{"paper_id":"afd464d3-c376-43b3-b680-fd66fafc27e4","body_text":"Changes of salivary metabolomics in patients with chronic erosive gastritis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Changes of salivary metabolomics in patients with chronic erosive gastritis Shaowei Liu, Shixiong Zhang, Haoyu Chen, Pingping Zhou, Tianxiao Yang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2028880/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 May, 2023 Read the published version in BMC Gastroenterology → Version 1 posted 14 You are reading this latest preprint version Abstract Introduction Chronic erosive gastritis (CEG) is closely related to gastric cancer and needs early diagnosis and intervention. The invasiveness and discomfort of electronic gastroscope make it difficult to apply to the extensive screening of CEG. Therefore, a simple and noninvasive screening method is needed in clinic. Objectives The aim of this study is to screen potential biomarkers that can identify diseases from saliva samples of CEG patients using metabolomics. Methods Saliva samples from 64 CEG patients and 30 healthy volunteers were collected, and metabolomic analysis was performed using uhplc-q-tof/ms in the positive and negative ion mode. Statistical analysis was performed using univariate (student's t-test) and multivariate (orthogonal partial least squares discriminant analysis). Receiver operating characteristic (ROC) analysis was used to determine potential predictors in saliva of CEG patients. Results By comparing saliva samples from CEG patients and healthy volunteers, we found 45 differentially expressed metabolites, of which 37 were up-regulated and 8 were down-regulated. These differential metabolites are related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. In the ROC analysis, the AUC values of 7 metabolites were greater than 0.8, among which the AUC values of 1,2-dioleoyl-sn-glycoro-3-phosphodylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) were greater than 0.9. Conclusions We identified salivary metabolites related to CEG and screened out 45 potential biomarkers, 1,2-dioleoyl-sn-glycoro-3-phosphorylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phosphorine (SOPC), which may have clinical application value. Chronic erosive gastritis Saliva Metabolomics Biomarker UHPLC-Q-TOF/MS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Chronic erosive gastritis (CEG) is a kind of gastritis characterized by impaired integrity of gastric mucosa. Under gastroscope, it shows flat or uplift erosion. The depth of wound rupture is no more than 1mm and does not reach the muscular layer(Dixon et al., 1996 ). Clinically, chronic erosive gastritis can be manifested as stomach pain, nausea, vomiting, anorexia, weight loss and other symptoms. In severe cases, anemia may be caused by bleeding, but many patients do not have any symptoms(Farthing et al., 1981 ). Long term use of NSAIDs is the most common cause of chronic erosive gastritis. In addition, Helicobacter pylori infection, bile reflux, alcohol, cocaine and ionizing radiation are also the factors causing chronic erosive gastritis(Chen et al., 2005 ; Watari et al., 2014 ). The morbidity of chronic erosive gastritis is high. A multicenter study on chronic gastritis in China showed that of the 8892 patients included, 3760 (42.3%) were diagnosed as chronic erosive gastritis under electronic gastroscope(Du et al., 2014 ). Long term chronic inflammation of gastric mucosa is closely related to gastric cancer(Noto et al., 2022 ). Therefore, early diagnosis and intervention of chronic erosive gastritis are of great significance to block the progress of chronic gastritis and prevent gastric cancer. At present, the diagnosis of chronic erosive gastritis depends on gastroscopy, but it is difficult to be used as a broad screening method because of its invasiveness and discomfort(Zheng et al., 2018 ). Consequently, a new detection method with small trauma, easy operation and low cost is needed in clinic. Metabonomics is a comprehensive and effective method to analyze the changes of endogenous small molecule metabolites(Mu et al., 2019 ). Metabonomics based on LC-MS plays an important role in biomarker identification and clinical disease diagnosis(Ye et al., 2021 ). Saliva is one of the important body fluids in human body and has many functions. Due to the physiological characteristics of saliva and salivary glands, biomarkers in blood circulation can be finally secreted into saliva(Zhang et al., 2016 ). Because of its cheapness, non invasiveness, safety and economy, saliva collection and detection is expected to become an alternative method of serum or urine detection in disease diagnosis, and has a good prospect of clinical diagnosis(Yoshizawa et al., 2013 ). At present, saliva based on metabonomics has been applied to the diagnosis of diabetes(Aitken et al., 2015 ), cardiovascular diseases(Kosaka et al., 2014 ) and various cancers(Hizir et al., 2014 ; Wei et al., 2014 ; Xie et al., 2015 ) including gastric cancer(Chen et al., 2018 ; Shu et al., 2017 ). In our study, we recruited a total of 94 participants, including 30 healthy volunteers and 64 CEG patients. LC-MS based metabolomics studies the changes of metabolites in saliva between CEG patients and healthy people. 2. Methods 2.1. Participants Participants came from the Department of Gastroenterology of Hebei Hospital of traditional Chinese medicine from September 2021 to June 2022, including 30 cases of healthy control group (Normal group) and 64 cases of CEG group. Written informed consent was obtained from all participants. This study was approved by the ethics committee of Hebei Hospital of traditional Chinese medicine. All participants met the diagnostic criteria of chronic erosive gastritis. The inclusion criteria of participants are as follows: (1) diagnosed as chronic erosive gastritis by endoscopy; (2) 25–70 years old; (3) willingness to participate in the test and undersign the written informed consent. The exclusion criteria for screening patients were as follows: (1) past or present use of NSAIDs or other agents that can cause chronic erosive gastritis; (2) suffering from any other digestive system diseases; (3) any types of cardiovascular disease; (4) illness of the haematological system; (5) any kind of mental disorder including depression; (6) incapability or restricted capability. 2.2. Sample collection and preparation The standardized collection method is selected to collect saliva dynamically. The saliva collection place is a quiet room from 9:00 a.m. to 11:00 a.m. Before sampling, participants should rinse their mouth with distilled water for 3–5 times to remove impurities in their mouth, spit out the water, and collect an appropriate amount of saliva naturally flowing out of participants in a quiet state in a sterile sputum cup. The collected saliva samples were stored in an ice box and immediately centrifuged at 14000 R / min at 4℃ for 10 min. The centrifuged supernatant was sub packed and stored at − 80℃ for standby. After thawing the frozen saliva sample slowly at 4℃, take an appropriate amount of sample, add precooled methanol / acetonitrile / aqueous solution (2:2:1, V / V), vortex mixing, low-temperature ultrasound for 30min, stand at -20℃ for 10min, 14000 g centrifuged at 4℃ for 20min, take the supernatant and vacuum dry, and add 100% for mass spectrometry analysis µ L acetonitrile aqueous solution (acetonitrile: water = 1:1, V / V) was re dissolved, vortex, 14000 g, centrifuged at 4℃ for 15 min, and the supernatant was injected for analysis. 2.3.LC-MS/MS Analysis Saliva analyses were performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600). For HILIC separation, samples were analyzed using a 2.1 mm × 100 mm ACQUIY UPLC BEH 1.7 µm column (waters, Ireland). In both ESI positive and negative modes, the mobile phase contained A = 25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B = acetonitrile. The gradient was 85% B for 1 min and was linearly reduced to 65% in 11 min, and then was reduced to40% in 0.1 min and kept for 4 min, and then increased to 85% in 0.1 min, with a 5 min re-equilibration period employed. For RPLC separation, a 2.1 mm × 100 mm ACQUIY UPLC HSS T3 1.8 µm column (waters, Ireland) was used. In ESI positive mode, the mobile phase contained A = water with 0.1% formic acid and B = acetonitrile with 0.1% formic acid; and in ESI negative mode, the mobile phase contained A = 0.5 mM ammonium fluoride in water and B = acetonitrile. The gradient was 1%B for 1.5 min and was linearly increased to 99% in 11.5 min and kept for 3.5 min. Then it was reduced to 1% in 0.1 min and a 3.4 min of re-equilibration period was employed. The gradients were at a flow rate of 0.3 mL/min, and the column temperatures were kept constant at 25℃. A 2 µL aliquot of each sample was injected. The ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature: 600℃, IonSpray Voltage Floating (ISVF) ± 5500 V. In MS only acquisition, the instrument was set to acquire over the m/z range 60-1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25-1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 V with ± 15 eV; declustering potential (DP), 60 V (+) and − 60 V (−); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10(Zhou et al., 2021 ). 2.4. Raw data processing and statistical analysis The raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert before importing into freely available XCMS software. After normalized to total peak intensity, the processed data were analyzed by R package (ropls), where it was subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The 7-fold cross-validation and response permutation testing were used to evaluate the robustness of the model. The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification. Metabolites with the VIP value > 1 was further applied to Student’s t-test at univariate level to measure the significance of each metabolite, the p values less than 0.05 were considered as statistically significant. 