Abstract Omics analysis can identify new biomarkers at various levels and illuminate the molecular mechanisms of disease. As the furthest downstream of omics analyses, metabolomics reveals rapid and direct changes and has become an important marker for many diseases. Key metabolites can promote the development of diseases. Screening important metabolites to identify biomarkers can not only provide diagnostic standards and identification points in clinical diagnosis but also guide research on molecularly targeted therapies for clinical use. However, the application of metabolomics in oral diseases is not yet mature. This paper reviews the metabolomics analysis of oral and maxillofacial pathology and analyses the changes in metabolites from different perspectives, such as diagnosis and treatment. Common diseases associated with oral and maxillofacial pathology were used as search terms to screen the literature on metabolomics. Finally, a total of 51 articles were selected. Metabolomic analysis has been performed for ameloblastomas, odontogenic keratocysts, salivary gland tumours, oral cancer, and periapical disease. In particular, relatively comprehensive metabolomics analyses of oral cancer, including risk factors and the effects of radiotherapy and chemotherapy on metabolites, have been performed. In addition, studies have focused on metabolites of oral cancer determined using different types of samples or different types of instruments. In summary, this review discusses the metabolomic analysis of common diseases related to oral and maxillofacial pathology. This analysis aims to elucidate the gaps in the metabolic spectrum of oral and maxillofacial pathology and the challenges that further research needs to address, thus suggesting research directions and strategies to cope with challenges and identify valuable metabolic markers to guide the diagnosis and treatment of diseases related to oral and maxillofacial pathology, which may help to inhibit disease development and progression and thereby improve patient survival rates and quality of life. Keywords: Metabolomic, Odontogenic tumours, Jaw cysts, Saliva gland tumours, Oral cancer, Periapical disease Introduction In recent years, metabolomics has advanced rapidly and has been applied to the study of various diseases. High-throughput techniques in metabolomics can detect changes in large quantities of metabolites. The metabolites in the human body often fluctuate with genetic differences, environmental changes, the occurrence of diseases, or the use of medications. The diversity of these factors and their interactions contribute to the diversity and complexity of metabolomics research. Comprehensive analysis of these fluctuations at the metabolic level facilitates the study of diseases and provides valuable information for understanding changes in disease, the diagnosis and treatment of disease, drug response, and the identification of biomarkers [[32]1–[33]5]. Metabolites are the final products of various metabolic pathways, so compared to other omics, metabolomics is most closely related to the system phenotype, with more rapid and direct responses. Metabolomics usually targets the analytical study of low-molecular-weight organic and inorganic metabolites [[34]2, [35]3]. Many metabolites are components of other molecules in the body (DNA, RNA, protein, etc.), provide vital necessities for the body, and play crucial roles in the metabolic cycle [[36]3]. Metabolomics is categorized into two main types: nontargeted and targeted. Nontargeted metabolomics involves a comprehensive analysis of all metabolites present in a sample, whereas targeted metabolomics focuses on the selective measurement of known metabolites [[37]2, [38]6]. Multiple analytical instruments have been used in metabolomics studies, but owing to the complexity of metabolomics and the diversity of metabolites, no analytical instrument has been able to detect all metabolites in a sample, and each instrument has its own characteristic advantages and disadvantages. Therefore, it is essential to incorporate multiple analytical platforms for the same sample, increase the detection rate of metabolites, and improve the quality and integrity of metabolomic analysis studies [[39]2, [40]3, [41]7]. With increasing understanding of diseases and advances in omics technologies, numerous studies have identified alterations in metabolites and metabolic pathways that participate in disease occurrence and progression as important indicators of diseases, such as tumours [[42]7–[43]9]. In both colon and gastric cancers, the Warburg effect has been observed, and the metabolites involved in the tricarboxylic acid cycle (TCA cycle) are altered [[44]10]. Lipids [[45]7] and amino acids are also increased to varying extents in both colon and gastric cancer [[46]10]. These metabolic disorders promote the progression and deterioration of cancer. According to the Global Oral Health Report, oral diseases affect nearly half of the world's population, particularly in low- and middle-income countries. These diseases not only compromise individual health but also diminish the quality of life for patients and their families. Common oral tumours can lead to significant aesthetic and functional challenges in the maxillofacial region, especially tumours that can erode bone tissue, such as ameloblastoma and other odontogenic tumours [[47]1, [48]11]. Early diagnosis is a particular challenge for oral cancer. Although metabolomics in oral diseases remains largely exploratory at this stage and clinical application is still theoretical, metabolomics still has significant research value. In the future, metabolomics research can reveal differences between cancer tissue and normal tissue. Unique metabolites can act as biomarkers to identify oral cancer or precancerous lesions, solving the problem of early diagnosis. Metabolomics can also identify new molecular targets for oral cancer treatment and monitor response to treatment. Clinicians can adjust the treatment plan according to the changes in metabolic pathways and metabolites after treatment begins. Moreover, metabolomics can also provide prognostic markers for oral cancer to monitor the risk of recurrence [[49]7, [50]9]. This review revealed that there are still gaps in metabolomics research on many oral diseases. In particular, the distribution of metabolomics studies on oral tumours is highly uneven; experimental researchers have extensively studied the metabolism of oral cancer, but data on other types of tumours are scarce. Therefore, further identification and analysis of the unique metabolic fluctuations associated with oral diseases are needed to understand disease progression and develop new therapies. Thus, clarifying the latest advancements in metabolomics related to these oral diseases is crucial. This review focuses primarily on the field of oral and maxillofacial pathology, comprehensively addressing various tumours and cysts that may occur in the oral cavity to provide a review and analysis of changes in disease metabolites and metabolic pathways. The aim is to provide clinical practitioners with conclusions from existing metabolomics research, offer insights for the future clinical diagnosis and treatment of these diseases, and encourage experimental researchers to fill the gaps in metabolomics within the field of oral and maxillofacial pathology, thereby reducing the imbalance in metabolomics research and facilitating more comprehensive studies of disease progression in the future. Methods and materials Search strategy In this review, common diseases associated with oral and maxillofacial pathology were retrieved from the PubMed database to screen the literature on how the metabolome of these diseases is altered. To search separately for common diseases in oral and maxillofacial pathology according to the latest histological classification, multiple keywords, such as “oral squamous cell carcinoma (OSCC)”, “head and neck squamous cell carcinoma (HNSCC)”, “oral cancer”, “mouth neoplasms”, “ameloblastoma”, “odontogenic keratocyst”, “radicular cyst”, and “metabolomic”, were used. A total of 51 articles were obtained, which summarized research progress on the metabolomics of various oral diseases and provided directions for future diagnosis and treatment. Study selection All the retrieved literature was reviewed to ensure that the inclusion and exclusion criteria were met. The inclusion criteria used in this review are as follows: 1) the disease must be able to develop in the oral cavity; 2) the changes in the metabolites and metabolic pathways of these diseases must be discussed; and 3) the impact of these diseases before