Identification of Potential Biomarkers for Breast Cancer Based on Salivary Metabolomics

Identification of Potential Biomarkers for Breast Cancer Based on Salivary Metabolomics | 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 Identification of Potential Biomarkers for Breast Cancer Based on Salivary Metabolomics XinYu Jiang, Yumei Jia, Bo Zhang, kai yang, Yang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6531859/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 Introduction: Breast cancer (BC) remains the most prevalent malignant tumor in women worldwide and a leading cause of cancer-related mortality. Early screening is essential to improve prognosis, yet current diagnostic methods are often invasive or lack sensitivity. Saliva is an accessible and non-invasive biofluid containing various metabolites that reflect systemic physiological and pathological changes. Thus, salivary metabolomics may provide novel insights into breast cancer-associated metabolic alterations and support the development of early diagnostic strategies. Objectives To explore the salivary metabolomic profile of breast cancer patients and identify potential non-invasive biomarkers for early breast cancer screening. Methods Saliva samples were collected from 30 breast cancer patients and 20 healthy controls. An untargeted metabolomics approach was applied using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Multivariate analyses (PCA, OPLS-DA), KEGG pathway enrichment, and ROC curve analysis were used to characterize metabolic differences and evaluate the diagnostic performance of candidate biomarkers. Results A total of 101 differential metabolites were identified, including 81 upregulated and 20 downregulated compounds. Significant alterations were observed in caffeine metabolism, choline metabolism, and amino acid metabolism pathways. Among them, 2-aminonicotinic acid and theobromine demonstrated moderate diagnostic value, with AUCs of 0.82 and 0.85, respectively. However, diagnostic thresholds and confidence intervals require further validation in larger cohorts. Conclusion The salivary metabolome of breast cancer patients displays distinct changes compared to healthy individuals. These metabolic alterations suggest disruptions in energy metabolism, oxidative stress response, and immune regulation in breast cancer. Salivary metabolites such as 2-aminonicotinic acid and theobromine may serve as promising non-invasive biomarkers, although further studies are needed to confirm their diagnostic utility and specificity. Biological sciences/Cancer/Breast cancer Biological sciences/Cancer/Cancer epidemiology Health sciences/Neurology Health sciences/Oncology Breast Cancer Caffeine Metabolism Salivary Metabolomics LC-MS/MS Biomarkers KEGG Pathway Analysis ROC Curve Figures Figure 1 Figure 2 Figure 3 1. Introduction Breast cancer (BC) is the most common malignant tumor among women, ranking first in both incidence and mortality among female malignancies. According to a report by the World Health Organization (WHO), more than 2.3 million new breast cancer cases were diagnosed globally in 2020, with approximately 685,000 related deaths(Arnold et al. 2022). These figures not only underscore the severe threat breast cancer poses to women’s health worldwide but also highlight its significance and urgency in public health(Qiu et al. 2021). The high incidence and mortality rates of breast cancer make it one of the major challenges to women's health(Jafari et al. 2018). There is an urgent need to enhance research and application of early screening, diagnosis, and treatment strategies. Currently, breast cancer detection primarily relies on imaging techniques such as mammography, ultrasound, and magnetic resonance imaging (MRI)(Moyya and Asaithambi 2022), as well as invasive procedures like tissue biopsy or minimally invasive blood tests(Frassica et al. 2000). Although these technologies play a crucial role in diagnosis, they often fail to fully meet the need for non-invasive, convenient, and efficient screening, particularly for the early detection of breast cancer(Tsang and Tse 2020). Therefore, developing a novel non-invasive diagnostic method is urgently needed. Metabolomics, as a powerful analytical tool, has been widely used to study the expression changes of metabolites in complex human diseases. Its high specificity and sensitivity offer distinct advantages in research across various diseases(Brindle et al. 2002). By quantitatively analyzing the overall metabolic profile changes of organisms under normal physiological conditions, pathological processes, or external stimuli, metabolomics provides an indispensable platform for discovering potential biomarkers(Wang et al. 2022). This technology not only reveals disease-related metabolic pathway abnormalities but also provides critical scientific evidence for early diagnosis, prognosis assessment, and the development of personalized treatment strategies. In recent years, the application of metabolomics in cancer research has expanded significantly. Untargeted metabolomics and lipidomics studies using liquid chromatography-tandem mass spectrometry (LC-MS/MS) have shown tremendous potential in discovering novel biomarkers and uncovering metabolic changes(Alonso et al. 2015). Mohit Jain et al. used LC-MS/MS technology to analyze the consumption and release (CORE) curves of 219 metabolites in the culture media of the NCI-60 cancer cell lines, including breast cancer cells, revealing unique metabolic characteristics of cancer cells(Jain et al. 2012). In further studies, they employed RRLC-MS/MS to successfully differentiate between breast cancer patients and healthy controls (HC), identifying 12 potential breast cancer biomarkers in urine samples(Chen et al. 2009). It is worth noting that, in addition to urinary metabolomics, salivary metabolomics has gained increasing attention in recent years and has shown promising diagnostic potential. Sugimoto et al. used CE-MS technology to analyze saliva samples and identified 14 amino acids as potential biomarkers for breast cancer diagnosis(Sugimoto et al. 2010a). Moreover, researchers have used salivary biomarkers to diagnose diseases such as oral cancer(Wei et al. 2011), pancreatic cancer(Zhang et al. 2010), and lung cancer(Xiao et al. 2012). However, despite the growing body of research on salivary metabolomics in other cancer types, its application in breast cancer remains relatively limited, with the associated metabolic characteristics not yet systematically analyzed. Further in-depth research is urgently needed to clarify its clinical application value. Saliva, as a bodily fluid rich in various metabolites, offers distinct advantages in disease screening and early diagnosis due to its non-invasive collection, ease of use, and high reproducibility(Nonaka and Wong 2022). Existing studies have shown significant metabolic abnormalities in breast cancer patients, including disruptions in glucose metabolism, amino acid metabolism, and lipid metabolism(Young et al. 2023). These metabolic changes may be reflected in saliva through the bloodstream or other pathways. Therefore, by integrating salivary metabolomics, it is possible to identify new non-invasive biomarkers for the early diagnosis of breast cancer, providing a more convenient screening method for clinical use. This study, by combining the non-invasive approach of salivary metabolomics, aims to develop a more accurate, non-invasive, and easily scalable breast cancer detection method. This approach not only has the potential to improve early diagnosis rates but also reduce patient suffering and healthcare costs, offering a new breakthrough in breast cancer prevention and control. Furthermore, emerging evidence has suggested that salivary metabolomics may reflect systemic metabolic changes associated with tumor progression through immune and endocrine signaling pathways(Sugimoto et al. 2010b), thereby supporting its potential use in early cancer detection. This theoretical foundation provides a rationale for exploring salivary biomarkers as viable tools for breast cancer screening, although further empirical studies are required to establish their specificity and clinical utility(Zhong et al. 2016). 2. Materials and Methods 2.1 Clinical Samples and Ethical Approval Saliva samples were collected from 30 breast cancer (BC) patients between December 2024 and February 2025, with an average age of 38 years (range: 32–45 years). All BC patients were from the Integrative Oncology Department of Hunan Provincial Cancer Hospital. Table 1 presents the detailed clinical characteristics of the saliva samples used in this study. The diagnosis of all BC participants was based on clinical and histopathological criteria. A control group consisting of 20 healthy women without a history of malignancy or related breast diseases was also included. This study was approved by the Ethics Committee of Hunan Provincial Cancer Hospital and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent and agreed to provide saliva samples for the study. Ethical approval number: This study was approved by the Ethics Committee of Hunan Cancer Hospital (Approval No. 2025-KY-KS-045), and all procedures were conducted in accordance with relevant guidelines and regulations. Table 1 Number of Patient Samples in This Study Characteristic BC Patients Number of subjects 30 Ethnicity Chinese Gender (Male/Female) 0/30 Clinical stage Early stage (I-II) 20 (I:6, II:14) Advanced stage (III-IV) 10 (III:8, IV:2) TNM status Tumor status (T) T1:13, T2:16, T3:0, T4:1 Regional lymph node status (N) N0:16, N1:6, N2:6, N3:2 Distant metastasis status (M) M0:29, M1:1 2.2 Reagents and Instruments The reagents used in this study are listed in Table 2 . The instruments used in this study are summarized in Table 3 . Table 2 Reagents Name CAS Number Purity Brand Methanol 67-56-1 LC-MS grade CNW Technologies Acetonitrile 75-05-8 LC-MS grade CNW Technologies Ammonium acetate 631-61-8 LC-MS grade SIGMA-ALDRICH Ammonium hydroxide 1336-21-6 LC-MS grade CNW Technologies Ultrapure water (ddH2O) - - Watsons Acetic acid 64-19-7 LC-MS grade SIGMA-ALDRICH 2-Propanol 67-63-0 LC-MS grade Table 3 Instruments Instrument Model Brand Ultra-high performance liquid chromatography (UHPLC) Vanquish Thermo Fisher Scientific High-resolution mass spectrometer (HRMS) Orbitrap Exploris 120 Thermo Fisher Scientific Centrifuge Heraeus Fresco17 Thermo Fisher Scientific Analytical balance BSA124S-CW Sartorius Ultrasonic cleaner PS-60AL Shenzhen Redbang Electronics Co., Ltd. Homogenizer JXFSTPRP-24 Shanghai Jingxin Technology Co., Ltd. Freeze dryer LGJ-10C Sihuan Frey Technology Development Co., Ltd. 2.3 Saliva Collection and Processing Participants were instructed to avoid eating, drinking, smoking, or using oral hygiene products for at least 1 hour before sample collection. They rinsed their mouths thoroughly with deionized water and expelled any residual saliva. Participants were seated comfortably with their eyes open, head slightly tilted forward, and instructed to rest for 5 minutes to minimize facial movements. Saliva was collected for 5 minutes using expectoration: participants were asked to accumulate saliva at the bottom of their mouths and expel it into a 50 mL centrifuge tube every 60 seconds (with a reminder not to expectorate mucus). The saliva samples were then centrifuged at 4°C, 2600 g for 15 minutes. The supernatant was quenched in liquid nitrogen and stored at -80°C. 2.4 Experimental Methods 2.4.1 Metabolite Extraction Samples were thawed on ice and subjected to metabolite extraction using the Starlid™ automated workstation. A 100 µL aliquot of each sample and 400 µL of extraction solvent (methanol:acetonitrile = 1:1, v/v, containing isotopically labeled internal standards) were transferred to a 96-well protein precipitation plate. The mixture was vortexed at 750 rpm for 5 minutes, left to stand for 5 minutes, filtered, and the filtrate was collected. An equal volume of supernatant from all samples was mixed to create a quality control (QC) sample for analysis. 