3. Results 3.1. Characteristics of the study population Among 64 patients in this study, there were 31 females and 33 males with an average age of 49.64 ± 6.80 years old. Among the 30 participants in the healthy control group, there were 14 males and 16 females, with an average age of 47.67 ± 9.88 years old. As shown in supplementary Fig. 1, the electronic gastroscopy of CEG patients showed that the mucosa at the gastric body or antrum was in sheet or strip erosion, local mucosa was congested, and old bleeding spots could also be seen. According to Table 1 , there were no significant differences in gender, age, ALT, AST, ALP between CEG group and normal group. Epigastric pain, gastric distension and heartburn are common clinical symptoms of CEG patients, but there is no significant difference in the frequency. Table 1 Baseline characteristics of patients included in the present study CEG group(n = 64) N group (n = 30) P-value Age, years 49.64 ± 6.80 47.67 ± 9.88 0.174 Male, n (%) 33 (51.56) 14 (46.67) 0.873 Symptoms Epigastric pain n (%) 34 (53.13) 0 0.000 Gastric distention n (%) 28 (43.75) 0 0.000 Heartburn n (%) 31 (48.44) 0 0.000 Hematological results ALT (U/L) 20.36 ± 6.95 28.73 ± 9.09 0.166 AST (U/L) 20.41 ± 3.75 19.77 ± 3.02 0.583 ALP (U/L) 78.32 ± 18.14 81.35 ± 11.18 0.554 3.2.LC-MS method validation We used XCMS software (SIMCA-P 14.1, Umetrics, Umea, Sweden) to extract the metabolite ion peaks of all samples. We obtained the principal compo-nent analysis (PCA) model by pareto-scaling conversion of all peaks. Through the pareto-scaling conversion of each peak, the principal component analysis model is obtained. As shown in Fig. 1 a,b, under the positive and negative ion mode, quality control (QC) samples are closely clustered, indicating that the repeatability of the experiment is good. We performed Pearson correlation analysis on QC samples. The abscissa and ordinate represent the logarithm of the ion peak signal intensity value. The general correlation coefficient greater than 0.9 indicates that the correlation is good. As shown in Fig. 1 c,d, the correlation coefficient between all QC samples is above 0.9, indicating that the analysis system of instrument is stable and the data can be used for subsequent analysis. 3.3. Multivariate statistical analysis In order to clearly show the relationship between each group of samples, we conducted principal component analysis (PCA) and there was a weak separation trend between the CEG groups and normal group. Therefore, it is necessary to adjust the model to show more apparent inter group differences. We constructed orthogonal partial least square discriminate analysis (OPLS-DA) model to distinguish samples. As shown in the OPLS-DA score plot (Fig. 2 a,b) that CEG and normal group can be significantly separated in both positive and negative ion modes, indicating that the metabolites in the saliva of CEG patients have changed significantly compared with the healthy group. The evaluation parameters Q 2 Y and R 2 of OPLS-DA model were obtained through cross-validation. R2y = 0.905 and Q2 = 0.574 in the positive ionization model, r2y = 0.911 and Q2 = 0.481 in the negative ionization model, showing acceptable applicability and predictability. In order to ensure the effectiveness of the model, the permutation test was used to verify the model, the results shown that the model was valid without overfitting (Fig. 2 c, d). Next, We used volcano plot to show the results of fold change (FC) analysis and t-test of two groups of samples. As shown in Fig. 2 e, f, the CEG group and the N group, were significantly separated in the positive and negative ion modes. 3.4. Screening biomarkers related with CEG Based on the Variable Importance for the Projection (VIP) obtained by OPLS-DA model and significant p value obtained from student t-test, the differential metabolites of CEG group and N group were screened to obtain the potential biomarkers of CEG with VIP > 1 and p value < 0.05. The biomarkers were identified according to their structures in the Human Metabolome Database (HMDB; http://www.hmdb.ca/ ). In the positive and negative ion mode, 45 potential biomarkers of CEG were screened and listed in the Supplementary Table 1. In order to more comprehensively and intuitively show the relationship between samples and the differences of metabolite expression patterns in different samples, accurately screen metabolic markers and study the changes of related metabolic processes, we used the metabolite expression level to cluster each group of samples. As shown in Fig. 3 , compared with the normal group, the metabolites of CEG patients changed significantly. The changes of these metabolites may be related to the pathogenesis of CEG. In order to more clearly show the change trend and amplitude of metabolites, we made FC plot.As shown in Fig. 4 , compared with the normal group, the expression of 37 metabolites in the CGE group was up-regulated, including sphingomyelin (d18:1/18:0), 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, 1-Stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC), Lys-Pro, 1-Palmitoyl-sn-glycero-3-phosphocholine, N-Acetylcadaverine, 1-Stearoyl-2-hydroxy-sn-glycero-3-phosphocholine,indoleacetic acid, (3-Carboxypropyl) trimethylammonium cation, thioetheramide-PC. It was also observed that the expression of 8 metabolites such as tetrahydrocorticosterone,norethindrone acetate, 2-Methylbenzoic acid, dioctyl phthalate, 2-Ethoxyethanol, arachidic acid, pinocembrin, behenic acid were down regulated. In addition, we also ranked the metabolites according to the FC value, and drew box plot of top 9 representative up-regulated or down-regulated metabolites (Fig. 5 ). 3.5 Receiver operating characteristic (ROC) analysis of the potential biomarkers ROC analysis of potential biomarkers were performed to identify metabolites with the capability to diagnose CEG. For the ROC curve analysis, 0.5 < AUC ≤ 0.7 means low diagnostic accuracy, 0.7 < AUC ≤ 0.9 means medium diagnostic accuracy, and 0.9 < AUC < 1.0 means high diagnostic accuracy. We performed ROC analysis on the top ten metabolites of FC value, and calculated the cumulative AUC of the top three and top ten metabolites (Table 2 and Fig. 6 ). The top three metabolites including sphingomyelin (d18:1/18:0), 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, 1-Stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine (SOPC) provided AUC value of 0.897 (95%CI:0.830–0.963), 0.925 (95%CI:0.869–0.981) and 0.922 (95%CI:0.860–0.985) respectively. The top three biomarkers cumulative AUC was up to 0.927 (95%CI:0.866–0.987). The top five biomarkers cumulative AUC was up to 0.948 (95%CI:0.891-1.000). The top ten biomarkers cumulative AUC was up to 0.975 (95%CI:0.937-1.000). The results implied a high accuracy in predicting. Table 2 ROC analysis of CEG top 10 biomarkers from saliva Metabolites Cut.offs Sensitivity Specificity AUC p value Sphingomyelin (d18:1/18:0) 7100.78 0.759 1.000 0.897 1.9426E-9 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine 3906.03 0.855 0.963 0.925 4.934E-10 1-Stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine (SOPC) 13346.74 0.830 1.000 0.922 7.7141E-10 Lys-Pro 10460.62 0.711 0.750 0.781 0.000105 1-Palmitoyl-sn-glycero-3-phosphocholine 5267.02 0.673 0.767 0.778 0.000038 N-Acetylcadaverine 10026.48 0.702 0.962 0.867 2.312E-7 1-Stearoyl-2-hydroxy-sn-glycero-3-phosphocholine 5135.58 0.654 0.867 0.762 0.000086 Indoleacetic acid 7754.61 0.649 0.962 0.843 6.1138E-7 (3-Carboxypropyl)trimethylammonium cation 52372.3 0.768 0.885 0.863 1.4293E-7 Thioetheramide-PC 103381.19 0.725 1.000 0.875 1.2218E-7 3.6. Bioinformatics analysis of CEG We classified the differential metabolites screened between CEG group and N group. These metabolites were mainly classified as organic acids and derivatives, lipids and lipid-like molecules, organoheterocyclic compounds,organic oxygen compounds,benzenoids. According to the different metabolites, we employed the Kyoto Encyclopedia of Genes and Genomes (KEGG) ( http://www.kegg.jp/ ) to further explore the most relevant pathways. In order to determine which metabolic and signal transduction pathways were significantly affected, we analyzed the significance level of metabolite enrichment in each pathway. The 6 significantly affected transduction pathways are described in Fig. 7 and include protein digestion and absorption, phenylalanine metabolism,neuroactive ligand-receptor interaction,nicotinate and nicotinamide metabolism, mTOR signaling pathway. 4. Discussion As a long-term chronic inflammation of gastric mucosa, CEG is closely related to gastric cancer(Al-Yassir et al., 2021 ). Therefore, early diagnosis and intervention of CEG are of great significance. At present, the diagnosis of CEG mainly depends on the examination of electronic gastroscope, but the characteristics of invasiveness and discomfort make it difficult to carry out widely. Consequently we need a simple and safe detection method. Saliva is one of the important human body fluids, which is easy to obtain and has shown its potential in disease diagnosis. The aim of this study was to find potential biomarkers of this disease from the saliva of CEG patients. In this study, the baseline data of sex, age, ALT, AST and ALP of the two groups were evenly matched, and there was no significant deviation. CEG patients showed epigastric pain, gastric distension and heartburn, but there was no significant difference in the incidence of the three symptoms. This is consistent with previous understanding. CEG patients can show a variety of common clinical symptoms of the upper digestive system, but these symptoms are not specific and cannot identify the occurrence of this disease(Juhasz et al., 2014 ). Metabonomics technology was used to verify the metabolic differences between CEG patients and normal controls in this study. Finally, 45 metabolites were identified and annotated as potential biomarkers and 6 metabolic pathways were enriched. In the CEG group, the levels of L-arginine, tyramine, indoleacetic acid, phenylpyruvate and N-acetylputrescine were all elevated in saliva. L-arginine is an essential amino acid, which plays an important role in physiology and biochemistry. Previous studies have shown that L-arginine can aggravate the damage of ethanol to rat gastric mucosa(Ferraz et al., 1994 ). L-arginine is a donor of No. No can promote gastric ulcer in gastric ischemia-reperfusion(Kobata et al., 