and after radiotherapy or chemotherapy must be considered. The exclusion criteria were the following topics: 1) metastatic tumours, that is, diseases with malignant tumours at sites other than the oral cavity; and 2) some less common tumours in the oral cavity that are more likely to occur in other parts of the body. Results Table [51]1 shows the advantages and disadvantages of different analysis platforms and our evaluation of these platforms. Table [52]2 summarizes the existing literature on metabolomics studies of diseases in oral and maxillofacial pathology, including the publication year, author, type of analytical instrument, and metabolomics results. Figure [53]1 shows a flow chart of this discussion. Table 1. Advantage and disadvantage of the platform Analysis platform Gas Chromatography- Mass Spectrometry Liquid Chromatography-Mass Spectrometry Capillary Electrophoresis-Mass Spectrometry Nuclear Magnetic Resonance Advantage Separate and analyse volatile metabolites, which can separate the specific metabolites of interest.It is suitable for volatile and insoluble substances The scope of analysis is wide.And compounds with strong polarity, difficult volatilization and thermal instability can be analysed. The number of metabolites detected is significantly higher than that of Gas Chromatography- Mass Spectrometry Fast and high-resolution analysis of charged metabolites such as nucleic acids, amino acids and carboxylic acids Samples can be collected in a noninvasive or minimally invasive way. Few samples are needed, few or no samples are prepared, and the analysis speed is fast Disadvantage Many metabolites are still not easy to evaporate, so they cannot be analysed by Gas Chromatography- Mass Spectrometry . Compared with Liquid Chromatography-Mass Spectrometry , sample preparation is more complicated The combined use of Liquid Chromatography-Mass Spectrometry can accurately qualitatively and quantify the number of metabolites is less than that of Gas Chromatography- Mass Spectrometry The ability to separate macromolecules is limited. Special equipment is needed. It is highly sensitive to changes in the composition of the buffer The equipment and accessories are expensive and the operation of the equipment is complicated Assess The relationship between the combination of Gas Chromatography- Mass Spectrometry and Liquid Chromatography-Mass Spectrometry is that some metabolites are repeated, and most metabolites are complementary. We suggest that the two platforms should be used together or select according to the research purpose. Low molecular weight metabolites (amino acids, fatty acids, carbohydrates, central metabolic pathways such as TCA cycle or glycolysis etc.) can choose the platform of Gas Chromatography- Mass Spectrometry, while lipophilic high molecular weight compounds such as lipids (glyceryl ester, phospholipids, bile acid quantification) can choose the Liquid Chromatography-Mass Spectrometry. In addition, Capillary Electrophoresis-Mass Spectrometry can be used for the analysis of charged substance.Due to its high repeatability, the sample is analysed in the Nuclear Magnetic Resonance tube, and the sample is not in contact with the operating components of the platform. It reduces pollution and maintenance, making sample high-throughput analysis possible [54]Open in a new tab Table 2. Findings regarding metabolomic changes in common oral diseases Authors Analytical method Specimen Statistics Main findings Pathway Li.et al. 2024 [[55]1] Liquid Chromatography-Mass Spectrometry Tissues unsupervised Principal Component Analysis and orthogonal Partial Least Squares Discriminant Analysis There are 20 metabolites that are different in odontogenic cyst, odontogenic keratocyst and ameloblastoma, such as:oleamide,D-phenylalanine,D-proline,L-leucine.The highest nucleic acids in ameloblastoma, and the highest peptides in odontogenic keratocyst Taurine and hypotaurine metabolism, nicotinate-nicotinamide metabolism, citrate cycle, pyrimidine metabolism, galactose metabolism, cysteine metabolism, methionine metabolism Filipe Fideles Duarte-Andrade et al. 2019 [[56]9] Gas Chromatography- Mass Spectrometry Formalin-Fixed and Paraffin-Embedded Tissues unsupervised Principal Component Analysis and Partial Least Squares Discriminant Analysis 11 metabolites were significantly differences:2-hydroxypyridine, β-cyano-L-alanine, Caprylic acid, Glycerol, Glycine,, L-glutamic acid, L-serine, Myristic acid, Phosphoric acid, D-(+)trehalose, Glycolicacid ABC transporters,Aminoacyl-tRNA biosynthesis,Cyanoamino acid metabolism Victor Coutinho Bastos et al 2022 [[57]12] Liquid Chromatography-Mass Spectrometry Formalin-Fixed and Paraffin-Embedded Tissues The metabolic spectrum of dental follicles changes with age C21-steroid hormone biosynthesis, bile acid biosynthesis, galactose metabolism, androgen and oestrogen biosynthesis, starch and sucrose metabolism, and lipoate metabolism S. Wang et al. 2024 [[58]13] Gas Chromatography- Mass Spectrometry Serum 73 metabolites were identified, including 24 metabolites showed a significant difference in odontogenic keratocyst, four of which have increased expression F. Leite-Lima et al G. 2022 [[59]14] Liquid Chromatography-Mass Spectrometry Formalin-Fixed and Paraffin-Embedded Tissues Partial Least Squares Discriminant Analysis In the odontogenic keratinocyst before marsupialization, the expression of the first 25 significantly changed metabolites was upregulated, that is to say, the premarsupialization tissue gathered into a group separately and separated from the other two groups, indicating that it is a different tissue from the other two groups Metabolism of oxylipin, linoleic acid, sialic acid, omega-3 fatty acid, galactose, glycolysis, gluconeogenesis, ubiquinone biosynthesis, glycosphingolipid, nicotinate/nicotinamide, ferroptosis A. M. Altaie et al. 2021 [[60]15] Gas Chromatography- Mass Spectrometry Tissues Healthy oral cavity are more susceptible to viral infection than concurrent periapical lesions. The balance of fatty acids can affect the viral load and result in significant upregulation of the associated genes and therefore higher viral load. In addition, palmitic, stearic, myristic acids and oleic acid increased at high levels during infection A. M. Altaie et al. 2021 [[61]16] Gas Chromatography- Mass Spectrometry Tissues Metabolites and related immune substances affect the occurrence and development of oral lesions.Saturated fatty acids such as palmitic acid and stearic acid in radicular cysts are associated with apoptosis Masahiro Sugimoto et al. 2010 [[62]17] capillary electrophoresis time-of-flight mass spectrometry Saliva unsupervised Principal Component Analysis Identified 27 salivary metabolites that distinguish oral cancer and healthy individuals, such as choline, tryptophan, valine, threonine, histidine, pipecolic acid, glutamic acid, carnitine, alanine, piperidine, taurine Qihui Wang et al. 2013[[63]18] Ultra-Performance Liquid Chromatography–Mass Spectrometry Saliva Four salivary metabolites, choline, betaine, pipecolinic acid, and L-carnitine, were significant differences at the stages of oral squamous cell carcinoma I-II and healthy groups and could serve as salivary metabolite biomarkers for early diagnosis of oral squamous cell carcinoma Jie Wei et al. 2010 [[64]19] Ultra-Performance Liquid Chromatography–Mass Spectrometry Saliva unsupervised Principal Component Analysis and orthogonal Partial Least Squares Discriminant Analysis The combination of Valine, lactic acid and phenylalanine distinguishes oral squamous cell carcinoma patients from healthy subjects or oral leukoplakia patients Ravindra Taware et al. 2018 [[65]20] Gas Chromatography- Mass Spectrometry Saliva unsupervised Principal Component Analysis and orthogonal Partial Least Squares Discriminant Analysis 1,4-dichlorobenzene, 1,2-decanediol, 2,5-bis 1,1-dimethylethylphenol and E-3-decen-2-ol are the four most sensitive salivary volatile organic metabolites for identifying oral cancers glycolysis or gluconeogenesis, pyruvate metabolism, selenoamino acid metabolism, taurine and hypotaurine metabolism, glycerolipid metabolism and tyrosine metabolism.nicotinate and nicotinamide metabolism Ashish Gupta et al. 2014 [[66]21] ^1H nuclear magnetic resonance (^1H NMR) Serum unsupervised Principal Component Analysis and orthogonal Partial Least Squares Discriminant Analysis Four biomarkers (glutamine, propionate, acetone, and choline) were able to distinguish oral squamous cell carcinoma from healthy control.And four biomarkers (glutamine, acetone, acetate, and choline) were able to distinguish oral squamous cell carcinoma from oral leukoplakia Xiangli Kong