2.4.2 Instrumental Analysis For polar metabolites, an ultra-high-performance liquid chromatography (UHPLC) system, Vanquish (Thermo Fisher Scientific), was used in conjunction with a Waters ACQUITY UPLC BEH Amide (2.1 mm × 50 mm, 1.7 µm) column for chromatographic separation of target compounds. The mobile phase consisted of A: water with 25 mmol/L ammonium acetate and 25 mmol/L ammonia, and B: acetonitrile. The sample tray was maintained at 4°C, and the injection volume was 2 µL. The Orbitrap Exploris 120 mass spectrometer, controlled by Xcalibur software (version 4.4, Thermo), was used for data acquisition in both full MS and MS/MS modes. Detailed parameters are as follows: Sheath gas flow rate: 50 Arb;Aux gas flow rate: 15 Arb; Capillary temperature: 320°C; Full MS resolution: 60,000; MS/MS resolution: 15,000;Collision energy: SNCE 20/30/40; Spray voltage: 3.8 kV (positive) or -3.4 kV (negative) 2.5 Data Analysis Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed using R-4.1.1. The importance of variables in the OPLS-DA model was assessed using the variable importance in projection (VIP) scores, and differential metabolites were selected based on VIP > 1, p < 0.05, and fold change (FC). The metabolites were annotated using the KEGG compound database and mapped to KEGG pathways for pathway analysis. Finally, the diagnostic potential of significantly different metabolites was evaluated using receiver operating characteristic (ROC) curves. The software and analysis tools used in this study are summarized in Table 4 . Table 4 Data Analysis Software Analysis Software (Version) PCA SIMCA (18.0.1) OPLS-DA SIMCA (18.0.1) OPLS-DA Permutation Test R (ggplot2) (3.3.5) Volcano Plot R (ggplot2) (3.3.5) Z-score Plot R (ggplot2) (3.3.5) Stick Plot R (ggplot2) (3.3.5) Correlation Heatmap R (corrplot) (0.89) Chord Diagram R (ggraph) (2.0.5) KEGG Annotation Plot R (base) (3.6.3) KEGG Pathway Annotation Classification R (ggplot2) (3.3.5) KEGG Enrichment Plot R (ggplot2) (3.3.5) Metabolic Pathway Bubble Plot R (KEGGgraph, ggplot2) (1.46.0, 3.3.5) Metabolic Pathway Tree Diagram R (KEGGgraph, treemap) (1.46.0, 2.4-2) ROC Curve Plot R (plotROC, pROC) (2.2.1, 1.16.2) 3. Results 3.1 Principal Component Analysis (PCA) The PCA score plot showed that the samples were primarily distributed within the 95% confidence interval, and the quality control (QC) samples exhibited good clustering, indicating the high stability and reliability of the experimental data (Fig. 1A). To further distinguish the metabolic differences between breast cancer (BC) patients and healthy female controls, orthogonal partial least squares discriminant analysis (OPLS-DA) was performed. The OPLS-DA score plot (Fig. 1B) revealed that the model's R²X, R²Y, and Q values were 0.312, 0.617, and 0.251, respectively, with clear separability between the two groups. The most significant metabolic features contributing to the group differences were identified. To validate the model's reliability, a permutation test (n = 200) was conducted, where the class variable Y's arrangement was randomly shuffled to generate random models with R² and Q² values (Fig. 1C). The results indicated that the model was not overfitted and showed high statistical significance.Based on the screening criteria (VIP > 1 and P < 0.05), 101 differential metabolites were selected from the preliminary analysis of metabolic features, of which 81 were significantly upregulated and 20 were significantly downregulated (Fig. 1D). To further analyze the distribution of these differential metabolites across groups, a Z-score analysis was performed on the top 10 most significantly upregulated and downregulated metabolites (Fig. 1E). The results revealed a clear difference in the distribution of metabolites between the two groups. Notably, the most significantly upregulated metabolites included 2-Aminonicotinic acid, N-Acetyl-D-galactosamine 4-sulfate, and 6,8-Di-O-methylaverufin, while the most significantly downregulated metabolites were Theophylline, 1,7-Dimethylxanthine, and 3-Hydroxyhept-4-enoylcarnitine. A stick chart further confirmed that 2-Aminonicotinic acid (upregulated) and Theophylline (downregulated) were the most significantly different metabolites between the two groups (Fig. 1F).Correlation analysis of the differential metabolites revealed a strong positive correlation between 2-Methyl-3-hydroxybutyric acid and 2-Methylbutyric acid, with a correlation coefficient close to 1 (Figs. 1G, H), suggesting that these two metabolites may act synergistically in the same biological process or be regulated by similar mechanisms. On the other hand, the correlation coefficient between Theophylline and Caffeine metabolism was negative and of substantial magnitude, indicating an inverse trend in their changes under experimental conditions. This may reflect their roles in different biological pathways or opposing regulatory influences under the experimental conditions. 3.2 Pathway Analysis To explore the metabolic changes between breast cancer (BC) patients and healthy controls and their biological significance, this study performed pathway annotation and enrichment analysis of the differential metabolites based on the KEGG database. The KEGG enrichment classification plot (Fig. 2 A) revealed that the differential metabolites were primarily enriched in pathways such as the biosynthesis of other secondary metabolites, cancer overview, and carbohydrate metabolism. These findings suggest that these pathways may play a significant role in the metabolic reprogramming of breast cancer. Additionally, the KEGG heatmap (Fig. 2 B) displayed the expression level changes of metabolites across the pathways, with color gradients from blue to red indicating increasing metabolite abundance. This further highlighted the metabolic differences between BC patients and healthy controls. Further KEGG pathway enrichment analysis indicated significant differences in several metabolic pathways between the experimental groups, with the highest proportions observed in caffeine metabolism, sphingolipid metabolism, and propanoate metabolism (Fig. 2 C). The KEGG enrichment bubble plot (Fig. 2 D) visually depicted the enrichment of these pathways, with caffeine metabolism and choline metabolism showing higher Rich Factors and significant P-values (P < 0.05), suggesting that these pathways may be significantly activated in breast cancer. 3.3 ROC Analysis of Differential Metabolites Receiver operating characteristic (ROC) curves were constructed for each comparison based on a series of binary classifications (defined by threshold values), with the true positive rate (sensitivity) plotted on the vertical axis and the false positive rate (1-specificity) on the horizontal axis. For each clearly identified differential metabolite, we plotted its ROC curve and calculated the area under the curve (AUC). The AUC value ranges between 0.5 and 1.0. An AUC closer to 1 indicates better diagnostic performance. An AUC between 0.5 and 0.7 reflects low accuracy, between 0.7 and 0.9 indicates moderate accuracy, and above 0.9 suggests high accuracy. Among the upregulated metabolites in this study, 2-Aminonicotinic acid exhibited the highest AUC value (Fig. 3 A), suggesting its potential as a biomarker for breast cancer. Furthermore, among the downregulated metabolites, Theobromine showed the highest AUC value (Fig. 3 B), indicating its strong diagnostic value in distinguishing between breast cancer and healthy controls. These results further confirm the critical role of specific metabolites in the metabolic changes associated with breast cancer and provide new avenues for non-invasive screening. Although ROC analysis demonstrated that theobromine and 2-aminonicotinic acid exhibited favorable diagnostic performance (AUC = 0.85 and 0.82, respectively), specific diagnostic thresholds, sensitivity, specificity, and confidence intervals have not been fully validated in large, independent cohorts. Future studies should include larger sample sizes to determine optimal cutoff values and further assess the reliability and clinical applicability of these biomarkers. 4. Discussion This study employed LC-MS/MS non-targeted metabolomics to deeply analyze the salivary metabolic profiles of breast cancer (BC) patients and healthy controls, successfully identifying a range of potential biomarkers with diagnostic value. During the metabolite screening process, we systematically identified differential metabolites using PCA, OPLS-DA, and permutation testing. Among these, Theobromine and 2-Aminonicotinic acid were significantly upregulated or downregulated in breast cancer patients, suggesting that these metabolites may play important roles in the development and progression of breast cancer. Furthermore, ROC curve analysis further validated the potential diagnostic value of Theobromine and 2-Aminonicotinic acid, with high AUC values indicating their ability to effectively differentiate breast cancer patients from healthy individuals, demonstrating high diagnostic sensitivity and specificity.Theobromine is a key intermediate in the caffeine metabolism pathway, and its level changes may be closely associated with alterations in cytochrome P450 enzymes (such as CYP1A2) in breast cancer patients. This suggests that the altered caffeine metabolism pathway may contribute to the metabolic reprogramming observed in breast cancer. Additionally, 2-Aminonicotinic acid, a derivative of nicotinic acid, could be implicated in the altered nicotinamide metabolism observed in cancer cells. Given that these metabolic pathways are known to be involved in energy metabolism, oxidative stress, and cell signaling, these findings point to the significant role of these metabolites in cancer pathophysiology. Studies have shown that the inhibition of CYP1A2 activity has been reported in various cancers, which may result in abnormal accumulation or excessive consumption of caffeine and its metabolites, such as theobromine(Mokkawes and de Visser 2023). Additionally, dysregulation of theobromine metabolism may reflect an increase in oxidative stress levels within the tumor microenvironment, which plays a crucial role in the metabolic adaptation and survival strategies of breast cancer cells(Tarka et al. 1983). Additionally, 2-Aminonicotinic acid, a key product of nicotinic acid metabolism, may serve as an indicator of abnormal nicotinic acid metabolic pathways in breast cancer patients. Nicotinic acid and its derivatives play vital roles in cellular energy metabolism and DNA repair, and their metabolic imbalance may impact the proliferation and survival of tumor cells(Chiang et al. 2024). Notably, the abnormal levels of 2-Aminonicotinic acid may be associated with dysregulation of nicotinamide adenine dinucleotide (NAD⁺) metabolism, which is widely recognized as one of the key mechanisms in cancer metabolic reprogramming(Covarrubias et al. 2021). In pathway analysis, significant differences were observed between the salivary metabolomes of breast cancer patients and healthy individuals, particularly in key metabolic pathways such as caffeine metabolism, sphingolipid metabolism, and propanoate metabolism. This study found that the caffeine metabolism, sphingolipid metabolism, and propanoate metabolism pathways contributed the most to the metabolic differences, which aligns with the phenomenon of metabolic reprogramming in breast cancer. The caffeine metabolism pathway also showed significant changes in breast cancer patients. Caffeine is primarily metabolized by cytochrome P450 enzymes, particularly CYP1A2, whose activity is suppressed in various cancers. Caffeine metabolism is