2007 ). Previous studies found that the metabolism of L-arginine in patients with advanced gastric adenocarcinoma was more active than that in patients with superficial gastritis(Hu et al., 2018 ), which was similar to the results of this experiment. The high metabolic level of L-arginine may be the factor leading to CEG, and suggests that CEG is at risk of further deterioration. Tyramine is a kind of biological trace amine, which is produced by tyrosine deacidification, and diet is its main source(Andersen et al., 2019 ). Tyramine can be used as an agonist of human trace amine-associated receptor to stimulate G cells in pyloric tissue to secrete gastrin, promote the secretion of gastric juice and change the movement state of stomach(Ohta et al., 2017 ). In this study, the content of tyramine in saliva of CEG patients is higher than that of normal people. The effect of tyramine may increase gastric acid secretion, cause high acid state in the stomach, damage gastric mucosa and delay its repair. In this study, we found that the levels of 6 substances related to lipid metabolism had changed significantly. The contents of arachidic acid and behenic acid in saliva of CEG patients decreased, and both of them are involved in the biosynthesis of unsaturated fatty acids. Previous studies have shown that unsaturated fatty acids can reduce oxidative damage and inflammatory response(Long et al., 2019 ), enhance the defense of gastric mucosa(Park et al., 2015 ), promote the repair of gastric mucosa(Hollander and Tarnawski, 1991 ) and reduce peptic ulcer(Manjari and Das, 2000 ). Similar to the results of this study, the levels of six unsaturated free fatty acids in serum of patients with gastric cancer were significantly lower than those of patients with benign gastric disease(Zhang et al., 2014 ). Polyunsaturated fatty acids showed tumoricidal action on gastric cancer cells in vitro(Dai et al., 2013 ) and it’s supplementation has been proposed as adjuvant treatment in cancer due anti-inflammatory properties(Mocellin et al., 2018 ). These results suggest that CEG is related to lipid metabolism. Through the metabolic pathway enrichment analysis, mTOR signaling pathway was found to be associated with CEG.mTOR signaling pathway is considered to be a key regulator of autophagy(Jung et al., 2010 ). Autophagy plays an important role in maintaining cell homeostasis and is closely related to the occurrence of many human diseases, including cancer(Klionsky et al., 2021 ). In previous studies(Arab et al., 2021 ; Chang et al., 2017 ), inhibition of autophagy can cause apoptosis of gastric mucosal epithelial cells and damage of gastric mucosa. Activation of autophagy by down regulating mTOR signaling pathway can ameliorate ethanol induced gastric mucosal epithelial cell injury. More importantly, a measure of autophagy can induce the apoptosis of gastric cancer cells, inhibit the proliferation of gastric cancer cells and increase their sensitivity to chemotherapeutic drugs(Cao et al., 2019 ). In this study, the level of L-arginine in CEG patients was higher than that in normal population. Arginine is an amino acid critically involved in multiple cellular processes, and is a direct activator of mTOR(Chen et al., 2021 ). Therefore, we speculate that L-arginine activates mTOR signaling pathway to regulate autophagy of gastric mucosal epithelial cells, which may be one of the mechanisms leading to CEG. However, the present study had some limitations. First, in order to design the test set and verification set to improve the reliability of diagnosis, we need to use a larger sample size in the future work. Second, in this study, the samples before CEG treatment were selected, and the treated samples were not selected. It is suggested that the treated samples can be added to the future work to verify the results of this study and explore new treatment methods and targets. 5. Conclusion In the present study, 45 potential biomarkers related to CEG were identified and 6 metabolic pathways were enriched. These differential metabolites are related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. At the same time, we found that 1,2-dioleoyl-sn-glycoro-3-phosphocholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) have the potential to become biomarkers for diagnosing CEG. The results of this study are helpful to identify potential biomarkers for non metabolic diseases. Declarations Acknowledgments We are grateful to all of the subjects who kindly agreed to participate in this study. Authorship contributions Shaowei Liu: Investigation, Data curation, Writing − original draft, Writing − review & editing, Visualization. Shixiong Zhang: Resources, Data curation. Haoyu Chen: Data curation, Methodology. Pingping Zhou: Investigation, Supervision. Tianxiao Yang: Data curation. Jingjing Lv: Visualization. Huixia Li: Conceptualization, Funding acquisition. Yangang Wang: Conceptualization, Formal analysis, Visualization, Supervision, Funding acquisition, Writing − review & editing. Funding This work was supported by the Natural Science Foundation of Hebei (No. H2020423207) and Scientific research plan project of Hebei administration of traditional Chinese Medicine (2022325). Data availability The data that support the findings of this study is available from the corresponding author upon reasonable request. Ethics approval and consent to participate All experiments were conducted in accordance with the Declaration of Helsinki. This study was approved by the ethics committee of Hebei Hospital of traditional Chinese medicine(ethics board approval HBZY2021-KY-045-01). 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ACS Appl Mater Interfaces. 2014;6:14772–8. https://doi.org/10.1021/am504190a . Hollander D, Tarnawski A. Is there a role for dietary essential fatty acids in gastroduodenal mucosal protection? J Clin Gastroenterol. 1991;13 Suppl 1:72–4. https://doi.org/10.1097/00004836-199112001-00012 . Hu YL, Pang W, Huang Y, Zhang Y, Zhang CJ. The Gastric Microbiome Is Perturbed in Advanced Gastric Adenocarcinoma Identified Through Shotgun Metagenomics. Front Cell Infect Microbiol. 2018;8:433. https://doi.org/10.3389/fcimb.2018.00433 . Juhasz M, Nagy VL, Szekely H, Kocsis D, Tulassay Z, Laszlo JF. Influence of inhomogeneous static magnetic field-exposure on patients with erosive gastritis: a randomized, self- and placebo-controlled, double-blind, single centre, pilot study. J R Soc Interface. 2014;11:20140601. https://doi.org/10.1098/rsif.2014.0601 . Jung CH, Ro SH, Cao J, Otto NM, Kim DH. mTOR regulation of autophagy. FEBS Lett. 2010;584:1287–95. https://doi.org/10.1016/j.febslet.2010.01.017 . Klionsky DJ, Petroni G, Amaravadi RK, Baehrecke EH, Ballabio A, Boya P, Bravo-San Pedro JM, Cadwell K, Cecconi F, Choi AMK, Choi ME, Chu CT, Codogno P, Colombo MI, Cuervo AM, Deretic V, Dikic I, Elazar Z, Eskelinen EL… Pietrocola F. Autophagy in major human diseases. EMBO J. 2021;40:e108863. https://doi.org/10.15252/embj.2021108863 . Kobata A, Kotani T, Komatsu Y, Amagase K, Kato S, Takeuchi K. Dual action of nitric oxide in the pathogenesis of ischemia/reperfusion-induced mucosal injury in mouse stomach. Digestion. 2007;75:188–97. https://doi.org/10.1159/000108590 . Kosaka T, Kokubo Y, Ono T, Sekine S, Kida M, Kikui M, Yamamoto M, Watanabe M, Amano A, Maeda Y, Miyamoto Y. Salivary inflammatory cytokines may be novel markers of carotid atherosclerosis in a Japanese general population: the Suita study. Atherosclerosis. 2014;237:123–8. https://doi.org/10.1016/j.atherosclerosis.2014.08.046 . Long X, Zhao X, Wang W, Zhang Y, Wang H, Liu X, Suo H. Protective effect of silkworm pupa oil on hydrochloric acid/ethanol-induced gastric ulcers. J Sci Food Agric. 2019;99:2974–86. https://doi.org/10.1002/jsfa.9511 . Manjari V, Das UN. Effect of polyunsaturated fatty acids on dexamethasone-induced gastric mucosal damage. Prostaglandins Leukot Essent Fatty Acids. 2000;62:85–96. https://doi.org/10.1054/plef.1999.0125 . Mocellin MC, Fernandes R, Chagas TR, Trindade E. A meta-analysis of n-3 polyunsaturated fatty acids effects on circulating acute-phase protein and cytokines in gastric cancer. Clin Nutr. 2018;37:840–50. https://doi.org/10.1016/j.clnu.2017.05.008 . Mu X, Ji C, Wang Q, Liu K, Hao X, Zhang G, Shi X, Zhang Y, Gonzalez FJ, Wang Q, Wang Y. Non-targeted metabolomics reveals diagnostic biomarker in the tongue coating of patients with chronic gastritis. J Pharm Biomed Anal. 2019;174:541–51. https://doi.org/10.1016/j.jpba.2019.06.025 . Noto CN, Hoft SG, Bockerstett KA, Jackson NM, Ford EL, Vest LS, DiPaolo RJ. IL13 Acts Directly on Gastric Epithelial Cells to Promote Metaplasia Development During Chronic Gastritis. Cell Mol Gastroenterol Hepatol. 2022;13:623–42. https://doi.org/10.1016/j.jcmgh.2021.09.012 . Ohta H, Takebe Y, Murakami Y, Takahama Y, Morimura S. Tyramine and beta-phenylethylamine, from fermented food products, as agonists for the human trace amine-associated receptor 1 (hTAAR1) in the stomach. Biosci Biotechnol Biochem. 2017;81:1002–6. https://doi.org/10.1080/09168451.2016.1274640 . Park JM, Han YM, Jeong M, Kim EH, Ko WJ, Cho JY, Hahm KB. Omega-3 polyunsaturated fatty acids as an angelus custos to rescue patients from NSAID-induced gastroduodenal damage. J Gastroenterol. 2015;50:614–25. https://doi.org/10.1007/s00535-014-1034-z . Shu J, Yu H, Li X, Zhang D, Liu X, Du H, Zhang J, Yang Z, Xie H, Li Z. Salivary glycopatterns as potential biomarkers for diagnosis of gastric cancer. Oncotarget. 2017;8:35718–27. https://doi.org/10.18632/oncotarget.16082 . Watari J, Chen N, Amenta PS, Fukui H, Oshima T, Tomita T, Miwa H, Lim KJ, Das KM. Helicobacter pylori associated chronic gastritis, clinical syndromes, precancerous lesions, and pathogenesis of gastric cancer development. World J Gastroenterol. 2014;20:5461–73. https://doi.org/10.3748/wjg.v20.i18.5461 . Wei F, Lin CC, Joon A, Feng Z, Troche G, Lira ME, Chia D, Mao M, Ho CL, Su WC, Wong DT. Noninvasive saliva-based EGFR gene mutation detection in patients with lung cancer. Am J Respir Crit Care Med. 2014;190:1117–26. https://doi.org/10.1164/rccm.201406-1003OC . Xie Z, Yin X, Gong B, Nie W, Wu B, Zhang X, Huang J, Zhang P, Zhou Z, Li Z. Salivary microRNAs show potential as a noninvasive biomarker for detecting resectable pancreatic cancer. Cancer Prev Res (Phila). 