et al. 2015 [[67]22] ^1H NMR Plasma unsupervised Principal Component Analysis and Partial Least Squares Discriminant Analysis Lactic acid, choline, glucose, proline, valine, isoleucine, aspartic acid and 2-hydroxybutyric acid may be relative to the mechanisms of oral cancer Ana Carolina B et al. 2018 [[68]23] ^1H NMR Cells Partial Least Squares Discriminant Analysis Lipid metabolism is related to the increased invasiveness as a result of the The expression level of malonate, methyl malonic acid, n-acetyl and unsaturated fatty acids (CH2) increased with enhanced metastatic invasion Guozhu Ye et al. 2014 [[69]24] Gas Chromatography- Mass Spectrometry Serum unsupervised Principal Component Analysis and Partial Least Squares Discriminant Analysis Most fatty acids, steroids, and antioxidant substances expression increased in the chemotherapy samples. In the samples with better efficacy, the differences in amino acids and carbohydrates were more pronounced, And lactic acid, glutamic acid, and aspartic acid can be used as landmark metabolites for the suitability and effectiveness of chemotherapy Hui Wang et al. 2015 [[70]25] ^1H NMR Cells unsupervised Principal Component Analysis and Partial Least Squares Discriminant Analysis Acacetate with higher expression levels and lactate with lower expression levels play important roles in the process of cell resistance M. Roś-Mazurczyk et al. 2017 [[71]26] Gas Chromatography- Mass Spectrometry Serum Significant changes occurred in 20 metabolites such as carboxylic acids, sugars, amines and amino acids before and after treatment. In the upregulated metabolites, 3-hydroxybutyric acid was the most significant metabolite, and its level was tripled in the treated sample Protein biosynthesis and amino acid metabolism, etc P. Kamarajan et al. 2017 [[72]27] Liquid Chromatography-Mass Spectrometry Gas Chromatography- Mass Spectrometry Tissues Saliva Plasma Cell lines unsupervised Principal Component Analysis There were 30 metabolites that could differentiate normal saliva from saliva in head and neck squamous cell carcinoma patients. There were 30 metabolites that distinguished normal tissues from primary and metastatic head and neck squamous cell carcinoma tissues. In addition, found that the glutamine metabolism pathway plays an important role in tumours Glutamine/glutamate metabolism, aerobic glycolysis (Warburg effect), oxidative phosphorylation (Pasteur’s effect), energy metabolism, TCA cycle anaplerotic flux, hexosamine pathway, osmoregulatory and antioxidant mechanisms J. Wang et al. 2014 [[73]28] Coupling Capillary Ion Chromatography with Q Exactive Mass Spectrometer Cells In the stem-like cancer cells of head and neck squamous cell carcinoma,D-fructose 6-phosphate and α-D-glucose 6-phosphate was higher than that in the nonstem cells, Pyruvate and lactate are also altered, indicating increased effects such as the glycolytic pathway and pyruvate metabolism in stem-like cancer cells Energy metabolism pathways, glycolysis, TCA cycle, and pyruvate metabolism are significantly changed M. Wu et al. 2022 [[74]29] Liquid Chromatography-Mass Spectrometry Serum unsupervised Principal Component Analysis and orthogonal Partial Least Squares Discriminant Analysis Differentially abundant metabolites in salivary gland tumours and normal controls, such as lactic acid, sn-Glycero-3-phosphocholine, Serine, aspartic acid, Swertiamarin, Vincanidine, Proline, 6-phosphate E. Sommella et al. 2022 [[75]30] Matrix-assisted laser desorption/ionization mass spectrometry imaging Tissues unsupervised Principal Component Analysis and Partial Least Squares Discriminant Analysis In tumour tissue by Matrix-assisted laser desorption/ionization mass spectrometry imaging, but decreased LPC 16:0, sphingomyelin, triacylglycerols. Matrix-assisted laser desorption/ionization mass spectrometry imaging can be used by clinicians as a diagnostic tool M. Grimaldi et al. 2018 [[76]31] NMR spectroscopy and processing Saliva unsupervised Principal Component Analysis and Partial Least Squares Discriminant Analysis D-glucose, L-alanine, L-threonine, L-serine, inositol, L-leucine and L-valine were higher in parotid tumours, but expression of formate, diamantine, succinate and pyruvate were lower in parotid tumours. L-alanine, L-leucine, lactate, glycine, L-tyrosine, glycerol, isopropyl alcohol became the characteristics of male patients Metabolite pathway enrichment analysis of differentially abundant metabolites revealed that altered pathways of alanine, aspartate, glutamate, serine and glycine N. Skaug et al. 1976 [[77]32] Cyst fluids Cyst fluid is high in cholesterol and contains more cholesterol than serum I. Slutzky-Goldberg et al.2013 [[78]33] Tissues The probability of cholesterol deposition is higher in elderly periapical biopsies, which can hinder the periapical healing process after cholesterol deposition, while the rapid healing rate of periapical lesions in younger patients may be due to low cholesterol levels M. Yashima et al. 1990 [[79]34] Cyst fluids In radicular cyst fluid, cholesterol content was associated with apo B and a correlation between apo B and heat-stabilized cholesterol binding protein activity, Antibodies against apolipoprotein B clearly inhibited cholesterol binding activity in radicular cyst fluid C. Plengwitthaya et al. 2019 [[80]35] Tissues The probability of finding cholesterol crystals in radicular cysts is higher than that of other diseases or significantly higher when the periapical lesion is greater than 100 mm^2 M. Kamboj et al. 2016 [[81]36] Tissues Cholesterol granuloma is the result of cholesterol crystal accumulation and can participate in bone erosion and lesion enlargement when it occurs in the cyst wall R. M. Browne et al 1971 [[82]37] Tissues Cholestogenic cleft exists in odontogenic cysts and is proportional to haemoflavin [83]Open in a new tab Fig. 1. [84]Fig. 1 [85]Open in a new tab Flow chart Metabolomics platforms Chromatography-Mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy are the most commonly used analytical methods. NMR detects hydrogen atoms in chemical substances. Samples are collected in a noninvasive or minimally invasive manner and are easy to prepare, increasing the accuracy of the identification and analysis of metabolites. However, the equipment is expensive, and the operation is complex. Mass spectrometry is an analytical technique that classifies ions on the basis of their mass‒to‒charge ratio after chemical substances are ionized. Mass spectrometry is often used in conjunction with separation techniques such as chromatography, which enhances quality resolution and determination ability [[86]2, [87]3, [88]7]. These methods include gas chromatography‒mass spectrometry (GC‒MS), liquid chromatography‒mass spectrometry (LC‒MS) and capillary electrophoresis‒mass spectrometry. GC‒MS uses gas as the mobile phase and is aimed primarily at volatile metabolites. LC‒MS uses liquid as the mobile phase and is mainly targeted at larger molecules that are difficult to vaporize and not easily volatile. The substances detected are usually more numerous than those detected by GC‒MS, and sample preparation is simpler for LC‒MS. Capillary electrophoresis‒mass spectrometry primarily targets charged substances, but it generally requires special equipment and is easily affected by the composition of the buffer solution. When selecting a technical platform, if the research purpose does not have specific requirements for the properties of metabolites, it is preferable to choose a platform that can detect a greater number of substances. When the target metabolites have clear specifications, the corresponding platform should be selected on the basis of their properties. In addition, under experimental conditions that allow high requirements for experimental results, the use of NMR can also reduce contamination and maintenance, thus providing researchers with high-throughput analysis and more accurate results. On the basis of the above analysis, each type of equipment targets specific substances, but there are also varying degrees of drawbacks. The choice of equipment platform depends on the experimental requirements. However, metabolomics analysis requires the combined use of different platforms to construct a complete metabolic profile. This means that more time and effort are needed to complete this work. This is also the reason why there is a research gap in the metabolomics of most oral diseases. Sample type In the study of metabolomics in organisms, different sample types also present metabolic differences. For example, the expression levels of the same metabolite may vary across different sample types, or the metabolites