the control group (Fig. 3C, D; Supplementary Fig. 4C, D).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eThe oral microbiome may alter cytokine and immune pathways and contribute to the onset of cancer\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIHC and ELISA experiments were conducted to further analyze the significant pathways identified (K08765, K06445, K01897, K00994, and K01535). Oral cancer tissues showed significantly elevated levels of the CPT1A protein; an increased presence of T regulatory cells (CD4\u003csup\u003e+\u003c/sup\u003e); and higher levels of OXSR1, IL-6, and TNF-\u0026alpha;. SCFAs production by oral microbial communities were lower in the oral cancer group than in the control group (Fig. 4A\u0026ndash;H).\u003c/p\u003e\n\u003cp\u003eMTT\u0026nbsp;assays revealed that CPT1A knockdown did not affect the survival of either cancerous or normal cells. Further exploring the relationship between specific microbiomes and cytokines/enzymes revealed that \u003cem\u003eStreptococcus\u003c/em\u003e correlated positively with IL-6, TNF-\u0026alpha;, CPT1A, and OXSR1 levels; \u003cem\u003eParvimonas\u003c/em\u003e correlated positively with IL-6 alone; and \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003ecorrelated negatively with IL-6, OXSR1, and TNF-\u0026alpha;, suggesting that oral cancer may affect cytokine and CPT1A levels (Fig. 4I\u0026ndash;K).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis case-control study revealed a promising association between\u0026nbsp;specific members of the oral\u0026nbsp;microbiome and the underlying pathways in oral cancer. Patients with oral cancer showed prominent alterations in certain enzymes, such as CPT1A, which is significantly associated with fatty acid metabolism. Moreover, patients with oral cancer showed lower SCFAs levels and higher concentrations of key markers (such as CPT1A, IL-6, OXSR1, and TNF-\u0026alpha;) than the control group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReports on the association between\u0026nbsp;the oral microbiome and oral cancer are limited\u0026nbsp;\u003csup\u003e19\u003c/sup\u003e; the role of CPT1A as a regulator of fatty acid metabolism with relevance to\u0026nbsp;the oral microbiome and oral cancer risk\u0026nbsp;has rarely been investigated. In this study, we elucidated associations between\u0026nbsp;the oral\u0026nbsp;microbiome, cellular enzymes, and cytokines in oral cancer in detail. We observed that the levels of \u003cem\u003eCorynebacterium\u003c/em\u003e and \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003edecreased significantly\u0026nbsp;in patients with oral cancer\u003cem\u003e,\u003c/em\u003e whereas the levels of \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eParvimonas\u003c/em\u003e increased significantly. Our findings are consistent with\u0026nbsp;those of earlier reports\u0026nbsp;suggesting that bacterial imbalance may upset the complex equilibrium of oral microbiome\u0026ndash;host relationships\u0026nbsp;and cause oral cancer\u0026nbsp;\u003csup\u003e20-24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur results suggest that oral cancer progression is intricately linked to altered fatty acid metabolism enzymes, as shown by\u0026nbsp;the increased CPT1A levels\u0026nbsp;driven by oral bacteria changes. Certain bacterial species, including \u003cem\u003eCorynebacterium\u003c/em\u003e \u003cem\u003eglutamicum\u003c/em\u003e and \u003cem\u003ePrevotella\u003c/em\u003e \u003cem\u003eintermedia\u003c/em\u003e, are linked to fatty acid metabolism via beta-oxidation and energy generation, as documented in the KEGG database\u003csup\u003e25, 26\u003c/sup\u003e. Extensive research\u0026nbsp;has indicated that \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eParvimonas\u003c/em\u003e are closely associated with\u0026nbsp;the production of diverse SCFAs and bile acids\u0026nbsp;\u003csup\u003e27, 28\u003c/sup\u003e, distinct from FA production metabolized by CPT1A in\u0026nbsp;the mitochondria\u0026nbsp;in different cancers\u0026nbsp;\u003csup\u003e16, 29\u003c/sup\u003e. Our current findings demonstrated a correlation between changes in oral bacterial compositions, increased lipid metabolism, and oxidative stress in oral cancer, which are linked to variations in CPT1A enzyme levels and the production of vital metabolites\u0026nbsp;such as fatty acids. These insights elucidate potential lipid-related mechanisms underlying oral cancer development and suggest that exploring imbalance in the\u0026nbsp;oral\u0026nbsp;microbiome is promising for\u0026nbsp;the\u0026nbsp;diagnosis and early prediction of oral cancer.\u003c/p\u003e\n\u003cp\u003eOur findings exemplify the vital influence of the oral microbiome in governing the function of core metabolic enzymes such as CPT1A, OXSR1 and the generation of specific metabolites such as SCFAs and cytokines such as TNF-\u0026alpha;, and IL-6 (Fig. 5). These molecules can trigger distinct immune responses and pathways linked to carcinogenesis\u0026nbsp;\u003csup\u003e30\u003c/sup\u003e. Our results are consistent with\u0026nbsp;those of previous studies, in which oral bacteria were found\u0026nbsp;to be associated with tumor metastasis through vascular inflammation and barrier disruption\u0026nbsp;\u003csup\u003e31, 32\u003c/sup\u003e. Previously,\u0026nbsp;FAO upregulation increased oxidative stress and potentially increased OXSR1 levels\u0026nbsp;\u003csup\u003e33\u003c/sup\u003e.\u0026nbsp;Here, we demonstrated that CPT1A knockdown did not significantly influence\u0026nbsp;the survival of cancerous or normal cells. Previous data emphasized the significance of CPT1A in cancer cell proliferation, metastasis, and induced tumor senescence via FAO regulation\u0026nbsp;\u003csup\u003e30, 32, 34-39\u003c/sup\u003e. Our findings affirm that CPT1A is a lipid-metabolism regulator related to\u0026nbsp;oral cancer risk.