not only closely linked to energy metabolism but also plays a role in other biological processes(Yu et al. 2021). Additionally, caffeine metabolism is not only closely related to energy metabolism, but its metabolites may also be linked to increased oxidative stress levels in the tumor microenvironment. Previous studies have shown that breast cancer cells can promote their survival and proliferation by regulating their redox state, and further influence cancer cell proliferation and drug resistance through the modulation of cell signaling pathways(Casas-Hinojosa et al. 2023). Specifically, caffeine and its metabolites can inhibit cancer cell proliferation and induce apoptosis by suppressing the PI3K/AKT/mTOR signaling pathway(Saiki et al. 2011). Additionally, caffeine metabolism regulates the AMPK signaling pathway, affecting energy metabolism and autophagy in cancer cells(Liang and Mills 2013). In this study, the significant changes in caffeine metabolism suggest that breast cancer cells may adapt to their energy demands and drug resistance by regulating caffeine metabolism. This finding provides new insights into the metabolic regulatory mechanisms of breast cancer and indicates that the caffeine metabolism pathway could serve as a potential therapeutic target. The importance of changes in sphingolipid metabolism in breast cancer has been widely recognized. Sphingolipid molecules, such as sphingosine-1-phosphate (S1P), play a key role in regulating cell proliferation, apoptosis, and migration(Furuya et al. 2011). Studies have shown that sphingosine-1-phosphate (S1P) promotes the invasion and metastasis of breast cancer cells by activating its receptors (S1PRs)(Terkelsen et al. 2020). Furthermore, sphingolipid metabolism is closely linked to immune regulation in the tumor microenvironment. For example, overexpression of sphingosine kinase 1 (SPHK1) can suppress the antitumor activity of immune cells(Huang et al. 2023). The abnormal sphingolipid metabolism observed in this study may indicate the activation of sphingolipid signaling pathways in breast cancer patients, providing potential grounds for targeted therapeutic strategies aimed at sphingolipid metabolism. The disruption of propionate metabolism may reflect an imbalance in short-chain fatty acid metabolism in breast cancer patients. Propionate, a key intermediate in energy metabolism, can be converted into propionyl-CoA and further enter the tricarboxylic acid (TCA) cycle to provide energy for the cells(Mann et al. 2024). However, in breast cancer, mitochondrial function is often impaired, leading to a reprogramming of energy metabolism pathways(Pelicano et al. 2014). Abnormal propionate metabolism may weaken the energy supply to breast cancer cells and impair their metabolic adaptation, thereby promoting tumor progression. Additionally, short-chain fatty acids (such as propionate) have been shown to play a crucial role in immune regulation, and their metabolic imbalance may affect immune responses in the tumor microenvironment, thereby influencing the initiation and progression of breast cancer(Silva et al. 2017). Notably, this study utilized saliva as a sample for early non-invasive breast cancer diagnosis. Compared to traditional methods like blood and tissue biopsies, saliva collection is more convenient, non-invasive, and better suited for large-scale screening. Additionally, saliva collection does not require specialized medical personnel, reducing patient compliance issues and healthcare costs, and improving the efficiency of early breast cancer screening. The results further confirm the potential of saliva metabolomics in breast cancer diagnosis. By conducting LC-MS/MS untargeted metabolomics analysis of saliva from breast cancer patients and healthy controls, we successfully identified a series of key metabolites closely associated with breast cancer, supporting saliva as a potential carrier for early detection biomarkers. Furthermore, it provides important scientific evidence for future non-invasive cancer screening. Future studies could combine multi-omics analysis (e.g., proteomics, transcriptomics) to explore the saliva metabolomic features of breast cancer subtypes, enhancing diagnostic accuracy and clinical translation value. However, there are certain limitations in this study. Firstly, the sample size is relatively small, including only 30 breast cancer patients and 20 healthy individuals, which may limit the generalizability of the findings. Therefore, future studies need to validate the findings in a larger independent cohort to ensure the stability and generalizability of the results. Secondly, although various statistical methods were employed for metabolite screening and pathway analysis, cell or animal experiments were not conducted to further validate the functions of these metabolites. Future studies could combine molecular biology experiments to further explore the mechanisms through which key metabolites influence the development of breast cancer. Additionally, this study did not differentiate between breast cancer subtypes, and metabolic characteristics may vary significantly across subtypes. Therefore, future research should explore the application value of saliva metabolomics in different breast cancer subtypes. In conclusion, this study demonstrates that LC-MS/MS-based untargeted saliva metabolomics analysis effectively identifies breast cancer-related metabolic features and selects a series of potential biomarkers. KEGG pathway analysis revealed the potential roles of metabolic pathways such as fatty acid biosynthesis, caffeine metabolism, and choline metabolism in breast cancer, providing new research directions for exploring the metabolic mechanisms of breast cancer. Furthermore, this study is the first to validate the feasibility of saliva metabolomics in non-invasive breast cancer screening, offering a new approach for future clinical testing. Future studies could expand the sample size, integrate molecular biology experiments, and explore the metabolic characteristics of different breast cancer subtypes to further advance early diagnosis and the optimization of personalized treatment strategies. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Xinyu Jiang contributed to the study design, sample collection, experimental work, data analysis, and original draft preparation.Yumei Jia assisted in sample processing, metabolomics data acquisition, and quality control.Bo Zhang contributed to data visualization and helped revise the manuscript.Kai Yang was responsible for statistical analysis and literature review.Yang Li supervised the study, provided funding support, and was responsible for reviewing and editing the manuscript.All authors have read and approved the final version of the manuscript. Funding This work was supported by the Natural Science Foundation of Hunan Province (Grant No. 2025JJ80822) and the project “Integrative Western and Traditional Chinese Medicine Focus-DCA Mode Intervention for Preventing Postoperative Infections in Elderly Patients with Gastrointestinal Malignancies” (Project No. A2023053), funded by the Hunan Provincial Administration of Traditional Chinese Medicine. Compliance with ethical Standards All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data Availability The datasets generated and/or analyzed during the current study are included in this published article and its supplementary information files. Additional data are available from the corresponding author upon reasonable request. References Alonso, A., Marsal, S., & Julià, A. (2015). Analytical methods in untargeted metabolomics: state of the art in 2015. Frontiers in Bioengineering and Biotechnology , 3 , 23. https://doi.org/10.3389/fbioe.2015.00023 Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., et al. (2022). Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast (Edinburgh, Scotland) , 66 , 15–23. https://doi.org/10.1016/j.breast.2022.08.010 Brindle, J. T., Antti, H., Holmes, E., Tranter, G., Nicholson, J. K., Bethell, H. W. L., et al. (2002). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. Nature Medicine , 8 (12), 1439–1444. https://doi.org/10.1038/nm1202-802 Casas-Hinojosa, I., Gómez-Oliván, L. M., Gutierrez-Noya, V. M., Gracía-Medina, S., Rosales-Pérez, K. E., Orozco-Hernández, J. M., et al. (2023). Integrative approach to elucidate the embryological effects of caffeine in Cyprinus carpio: Bioconcentration and alteration of oxidative stress-related gene expression patterns. The Science of the Total Environment , 894 , 165016. https://doi.org/10.1016/j.scitotenv.2023.165016 Chen, Y., Zhang, R., Song, Y., He, J., Sun, J., Bai, J., et al. (2009). RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer. The Analyst , 134 (10), 2003–2011. https://doi.org/10.1039/b907243h Chiang, Y.-F., Huang, K.-C., Huang, T.-C., Chen, H.-Y., Ali, M., Al-Hendy, A., et al. (2024). Regulatory roles of NAMPT and NAD+ metabolism in uterine leiomyoma progression: Implications for ECM accumulation, stemness, and microenvironment. Redox Biology , 78 , 103411. https://doi.org/10.1016/j.redox.2024.103411 Covarrubias, A. J., Perrone, R., Grozio, A., & Verdin, E. (2021). NAD+ metabolism and its roles in cellular processes during ageing. Nature Reviews. Molecular Cell Biology , 22 (2), 119–141. https://doi.org/10.1038/s41580-020-00313-x Frassica, F. J., McCarthy, E. F., & Bluemke, D. A. (2000). Soft-tissue masses: when and how to biopsy. Instructional Course Lectures , 49 , 437–442. Furuya, H., Shimizu, Y., & Kawamori, T. (2011). Sphingolipids in cancer. Cancer Metastasis Reviews , 30 (3–4), 567–576. https://doi.org/10.1007/s10555-011-9304-1 Huang, C., Su, L., Chen, Y., Wu, S., Sun, R., Xu, Q., et al. (2023). Ceramide kinase confers tamoxifen resistance in estrogen receptor-positive breast cancer by altering sphingolipid metabolism. Pharmacological Research , 187 , 106558. https://doi.org/10.1016/j.phrs.2022.106558 Jafari, S. H., Saadatpour, Z., Salmaninejad, A., Momeni, F., Mokhtari, M., Nahand, J. S., et al. (2018). Breast cancer diagnosis: Imaging techniques and biochemical markers. Journal of Cellular Physiology , 233 (7), 5200–5213. https://doi.org/10.1002/jcp.26379 Jain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A. L., et al. (2012). Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science (New York, N.Y.) , 336 (6084), 1040–1044. https://doi.org/10.1126/science.1218595 Liang, J., & Mills, G. B. (2013). AMPK: a contextual oncogene or tumor suppressor? Cancer Research , 73 (10), 2929–2935. https://doi.org/10.1158/0008-5472.CAN-12-3876 Mann, E. R., Lam, Y. K., & Uhlig, H. H. (2024). Short-chain fatty acids: linking diet, the microbiome and immunity. Nature Reviews. Immunology , 24 (8), 577–595. https://doi.org/10.1038/s41577-024-01014-8 Mokkawes, T., & de Visser, S. P. (2023). Caffeine Biodegradation by Cytochrome P450 1A2. What Determines the Product Distributions? Chemistry (Weinheim an Der Bergstrasse, Germany) , 29 (32), e202203875. https://doi.org/10.1002/chem.202203875 Moyya, P. D., & Asaithambi, M. (2022). Radiomics - Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review. Current Medical Imaging , 18 (1), 3–17. https://doi.org/10.2174/1573405617666210303102526 Nonaka, T., & Wong, D. T. W. (2022). Saliva Diagnostics. Annual Review of Analytical Chemistry (Palo Alto, Calif.) , 15 (1), 107–121. https://doi.org/10.1146/annurev-anchem-061020-123959 Pelicano, H., Zhang, W., Liu, J., Hammoudi, N., Dai, J., Xu, R.