between different sample types may be completely different. Sample types that can be collected include blood, urine, cerebrospinal fluid, lymphatic fluid, bile, faeces, saliva, cells, and tissues [[89]3].Saliva, gingival crevicular fluid, serum, and tissue are the most commonly used sample types in most oral diseases [[90]7, [91]10].Serum, tissues, saliva, and other samples are biological samples in addition to cell lines. The metabolic responses within an organism often exhibit dynamic changes and are influenced by various external factors, which can affect the accuracy of the results. As subjects of metabolomics research, cell lines have controllable experimental conditions. After metabolomics results are obtained, they can be directly combined with other omics analyses, making the experimental results more comprehensive. However, cell metabolomics is a type of in vitro experiment and cannot fully simulate complex metabolic changes in living organisms. Therefore, compared with cell metabolomics, biological samples truly reflect the complex internal environment of the organism and the metabolic characteristics after systemic regulation. In terms of acquisition, cell lines are easier to obtain. However, biological samples, such as saliva and urine, can be collected via noninvasive, safe, simple, and cost-effective methods, making them relatively easy to obtain. On the other hand, types such as serum and tissue are not completely safe and are not noninvasive, making them indeed more difficult to acquire than cell lines [[92]38]. Oral and maxillofacial cysts Oral and maxillofacial cysts include soft tissue cysts and jaw cysts, among which soft tissue cysts include salivary gland cysts, sebaceous cysts, and dermoid cysts. However, a review of the literature reveals that there has yet to be a study focusing on metabolomics in relation to soft tissue cysts; therefore, this study does not address them in detail. With respect to jaw cysts, the latest classification from 2022 did not introduce further subdivisions. In previous classifications, jaw cysts were categorized into developmental and inflammatory types, with developmental cysts further divided into odontogenic and nonodontogenic categories. Each type of lesion exhibits distinct biological behaviours, histological characteristics, and clinical manifestations [[93]39].Odontogenic cysts include radicular cysts, odontogenic keratocysts, etc., and both have metabolomics findings. Oral and maxillofacial tumours Oral and maxillofacial tumours include benign tumours, tumour-like lesions, and malignancies. Examples include ameloblastomas and oral cancers. The tissue sources of benign tumours primarily originate from dental and epithelial tissues, whereas malignant tumours are more frequently derived from epithelial origins. Sarcomas are less common in the oral and maxillofacial regions. Overall, the prevalence of benign tumours is greater than that of malignant tumours among oral and maxillofacial tumours. This review focuses primarily on these types of tumours. Benign tumours and tumour-like lesions In addition to cysts, benign tumours can also occur in the mouth and maxillofacial cavity. Research has shown that there have been studies on the metabolomics of ameloblastoma. Ameloblastoma is an odontogenic tumour and is the most prevalent type of odontogenic tumour, accounting for more than 60% of cases. Furthermore, it has imaging characteristics similar to those of odontogenic keratocysts. This study also compared the metabolomics of ameloblastoma and odontogenic keratocysts. Additionally, the incidence of tumours varies among different salivary glands, and the risk of transformation into malignancy also differ. By reviewing the metabolomics of benign tumours, we hope to uncover the underlying mechanisms of their metabolism, which may aid in their treatment and help prevent deterioration. Malignancy Cancers are most common in oral and maxillofacial tumours, and sarcoma is rare. Among cancers, squamous cell carcinoma is the most common. A literature review revealed that the metabolic spectrum analysis of oral squamous cell carcinoma is the most comprehensive and thorough. We believe that this metabolic analysis of oral squamous cell carcinoma provides a complete and rigorous method and a clear analysis direction for studying the metabolism of other oral and maxillofacial tumours. Discussion Jaw cysts (application of cholesterol in jaw cysts) Cholesterol is often found in jaw cysts, which are rich in cholesterol and more cholesterol than serum is [[94]32]. Cholesterol crystals formed by cholesterol accumulation are also common. The cystic fluid of cholesterol-containing crystals is gold in colour [[95]35]. Cholesterol crystallization is a crystalline substance that increases the concentration of cholesterol in bile and surpasses the solubility limits. Cholesterol crystals are often found in odontogenic cysts, with a notably greater incidence in inflammatory cysts than in noninflammatory cysts. This observation suggests that the inflammatory response may be linked to the formation of cholesterol crystals [[96]35–[97]37, [98]40]. Cholesterol granulomas on the cyst wall result from the accumulation of cholesterol crystals. This condition can lead to the expansion of associated oral lesions and bone erosion, as well as promote the growth of jaw cysts, particularly inflammatory cysts [[99]36, [100]41]. The cholesterol crystallium that accumulates in the capsule wall can act as a stimulus that can cause a foreign body reaction [[101]33, [102]40]. The release of interleukin (IL)−1α, IL-1β, IL-6, IL-8, and tumour necrosis factor. These factors increase the susceptibility of odontogenic cysts to dilation, potentially extending to the maxillary sinus. Additionally, cholesterol crystallization may impact lesions associated with apical periodontitis in the long term, thereby hindering the healing process [[103]40]. Cholesterol fissures are often observed in periapical biopsies, with an incidence rate as high as 44%, and the higher probability of cholesterol deposition in elderly periapical biopsies and cholesterol deposition can hinder the periapical healing process. The high incidence of cholesterol in elderly patients makes the periapical lesion healing rate lower than that in other age groups, and high cholesterol may be due to the lack of repair in elderly patients. Therefore, elderly people need to pay attention to the risk of subsequent lesions after receiving endodontic treatment [[104]33]. In the study by C. Plengwitthaya et al., the probability of detecting cholesterol crystals in radicular cysts was greater than that in other diseases or significantly greater when periapical lesions were greater than 100mm^2 [[105]35]. Cholesterol crystals have become one of the characteristics of radicular cysts, triglycerides are the source of cholesterol in radicular cysts, and triglyceride and cholesterol content increases with age [[106]33, [107]34]. In radicular cyst fluid, cholesterol content was found to be associated with apolipoprotein B. Additionally, a correlation between apolipoprotein B and heat-stable cholesterol binding protein activity and heat-stable cholesterol binding protein was found to be involved in cholesterol accumulation within radicular cysts. Antibodies against apolipoprotein B significantly inhibited cholesterol-binding activity in radicular cyst fluid [[108]34, [109]42]. These findings suggest that cholesterol metabolism may contribute to the progression and healing outcomes of inflammatory oral lesions, particularly in ageing populations. Metabolomic analysis of odontogenic keratocysts Odontogenic keratocysts are more common odontogenic cysts than ameloblastomas are [[110]43]. Clinically, they typically present with slow growth, aggressive behaviour, and cysts that often expand towards the buccal side. Bone expansion can lead to maxillofacial asymmetry and tooth displacement [[111]44]. Compared with those in the healthy group, 24 metabolites were altered in the serum metabolic profile of odontogenic keratocysts, of which 4 were upregulated [[112]45]. Moreover, inflammatory responses, such as differences in the metabolic profiles of healthy dental pulp and periapical lesions, can influence metabolomics. Additionally, there are distinctions between noninflammatory and inflammatory odontogenic keratocysts. In this context, 8 metabolites presented decreased levels, whereas 13 metabolites presented increased levels during inflammation, highlighting 8 differential metabolic pathways [[113]1]. Treatment modalities also affect the metabolic profile of odontogenic keratocysts, where marsupialization is a conservative