2015;8:165–73. https://doi.org/10.1158/1940-6207.CAPR-14-0192 . Ye X, Wang X, Wang Y, Sun W, Chen Y, Wang D, Li Z, Li Z. A urine and serum metabolomics study of gastroesophageal reflux disease in TCM syndrome differentiation using UPLC-Q-TOF/MS. J Pharm Biomed Anal. 2021;206:114369. https://doi.org/10.1016/j.jpba.2021.114369 . Yoshizawa JM, Schafer CA, Schafer JJ, Farrell JJ, Paster BJ, Wong DT. Salivary biomarkers: toward future clinical and diagnostic utilities. Clin Microbiol Rev. 2013;26:781–91. https://doi.org/10.1128/CMR.00021-13 . Zhang CZ, Cheng XQ, Li JY, Zhang P, Yi P, Xu X, Zhou XD. Saliva in the diagnosis of diseases. Int J Oral Sci. 2016;8:133–7. https://doi.org/10.1038/ijos.2016.38 . Zhang Y, Qiu L, Wang Y, He C, Qin X, Liu Y, Li Z. Unsaturated free fatty acids: a potential biomarker panel for early detection of gastric cancer. Biomarkers. 2014;19:667–73. https://doi.org/10.3109/1354750X.2014.977951 . Zheng HR, Zhang XQ, Li LZ, Wang YL, Wei Y, Chen YM, Shao JL, Wang XR, Yu WF, Su DS. Multicentre prospective cohort study evaluating gastroscopy without sedation in China. Br J Anaesth. 2018;121:508–11. https://doi.org/10.1016/j.bja.2018.04.027 . Zhou P, Hao X, Liu Y, Yang Z, Xu M, Liu S, Zhang S, Yang T, Wang X, Wang Y. Determination of the protective effects of Hua-Zhuo-Jie-Du in chronic atrophic gastritis by regulating intestinal microbiota and metabolites: combination of liquid chromatograph mass spectrometer metabolic profiling and 16S rRNA gene sequencing. Chin Med. 2021;16:37. https://doi.org/10.1186/s13020-021-00445-y . Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.docx supplementaryfig1.tif Cite Share Download PDF Status: Published Journal Publication published 19 May, 2023 Read the published version in BMC Gastroenterology → Version 1 posted Editorial decision: Major revision 05 Jan, 2023 Reviews received at journal 05 Jan, 2023 Reviews received at journal 01 Dec, 2022 Reviewers agreed at journal 17 Nov, 2022 Reviewers agreed at journal 17 Nov, 2022 Reviews received at journal 21 Oct, 2022 Reviews received at journal 10 Oct, 2022 Reviewers agreed at journal 10 Oct, 2022 Reviewers agreed at journal 07 Oct, 2022 Reviewers invited by journal 07 Oct, 2022 Editor assigned by journal 07 Oct, 2022 Editor invited by journal 17 Sep, 2022 Submission checks completed at journal 17 Sep, 2022 First submitted to journal 03 Sep, 2022 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-2028880\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":137470758,\"identity\":\"7c1ad453-4f58-42c1-9210-5550c2dbd6c8\",\"order_by\":0,\"name\":\"Shaowei Liu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hebei University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shaowei\",\"middleName\":\"\",\"lastName\":\"Liu\",\"suffix\":\"\"},{\"id\":137470759,\"identity\":\"98853439-d1ed-4218-90d9-67eff2afc1ff\",\"order_by\":1,\"name\":\"Shixiong Zhang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hebei University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shixiong\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"},{\"id\":137470760,\"identity\":\"aa5386c3-abc9-4f80-96d6-16816d3e4c0d\",\"order_by\":2,\"name\":\"Haoyu Chen\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hebei University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Haoyu\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":137470761,\"identity\":\"f2a6627d-cdb1-4427-9868-bd9425c84016\",\"order_by\":3,\"name\":\"Pingping Zhou\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hebei University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Pingping\",\"middleName\":\"\",\"lastName\":\"Zhou\",\"suffix\":\"\"},{\"id\":137470762,\"identity\":\"e71ce4c5-453b-4f96-992b-b8653914399f\",\"order_by\":4,\"name\":\"Tianxiao Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hebei University of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Tianxiao\",\"middleName\":\"\",\"lastName\":\"Yang\",\"suffix\":\"\"},{\"id\":137470763,\"identity\":\"d49dd2d5-95bb-4750-9240-82def005d6c7\",\"order_by\":5,\"name\":\"Jingjing Lv\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Hebei Province Hospital of Chinese Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jingjing\",\"middleName\":\"\",\"lastName\":\"Lv\",\"suffix\":\"\"},{\"id\":137470764,\"identity\":\"881de982-b7e2-4a66-8069-9c02f3502fa2\",\"order_by\":6,\"name\":\"Huixia Li\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Beijing University of Chinese Medicine Third Affiliated Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Huixia\",\"middleName\":\"\",\"lastName\":\"Li\",\"suffix\":\"\"},{\"id\":137470765,\"identity\":\"07527688-b1e0-4ca4-9104-492377fdbd1d\",\"order_by\":7,\"name\":\"Yangang Wang\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYDACZiBOMJDg4WdmPvyABC0VNjKS7WxpBiRYdSbNxuA8j4IEUYrN2XkMHzxsO8xjfJiHwYChxiaaoBbLZh5jg0SgFrPDvAceMBxLy20gpMXgMO82CYgWvgQDxobDRGnZ/gOkxbiZx0CCWC3bGBLOpPEYMBOrxbKZ/7MEMJB5JA4DAzmBGL+Y8x9L/PjDQMKev//w4QcfamyIcBgKL4GQckwto2AUjIJRMAqwAQC82jsatYUrwwAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Hebei University of Chinese Medicine\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Yangang\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2022-09-03 14:29:12\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-2028880/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-2028880/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12876-023-02803-6\",\"type\":\"published\",\"date\":\"2023-05-19T20:52:41+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":26672325,\"identity\":\"9899fa30-d035-4178-900c-4e6fba4787a8\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:37:40\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":802881,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eQuality control (QC) chart for samples. A, B Principal component analysis (PCA) score charts in the positive and negative ion modes, different graphs represent different samples. CEG group samples = Purple triangle, N group samples = Yellow rhombus, and QC samples = Light blue dots. C, D QC sample correlation map for the positive and negative ion modes\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/69cf118937400c245d9aed93.png\"},{\"id\":26670677,\"identity\":\"1877038d-05ae-47e5-8555-0d0290ff50c0\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:27:39\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":859897,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOrthogonal partial least square-discriminate analysis (OPLS-DA) score charts and cross-validation test in the positive and negative ion mode (A-D). A, C OPLS-DA score graph and mode cross-validation graph for the CEG and N groups in the positive ion mode. B, D OPLS-DA score graph and mode cross-validation graph for the CEG and N groups in the negative ion mode. CEG = Green square and N = Dark blue dots. Volcano map based on fold change (FC) analysis and t test (E-F). E, F CEG and N volcano diagrams in the positive and negative ion modes. Up regulated metabolites = Red circle, Down regulated metabolites = Blue circle\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/f94ecce11a3c52fded0e153f.png\"},{\"id\":26670679,\"identity\":\"9d258082-48e1-4bc5-bac7-3da1ae38e3ae\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:27:39\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":792688,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eDifferential metabolite hierarchical clustering diagram in positive(A) and negative(B) ion mode. The ordinate represents the metabolites that are significantly differently expressed, and the abscissa is the sample information. Red represents significantly up-regulated metabolites, blue represents significantly down-regulated metabolites, and the gray part represents no quantitative information on the metabolite\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/a0ced9c2138e52d7f38eddb7.png\"},{\"id\":26671557,\"identity\":\"b7c0ed93-ff42-47aa-93d5-6e32664e2d69\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:32:40\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":450559,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eVolcano map based on fold change (FC) analysis in positive(A) and negative(B) ion mode. Up regulated metabolites = Red bar, Down regulated metabolites = Green bar\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/887b34070daa264b6a1f0142.png\"},{\"id\":26670684,\"identity\":\"e07f7576-2340-4444-8b90-c51518d1b662\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:27:40\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":500095,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBox plots of top nine representative biomarkers screened according to their FC (Fold Change) values\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/34fe15866ba4dea1f9cf4497.png\"},{\"id\":26672324,\"identity\":\"172c3427-823a-4328-a729-0f5f2c77ac68\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:37:39\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":712933,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSaliva ROC curve of Sphingomyelin (d18:1/18:0), 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, 1-Stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC) and merged biomarker\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/52247873cb0d7129dab240da.png\"},{\"id\":26672327,\"identity\":\"cd57ef06-c3b4-4fe6-864c-c03cb582d37a\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:37:40\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":145194,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis of differentially expressed metabolites\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/0a80ad0c597525d58956cf32.png\"},{\"id\":44730953,\"identity\":\"24072f03-04f4-4f15-b6d1-61ee343a2a78\",\"added_by\":\"auto\",\"created_at\":\"2023-10-16 