-H., et al. (2014). Mitochondrial dysfunction in some triple-negative breast cancer cell lines: role of mTOR pathway and therapeutic potential. Breast cancer research: BCR , 16 (5), 434. https://doi.org/10.1186/s13058-014-0434-6 Qiu, H., Cao, S., & Xu, R. (2021). Cancer incidence, mortality, and burden in China: a time‐trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Communications , 41 (10), 1037–1048. https://doi.org/10.1002/cac2.12197 Saiki, S., Sasazawa, Y., Imamichi, Y., Kawajiri, S., Fujimaki, T., Tanida, I., et al. (2011). Caffeine induces apoptosis by enhancement of autophagy via PI3K/Akt/mTOR/p70S6K inhibition. Autophagy , 7 (2), 176–187. https://doi.org/10.4161/auto.7.2.14074 Silva, C. L., Perestrelo, R., Silva, P., Tomás, H., & Câmara, J. S. (2017). Volatile metabolomic signature of human breast cancer cell lines. Scientific Reports , 7 , 43969. https://doi.org/10.1038/srep43969 Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M. (2010a). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics: Official Journal of the Metabolomic Society , 6 (1), 78–95. https://doi.org/10.1007/s11306-009-0178-y Sugimoto, M., Wong, D. T., Hirayama, A., Soga, T., & Tomita, M. (2010b). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. Metabolomics: Official Journal of the Metabolomic Society , 6 (1), 78–95. https://doi.org/10.1007/s11306-009-0178-y Tarka, S. M., Arnaud, M. J., Dvorchik, B. H., & Vesell, E. S. (1983). Theobromine kinetics and metabolic disposition. Clinical Pharmacology and Therapeutics , 34 (4), 546–555. https://doi.org/10.1038/clpt.1983.212 Terkelsen, T., Russo, F., Gromov, P., Haakensen, V. D., Brunak, S., Gromova, I., et al. (2020). Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration. Breast cancer research: BCR , 22 (1), 73. https://doi.org/10.1186/s13058-020-01295-6 Tsang, J. Y. S., & Tse, G. M. (2020). Molecular Classification of Breast Cancer. Advances in Anatomic Pathology , 27 (1), 27–35. https://doi.org/10.1097/PAP.0000000000000232 Wang, G., Qiu, M., Xing, X., Zhou, J., Yao, H., Li, M., et al. (2022). Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. Science Translational Medicine , 14 (630), eabk2756. https://doi.org/10.1126/scitranslmed.abk2756 Wei, J., Xie, G., Zhou, Z., Shi, P., Qiu, Y., Zheng, X., et al. (2011). Salivary metabolite signatures of oral cancer and leukoplakia. International Journal of Cancer , 129 (9), 2207–2217. https://doi.org/10.1002/ijc.25881 Xiao, H., Zhang, L., Zhou, H., Lee, J. M., Garon, E. B., & Wong, D. T. W. (2012). Proteomic analysis of human saliva from lung cancer patients using two-dimensional difference gel electrophoresis and mass spectrometry. Molecular & cellular proteomics: MCP , 11 (2), M111.012112. https://doi.org/10.1074/mcp.M111.012112 Young, C. M., Beziaud, L., Dessen, P., Madurga Alonso, A., Santamaria-Martínez, A., & Huelsken, J. (2023). Metabolic dependencies of metastasis-initiating cells in female breast cancer. Nature Communications , 14 (1), 7076. https://doi.org/10.1038/s41467-023-42748-8 Yu, J., Wang, N., Gong, Z., Liu, L., Yang, S., Chen, G. G., & Lai, P. B. S. (2021). Cytochrome P450 1A2 overcomes nuclear factor kappa B-mediated sorafenib resistance in hepatocellular carcinoma. Oncogene , 40 (3), 492–507. https://doi.org/10.1038/s41388-020-01545-z Zhang, L., Farrell, J. J., Zhou, H., Elashoff, D., Akin, D., Park, N.-H., et al. (2010). Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology , 138 (3), 949-957.e1–7. https://doi.org/10.1053/j.gastro.2009.11.010 Zhong, L., Cheng, F., Lu, X., Duan, Y., & Wang, X. (2016). Untargeted saliva metabonomics study of breast cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. Talanta , 158 , 351–360. https://doi.org/10.1016/j.talanta.2016.04.049 Additional Declarations No competing interests reported. Supplementary Files aucup.csv aucdown.csv chordplotAnalysis.xlsx correlationalmatrix.xlsx DifferentiallyExpressedMetabolites.xlsx EnrichmentAnalysis.xlsx KEGGClassification.xlsx PathwayAnalysis.xlsx MatchstickAnalysis.xlsx Statistical.xlsx 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-6531859","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":455896675,"identity":"5ff6bfb6-aeb1-4cf3-888a-80589da22fbf","order_by":0,"name":"XinYu Jiang","email":"","orcid":"","institution":"Hunan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"XinYu","middleName":"","lastName":"Jiang","suffix":""},{"id":455896676,"identity":"f8ebff62-16a4-4f6c-afe1-054bec1dd7ef","order_by":1,"name":"Yumei Jia","email":"","orcid":"","institution":"Hunan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yumei","middleName":"","lastName":"Jia","suffix":""},{"id":455896677,"identity":"f5bf5b1e-70f0-45c8-baa3-b96571f5191d","order_by":2,"name":"Bo Zhang","email":"","orcid":"","institution":"Hunan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Zhang","suffix":""},{"id":455896678,"identity":"002e16ae-fdc9-4bc2-bce3-207d5be5a960","order_by":3,"name":"kai yang","email":"","orcid":"","institution":"Hunan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"kai","middleName":"","lastName":"yang","suffix":""},{"id":455896679,"identity":"3af47871-6dec-493b-9e0d-c99036f405eb","order_by":4,"name":"Yang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYBACPmYwZZNAvBY2iJY0UrRAqMOkaGHnMfxc8Ot8njn74QcMH/fUMvDPbiDkMB5j6Zl9t4ste9IMGGc8O84gcecAQS0G0rw9txM33GAwYOY5cIzBQIKAI0G2/ObtOQfUwv6BaC1m0jw/DgC18IBsqSFGC1uZNW9DcuKGMzkFB2ccOMAjcYOAFn7+w5tv8/yxS9xw/PjGBx8O1MnxzyCghYGBw4CBsQ3CPACMIB5C6oGA/QEDwx84r44IHaNgFIyCUTDSAAD9UkCkTLp/2gAAAABJRU5ErkJggg==","orcid":"","institution":"Hunan cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-25 23:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6531859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6531859/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82899547,"identity":"6a2cd77d-3234-4b9e-a5ee-c3285c36cbac","added_by":"auto","created_at":"2025-05-16 13:20:14","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":452294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic analysis of saliva. (A) PCA of breast cancer patients (BC) and normal controls (NC); (B) OPLS-DA analysis of BC and NC; (C) Results of permutation test for OPLS-DA model; Ry(cum) represents the cumulative explained variance in the y-direction of the model; Q(cum) represents the proportion of the predicted variance of the model; (D) Volcano plot for differential metabolite screening; (E) Z-score plot for differential metabolites; (F) Stick diagram of differential metabolites; (G) Chord diagram for correlation analysis of differential metabolites; (H) Heatmap for correlation analysis of differential metabolites.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/e252532b48644772336a2f7b.jpeg"},{"id":82899539,"identity":"65dab806-d8a0-4780-8906-10ddcf8fb054","added_by":"auto","created_at":"2025-05-16 13:20:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":639194,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Metabolite Pathway Analysis(A) KEGG Enrichment Analysis Plot(B) KEGG Heatmap(C) Rectangular Tree Map of Differential Metabolite Pathways(D) Bubble Plot of Differential Metabolite Pathways.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/f324a4071ba6101715005424.png"},{"id":82899541,"identity":"93b1614b-01a6-4241-aa5d-7952f8a13177","added_by":"auto","created_at":"2025-05-16 13:20:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":700989,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC analysis of differential metabolites.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/e8b32b86acc65d5a1bac25f5.png"},{"id":89625606,"identity":"d1e6b60e-ddbc-4e47-bf63-185bd9595413","added_by":"auto","created_at":"2025-08-22 05:46:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2798357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/367904f9-37a7-410c-bd4c-967c7932ba32.pdf"},{"id":82898595,"identity":"3b49c873-c417-47ad-8572-57967f1ca608","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"csv","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1330,"visible":true,"origin":"","legend":"","description":"","filename":"aucup.csv","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/347eaac8da496e1cc442b63e.csv"},{"id":82899538,"identity":"0a673cbb-81a0-405b-9ffb-4d84d60d1813","added_by":"auto","created_at":"2025-05-16 13:20:14","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1108,"visible":true,"origin":"","legend":"","description":"","filename":"aucdown.csv","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/c08fbf87b57b98154b867f8b.csv"},{"id":82899540,"identity":"8736a9bf-1f5a-4365-9d5d-be9187c0449e","added_by":"auto","created_at":"2025-05-16 13:20:14","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23252,"visible":true,"origin":"","legend":"","description":"","filename":"chordplotAnalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/1840ee12060bb265f7ffeffd.xlsx"},{"id":82898604,"identity":"44c0c002-0f2c-4b72-9b33-6c42b50822d5","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":16481,"visible":true,"origin":"","legend":"","description":"","filename":"correlationalmatrix.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/b38145ee20cdc858d92b5b6a.xlsx"},{"id":82898606,"identity":"51faf4b5-3302-4228-ba70-0ab3cbdb2e6d","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":12235,"visible":true,"origin":"","legend":"","description":"","filename":"DifferentiallyExpressedMetabolites.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/72680f9175da1f854a62ee88.xlsx"},{"id":82898607,"identity":"1d2f9dfe-9ba3-48ad-838e-65836c9ad99d","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":13134,"visible":true,"origin":"","legend":"","description":"","filename":"EnrichmentAnalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/c7f8d4e7ed3ab8f6d25ed7a0.xlsx"},{"id":82898616,"identity":"21a53ecb-ca05-434e-b4da-7ad497fff327","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11949,"visible":true,"origin":"","legend":"","description":"","filename":"KEGGClassification.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/512978a478e1080d8ae35831.xlsx"},{"id":82898613,"identity":"014ee3b6-412d-4b52-a310-9b82fe1c609e","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10412,"visible":true,"origin":"","legend":"","description":"","filename":"PathwayAnalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/1639eb33c58c2b58e517c977.xlsx"},{"id":82898622,"identity":"f2e5dc09-f511-4967-993b-ca5876be93f1","added_by":"auto","created_at":"2025-05-16 13:12:14","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1286427,"visible":true,"origin":"","legend":"","description":"","filename":"MatchstickAnalysis.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/61666d709087c45214beae79.xlsx"},{"id":82898627,"identity":"8413c964-29e7-4a86-b6e8-3ffd4710bb9f","added_by":"auto","created_at":"2025-05-16 13:12:15","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":5747406,"visible":true,"origin":"","legend":"","description":"","filename":"Statistical.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6531859/v1/50c6c1fb21a21c3489736c08.