treatment in the odontogenic keratocyst treatment approach. Some biological changes in the odontogenic keratocyst epithelium that gradually resemble the oral mucosa after marsupialization were reported by Flavia Leite-Lima et al. However, odontogenic keratocysts before marsupialization differ from those in the adjacent oral mucosa. During this process of biological changes, the epithelial cells of odontogenic keratocysts undergo a series of metabolic changes, and the metabolic profile after marsupialization is more similar to that of the oral mucosa. A comparison of the metabolic pathways associated with odontogenic keratocysts before marsupialization and oral mucosa analysis revealed that oxylipin, linoleic acid, vitamin E, sialic acid, −3fatty acid and galactose, glycolysis and gluconeogenesis, and carnitine and de novo fatty acid biosynthesis, which are metabolic pathways that significantly changed and may be associated with the development of lesions, maintenance, and invasion ability. Phenotypic differences in the oral mucosa of odontogenic keratocysts after marsupialization were associated with metabolic pathways such as ferroptosis, nicotinate/nicotinamide metabolism, and glycosphingolipid, ubiquitin and dicyunsaturated fatty acid oxidation. These pathways contribute to the regulation of cell growth, proliferation, injury, and death. However, the biosynthetic pathways of nicotinic acid/nicotinamide metabolism, α-tocopherol degradation and polyunsaturated fatty acids significantly differed before and after marsuparization. These differences in metabolic pathways provide new directions for the treatment of odontogenic keratocysts [[114]14]. Changes in the partial environment can also alter the metabolic profile of odontogenic keratocysts in relation to oxidative stress. In osteosarcoma, alterations in metabolites and their associated metabolic pathways drive the progression of osteoclasts, and bisphosphonates can affect collagen synthesis and thus influence bone homeostasis [[115]1]. Periapical lesions Periapical lesions and fatty acids A study of the metabolomics of periapical lesions (periapical abscesses, radicular cysts, and preapical granulomas) and healthy mouths revealed that the level of fatty acids in periapical lesions is lower than that in healthy mouths [[116]15]. Compared with periapical lesions, palmitic acid, stearic acid, and myristic acid were the most abundantly expressed saturated fatty acids in the metabolic profile of the healthy group. Additionally, palmitic acid, stearic acid, myristic acid, and oleic acid also increased at high levels during infection, while L-lactic acid was found to be higher only in periapical granulomas during infection. Furthermore, palmitic acid and stearic acid demonstrate weak antiviral activity and proinflammatory effects, whereas L-lactic acid possesses both antiviral and anti-inflammatory properties [[117]15, [118]16]. Fatty acids and viral infections Viral infections can be influenced by metabolites, which may either protect the body from infection or worsen the disease. The oral cavity is one of the primary routes for the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Angiotensin-converting enzyme 2 (ACE-2), neuropilin-1 (NRP-1), and transmembrane serine protease 2 (TMPRSS2), which are associated with SARS-CoV-2 infection, have been identified in the oral cavity. The viral load can change due to the balance of fatty acids. High levels of fatty acids in the healthy mouth promote the upregulation of related genes, resulting in increased viral load and increased viral infection rates. However, IL-6 gene expression was greater in periapical abscesses and periapical granulomas than in radicular cysts and healthy controls, while SARS-CoV-2 receptor expression was low, indicating an inverse relationship between inflammation and the expression of viral receptors. Therefore, a healthy oral cavity is more susceptible to SARS-CoV-2 infection [[119]15]. Fatty acid and viral infection-related gene expression Analysis of the relationship between fatty acid content and gene expression revealed that the expression of the ACE-2 gene was significantly upregulated when the concentrations of palmitic acid, stearic acid, and 1-nonadelene increased. However, when the concentrations of oleic acid, 17-octadenoic acid, and L-lactic acid increased, the expression of the ACE-2 gene was upregulated slightly. When palmitic acid, 1-nonadelene, stearic acid, and 17-octadecenic acid were present at high concentrations, the expression of the NRP-1 gene was slightly upregulated. However, when the concentration of oleic acid or L-lactic acid was increased, the expression of NRP-1 decreased. When the concentration of fatty acids was high, the expression of TMPRSS2 was upregulated, especially palmitic acid, stearic acid, 1-nonadelene and L-lactic acid. However, 1-nonadelene is most abundant in radicular cysts, so the viral load in radicular cysts may be lower than that in healthy mouths but higher than that in other lesion groups [[120]15]. Fatty acids and other metabolites in relation to immune responses In addition, unique metabolites within the metabolic pathways of immune cells may serve as markers during inflammation. Furthermore, metabolites and immune responses play significant roles in the onset and progression of oral diseases. The metabolites were extracted from periapical lesions (periapical abscess, periapical granuloma, and radicular cysts), and their metabolic profiles were then compared with those of healthy dental pulp tissue to categorize these metabolites into related immune cell groups. Lipid metabolism pathways such as 16–20-HETE and 17-octadecanoic acid in periapical abscesses can upregulate MMP-9, interleukin-8, cytochrome P450 4 F3 and vascular endothelial growth factor. Saturated fatty acids such as palmitic acid and stearic acid in radicular cysts are associated with apoptosis, and nonalkane alkanes and their derivatives, such as 5-butylfethane and nonyltetrayl ether, are associated with lipid phagocytosis. The formation of granulomas in periapical granulomas is associated with L-(+)lactate and ethylene glycol [[121]16]. In summary, the fatty acid content in periapical lesions is lower than that in healthy oral environments. Fatty acids can promote the expression of genes associated with viral infections, resulting in an increased viral load. Consequently, a healthy oral environment may be more susceptible to viral infections, and other metabolites, such as fatty acids, can also influence various immune responses. Odontogenic tumours Odontogenic tumours are usually composed of odontogenic tissue, the odontogenic epithelium, odontogenic mesenchyme, or odontogenic epithelium and mesenchyme, mainly within the jaw. Classification is based on biological behaviour (malignant or benign) and tissue of origin (epithelial, interlobar, or mixed) [[122]39, [123]46]. Odontogenic tumours can lead to root or bone absorption, cause tooth movement misalignment, lead to changes in bone density, and may impact facial function, aesthetics, etc. However, owing to the radiological similarities among these lesions, differential diagnosis also faces challenges, and misdiagnosis leads to mistreatment. Omics helps us once again recognize another feature of tooth-related tumour changes, helping us to discover more new technologies in many aspects, such as the diagnosis, treatment, and prognosis of diseases [[124]39]. Ameloblastoma The primary source of ameloblastoma is the enamel organ, the residual epithelium of the dental plate, or the epithelial surplus in the periodontal tissue. The dental follicle is a layer of loose connective tissue surrounding the developing enamel organ and the dental papilla. It is composed of dental follicle cells derived from ectodermal interleaf cells. These dental follicle cells can differentiate in different directions into osteoblasts, fibroblasts, etc., and eventually form cementum, the periodontal membrane, and intrinsic alveolar bone. The dental follicle plays a crucial role in the development of the tooth root and the process of tooth eruption [[125]47]. Metabolomic analysis of the dental follicle revealed that it undergoes metabolic changes with age. The differences in metabolic pathways between young individuals and adults primarily involve c21-steroid hormone biosynthesis, bile acid biosynthesis, galactose metabolism, androgen and oestrogen biosynthesis, starch and sucrose metabolism, and lipoic acid metabolism [[126]1, [127]12]. Li et al. reported that ameloblastomas had a higher nucleic acid content than odontogenic keratocysts did. Additionally, they performed an enrichment analysis of