21:37:59\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1920903,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/2e92ca70-dbe9-4737-81a3-55e2bc480256.pdf\"},{\"id\":26670681,\"identity\":\"db0c0286-1a0e-4f8f-976d-9f80543eb1ee\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:27:40\",\"extension\":\"docx\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":20890,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryTable1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/8eecb42891c6e64d00a0eaa1.docx\"},{\"id\":26670685,\"identity\":\"0f72eba9-cb21-468e-a71e-3e76e40a4043\",\"added_by\":\"auto\",\"created_at\":\"2022-09-19 20:27:41\",\"extension\":\"tif\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":31677952,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supplementaryfig1.tif\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-2028880/v1/10ea33333d15cfd55083a0f3.tif\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Changes of salivary metabolomics in patients with chronic erosive gastritis\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eChronic erosive gastritis (CEG) is a kind of gastritis characterized by impaired integrity of gastric mucosa. Under gastroscope, it shows flat or uplift erosion. The depth of wound rupture is no more than 1mm and does not reach the muscular layer(Dixon et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e). Clinically, chronic erosive gastritis can be manifested as stomach pain, nausea, vomiting, anorexia, weight loss and other symptoms. In severe cases, anemia may be caused by bleeding, but many patients do not have any symptoms(Farthing et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e1981\\u003c/span\\u003e). Long term use of NSAIDs is the most common cause of chronic erosive gastritis. In addition, Helicobacter pylori infection, bile reflux, alcohol, cocaine and ionizing radiation are also the factors causing chronic erosive gastritis(Chen et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Watari et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). The morbidity of chronic erosive gastritis is high. A multicenter study on chronic gastritis in China showed that of the 8892 patients included, 3760 (42.3%) were diagnosed as chronic erosive gastritis under electronic gastroscope(Du et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Long term chronic inflammation of gastric mucosa is closely related to gastric cancer(Noto et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Therefore, early diagnosis and intervention of chronic erosive gastritis are of great significance to block the progress of chronic gastritis and prevent gastric cancer. At present, the diagnosis of chronic erosive gastritis depends on gastroscopy, but it is difficult to be used as a broad screening method because of its invasiveness and discomfort(Zheng et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Consequently, a new detection method with small trauma, easy operation and low cost is needed in clinic.\\u003c/p\\u003e \\u003cp\\u003eMetabonomics is a comprehensive and effective method to analyze the changes of endogenous small molecule metabolites(Mu et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Metabonomics based on LC-MS plays an important role in biomarker identification and clinical disease diagnosis(Ye et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Saliva is one of the important body fluids in human body and has many functions. Due to the physiological characteristics of saliva and salivary glands, biomarkers in blood circulation can be finally secreted into saliva(Zhang et al., \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Because of its cheapness, non invasiveness, safety and economy, saliva collection and detection is expected to become an alternative method of serum or urine detection in disease diagnosis, and has a good prospect of clinical diagnosis(Yoshizawa et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). At present, saliva based on metabonomics has been applied to the diagnosis of diabetes(Aitken et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), cardiovascular diseases(Kosaka et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) and various cancers(Hizir et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Wei et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Xie et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e) including gastric cancer(Chen et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Shu et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e In our study, we recruited a total of 94 participants, including 30 healthy volunteers and 64 CEG patients. LC-MS based metabolomics studies the changes of metabolites in saliva between CEG patients and healthy people.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Participants\\u003c/h2\\u003e \\u003cp\\u003e Participants came from the Department of Gastroenterology of Hebei Hospital of traditional Chinese medicine from September 2021 to June 2022, including 30 cases of healthy control group (Normal group) and 64 cases of CEG group. Written informed consent was obtained from all participants. This study was approved by the ethics committee of Hebei Hospital of traditional Chinese medicine.\\u003c/p\\u003e \\u003cp\\u003eAll participants met the diagnostic criteria of chronic erosive gastritis. The inclusion criteria of participants are as follows: (1) diagnosed as chronic erosive gastritis by endoscopy; (2) 25\\u0026ndash;70 years old; (3) willingness to participate in the test and undersign the written informed consent. The exclusion criteria for screening patients were as follows: (1) past or present use of NSAIDs or other agents that can cause chronic erosive gastritis; (2) suffering from any other digestive system diseases; (3) any types of cardiovascular disease; (4) illness of the haematological system; (5) any kind of mental disorder including depression; (6) incapability or restricted capability.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Sample collection and preparation\\u003c/h2\\u003e \\u003cp\\u003eThe standardized collection method is selected to collect saliva dynamically. The saliva collection place is a quiet room from 9:00 a.m. to 11:00 a.m. Before sampling, participants should rinse their mouth with distilled water for 3\\u0026ndash;5 times to remove impurities in their mouth, spit out the water, and collect an appropriate amount of saliva naturally flowing out of participants in a quiet state in a sterile sputum cup. The collected saliva samples were stored in an ice box and immediately centrifuged at 14000 R / min at 4℃ for 10 min. The centrifuged supernatant was sub packed and stored at \\u0026minus;\\u0026thinsp;80℃ for standby.\\u003c/p\\u003e \\u003cp\\u003eAfter thawing the frozen saliva sample slowly at 4℃, take an appropriate amount of sample, add precooled methanol / acetonitrile / aqueous solution (2:2:1, V / V), vortex mixing, low-temperature ultrasound for 30min, stand at -20℃ for 10min, 14000 g centrifuged at 4℃ for 20min, take the supernatant and vacuum dry, and add 100% for mass spectrometry analysis \\u0026micro; L acetonitrile aqueous solution (acetonitrile: water\\u0026thinsp;=\\u0026thinsp;1:1, V / V) was re dissolved, vortex, 14000 g, centrifuged at 4℃ for 15 min, and the supernatant was injected for analysis.\\u003c/p\\u003e \\u003cp\\u003e2.3.LC-MS/MS Analysis\\u003c/p\\u003e \\u003cp\\u003eSaliva analyses were performed using an UHPLC (1290 Infinity LC, Agilent Technologies) coupled to a quadrupole time-of-flight (AB Sciex TripleTOF 6600). For HILIC separation, samples were analyzed using a 2.1 mm \\u0026times; 100 mm ACQUIY UPLC BEH 1.7 \\u0026micro;m column (waters, Ireland). In both ESI positive and negative modes, the mobile phase contained A\\u0026thinsp;=\\u0026thinsp;25 mM ammonium acetate and 25 mM ammonium hydroxide in water and B\\u0026thinsp;=\\u0026thinsp;acetonitrile. The gradient was 85% B for 1 min and was linearly reduced to 65% in 11 min, and then was reduced to40% in 0.1 min and kept for 4 min, and then increased to 85% in 0.1 min, with a 5 min re-equilibration period employed.\\u003c/p\\u003e \\u003cp\\u003eFor RPLC separation, a 2.1 mm \\u0026times; 100 mm ACQUIY UPLC HSS T3 1.8 \\u0026micro;m column (waters, Ireland) was used. In ESI positive mode, the mobile phase contained A\\u0026thinsp;=\\u0026thinsp;water with 0.1% formic acid and B\\u0026thinsp;=\\u0026thinsp;acetonitrile with 0.1% formic acid; and in ESI negative mode, the mobile phase contained A\\u0026thinsp;=\\u0026thinsp;0.5 mM ammonium fluoride in water and B\\u0026thinsp;=\\u0026thinsp;acetonitrile. The gradient was 1%B for 1.5 min and was linearly increased to 99% in 11.5 min and kept for 3.5 min. Then it was reduced to 1% in 0.1 min and a 3.4 min of re-equilibration period was employed. The gradients were at a flow rate of 0.3 mL/min, and the column temperatures were kept constant at 25℃. A 2 \\u0026micro;L aliquot of each sample was injected.\\u003c/p\\u003e \\u003cp\\u003eThe ESI source conditions were set as follows: Ion Source Gas1 (Gas1) as 60, Ion Source Gas2 (Gas2) as 60, curtain gas (CUR) as 30, source temperature: 600℃, IonSpray Voltage Floating (ISVF)\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5500 V. In MS only acquisition, the instrument was set to acquire over the m/z range 60-1000 Da, and the accumulation time for TOF MS scan was set at 0.20 s/spectra. In auto MS/MS acquisition, the instrument was set to acquire over the m/z range 25-1000 Da, and the accumulation time for product ion scan was set at 0.05 s/spectra. The product ion scan is acquired using information dependent acquisition (IDA) with high sensitivity mode selected. The parameters were set as follows: the collision energy (CE) was fixed at 35 V with \\u0026plusmn;\\u0026thinsp;15 eV; declustering potential (DP), 60 V (+) and \\u0026minus;\\u0026thinsp;60 V (\\u0026minus;); exclude isotopes within 4 Da, candidate ions to monitor per cycle: 10(Zhou et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. Raw data processing and statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThe raw MS data (wiff.scan files) were converted to MzXML files using ProteoWizard MSConvert before importing into freely available XCMS software. After normalized to total peak intensity, the processed data were analyzed by R package (ropls), where it was subjected to multivariate data analysis, including Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The 7-fold cross-validation and response permutation testing were used to evaluate the robustness of the model. The variable importance in the projection (VIP) value of each variable in the OPLS-DA model was calculated to indicate its contribution to the classification. Metabolites with the VIP value\\u0026thinsp;\\u0026gt;\\u0026thinsp;1 was further applied to Student\\u0026rsquo;s t-test at univariate level to measure the significance of each metabolite, the p values less than 0.05 were considered as statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Characteristics of the study population\\u003c/h2\\u003e \\u003cp\\u003eAmong 64 patients in this study, there were 31 females and 33 males with an average age of 49.