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Potential Biomarkers for Breast Cancer Based on Salivary Metabolomics","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBreast cancer (BC) is the most common malignant tumor among women, ranking first in both incidence and mortality among female malignancies. According to a report by the World Health Organization (WHO), more than 2.3\u0026nbsp;million new breast cancer cases were diagnosed globally in 2020, with approximately 685,000 related deaths(Arnold et al. 2022). These figures not only underscore the severe threat breast cancer poses to women\u0026rsquo;s health worldwide but also highlight its significance and urgency in public health(Qiu et al. 2021). The high incidence and mortality rates of breast cancer make it one of the major challenges to women's health(Jafari et al. 2018). There is an urgent need to enhance research and application of early screening, diagnosis, and treatment strategies. Currently, breast cancer detection primarily relies on imaging techniques such as mammography, ultrasound, and magnetic resonance imaging (MRI)(Moyya and Asaithambi 2022), as well as invasive procedures like tissue biopsy or minimally invasive blood tests(Frassica et al. 2000). Although these technologies play a crucial role in diagnosis, they often fail to fully meet the need for non-invasive, convenient, and efficient screening, particularly for the early detection of breast cancer(Tsang and Tse 2020). Therefore, developing a novel non-invasive diagnostic method is urgently needed. Metabolomics, as a powerful analytical tool, has been widely used to study the expression changes of metabolites in complex human diseases. Its high specificity and sensitivity offer distinct advantages in research across various diseases(Brindle et al. 2002). By quantitatively analyzing the overall metabolic profile changes of organisms under normal physiological conditions, pathological processes, or external stimuli, metabolomics provides an indispensable platform for discovering potential biomarkers(Wang et al. 2022). This technology not only reveals disease-related metabolic pathway abnormalities but also provides critical scientific evidence for early diagnosis, prognosis assessment, and the development of personalized treatment strategies. In recent years, the application of metabolomics in cancer research has expanded significantly. Untargeted metabolomics and lipidomics studies using liquid chromatography-tandem mass spectrometry (LC-MS/MS) have shown tremendous potential in discovering novel biomarkers and uncovering metabolic changes(Alonso et al. 2015). Mohit Jain et al. used LC-MS/MS technology to analyze the consumption and release (CORE) curves of 219 metabolites in the culture media of the NCI-60 cancer cell lines, including breast cancer cells, revealing unique metabolic characteristics of cancer cells(Jain et al. 2012). In further studies, they employed RRLC-MS/MS to successfully differentiate between breast cancer patients and healthy controls (HC), identifying 12 potential breast cancer biomarkers in urine samples(Chen et al. 2009). It is worth noting that, in addition to urinary metabolomics, salivary metabolomics has gained increasing attention in recent years and has shown promising diagnostic potential. Sugimoto et al. used CE-MS technology to analyze saliva samples and identified 14 amino acids as potential biomarkers for breast cancer diagnosis(Sugimoto et al. 2010a). Moreover, researchers have used salivary biomarkers to diagnose diseases such as oral cancer(Wei et al. 2011), pancreatic cancer(Zhang et al. 2010), and lung cancer(Xiao et al. 2012). However, despite the growing body of research on salivary metabolomics in other cancer types, its application in breast cancer remains relatively limited, with the associated metabolic characteristics not yet systematically analyzed. Further in-depth research is urgently needed to clarify its clinical application value. Saliva, as a bodily fluid rich in various metabolites, offers distinct advantages in disease screening and early diagnosis due to its non-invasive collection, ease of use, and high reproducibility(Nonaka and Wong 2022). Existing studies have shown significant metabolic abnormalities in breast cancer patients, including disruptions in glucose metabolism, amino acid metabolism, and lipid metabolism(Young et al. 2023). These metabolic changes may be reflected in saliva through the bloodstream or other pathways. Therefore, by integrating salivary metabolomics, it is possible to identify new non-invasive biomarkers for the early diagnosis of breast cancer, providing a more convenient screening method for clinical use. This study, by combining the non-invasive approach of salivary metabolomics, aims to develop a more accurate, non-invasive, and easily scalable breast cancer detection method. This approach not only has the potential to improve early diagnosis rates but also reduce patient suffering and healthcare costs, offering a new breakthrough in breast cancer prevention and control. Furthermore, emerging evidence has suggested that salivary metabolomics may reflect systemic metabolic changes associated with tumor progression through immune and endocrine signaling pathways(Sugimoto et al. 2010b), thereby supporting its potential use in early cancer detection. This theoretical foundation provides a rationale for exploring salivary biomarkers as viable tools for breast cancer screening, although further empirical studies are required to establish their specificity and clinical utility(Zhong et al. 2016).\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Clinical Samples and Ethical Approval\u003c/h2\u003e \u003cp\u003eSaliva samples were collected from 30 breast cancer (BC) patients between December 2024 and February 2025, with an average age of 38 years (range: 32\u0026ndash;45 years). All BC patients were from the Integrative Oncology Department of Hunan Provincial Cancer Hospital. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the detailed clinical characteristics of the saliva samples used in this study. The diagnosis of all BC participants was based on clinical and histopathological criteria. A control group consisting of 20 healthy women without a history of malignancy or related breast diseases was also included. This study was approved by the Ethics Committee of Hunan Provincial Cancer Hospital and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent and agreed to provide saliva samples for the study. Ethical approval number: This study was approved by the Ethics Committee of Hunan Cancer Hospital (Approval No. 2025-KY-KS-045), and all procedures were conducted in accordance with relevant guidelines and regulations.\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\u003eNumber of Patient Samples in This Study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBC Patients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly stage (I-II)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (I:6, II:14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdvanced stage (III-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (III:8, IV:2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTNM status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor status (T)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1:13, T2:16, T3:0, T4:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional lymph node status (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN0:16, N1:6, N2:6, N3:2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistant metastasis status (M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM0:29, M1:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Reagents and Instruments\u003c/h2\u003e \u003cp\u003eThe reagents used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The instruments used in this study are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003eReagents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAS Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePurity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBrand\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e67-56-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCNW Technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetonitrile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e75-05-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCNW Technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmonium acetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e631-61-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIGMA-ALDRICH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmmonium hydroxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e1336-21-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCNW Technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrapure water (ddH2O)\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\u003eWatsons\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e64-19-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSIGMA-ALDRICH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2-Propanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e67-63-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLC-MS grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInstruments\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=\"left\" 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\"\u003e \u003cp\u003eInstrument\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBrand\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltra-high performance liquid chromatography (UHPLC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVanquish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher Scientific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-resolution mass spectrometer (HRMS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrbitrap Exploris 120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher Scientific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentrifuge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeraeus Fresco17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThermo Fisher Scientific\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalytical balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBSA124S-CW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSartorius\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUltrasonic cleaner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePS-60AL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShenzhen Redbang Electronics Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomogenizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJXFSTPRP-24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eShanghai Jingxin Technology Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFreeze dryer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGJ-10C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSihuan Frey Technology Development Co., Ltd.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Saliva Collection and Processing\u003c/h2\u003e \u003cp\u003e Participants were instructed to avoid eating, drinking, smoking, or using oral hygiene products for at least 1 hour before sample collection. They rinsed their mouths thoroughly with deionized water and expelled any residual saliva. Participants were seated comfortably with their eyes open, head slightly tilted forward, and instructed to rest for 5 minutes to minimize facial movements. Saliva was collected for 5 minutes using expectoration: participants were asked to accumulate saliva at the bottom of their mouths and expel it into a 50 mL centrifuge tube every 60 seconds (with a reminder not to expectorate mucus). The saliva samples were then centrifuged at 4\u0026deg;C, 2600 g for 15 minutes. The supernatant was quenched in liquid nitrogen and stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Experimental Methods\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Metabolite Extraction\u003c/h2\u003e \u003cp\u003eSamples were thawed on ice and subjected to metabolite extraction using the Starlid\u0026trade; automated workstation. A 100 \u0026micro;L aliquot of each sample and 400 \u0026micro;L of extraction solvent (methanol:acetonitrile\u0026thinsp;=\u0026thinsp;1:1, v/v, containing isotopically labeled internal standards) were transferred to a 96-well protein precipitation plate. The mixture was vortexed at 750 rpm for 5 minutes, left to stand for 5 minutes, filtered, and the filtrate was collected. An equal volume of supernatant from all samples was mixed to create a quality control (QC) sample for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Instrumental Analysis\u003c/h2\u003e \u003cp\u003eFor polar metabolites, an ultra-high-performance liquid chromatography (UHPLC) system, Vanquish (Thermo Fisher Scientific), was used in conjunction with a Waters ACQUITY UPLC BEH Amide (2.1 mm \u0026times; 50 mm, 1.7 \u0026micro;m) column for chromatographic separation of target compounds. The mobile