metabolites that exhibited significant differences in abundance, revealing that L-carnitine and hypoxanthine were enriched in ameloblastoma. [[128]1] A metabolomic analysis of formalin-fixed paraffin-embedded ameloblastoma tissues was conducted via gas chromatography‒mass spectrometry, and 26 metabolites were identified. Compared with those in normal dental follicle tissue, the expression of 11 metabolites, including amino acids, fatty acids, carbohydrates, inorganic acids, and organic heterocyclic compounds, significantly differed. Among them, the expression levels of serine and glycine are elevated, and the associated pathways are involved in glycolysis. Both biosynthetic processes are involved in tumour growth and promote tumour growth. Related metabolic pathways, including aminoacyl-tRNA biosynthesis, cyanoamino acid metabolism, and ABC transporters, can be used to differentiate between tumour and normal tissues. The metabolic profile of recurrent ameloblastoma patients was compared with that of primary ameloblastoma patients. There were no metabolic differences between the recurrent and primary groups. A decrease in glycerol abundance was noted in ameloblastomas with the BRAF V600E mutation, likely due to an increased rate of glycolysis [[129]9]. Ameloblastoma and odontogenic keratocysts Ameloblastomas and odontogenic keratocysts have the smallest differences in imaging, and both can cause destruction to the jaw. However, ameloblastomas cause the most damage to bone, whereas odontogenic keratocysts are weak. Owing to the lack of diagnostic treatment and invasive and recurrent characteristics, jaw destruction may lead to severe pathological fractures, which further increase the difficulty of treatment [[130]1]. The imaging characteristics of odontogenic keratocysts and ameloblastomas differ minimally; however, differentiation can be achieved through histological examination via biopsy. Furthermore, this review identified distinct differences in their metabolic profiles in addition to histopathology. Li et al. used tartrate-resistant acid phosphatase, immunohistochemistry and high-throughput targeted metabolomics to assess bone destruction activity and metabolic profiles to identify new diagnostic markers and treatments [[131]1]. A comparison of ameloblastoma and odontogenic keratocysts revealed that 29 metabolites differed: 12 increased, and 17 decreased. The metabolic pathways of cysteine and methionine were the most divergent. In contrast to ameloblastoma, the polypeptide content was greater in odontogenic keratocysts than in ameloblastomas, with isoleucine and L-methionine sulfone being particularly enriched in odontogenic keratocysts. Cysteine levels are lower in odontogenic keratocysts than in ameloblastomas. KEGG analysis of cysteine metabolism pathways revealed that cysteine metabolism is an important metabolic process in cells. Cystathionine γ-lyase (CTH) regulates this pathway, catalysing cysteine and hydrogen sulfide, while the resulting hydrogen sulfide stimulates the activation of NF-κB signalling and the nitrogen fixation 1 homologue, increasing the expression of cathepsin K (CTSK) and matrix metalloproteinase 9 (MMP 9). CTSK and MMP 9 cause bone destruction, whose increased expression represents enhanced bone destruction but also increases the tolerance of iron death, increases tumour invasion, and increases the vitality of tumour cells. Among them, CTSK expression was greater in ameloblastomas than in odontogenic keratocysts, whereas MMP 9 expression was greater in odontogenic keratocysts. The generation of sulfide and glutathione and the depletion of cysteine are involved in the mechanism of iron death. Iron death is a form of programmed cell death, and CTH protects cells and reduces the occurrence of iron death. Through their study, they reported that CTH affects bone destruction activity by regulating the sensitivity of epithelial cells to iron death and that reducing the expression of CTH can promote iron-induced cell death. CTH expression was greater in ameloblastomas than in odontogenic keratocysts. Like cysteine metabolism, differences in cysteine metabolism may lead to different destruction capacities. Cysteine can also be used as a biomarker for ameloblastoma or a predictor of invasion, helping in its diagnosis. Disorders of metabolites and metabolic pathways affect the ability of tumours to destroy bone [[132]1]. Saliva gland tumours The three major salivary glands in the oral cavity are the parotid, submandibular, and sublingual glands. Most salivary gland tumours occur in the parotid gland. Malignant tumours include mucoepidermoid carcinoma, adenoid cystic carcinoma, and acinar cell carcinoma, among others [[133]29].Benign tumours, such as pleomorphic adenomas, can progress into malignant tumours. Salivary gland cancer often reaches advanced stages before being detected by patients, resulting in a poor prognosis and low survival in patients with salivary gland cancer. Both benign and malignant tumours directly influence the extent of surgical intervention. The metabolites of salivary gland tumours provide relevant information for studying the mechanisms of tumour occurrence and development, early diagnosis and treatment prognosis [[134]30, [135]31]. Wu et al. conducted a study that analysed the serum metabolites of both normal patients and those with salivary gland tumours. They identified 32 differentially abundant metabolites enriched in amino acid metabolism pathways, particularly aspartic acid. Both benign and malignant salivary gland tumours presented increased levels of serine and lactate, with malignant tumours showing higher concentrations of both metabolites than benign tumours. A risk assessment formula for salivary gland tumours was developed according to the levels of serine and lactate. This formula identified both benign and malignant patients, speculated on the risk of disease progression, identified high-risk groups, and facilitated close monitoring of these groups. Additionally, personalized treatment plans have been created. The study revealed that age and sex did not influence the serum metabolic profile of patients with salivary gland tumours [[136]29]. This finding contrasts with the findings of another study by Grimaldi et al., which examined the clinical manifestations of salivary gland cancer. The incidence of this cancer is gradually increasing among male patients over the age of 50. The study of saliva metabolites in male patients revealed differences in the levels of alanine, leucine, lactate, isopropanol, tyrosine, glycine, and glycerol, which are characteristic of this population. Metabolite pathway enrichment analysis of these differentially abundant metabolites revealed altered pathways for aspartate, glutamate, serine and so on [[137]31]. When the tissues of all patients with salivary gland tumours were compared, metabolic profiling revealed significant differences in lipid responses. The analysis indicated that the levels of glycerophospholipids increased in tumour regions, whereas phosphatidylcholine and phosphatidylethanolamine metabolism accounted for the primary changes [[138]30]. However, sphingomyelin and triacylglycerol levels decreased in the tumour area. Sphingolipids are essential components of cell membranes and play a role in cancer cell signalling pathways. Triacylglycerol is involved in energy production, which may be linked to the high energy demands of cancer cell proliferation. Among the amino acids, glutamine, glutamate, and aspartic acid levels were significantly elevated, indicating that glutamate metabolism is altered to supply a carbon source for the TCA cycle [[139]30]. Amino acids are also altered in the saliva of patients with salivary glands, and leucine, serine, alanine, pyroglutamate, and dimethylamine are characteristic features of salivary gland tumours. Further analysis revealed that glucose, alanine, threonine, serine, inositol, leucine, and valine were present at high levels in parotid tumours but were expressed at low levels in formate, diamantine, succinic acid, and pyruvate [[140]31]. These metabolites associated with glycolytic effects often differ in other cancers (oral cancer, thyroid cancer, breast cancer, etc.), consistently resulting in faster glucose uptake by cancer cells and significantly higher rates of glycolytic effects. These differentially abundant metabolites offer valuable insights for the development of new diagnostic methods and treatments, enhancing the prognostic evaluation of salivary gland tumours and improving patient survival rates. Oral cancer Oral cancer is one of the most common malignancies of the head and neck, with the majority classified