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.80 years old. Among the 30 participants in the healthy control group, there were 14 males and 16 females, with an average age of 47.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.88 years old.\\u003c/p\\u003e \\u003cp\\u003eAs shown in supplementary Fig.\\u0026nbsp;1, the electronic gastroscopy of CEG patients showed that the mucosa at the gastric body or antrum was in sheet or strip erosion, local mucosa was congested, and old bleeding spots could also be seen.\\u003c/p\\u003e \\u003cp\\u003eAccording to Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, there were no significant differences in gender, age, ALT, AST, ALP between CEG group and normal group. Epigastric pain, gastric distension and heartburn are common clinical symptoms of CEG patients, but there is no significant difference in the frequency.\\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\\u003eBaseline characteristics of patients included in the present study\\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=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCEG group(n\\u0026thinsp;=\\u0026thinsp;64)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN group (n\\u0026thinsp;=\\u0026thinsp;30)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge, years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e49.64\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e47.67\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.174\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale, n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e33 (51.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14 (46.67)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.873\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSymptoms\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEpigastric pain n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34 (53.13)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGastric distention n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28 (43.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHeartburn n (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e31 (48.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eHematological results\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eALT (U/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.36\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e28.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.166\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAST (U/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.41\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.75\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.77\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.583\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eALP (U/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e78.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e81.35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.554\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e3.2.LC-MS method validation\\u003c/p\\u003e \\u003cp\\u003eWe used XCMS software (SIMCA-P 14.1, Umetrics, Umea, Sweden) to extract the metabolite ion peaks of all samples. We obtained the principal compo-nent analysis (PCA) model by pareto-scaling conversion of all peaks. Through the pareto-scaling conversion of each peak, the principal component analysis model is obtained. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ea,b, under the positive and negative ion mode, quality control (QC) samples are closely clustered, indicating that the repeatability of the experiment is good. We performed Pearson correlation analysis on QC samples. The abscissa and ordinate represent the logarithm of the ion peak signal intensity value. The general correlation coefficient greater than 0.9 indicates that the correlation is good. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec,d, the correlation coefficient between all QC samples is above 0.9, indicating that the analysis system of instrument is stable and the data can be used for subsequent analysis.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Multivariate statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eIn order to clearly show the relationship between each group of samples, we conducted principal component analysis (PCA) and there was a weak separation trend between the CEG groups and normal group. Therefore, it is necessary to adjust the model to show more apparent inter group differences. We constructed orthogonal partial least square discriminate analysis (OPLS-DA) model to distinguish samples. As shown in the OPLS-DA score plot (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea,b) that CEG and normal group can be significantly separated in both positive and negative ion modes, indicating that the metabolites in the saliva of CEG patients have changed significantly compared with the healthy group. The evaluation parameters Q\\u003csup\\u003e2\\u003c/sup\\u003eY and R\\u003csup\\u003e2\\u003c/sup\\u003e of OPLS-DA model were obtained through cross-validation. R2y\\u0026thinsp;=\\u0026thinsp;0.905 and Q2\\u0026thinsp;=\\u0026thinsp;0.574 in the positive ionization model, r2y\\u0026thinsp;=\\u0026thinsp;0.911 and Q2\\u0026thinsp;=\\u0026thinsp;0.481 in the negative ionization model, showing acceptable applicability and predictability. In order to ensure the effectiveness of the model, the permutation test was used to verify the model, the results shown that the model was valid without overfitting (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec, d). Next, We used volcano plot to show the results of fold change (FC) analysis and t-test of two groups of samples. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee, f, the CEG group and the N group, were significantly separated in the positive and negative ion modes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. Screening biomarkers related with CEG\\u003c/h2\\u003e \\u003cp\\u003eBased on the Variable Importance for the Projection (VIP) obtained by OPLS-DA model and significant \\u003cem\\u003ep\\u003c/em\\u003e value obtained from student t-test, the differential metabolites of CEG group and N group were screened to obtain the potential biomarkers of CEG with VIP\\u0026thinsp;\\u0026gt;\\u0026thinsp;1 and \\u003cem\\u003ep\\u003c/em\\u003e value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05. The biomarkers were identified according to their structures in the Human Metabolome Database (HMDB; \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.hmdb.ca/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.hmdb.ca/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). In the positive and negative ion mode, 45 potential biomarkers of CEG were screened and listed in the Supplementary Table\\u0026nbsp;1. In order to more comprehensively and intuitively show the relationship between samples and the differences of metabolite expression patterns in different samples, accurately screen metabolic markers and study the changes of related metabolic processes, we used the metabolite expression level to cluster each group of samples. As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, compared with the normal group, the metabolites of CEG patients changed significantly. The changes of these metabolites may be related to the pathogenesis of CEG.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn order to more clearly show the change trend and amplitude of metabolites, we made FC plot.As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, compared with the normal group, the expression of 37 metabolites in the CGE group was up-regulated, including sphingomyelin (d18:1/18:0), 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, 1-Stearoyl-2-oleoyl-sn-glycerol 3-phosphocholine (SOPC), Lys-Pro, 1-Palmitoyl-sn-glycero-3-phosphocholine, N-Acetylcadaverine, 1-Stearoyl-2-hydroxy-sn-glycero-3-phosphocholine,indoleacetic acid, (3-Carboxypropyl) trimethylammonium cation, thioetheramide-PC. It was also observed that the expression of 8 metabolites such as tetrahydrocorticosterone,norethindrone acetate, 2-Methylbenzoic acid, dioctyl phthalate, 2-Ethoxyethanol, arachidic acid, pinocembrin, behenic acid were down regulated. In addition, we also ranked the metabolites according to the FC value, and drew box plot of top 9 representative up-regulated or down-regulated metabolites (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Receiver operating characteristic (ROC) analysis of the potential biomarkers\\u003c/h2\\u003e \\u003cp\\u003eROC analysis of potential biomarkers were performed to identify metabolites with the capability to diagnose CEG. For the ROC curve analysis, 0.5\\u0026thinsp;\\u0026lt;\\u0026thinsp;AUC\\u0026thinsp;\\u0026le;\\u0026thinsp;0.7 means low diagnostic accuracy, 0.7\\u0026thinsp;\\u0026lt;\\u0026thinsp;AUC\\u0026thinsp;\\u0026le;\\u0026thinsp;0.9 means medium diagnostic accuracy, and 0.9\\u0026thinsp;\\u0026lt;\\u0026thinsp;AUC\\u0026thinsp;\\u0026lt;\\u0026thinsp;1.0 means high diagnostic accuracy. We performed ROC analysis on the top ten metabolites of FC value, and calculated the cumulative AUC of the top three and top ten metabolites (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). The top three metabolites including sphingomyelin (d18:1/18:0), 1,2-dioleoyl-sn-glycero-3-phosphatidylcholine, 1-Stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine\\u003c/p\\u003e \\u003cp\\u003e(SOPC) provided AUC value of 0.897 (95%CI:0.830\\u0026ndash;0.963), 0.925 (95%CI:0.869\\u0026ndash;0.981) and 0.922 (95%CI:0.860\\u0026ndash;0.985) respectively. The top three biomarkers cumulative AUC was up to 0.927 (95%CI:0.866\\u0026ndash;0.987). The top five biomarkers cumulative AUC was up to 0.948 (95%CI:0.891-1.000). The top ten biomarkers cumulative AUC was up to 0.975 (95%CI:0.937-1.000). The results implied a high accuracy in predicting.