phase consisted of A: water with 25 mmol/L ammonium acetate and 25 mmol/L ammonia, and B: acetonitrile. The sample tray was maintained at 4\u0026deg;C, and the injection volume was 2 \u0026micro;L. The Orbitrap Exploris 120 mass spectrometer, controlled by Xcalibur software (version 4.4, Thermo), was used for data acquisition in both full MS and MS/MS modes. Detailed parameters are as follows: Sheath gas flow rate: 50 Arb;Aux gas flow rate: 15 Arb; Capillary temperature: 320\u0026deg;C; Full MS resolution: 60,000; MS/MS resolution: 15,000;Collision energy: SNCE 20/30/40; Spray voltage: 3.8 kV (positive) or -3.4 kV (negative)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data Analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed using R-4.1.1. The importance of variables in the OPLS-DA model was assessed using the variable importance in projection (VIP) scores, and differential metabolites were selected based on VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and fold change (FC). The metabolites were annotated using the KEGG compound database and mapped to KEGG pathways for pathway analysis. Finally, the diagnostic potential of significantly different metabolites was evaluated using receiver operating characteristic (ROC) curves. The software and analysis tools used in this study are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData Analysis Software\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoftware (Version)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIMCA (18.0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPLS-DA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIMCA (18.0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOPLS-DA Permutation Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggplot2) (3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolcano Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggplot2) (3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-score Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggplot2) (3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStick Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggplot2) (3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrelation Heatmap\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (corrplot) (0.89)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChord Diagram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggraph) (2.0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG Annotation Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (base) (3.6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG Pathway Annotation Classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggplot2) (3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG Enrichment Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (ggplot2) (3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Pathway Bubble Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (KEGGgraph, ggplot2) (1.46.0, 3.3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Pathway Tree Diagram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (KEGGgraph, treemap) (1.46.0, 2.4-2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eROC Curve Plot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR (plotROC, pROC) (2.2.1, 1.16.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Principal Component Analysis (PCA)\u003c/h2\u003e \u003cp\u003eThe PCA score plot showed that the samples were primarily distributed within the 95% confidence interval, and the quality control (QC) samples exhibited good clustering, indicating the high stability and reliability of the experimental data (Fig.\u0026nbsp;1A). To further distinguish the metabolic differences between breast cancer (BC) patients and healthy female controls, orthogonal partial least squares discriminant analysis (OPLS-DA) was performed. The OPLS-DA score plot (Fig.\u0026nbsp;1B) revealed that the model's R\u0026sup2;X, R\u0026sup2;Y, and Q values were 0.312, 0.617, and 0.251, respectively, with clear separability between the two groups. The most significant metabolic features contributing to the group differences were identified. To validate the model's reliability, a permutation test (n\u0026thinsp;=\u0026thinsp;200) was conducted, where the class variable Y's arrangement was randomly shuffled to generate random models with R\u0026sup2; and Q\u0026sup2; values (Fig.\u0026nbsp;1C). The results indicated that the model was not overfitted and showed high statistical significance.Based on the screening criteria (VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), 101 differential metabolites were selected from the preliminary analysis of metabolic features, of which 81 were significantly upregulated and 20 were significantly downregulated (Fig.\u0026nbsp;1D). To further analyze the distribution of these differential metabolites across groups, a Z-score analysis was performed on the top 10 most significantly upregulated and downregulated metabolites (Fig.\u0026nbsp;1E). The results revealed a clear difference in the distribution of metabolites between the two groups. Notably, the most significantly upregulated metabolites included 2-Aminonicotinic acid, N-Acetyl-D-galactosamine 4-sulfate, and 6,8-Di-O-methylaverufin, while the most significantly downregulated metabolites were Theophylline, 1,7-Dimethylxanthine, and 3-Hydroxyhept-4-enoylcarnitine. A stick chart further confirmed that 2-Aminonicotinic acid (upregulated) and Theophylline (downregulated) were the most significantly different metabolites between the two groups (Fig.\u0026nbsp;1F).Correlation analysis of the differential metabolites revealed a strong positive correlation between 2-Methyl-3-hydroxybutyric acid and 2-Methylbutyric acid, with a correlation coefficient close to 1 (Figs.\u0026nbsp;1G, H), suggesting that these two metabolites may act synergistically in the same biological process or be regulated by similar mechanisms. On the other hand, the correlation coefficient between Theophylline and Caffeine metabolism was negative and of substantial magnitude, indicating an inverse trend in their changes under experimental conditions. This may reflect their roles in different biological pathways or opposing regulatory influences under the experimental conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pathway Analysis\u003c/h2\u003e \u003cp\u003eTo explore the metabolic changes between breast cancer (BC) patients and healthy controls and their biological significance, this study performed pathway annotation and enrichment analysis of the differential metabolites based on the KEGG database. The KEGG enrichment classification plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed that the differential metabolites were primarily enriched in pathways such as the biosynthesis of other secondary metabolites, cancer overview, and carbohydrate metabolism. These findings suggest that these pathways may play a significant role in the metabolic reprogramming of breast cancer. Additionally, the KEGG heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) displayed the expression level changes of metabolites across the pathways, with color gradients from blue to red indicating increasing metabolite abundance. This further highlighted the metabolic differences between BC patients and healthy controls. Further KEGG pathway enrichment analysis indicated significant differences in several metabolic pathways between the experimental groups, with the highest proportions observed in caffeine metabolism, sphingolipid metabolism, and propanoate metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The KEGG enrichment bubble plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) visually depicted the enrichment of these pathways, with caffeine metabolism and choline metabolism showing higher Rich Factors and significant P-values (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that these pathways may be significantly activated in breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 ROC Analysis of Differential Metabolites\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were constructed for each comparison based on a series of binary classifications (defined by threshold values), with the true positive rate (sensitivity) plotted on the vertical axis and the false positive rate (1-specificity) on the horizontal axis. For each clearly identified differential metabolite, we plotted its ROC curve and calculated the area under the curve (AUC). The AUC value ranges between 0.5 and 1.0. An AUC closer to 1 indicates better diagnostic performance. An AUC between 0.5 and 0.7 reflects low accuracy, between 0.7 and 0.9 indicates moderate accuracy, and above 0.9 suggests high accuracy. Among the upregulated metabolites in this study, 2-Aminonicotinic acid exhibited the highest AUC value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), suggesting its potential as a biomarker for breast cancer. Furthermore, among the downregulated metabolites, Theobromine showed the highest AUC value (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), indicating its strong diagnostic value in distinguishing between breast cancer and healthy controls. These results further confirm the critical role of specific metabolites in the metabolic changes associated with breast cancer and provide new avenues for non-invasive screening. Although ROC analysis demonstrated that theobromine and 2-aminonicotinic acid exhibited favorable diagnostic performance (AUC\u0026thinsp;=\u0026thinsp;0.85 and 0.82, respectively), specific diagnostic thresholds, sensitivity, specificity, and confidence intervals have not been fully validated in large, independent cohorts. Future studies should include larger sample sizes to determine optimal cutoff values and further assess the reliability and clinical applicability of these biomarkers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study employed LC-MS/MS non-targeted metabolomics to deeply analyze the salivary metabolic profiles of breast cancer (BC) patients and healthy controls, successfully identifying a range of potential biomarkers with diagnostic value. During the metabolite screening process, we systematically identified differential metabolites using PCA, OPLS-DA, and permutation testing. Among these, Theobromine and 2-Aminonicotinic acid were significantly upregulated or downregulated in breast cancer patients, suggesting that these metabolites may play important roles in the development and progression of breast cancer. Furthermore, ROC curve analysis further validated the potential diagnostic value of Theobromine and 2-Aminonicotinic acid, with high AUC values indicating their ability to effectively differentiate breast cancer patients from healthy individuals, demonstrating high diagnostic sensitivity and specificity.Theobromine is a key intermediate in the caffeine metabolism pathway, and its level changes may be closely associated with alterations in cytochrome P450 enzymes (such as CYP1A2) in breast cancer patients. This suggests that the altered caffeine metabolism pathway may contribute to the metabolic reprogramming observed in breast cancer. Additionally, 2-Aminonicotinic acid, a derivative of nicotinic acid, could be implicated in the altered nicotinamide metabolism observed in cancer cells. Given that these metabolic pathways are known to be involved in energy metabolism, oxidative stress, and cell signaling, these findings point to the significant role of these metabolites in cancer pathophysiology. Studies have shown that the inhibition of CYP1A2 activity has been reported in various cancers, which may result in abnormal accumulation or excessive consumption of caffeine and its metabolites, such as theobromine(Mokkawes and de Visser 2023). Additionally, dysregulation of theobromine metabolism may reflect an increase in oxidative stress levels within the tumor microenvironment, which plays a crucial role in the metabolic adaptation and survival strategies of breast cancer cells(Tarka et al. 1983). Additionally, 2-Aminonicotinic acid, a key product of nicotinic acid metabolism, may serve as an indicator of abnormal nicotinic acid metabolic pathways in breast cancer patients. Nicotinic acid and its derivatives play vital roles in cellular energy metabolism and DNA repair, and their metabolic imbalance may impact the proliferation and survival of tumor cells(Chiang et al. 2024). Notably, the abnormal levels of 2-Aminonicotinic acid may be associated with dysregulation of nicotinamide adenine dinucleotide (NAD⁺) metabolism, which is widely recognized as one of the key mechanisms in cancer metabolic reprogramming(Covarrubias et al. 2021).