\u003c/p\u003e\n\u003cp\u003eThis case-control study had several limitations, including a lack of available research data and the time of saliva-sample collection from the patients with cancer and the control participants. The 16S rRNA sequencing iSeq100 protocol had constraints such as short read lengths, a low throughput, and limited scalability, which can particularly impact large-scale projects. Nevertheless, this approach offered notable advantages, including affordability, rapid turnaround, and a user-friendly interface, making it well-suited for targeted applications within the budget and time constraints\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, our findings suggest a potential association between\u0026nbsp;oral\u0026nbsp;microbiome dysbiosis and the plausible involvement of metabolic enzymes (such as CPT1A) and\u0026nbsp;immunological pathways in oral cancer carcinogenesis. Furthermore,\u0026nbsp;specific shifts in the\u0026nbsp;bacterial composition may influence fundamental metabolic pathways, potentially contributing to\u0026nbsp;cell growth, immune activation, and oxidative stress during oral cancer development. Our findings suggest the potential applicability of \u003cem\u003eStreptococcus\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Parvimonas\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, and \u003cem\u003ePrevotella\u003c/em\u003e as oral cancer biomarkers. However, we did not investigate oral cancer progression; hence, additional research is required to elucidate the precise nature of these associations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Dr. Pir Mohd Ishfaq and Dr. Yeon-hee Kim from the Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea, for their valuable insights in manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K. Kim, contributed to conceptualization, design, data analysis and interpretation, drafted and critically revised the manuscript; Z. Praveen, contributed to conceptualization, design, data analysis, drafted and critically revised the manuscript; S.W. Choi, contributed to conceptualization, design, data analysis, drafted and critically revised the manuscript; J.H. Lee, contributed to data analysis, critically revised the manuscript; J.Y. Park, contributed to data analysis, critically revised the manuscript; H.J. Oh, contributed to data analysis, critically revised the manuscript; I.J. Kwon, contributed to data analysis, critically revised the manuscript; J.H. Park, contributed to data analysis, critically revised the manuscript. All authors gave their final approval and agree to be accountable for all aspects of the work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Cancer Center (NCC), Republic of Korea (grant numbers 2211690 and 2210660), and a grant by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT) (grant number 2020R1A2C1009369).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eInstitutional review board statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted in compliance with the principles outlined in the Declaration of Helsinki for all institutions\u0026nbsp;involved.\u0026nbsp;The participating institutions were the National Cancer Center of Korea (IRB approval number\u0026nbsp;NCC 2019-0050), including the Cancer Epidemiology Branch, Division of Cancer Epidemiology and Prevention, Department of Oral Cancer, Department of Cancer AI \u0026amp; Digital Health at the National Cancer Center Graduate School of Cancer Science and Policy (protocol code NCC2020-0214; date of approval: February 14, 2020), and Seoul National University Dental Hospital (IRB approval number CRI15017).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants involved in the study. Written informed consent was obtained from all patients (s) for the publication of this paper.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., \u0026amp; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 71(3):209-249 (2021).\u003c/li\u003e\n\u003cli\u003eLouredo BV, Vargas PA, P\u0026eacute;rez-de-Oliveira ME, Lopes MA, Kowalski LP, Curado MP. Epidemiology and survival outcomes of lip, oral cavity, and oropharyngeal squamous cell carcinoma in a southeast Brazilian population. \u003cem\u003eMed Oral Patol Oral Cir Bucal\u003c/em\u003e. 27(3):e274-e284 (2022).\u003c/li\u003e\n\u003cli\u003eShi, L., Yang, Y., Li, M., Li, C., Zhou, Z., Tang, G., Wu, L., Yao, Y., Shen, X., Hou, Z., \u0026amp; Jia, H.. 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Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases. \u003cem\u003eCells\u003c/em\u003e.12(3):493 (2023). \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4300099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4300099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The oral microbiome plays vital roles in the human microbiome and human health. Although oral-microbiome dysbiosis can cause oral diseases and contribute to oral cancer, these relationships remain unclear. In this case-control study, we aimed to elucidate the link between the oral microbiome and potential mechanisms that can promote oral cancer progression. This study involved 1,022 participants, with the discovery and validation datasets including 637 patients (104 with oral cancer cases and 533 controls) and 385 patients (53 with oral cancer cases and 332 controls), respectively. Machine learning was employed, and the LightGBM model revealed four bacterial genera that were significantly associated with oral cancer. Streptococcus and Parvimonas were more abundant in the oral cancer group, whereas Corynebacterium and Prevotella showed decreased abundances. The levels of enzymes associated with fatty acid oxidation, such as carnitine O-palmitoyltransferase 1 (CPT1A), long-chain acyl-CoA synthetase, acyl-CoA dehydrogenase, diacylglycerol choline phosphotransferase, and H+-transporting ATPase, were significantly higher in patients with oral cancer than in control subjects. Streptococcus and Parvimonas correlated positively with the cytokines interleukin-6, tumor necrosis factor-alpha, and CPT1A, whereas Corynebacterium and Prevotella showed negative correlations. Our findings suggest a potential association between the changes in four distinct microbiomes and alterations in specific cytokines and enzymes in the context of oral cancer carcinogenesis. These microbiomes may function as promising predictive markers for oral cancer and improve its diagnostic accuracy.","manuscriptTitle":"Oral Microbiome and CPT1A Function in Fatty Acid Metabolism in Oral Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 18:59:51","doi":"10.21203/rs.3.rs-4300099/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4e538f85-cdce-4934-90aa-c8eabeab278e","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-15T10:10:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-07 18:59:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4300099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4300099","identity":"rs-4300099","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}