\\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\\u003eROC analysis of CEG top 10 biomarkers from saliva\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetabolites\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCut.offs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSensitivity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSpecificity\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eAUC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ep value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSphingomyelin (d18:1/18:0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7100.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.759\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.897\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.9426E-9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1,2-dioleoyl-sn-glycero-3-phosphatidylcholine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3906.03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.855\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.963\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.925\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.934E-10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1-Stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine (SOPC)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13346.74\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.830\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.922\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.7141E-10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLys-Pro\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10460.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.711\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.750\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.781\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.000105\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1-Palmitoyl-sn-glycero-3-phosphocholine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5267.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.673\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.767\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.778\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.000038\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN-Acetylcadaverine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10026.48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.702\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.962\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.867\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.312E-7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1-Stearoyl-2-hydroxy-sn-glycero-3-phosphocholine\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5135.58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.654\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.867\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.762\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.000086\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIndoleacetic acid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7754.61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.649\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.962\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.843\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6.1138E-7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(3-Carboxypropyl)trimethylammonium cation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e52372.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.768\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.885\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.863\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.4293E-7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThioetheramide-PC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e103381.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.725\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.875\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.2218E-7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6. Bioinformatics analysis of CEG\\u003c/h2\\u003e \\u003cp\\u003eWe classified the differential metabolites screened between CEG group and N group. These metabolites were mainly classified as organic acids and derivatives, lipids and lipid-like molecules, organoheterocyclic compounds,organic oxygen compounds,benzenoids. According to the different metabolites, we employed the Kyoto Encyclopedia of Genes and Genomes (KEGG) (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.kegg.jp/\\u003c/span\\u003e\\u003cspan address=\\\"http://www.kegg.jp/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to further explore the most relevant pathways. In order to determine which metabolic and signal transduction pathways were significantly affected, we analyzed the significance level of metabolite enrichment in each pathway. The 6 significantly affected transduction pathways are described in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e and include protein digestion and absorption, phenylalanine metabolism,neuroactive ligand-receptor interaction,nicotinate and nicotinamide metabolism, mTOR signaling pathway.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eAs a long-term chronic inflammation of gastric mucosa, CEG is closely related to gastric cancer(Al-Yassir et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Therefore, early diagnosis and intervention of CEG are of great significance. At present, the diagnosis of CEG mainly depends on the examination of electronic gastroscope, but the characteristics of invasiveness and discomfort make it difficult to carry out widely. Consequently we need a simple and safe detection method. Saliva is one of the important human body fluids, which is easy to obtain and has shown its potential in disease diagnosis. The aim of this study was to find potential biomarkers of this disease from the saliva of CEG patients.\\u003c/p\\u003e \\u003cp\\u003eIn this study, the baseline data of sex, age, ALT, AST and ALP of the two groups were evenly matched, and there was no significant deviation. CEG patients showed epigastric pain, gastric distension and heartburn, but there was no significant difference in the incidence of the three symptoms. This is consistent with previous understanding. CEG patients can show a variety of common clinical symptoms of the upper digestive system, but these symptoms are not specific and cannot identify the occurrence of this disease(Juhasz et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMetabonomics technology was used to verify the metabolic differences between CEG patients and normal controls in this study. Finally, 45 metabolites were identified and annotated as potential biomarkers and 6 metabolic pathways were enriched.\\u003c/p\\u003e \\u003cp\\u003eIn the CEG group, the levels of L-arginine, tyramine, indoleacetic acid, phenylpyruvate and N-acetylputrescine were all elevated in saliva. L-arginine is an essential amino acid, which plays an important role in physiology and biochemistry. Previous studies have shown that L-arginine can aggravate the damage of ethanol to rat gastric mucosa(Ferraz et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e). L-arginine is a donor of No. No can promote gastric ulcer in gastric ischemia-reperfusion(Kobata et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). Previous studies found that the metabolism of L-arginine in patients with advanced gastric adenocarcinoma was more active than that in patients with superficial gastritis(Hu et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), which was similar to the results of this experiment. The high metabolic level of L-arginine may be the factor leading to CEG, and suggests that CEG is at risk of further deterioration. Tyramine is a kind of biological trace amine, which is produced by tyrosine deacidification, and diet is its main source(Andersen et al., \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Tyramine can be used as an agonist of human trace amine-associated receptor to stimulate G cells in pyloric tissue to secrete gastrin, promote the secretion of gastric juice and change the movement state of stomach(Ohta et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). In this study, the content of tyramine in saliva of CEG patients is higher than that of normal people. The effect of tyramine may increase gastric acid secretion, cause high acid state in the stomach, damage gastric mucosa and delay its repair.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we found that the levels of 6 substances related to lipid metabolism had changed significantly. The contents of arachidic acid and behenic acid in saliva of CEG patients decreased, and both of them are involved in the biosynthesis of unsaturated fatty acids. Previous studies have shown that unsaturated fatty acids can reduce oxidative damage and inflammatory response(Long et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), enhance the defense of gastric mucosa(Park et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), promote the repair of gastric mucosa(Hollander and Tarnawski, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e1991\\u003c/span\\u003e) and reduce peptic ulcer(Manjari and Das, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Similar to the results of this study, the levels of six unsaturated free fatty acids in serum of patients with gastric cancer were significantly lower than those of patients with benign gastric disease(Zhang et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Polyunsaturated fatty acids showed tumoricidal action on gastric cancer cells in vitro(Dai et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) and it\\u0026rsquo;s supplementation has been proposed as adjuvant treatment in cancer due anti-inflammatory properties(Mocellin et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). These results suggest that CEG is related to lipid metabolism.