\u003c/p\u003e \u003cp\u003eIn pathway analysis, significant differences were observed between the salivary metabolomes of breast cancer patients and healthy individuals, particularly in key metabolic pathways such as caffeine metabolism, sphingolipid metabolism, and propanoate metabolism. This study found that the caffeine metabolism, sphingolipid metabolism, and propanoate metabolism pathways contributed the most to the metabolic differences, which aligns with the phenomenon of metabolic reprogramming in breast cancer. The caffeine metabolism pathway also showed significant changes in breast cancer patients. Caffeine is primarily metabolized by cytochrome P450 enzymes, particularly CYP1A2, whose activity is suppressed in various cancers. Caffeine metabolism is not only closely linked to energy metabolism but also plays a role in other biological processes(Yu et al. 2021). Additionally, caffeine metabolism is not only closely related to energy metabolism, but its metabolites may also be linked to increased oxidative stress levels in the tumor microenvironment. Previous studies have shown that breast cancer cells can promote their survival and proliferation by regulating their redox state, and further influence cancer cell proliferation and drug resistance through the modulation of cell signaling pathways(Casas-Hinojosa et al. 2023). Specifically, caffeine and its metabolites can inhibit cancer cell proliferation and induce apoptosis by suppressing the PI3K/AKT/mTOR signaling pathway(Saiki et al. 2011). Additionally, caffeine metabolism regulates the AMPK signaling pathway, affecting energy metabolism and autophagy in cancer cells(Liang and Mills 2013). In this study, the significant changes in caffeine metabolism suggest that breast cancer cells may adapt to their energy demands and drug resistance by regulating caffeine metabolism. This finding provides new insights into the metabolic regulatory mechanisms of breast cancer and indicates that the caffeine metabolism pathway could serve as a potential therapeutic target. The importance of changes in sphingolipid metabolism in breast cancer has been widely recognized. Sphingolipid molecules, such as sphingosine-1-phosphate (S1P), play a key role in regulating cell proliferation, apoptosis, and migration(Furuya et al. 2011). Studies have shown that sphingosine-1-phosphate (S1P) promotes the invasion and metastasis of breast cancer cells by activating its receptors (S1PRs)(Terkelsen et al. 2020). Furthermore, sphingolipid metabolism is closely linked to immune regulation in the tumor microenvironment. For example, overexpression of sphingosine kinase 1 (SPHK1) can suppress the antitumor activity of immune cells(Huang et al. 2023). The abnormal sphingolipid metabolism observed in this study may indicate the activation of sphingolipid signaling pathways in breast cancer patients, providing potential grounds for targeted therapeutic strategies aimed at sphingolipid metabolism. The disruption of propionate metabolism may reflect an imbalance in short-chain fatty acid metabolism in breast cancer patients. Propionate, a key intermediate in energy metabolism, can be converted into propionyl-CoA and further enter the tricarboxylic acid (TCA) cycle to provide energy for the cells(Mann et al. 2024). However, in breast cancer, mitochondrial function is often impaired, leading to a reprogramming of energy metabolism pathways(Pelicano et al. 2014). Abnormal propionate metabolism may weaken the energy supply to breast cancer cells and impair their metabolic adaptation, thereby promoting tumor progression. Additionally, short-chain fatty acids (such as propionate) have been shown to play a crucial role in immune regulation, and their metabolic imbalance may affect immune responses in the tumor microenvironment, thereby influencing the initiation and progression of breast cancer(Silva et al. 2017).\u003c/p\u003e \u003cp\u003eNotably, this study utilized saliva as a sample for early non-invasive breast cancer diagnosis. Compared to traditional methods like blood and tissue biopsies, saliva collection is more convenient, non-invasive, and better suited for large-scale screening. Additionally, saliva collection does not require specialized medical personnel, reducing patient compliance issues and healthcare costs, and improving the efficiency of early breast cancer screening. The results further confirm the potential of saliva metabolomics in breast cancer diagnosis. By conducting LC-MS/MS untargeted metabolomics analysis of saliva from breast cancer patients and healthy controls, we successfully identified a series of key metabolites closely associated with breast cancer, supporting saliva as a potential carrier for early detection biomarkers. Furthermore, it provides important scientific evidence for future non-invasive cancer screening. Future studies could combine multi-omics analysis (e.g., proteomics, transcriptomics) to explore the saliva metabolomic features of breast cancer subtypes, enhancing diagnostic accuracy and clinical translation value. However, there are certain limitations in this study. Firstly, the sample size is relatively small, including only 30 breast cancer patients and 20 healthy individuals, which may limit the generalizability of the findings. Therefore, future studies need to validate the findings in a larger independent cohort to ensure the stability and generalizability of the results. Secondly, although various statistical methods were employed for metabolite screening and pathway analysis, cell or animal experiments were not conducted to further validate the functions of these metabolites. Future studies could combine molecular biology experiments to further explore the mechanisms through which key metabolites influence the development of breast cancer. Additionally, this study did not differentiate between breast cancer subtypes, and metabolic characteristics may vary significantly across subtypes. Therefore, future research should explore the application value of saliva metabolomics in different breast cancer subtypes. In conclusion, this study demonstrates that LC-MS/MS-based untargeted saliva metabolomics analysis effectively identifies breast cancer-related metabolic features and selects a series of potential biomarkers. KEGG pathway analysis revealed the potential roles of metabolic pathways such as fatty acid biosynthesis, caffeine metabolism, and choline metabolism in breast cancer, providing new research directions for exploring the metabolic mechanisms of breast cancer. Furthermore, this study is the first to validate the feasibility of saliva metabolomics in non-invasive breast cancer screening, offering a new approach for future clinical testing. Future studies could expand the sample size, integrate molecular biology experiments, and explore the metabolic characteristics of different breast cancer subtypes to further advance early diagnosis and the optimization of personalized treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Xinyu Jiang contributed to the study design, sample collection, experimental work, data analysis, and original draft preparation.Yumei Jia assisted in sample processing, metabolomics data acquisition, and quality control.Bo Zhang contributed to data visualization and helped revise the manuscript.Kai Yang was responsible for statistical analysis and literature review.Yang Li supervised the study, provided funding support, and was responsible for reviewing and editing the manuscript.All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Hunan Province (Grant No. 2025JJ80822) and the project \u0026ldquo;Integrative Western and Traditional Chinese Medicine Focus-DCA Mode Intervention for Preventing Postoperative Infections in Elderly Patients with Gastrointestinal Malignancies\u0026rdquo; (Project No. A2023053), funded by the Hunan Provincial Administration of Traditional Chinese Medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are included in this published article and its supplementary information files. Additional data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlonso, A., Marsal, S., \u0026amp; Juli\u0026agrave;, A. (2015). Analytical methods in untargeted metabolomics: state of the art in 2015. \u003cem\u003eFrontiers in Bioengineering and Biotechnology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 23. https://doi.org/10.3389/fbioe.2015.00023\u003c/li\u003e\n\u003cli\u003eArnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., et al. (2022). Current and future burden of breast cancer: Global statistics for 2020 and 2040. \u003cem\u003eBreast (Edinburgh, Scotland)\u003c/em\u003e, \u003cem\u003e66\u003c/em\u003e, 15\u0026ndash;23. https://doi.org/10.1016/j.breast.2022.08.010\u003c/li\u003e\n\u003cli\u003eBrindle, J. T., Antti, H., Holmes, E., Tranter, G., Nicholson, J. K., Bethell, H. W. L., et al. (2002). Rapid and noninvasive diagnosis of the presence and severity of coronary heart disease using 1H-NMR-based metabonomics. \u003cem\u003eNature Medicine\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(12), 1439\u0026ndash;1444. https://doi.org/10.1038/nm1202-802\u003c/li\u003e\n\u003cli\u003eCasas-Hinojosa, I., G\u0026oacute;mez-Oliv\u0026aacute;n, L. M., Gutierrez-Noya, V. M., Grac\u0026iacute;a-Medina, S., Rosales-P\u0026eacute;rez, K. E., Orozco-Hern\u0026aacute;ndez, J. M., et al. (2023). Integrative approach to elucidate the embryological effects of caffeine in Cyprinus carpio: Bioconcentration and alteration of oxidative stress-related gene expression patterns. \u003cem\u003eThe Science of the Total Environment\u003c/em\u003e, \u003cem\u003e894\u003c/em\u003e, 165016. https://doi.org/10.1016/j.scitotenv.2023.165016\u003c/li\u003e\n\u003cli\u003eChen, Y., Zhang, R., Song, Y., He, J., Sun, J., Bai, J., et al. (2009). RRLC-MS/MS-based metabonomics combined with in-depth analysis of metabolic correlation network: finding potential biomarkers for breast cancer. \u003cem\u003eThe Analyst\u003c/em\u003e, \u003cem\u003e134\u003c/em\u003e(10), 2003\u0026ndash;2011. https://doi.org/10.1039/b907243h\u003c/li\u003e\n\u003cli\u003eChiang, Y.-F., Huang, K.-C., Huang, T.-C., Chen, H.-Y., Ali, M., Al-Hendy, A., et al. (2024). Regulatory roles of NAMPT and NAD+ metabolism in uterine leiomyoma progression: Implications for ECM accumulation, stemness, and microenvironment. \u003cem\u003eRedox Biology\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e, 103411. https://doi.org/10.1016/j.redox.2024.103411\u003c/li\u003e\n\u003cli\u003eCovarrubias, A. J., Perrone, R., Grozio, A., \u0026amp; Verdin, E. (2021). NAD+ metabolism and its roles in cellular processes during ageing. \u003cem\u003eNature Reviews. Molecular Cell Biology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 119\u0026ndash;141. https://doi.org/10.1038/s41580-020-00313-x\u003c/li\u003e\n\u003cli\u003eFrassica, F. J., McCarthy, E. F., \u0026amp; Bluemke, D. A. (2000). Soft-tissue masses: when and how to biopsy. \u003cem\u003eInstructional Course Lectures\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e, 437\u0026ndash;442.