as squamous cell carcinoma. The size of the tumour and the likelihood of recurrence contribute to its poor prognosis and survival rates [[141]48]. Saliva, serum, tissue, and even urine samples from patients with oral cancer can be analysed via metabolomics to identify potential biomarkers of the disease. Salivary biomarkers Saliva is produced by the secretions of salivary glands and consists of water, organic compounds, inorganic substances, proteins, cells, microorganisms, and various other molecules [[142]13]. As a noninvasive and convenient sampling method, saliva can reflect the metabolic changes associated with oral diseases, oral biofilms, or systemic metabolic disorders. However, saliva is also influenced by various external and physiological factors, including the time of collection and the methods used [[143]10]. Compared with normal saliva, 27 metabolites can be used to identify oral cancer patients, and the expression of polyamines in oral cancer patients is significantly increased [[144]17]. Polyamines play a crucial role in promoting cell growth and proliferation, which in turn influences the metastasis and invasive capabilities of oral cancer. The upregulation of tryptophan may indicate the progression of oral cancer. Additionally, the levels of putrescine can fluctuate in response to radiation or chemotherapy. Compared with those in the healthy group, the concentrations of putrescine and cadaverine in the radiotherapy group decreased but remained elevated. Taurine and piperidine are believed to be specific metabolites associated with oral cancer [[145]17]. In early OSCC, the levels of choline, betaine and piperidinic acid are greater, whereas the level of L-carnitine is lower [[146]18], and betaine is a highly oxidized form of choline. Choline is a crucial component of biofilms, and fluctuations in choline levels may indicate abnormalities in biofilm synthesis and degradation, potentially due to the rapid proliferation of cancer cells [[147]17]. However, the reduced expression level of L-carnitine may be attributed to the downregulation of fatty acid metabolism in OSCC. These four salivary metabolites can serve as markers to differentiate between healthy and cancerous tissues, which is beneficial for the early diagnosis of OSCC. With advancements in the precision of metabolite extraction and identification, propionylcholine, n-acetyl-phenylalanine, sphingosine, plant sphingosine, and s-carboxymethyl-l-cysteine were further identified as markers that distinguish OSCC from healthy tissue [[148]18]. Salivary metabolomics can be utilized to differentiate between early OSCC lesions and precancerous lesions (oral lichen planus and oral leukoplakia). The combination of valine, phenylalanine, and lactate in saliva effectively distinguishes between oral cancer and oral leukoplakia [[149]19, [150]28]. An analysis of volatile substances in the saliva of oral cancer patients revealed that 1,4-dichlorobenzene, 1,2-decandiol, 2,5-double 1,1-dimethyl ethylphenol, and e-3-decene-2-alcohol could be markers for the identification of malignant diseases [[151]20]. Serum profile In contrast to the normal serum metabolic profile, glutamine, propionate, acetone, and choline serve as serum markers to distinguish healthy and cancer tissues, whereas glutamine, acetone, acetic acid, and choline can be used as serum markers to distinguish oral leukoplakia from cancer tissue. In addition, the upregulation of lactic acid, choline, and glucose, along with the downregulation of proline, valine, isoleucine, aspartic acid, and 2-hydroxybutyric acid, is beneficial for the progression of oral cancer [[152]21, [153]22]. Tissue metabolism In the tissue metabolic profile, the first observation was the downregulation of glucose and glutamine expression in OSCC but the upregulation of lactate, which reflects the enhanced glycolytic response in OSCC [[154]10]. In addition, glucose transporters are also upregulated. In particular, glucose transporter protein 1 and glucose transporter protein 3 [[155]48]. High levels of glutamine/glutamate provide carbon and nitrogen sources for cancer cells, which are captured by the glycolytic pathway to provide energy to cancer cells and promote tumour growth. A nitrogen source was used for the generation of purines and pyrimidines. This phenomenon has also been observed in salivary gland tumours, with increased glutamine/glutamate expression and increased hypoxanthine, xanthine, and related nucleotide metabolism [[156]30]. Glutamionolysis into glutamate in HNSCC cells or tissues results in increased glutamate and decreased glutamine levels. The expression of glutaminase and aldehyde dehydrogenase, which catalyse the reaction, increased in head and neck cancer tissues and cells. This phenomenon indicates that tumour development depends on glutaminolysis. In addition to tissues and cells, tumour spheres of HNSCC similarly exhibit this phenomenon [[157]27]. Lactate in OSCC can originate not only from cancer cells but also from surrounding normal tissue cells. After lactate is taken up by cancer cells, it increases their proliferation ability and invasive force and can be used as a promising biomarker for the diagnosis of OSCC. Glutamine offers a pathway for the targeted treatment of cancer. Its targeted treatment can be divided into two main types: glutaminase inhibitors and amino acid transporter inhibitors. Among these, telaglenastat (CB-839) is a glutaminase inhibitor that has demonstrated significant anti-proliferative effects in a variety of cancers and is currently being utilized in cancer therapy. V-9302 serves as a competitive inhibitor of amino acid transporter proteins, and the combined use of both agents to inhibit glutamine has emerged as a novel treatment strategy for liver cancer [[158]49]. Second, in addition to glutamine, the levels of various amino acids (valine, methionine, etc.) also change. The synthesis, decomposition, and transport pathways of these compounds are significantly increased, influencing the occurrence and development of OSCC from various perspectives. Furthermore, lipid metabolism is disrupted in OSCC. Upregulated cholesterol levels can promote oral carcinogenesis, and prostaglandin E2 also influences the proliferation of cancer cells to some extent. Among the metabolic pathways related to fatty acid uptake, various intermediates, such as CD36 and fatty acid-binding proteins, have been shown to play a role in the proliferation and migration of OSCC, as well as in lymph node metastasis. Therefore, the uptake of fatty acids by cancer cells is also beneficial [[159]13]. In another study, the knockdown of SOX11 also affected glycolysis and the tricarboxylic acid cycle pathway, thereby inhibiting the proliferative capacity of cancer cells [[160]28]. However, when the serum and tissue metabolomics of OSCC were compared, amino acids were upregulated, whereas glycolytic metabolites were downregulated. Conversely, the changes in the levels of certain metabolites in the serum contrasted with those in the tissues. The upregulation of metabolites in tissues may be attributed to the increased uptake of these metabolites from serum by cancer cells [[161]10]. Metabolite changes in the cell lines Compared with those in normal fibroblasts, the levels of malonate, methylmalonate, n-acetyl, and unsaturated fatty acids are increased, resulting in greater metastatic invasion [[162]23]. In the stem-like cancer cells of HNSCC, the levels of glycolysis-associated d-fructose 6-phosphate and α-d-glucose 6-phosphate are elevated compared with those in nonstem cells. Additionally, many glycolytic intermediates, including pyruvate and lactate, are altered, indicating enhanced roles for the glycolytic pathway and pyruvate metabolism in stem-like cancer cells [[163]28]. The significance of metabolites in treatment As an adjuvant means other than surgery and radiotherapy, the efficacy of chemotherapy can also be assessed through the metabolite response. Following induction chemotherapy with docetaxel, cisplatin, and fluorouracil, the expression levels of fatty acids, steroids, and antioxidants are elevated in patients with OSCC. In patients with better efficacy, the difference between amino acids and carbohydrates is more significant. In particular, lactate, glutamic acid, and aspartic acid may serve as biomarkers indicative of a significant response to chemotherapy [[164]24]. However, multidrug resistance remains the primary obstacle in the chemotherapy process. In resistant OSCC cell lines, acetate expression is elevated, whereas lactate expression is diminished, both of which may influence cellular resistance [[165]25]. The impact of chemotherapeutic