\\u003c/p\\u003e \\u003cp\\u003eThrough the metabolic pathway enrichment analysis, mTOR signaling pathway was found to be associated with CEG.mTOR signaling pathway is considered to be a key regulator of autophagy(Jung et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e). Autophagy plays an important role in maintaining cell homeostasis and is closely related to the occurrence of many human diseases, including cancer(Klionsky et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In previous studies(Arab et al., \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Chang et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), inhibition of autophagy can cause apoptosis of gastric mucosal epithelial cells and damage of gastric mucosa. Activation of autophagy by down regulating mTOR signaling pathway can ameliorate ethanol induced gastric mucosal epithelial cell injury. More importantly, a measure of autophagy can induce the apoptosis of gastric cancer cells, inhibit the proliferation of gastric cancer cells and increase their sensitivity to chemotherapeutic drugs(Cao et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). In this study, the level of L-arginine in CEG patients was higher than that in normal population. Arginine is an amino acid critically involved in multiple cellular processes, and is a direct activator of mTOR(Chen et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Therefore, we speculate that L-arginine activates mTOR signaling pathway to regulate autophagy of gastric mucosal epithelial cells, which may be one of the mechanisms leading to CEG.\\u003c/p\\u003e \\u003cp\\u003eHowever, the present study had some limitations. First, in order to design the test set and verification set to improve the reliability of diagnosis, we need to use a larger sample size in the future work. Second, in this study, the samples before CEG treatment were selected, and the treated samples were not selected. It is suggested that the treated samples can be added to the future work to verify the results of this study and explore new treatment methods and targets.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eIn the present study, 45 potential biomarkers related to CEG were identified and 6 metabolic pathways were enriched. These differential metabolites are related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. At the same time, we found that 1,2-dioleoyl-sn-glycoro-3-phosphocholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) have the potential to become biomarkers for diagnosing CEG. The results of this study are helpful to identify potential biomarkers for non metabolic diseases.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments \\u0026nbsp;\\u003c/strong\\u003eWe are grateful to all of the subjects who kindly agreed to participate in this study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthorship contributions \\u0026nbsp;\\u003c/strong\\u003eShaowei Liu:\\u0026nbsp;Investigation, Data curation, Writing \\u0026minus; original draft, Writing \\u0026minus; review \\u0026amp; editing, Visualization. Shixiong Zhang:\\u0026nbsp;Resources, Data curation. Haoyu Chen:\\u0026nbsp;Data curation, Methodology. Pingping Zhou:\\u0026nbsp;Investigation, Supervision. Tianxiao Yang: Data curation. Jingjing Lv: Visualization. Huixia Li: Conceptualization, Funding acquisition. Yangang Wang: Conceptualization, Formal analysis, Visualization, Supervision, Funding acquisition, Writing \\u0026minus; review \\u0026amp; editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding \\u0026nbsp;\\u003c/strong\\u003eThis work was supported by the Natural Science Foundation of Hebei (No. H2020423207) and Scientific research plan project of Hebei administration of traditional Chinese Medicine (2022325).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability \\u0026nbsp;\\u003c/strong\\u003eThe data that support the findings of this study is available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll experiments were conducted in accordance with the Declaration of Helsinki. This study was approved by the ethics committee of Hebei Hospital of traditional Chinese medicine(ethics board approval HBZY2021-KY-045-01). All patients have given written informed consent before inclusion.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAitken JP, Ortiz C, Morales-Bozo I, Rojas-Alcayaga G, Baeza M, Beltran C, Escobar A. 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Chin Med. 2021;16:37. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s13020-021-00445-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13020-021-00445-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-gastroenterology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmge\",\"sideBox\":\"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bmge/default.aspx\",\"title\":\"BMC Gastroenterology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Chronic erosive gastritis, Saliva, Metabolomics, Biomarker, UHPLC-Q-TOF/MS\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-2028880/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-2028880/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eIntroduction \\u003c/strong\\u003eChronic erosive gastritis (CEG) is closely related to gastric cancer and needs early diagnosis and intervention. The invasiveness and discomfort of electronic gastroscope make it difficult to apply to the extensive screening of CEG. Therefore, a simple and noninvasive screening method is needed in clinic.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eObjectives \\u003c/strong\\u003eThe aim of this study is to screen potential biomarkers that can identify diseases from saliva samples of CEG patients using metabolomics.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods \\u003c/strong\\u003eSaliva samples from 64 CEG patients and 30 healthy volunteers were collected, and metabolomic analysis was performed using uhplc-q-tof/ms in the positive and negative ion mode. Statistical analysis was performed using univariate (student's t-test) and multivariate (orthogonal partial least squares discriminant analysis). Receiver operating characteristic (ROC) analysis was used to determine potential predictors in saliva of CEG patients.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults \\u003c/strong\\u003eBy comparing saliva samples from CEG patients and healthy volunteers, we found 45 differentially expressed metabolites, of which 37 were up-regulated and 8 were down-regulated. These differential metabolites are related to amino acid, lipid, phenylalanine metabolism, protein digestion and absorption, and mTOR signaling pathway. In the ROC analysis, the AUC values of 7 metabolites were greater than 0.8, among which the AUC values of 1,2-dioleoyl-sn-glycoro-3-phosphodylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phospholine (SOPC) were greater than 0.9.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions \\u003c/strong\\u003eWe identified salivary metabolites related to CEG and screened out 45 potential biomarkers, 1,2-dioleoyl-sn-glycoro-3-phosphorylcholine and 1-stearoyl-2-oleoyl-sn-glycoro-3-phosphorine (SOPC), which may have clinical application value.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Changes of salivary metabolomics in patients with chronic erosive gastritis\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2022-09-19 20:27:37\",\"doi\":\"10.21203/rs.3.rs-2028880/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Major revision\",\"date\":\"2023-01-05T19:18:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2023-01-05T06:07:28+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2022-12-01T09:46:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"2c25c343-7702-4e13-814e-e3888cfc60f4\",\"date\":\"2022-11-17T07:24:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"3834c1a3-ebcb-41ad-9a3c-d4ea21c16796\",\"date\":\"2022-11-17T05:31:11+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2022-10-21T10:48:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2022-10-10T13:10:49+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"1d67e9dc-a4a1-4768-b1b4-61be46bb7290\",\"date\":\"2022-10-10T08:14:42+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"aa33d35f-d17e-4816-a3c2-6b7c0e41860c\",\"date\":\"2022-10-07T07:30:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2022-10-07T07:10:07+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2022-10-07T07:05:18+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2022-09-17T17:05:10+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2022-09-17T16:58:38+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Gastroenterology\",\"date\":\"2022-09-03T14:17:21+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-gastroenterology\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bmge\",\"sideBox\":\"Learn more about [BMC Gastroenterology](http://bmcgastroenterol.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/bmge/default.aspx\",\"title\":\"BMC Gastroenterology\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"0334a255-036b-4faf-8aaa-1c3e0d169010\",\"owner\":[],\"postedDate\":\"September 19th, 2022\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2023-10-16T21:20:51+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-2028880\",\"link\":\"https://doi.org/10.1186/s12876-023-02803-6\",\"journal\":{\"identity\":\"bmc-gastroenterology\",\"isVorOnly\":false,\"title\":\"BMC Gastroenterology\"},\"publishedOn\":\"2023-05-19 20:52:41\",\"publishedOnDateReadable\":\"May 19th, 2023\"},\"versionCreatedAt\":\"2022-09-19 20:27:37\",\"video\":\"\",\"vorDoi\":\"10.1186/s12876-023-02803-6\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12876-023-02803-6\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-2028880\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-2028880\",\"identity\":\"rs-2028880\",\"version\":[\"v1\"]},\"buildId\":\"_2-kVJe1T_tPrBINL-cwx\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}