\u003c/li\u003e\n\u003cli\u003eFuruya, H., Shimizu, Y., \u0026amp; Kawamori, T. (2011). Sphingolipids in cancer. \u003cem\u003eCancer Metastasis Reviews\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(3\u0026ndash;4), 567\u0026ndash;576. https://doi.org/10.1007/s10555-011-9304-1\u003c/li\u003e\n\u003cli\u003eHuang, C., Su, L., Chen, Y., Wu, S., Sun, R., Xu, Q., et al. (2023). Ceramide kinase confers tamoxifen resistance in estrogen receptor-positive breast cancer by altering sphingolipid metabolism. \u003cem\u003ePharmacological Research\u003c/em\u003e, \u003cem\u003e187\u003c/em\u003e, 106558. https://doi.org/10.1016/j.phrs.2022.106558\u003c/li\u003e\n\u003cli\u003eJafari, S. H., Saadatpour, Z., Salmaninejad, A., Momeni, F., Mokhtari, M., Nahand, J. S., et al. (2018). Breast cancer diagnosis: Imaging techniques and biochemical markers. \u003cem\u003eJournal of Cellular Physiology\u003c/em\u003e, \u003cem\u003e233\u003c/em\u003e(7), 5200\u0026ndash;5213. https://doi.org/10.1002/jcp.26379\u003c/li\u003e\n\u003cli\u003eJain, M., Nilsson, R., Sharma, S., Madhusudhan, N., Kitami, T., Souza, A. L., et al. (2012). Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. \u003cem\u003eScience (New York, N.Y.)\u003c/em\u003e, \u003cem\u003e336\u003c/em\u003e(6084), 1040\u0026ndash;1044. https://doi.org/10.1126/science.1218595\u003c/li\u003e\n\u003cli\u003eLiang, J., \u0026amp; Mills, G. B. (2013). AMPK: a contextual oncogene or tumor suppressor? \u003cem\u003eCancer Research\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(10), 2929\u0026ndash;2935. https://doi.org/10.1158/0008-5472.CAN-12-3876\u003c/li\u003e\n\u003cli\u003eMann, E. R., Lam, Y. K., \u0026amp; Uhlig, H. H. (2024). Short-chain fatty acids: linking diet, the microbiome and immunity. \u003cem\u003eNature Reviews. Immunology\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(8), 577\u0026ndash;595. https://doi.org/10.1038/s41577-024-01014-8\u003c/li\u003e\n\u003cli\u003eMokkawes, T., \u0026amp; de Visser, S. P. (2023). Caffeine Biodegradation by Cytochrome P450 1A2. What Determines the Product Distributions? \u003cem\u003eChemistry (Weinheim an Der Bergstrasse, Germany)\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(32), e202203875. https://doi.org/10.1002/chem.202203875\u003c/li\u003e\n\u003cli\u003eMoyya, P. D., \u0026amp; Asaithambi, M. (2022). Radiomics - Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review. \u003cem\u003eCurrent Medical Imaging\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 3\u0026ndash;17. https://doi.org/10.2174/1573405617666210303102526\u003c/li\u003e\n\u003cli\u003eNonaka, T., \u0026amp; Wong, D. T. W. (2022). Saliva Diagnostics. \u003cem\u003eAnnual Review of Analytical Chemistry (Palo Alto, Calif.)\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 107\u0026ndash;121. https://doi.org/10.1146/annurev-anchem-061020-123959\u003c/li\u003e\n\u003cli\u003ePelicano, H., Zhang, W., Liu, J., Hammoudi, N., Dai, J., Xu, R.-H., et al. (2014). Mitochondrial dysfunction in some triple-negative breast cancer cell lines: role of mTOR pathway and therapeutic potential. \u003cem\u003eBreast cancer research: BCR\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 434. https://doi.org/10.1186/s13058-014-0434-6\u003c/li\u003e\n\u003cli\u003eQiu, H., Cao, S., \u0026amp; Xu, R. (2021). Cancer incidence, mortality, and burden in China: a time‐trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. \u003cem\u003eCancer Communications\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(10), 1037\u0026ndash;1048. https://doi.org/10.1002/cac2.12197\u003c/li\u003e\n\u003cli\u003eSaiki, S., Sasazawa, Y., Imamichi, Y., Kawajiri, S., Fujimaki, T., Tanida, I., et al. (2011). Caffeine induces apoptosis by enhancement of autophagy via PI3K/Akt/mTOR/p70S6K inhibition. \u003cem\u003eAutophagy\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(2), 176\u0026ndash;187. https://doi.org/10.4161/auto.7.2.14074\u003c/li\u003e\n\u003cli\u003eSilva, C. L., Perestrelo, R., Silva, P., Tom\u0026aacute;s, H., \u0026amp; C\u0026acirc;mara, J. S. (2017). Volatile metabolomic signature of human breast cancer cell lines. \u003cem\u003eScientific Reports\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e, 43969. https://doi.org/10.1038/srep43969\u003c/li\u003e\n\u003cli\u003eSugimoto, M., Wong, D. T., Hirayama, A., Soga, T., \u0026amp; Tomita, M. (2010a). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. \u003cem\u003eMetabolomics: Official Journal of the Metabolomic Society\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 78\u0026ndash;95. https://doi.org/10.1007/s11306-009-0178-y\u003c/li\u003e\n\u003cli\u003eSugimoto, M., Wong, D. T., Hirayama, A., Soga, T., \u0026amp; Tomita, M. (2010b). Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles. \u003cem\u003eMetabolomics: Official Journal of the Metabolomic Society\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 78\u0026ndash;95. https://doi.org/10.1007/s11306-009-0178-y\u003c/li\u003e\n\u003cli\u003eTarka, S. M., Arnaud, M. J., Dvorchik, B. H., \u0026amp; Vesell, E. S. (1983). Theobromine kinetics and metabolic disposition. \u003cem\u003eClinical Pharmacology and Therapeutics\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(4), 546\u0026ndash;555. https://doi.org/10.1038/clpt.1983.212\u003c/li\u003e\n\u003cli\u003eTerkelsen, T., Russo, F., Gromov, P., Haakensen, V. D., Brunak, S., Gromova, I., et al. (2020). Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration. \u003cem\u003eBreast cancer research: BCR\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 73. https://doi.org/10.1186/s13058-020-01295-6\u003c/li\u003e\n\u003cli\u003eTsang, J. Y. S., \u0026amp; Tse, G. M. (2020). Molecular Classification of Breast Cancer. \u003cem\u003eAdvances in Anatomic Pathology\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(1), 27\u0026ndash;35. https://doi.org/10.1097/PAP.0000000000000232\u003c/li\u003e\n\u003cli\u003eWang, G., Qiu, M., Xing, X., Zhou, J., Yao, H., Li, M., et al. (2022). Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis. \u003cem\u003eScience Translational Medicine\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(630), eabk2756. https://doi.org/10.1126/scitranslmed.abk2756\u003c/li\u003e\n\u003cli\u003eWei, J., Xie, G., Zhou, Z., Shi, P., Qiu, Y., Zheng, X., et al. (2011). Salivary metabolite signatures of oral cancer and leukoplakia. \u003cem\u003eInternational Journal of Cancer\u003c/em\u003e, \u003cem\u003e129\u003c/em\u003e(9), 2207\u0026ndash;2217. https://doi.org/10.1002/ijc.25881\u003c/li\u003e\n\u003cli\u003eXiao, H., Zhang, L., Zhou, H., Lee, J. M., Garon, E. B., \u0026amp; Wong, D. T. W. (2012). Proteomic analysis of human saliva from lung cancer patients using two-dimensional difference gel electrophoresis and mass spectrometry. \u003cem\u003eMolecular \u0026amp; cellular proteomics: MCP\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), M111.012112. https://doi.org/10.1074/mcp.M111.012112\u003c/li\u003e\n\u003cli\u003eYoung, C. M., Beziaud, L., Dessen, P., Madurga Alonso, A., Santamaria-Mart\u0026iacute;nez, A., \u0026amp; Huelsken, J. (2023). Metabolic dependencies of metastasis-initiating cells in female breast cancer. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 7076. https://doi.org/10.1038/s41467-023-42748-8\u003c/li\u003e\n\u003cli\u003eYu, J., Wang, N., Gong, Z., Liu, L., Yang, S., Chen, G. G., \u0026amp; Lai, P. B. S. (2021). Cytochrome P450 1A2 overcomes nuclear factor kappa B-mediated sorafenib resistance in hepatocellular carcinoma. \u003cem\u003eOncogene\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(3), 492\u0026ndash;507. https://doi.org/10.1038/s41388-020-01545-z\u003c/li\u003e\n\u003cli\u003eZhang, L., Farrell, J. J., Zhou, H., Elashoff, D., Akin, D., Park, N.-H., et al. (2010). Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. \u003cem\u003eGastroenterology\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e(3), 949-957.e1\u0026ndash;7. https://doi.org/10.1053/j.gastro.2009.11.010\u003c/li\u003e\n\u003cli\u003eZhong, L., Cheng, F., Lu, X., Duan, Y., \u0026amp; Wang, X. (2016). Untargeted saliva metabonomics study of breast cancer based on ultra performance liquid chromatography coupled to mass spectrometry with HILIC and RPLC separations. \u003cem\u003eTalanta\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e, 351\u0026ndash;360. https://doi.org/10.1016/j.talanta.2016.04.049\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":"Breast Cancer, Caffeine Metabolism, Salivary Metabolomics, LC-MS/MS, Biomarkers, KEGG Pathway Analysis, ROC Curve","lastPublishedDoi":"10.21203/rs.3.rs-6531859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6531859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eBreast cancer (BC) remains the most prevalent malignant tumor in women worldwide and a leading cause of cancer-related mortality. Early screening is essential to improve prognosis, yet current diagnostic methods are often invasive or lack sensitivity. Saliva is an accessible and non-invasive biofluid containing various metabolites that reflect systemic physiological and pathological changes. Thus, salivary metabolomics may provide novel insights into breast cancer-associated metabolic alterations and support the development of early diagnostic strategies.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo explore the salivary metabolomic profile of breast cancer patients and identify potential non-invasive biomarkers for early breast cancer screening.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSaliva samples were collected from 30 breast cancer patients and 20 healthy controls. An untargeted metabolomics approach was applied using liquid chromatography\u0026ndash;tandem mass spectrometry (LC-MS/MS). Multivariate analyses (PCA, OPLS-DA), KEGG pathway enrichment, and ROC curve analysis were used to characterize metabolic differences and evaluate the diagnostic performance of candidate biomarkers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 101 differential metabolites were identified, including 81 upregulated and 20 downregulated compounds. Significant alterations were observed in caffeine metabolism, choline metabolism, and amino acid metabolism pathways. Among them, 2-aminonicotinic acid and theobromine demonstrated moderate diagnostic value, with AUCs of 0.82 and 0.85, respectively. However, diagnostic thresholds and confidence intervals require further validation in larger cohorts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe salivary metabolome of breast cancer patients displays distinct changes compared to healthy individuals. These metabolic alterations suggest disruptions in energy metabolism, oxidative stress response, and immune regulation in breast cancer. Salivary metabolites such as 2-aminonicotinic acid and theobromine may serve as promising non-invasive biomarkers, although further studies are needed to confirm their diagnostic utility and specificity.\u003c/p\u003e","manuscriptTitle":"Identification of Potential Biomarkers for Breast Cancer Based on Salivary Metabolomics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 13:12:09","doi":"10.21203/rs.3.rs-6531859/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":"5b3298fd-0ef4-463b-8734-3c88a5aea599","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48460331,"name":"Biological sciences/Cancer/Breast cancer"},{"id":48460332,"name":"Biological sciences/Cancer/Cancer epidemiology"},{"id":48460333,"name":"Health sciences/Neurology"},{"id":48460334,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-08-22T05:38:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 13:12:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6531859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6531859","identity":"rs-6531859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}