drugs (cisplatin, etc.) on the metabolic profile of tumours has also been observed in osteosarcoma [[166]50, [167]51]. After radiotherapy, the levels of twelve metabolites in the serum of head and neck cancer patients increased, the main pathways involved were methionine and galactose metabolism, the levels of 6 metabolites decreased, and the main metabolic pathways involved were glycine, serine, etc. Among these pathways, 3-hydroxybutyrate changed the most, with threefold upregulation after radiotherapy, which may be associated with the oxidative stress response triggered by radiation exposure [[168]26]. The significance of studying the metabolomics of OSCC for the surgical process is that, compared with the surrounding healthy tissue, some amino acids can effectively distinguish healthy tissue from tumour tissue to confirm the boundary between tumour tissue and normal tissue, which can be utilized to determine the surgical margin in real time [[169]13]. The significance of metabolites in aetiology Cigarette smoking, as a risk factor, increases the probability of developing oral cancer [[170]48], and salivary metabolites also differ between smokers and nonsmokers. Compared with nonsmokers, smokers presented higher concentrations of citric acid, lactate, pyruvate, and sucrose but lower levels of formate [[171]7, [172]17]. These metabolites from different levels can be used to clarify the characteristics of oral cancer metabolites. Changes in the metabolic pathways of oral cancer may be involved in metastatic invasion, and the identification of drug-induced resistance metabolites for oral cancer resistance provides a new research direction. The diagnosis and treatment of oral cancer may provide a new biological target. Other In the oral biofilm metabolic profile, the carbon metabolism pathway (Embden–Meyerhof–Parnas, Pentose Phosphate pathway, TCA) plays a role in oral biofilms; after glucose addition, the levels of glucose 6-phosphate, fructose 6-phosphate, fructose 1,6-diphosphate, 3-phosphogic acid, and phosphoenolpyruvate decrease. These findings indicate that phosphoenolpyruvate is involved in the glucose uptake of oral biofilms. Furthermore, the ability of fluoride to prevent the occurrence of caries is likely due to its ability to inhibit various responses within the central carbon metabolism pathways of oral biofilms [[173]10]. Challenges and prospects On the basis of the above analysis, we conclude that metabolomics research is both flexible and dynamic. There are still many challenges at the methodological level. First, there are sample-related issues: 1) The factors affecting metabolite fluctuations (such as genetics, diseases, medications, etc.) are diverse and complex. Metabolite concentrations are significantly influenced by physiological state, sampling time, storage duration, sample processing method (such as formalin-fixed paraffin-embedded tissues or direct freezing after liquid nitrogen treatment), and the natural degradation of metabolites during storage. 2) Studies with small sample sizes are prone to false positives, so some studies included in this review may still have small sample sizes, which could diminish the robustness of the research findings. These metabolic changes require further validation to clarify their clinical significance. Whereas studies with excessively large sample sizes can lead to resource waste. Second, there are data challenges: 1) Metabolic experiments often involve information on tens of thousands of metabolites, but the number of metabolites accurately annotated in public databases is limited, making comprehensive analysis difficult. 2) Isomers may be difficult to distinguish because of their similar mass spectrometry signals, and distinguishing them would require increased experimental costs. 3) Low-abundance metabolites are easily masked by high-abundance signals. The third is the technical platform: 1) Different analysis platforms have their own differences. 2) Real-time capabilities of the analysis platforms. 3) Differences in experimental procedures between different analysis platforms. The fourth aspect is reproducibility and reliability. There are numerous types and quantities of metabolites present in the human body, and even under completely identical experimental conditions, the results of metabolite detection and analysis may not be entirely consistent. This makes it difficult to standardize tumour biomarkers, and repeated confirmations may be needed before they can be used. In the face of this challenge, developing standardized processes is necessary. This means creating standardized sample processing methods and experimental procedures on the basis of the characteristics of different platforms and sample types to ensure the reproducibility and reliability of the results. However, this standardization process may lead to issues of resource waste in experiments. Notably, among the advantages and disadvantages of the analysis platform shown in Table [174]1, high repeatability is already a strength of NMR. Improving the stability of analysis platforms in the future can become the direction of metabolomics research. The comprehensive analysis of the tumour metabolism spectrum presents a significant challenge in the field of metabolomics. Conducting a thorough examination of the metabolomics associated with a disease requires substantial time, skilled laboratory personnel, and various experimental consumables. And another challenge is likely the lack of expertise in these technologies among some dental surgeons, as metabolomic data interpretation often requires collaboration with highly trained specialists. Nevertheless, as a downstream discipline of genomics, transcriptomics, and other omics, metabolomics plays a crucial role in understanding the metabolic profiles of diseases. These findings increase our understanding of disease development mechanisms on the basis of physiological phenotypes, and the identified biomarkers can serve as critical indicators for clinical diagnosis and as molecular targets for therapeutic interventions. For the aforementioned metabolomics research on oral diseases, we recommend supplementing the metabolic profiles of various sample types and selecting an appropriate analytical platform for supplementation according to the metabolites of different properties. In addition, multigroup joint analyses can be conducted in conjunction with other omics or through further examination of metabolomics across different pathologies on the basis of histological classifications. Furthermore, treatment methods and pathogenic risk factors should be incorporated into metabolomics analyses. The metabolomics research program focused on oral cancer provides us with a good example. The improvement of oral metabolomics requires joint efforts between academic researchers and clinical practitioners. Conclusion In conclusion, this review revealed that metabolic profiles have been analysed in several tumours and cysts. However, metabolomics has been inadequately studied in other oral diseases in addition to oral cancer. Nevertheless, metabolomics has introduced new concepts for diagnosis and treatment. From the standpoint of developmental mechanisms, risk factors influence changes in metabolites, and certain metabolites contribute to tumour growth, thereby affecting the invasive potential of tumours. These findings provide metabolomic information for tumorigenesis and development. From a diagnostic perspective, biomarkers can identify tumour tissue and even indicate early precancerous lesions, facilitating early diagnosis. Additionally, the metabolic profile can distinguish tumours with similar imaging features that are difficult to differentiate. From a treatment perspective, personalized treatment plans can be effectively developed on the basis of metabolomics. Finally, among patients undergoing radiotherapy and chemotherapy, the efficacy of radiotherapy chemotherapy can be evaluated by changes in the metabolic profile. During the procedure, surgical margins can be determined through metabolic differences between normal and tumour tissues. Consequently, metabolomics has expanded the understanding of oral diseases. In the future, this review suggests that researchers engaged in experimental work will broaden the scope of metabolomics investigations to encompass diseases beyond oral cancer. This expansion should include the characterization of metabolic profiles across various sample types, an increase in sample sizes to increase the precision of findings, and the establishment of reference standards for diverse analytical platforms to strengthen the reliability of the results. Acknowledgements