Volatile organic compounds in urine reveals distinct diagnostic signatures for gastric cancer

Volatile organic compounds in urine reveals distinct diagnostic signatures for gastric cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Volatile organic compounds in urine reveals distinct diagnostic signatures for gastric cancer Tao Sha, Wenyan Fei, Yun Zhao, Lin Bai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4609159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Gastric cancer (GC) remains a significant contributor to cancer-related mortality, underscoring the critical necessity for specific biomarkers to enable early diagnosis and prognosis. Analyzing volatile organic compounds (VOCs) in vivo offers a promising non-invasive approach for assessing metabolic processes. Methods A total of 201 metabolic samples were acquired from 63 GC patients and 65 healthy controls. Employing solid-phase microextraction and gas chromatography-ion mobility spectrometry-based analytical procedures, we conducted qualitative and signal response analysis of VOCs in blood, feces and urine. Volatolomics was comprehensively investigated across multiple human matrices, and a machine learning-based marker importance assessment framework was employed to evaluate diagnostic biomarkers of GC. Furthermore, a single urine test diagnostic method was established to assess the sensitivity and accuracy of VOCs in diagnosing GC. Results We underscored the specific VOCs alterations in human matrices, with particular emphasis on serum, feces and urine. We confirmed the dysregulation of GC metabolism during tumor development, as evidenced by VOCs such as short-chain fatty acids and ketones. Our developed urine-based VOCs targeted assay demonstrated superior diagnostic efficacy (AUC = 0.85, accuracy = 0.76, precision = 0.78, sensitivity = 0.75, F1 score = 0.75) compared to conventional serum markers (AUC = 0.68, accuracy = 0.63, precision = 0.70, sensitivity = 0.72, F1 score = 0.69). Conclusions Urine VOCs testing enhances GC detection efficacy and represents a novel strategy for cancer diagnosis. The confirmed robustness and precision underscore its potential for clinical translation. Trial registration ChiCTR, ChiCTR2300073117. Registered 2 July 2023 Retrospectively registered, https//www.chictr.org.cn/showproj.html?proj=200842 Biological sciences/Cancer Health sciences/Biomarkers Gastric cancer Volatolomics Machine learning Urine Noninvasive diagnosis Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Gastric cancer (GC) is the leading cause of cancer-related mortality worldwide. Early diagnosis and treatment are critical for improving survival [ 1 , 2 ]. Detecting and diagnosing GC at an early stage remain challenging due to the absence of clear clinical signs and distinctive biomarkers. Annual endoscopic examination has been proven to substantially reduce cancer-specific mortality. Nevertheless, implementing this invasive method presents various challenges. High costs, potential risks of complications such as bleeding and perforation, and depending heavily on skills of endoscopist have limited its acceptability and application in population screening [ 3 ]. Therefore, there is an urgent clinical need for a highly accurate and non-invasive tool for GC screening. A variety of analytical methods have been developed for disease diagnosis, including fecal occult blood test, and (bio)compounds used as markers for disease screening and therapeutic management, such as faecal calprotectin, but accuracy is far from optimal [ 4 , 5 ]. Metabolomics is a method of studying the metabolic activities within organisms by analyzing the composition and changes of metabolites in biological systems. Due to the influence of various factors such as gene expression, environmental factors, and lifestyle, metabolic activities are subject to change. Therefore, different physiological and pathological states typically exhibit specific metabolic profiles. Metabolomics provides a comprehensive approach to understanding metabolic activities within organisms, thus holding potential value in disease diagnosis and monitoring. Volatile metabolomics, as a significant subfield of metabolomics, entails the chemical profiling of volatile organic compounds (VOCs) involved in biological processes [ 6 ]. Among this strategy, only a few volatile biomarkers have been proposed for clinical use, e.g., nitric oxide (a biomarker for asthma) and carbon monoxide (a biomarker for Helicobacter pylori infection) [ 7 – 9 ]. These biomarkers can only be determined through breathing, which presents significant technical challenges such as standardized sample collection and storage, thereby limiting the information they can offer [ 10 ]. This makes the analysis of bodily fluid volatiles clinically significant. Disease-specific VOCs are produced mainly through changes in specific biochemical pathways in the body [ 11 , 12 ]. Following their production, VOCs are emitted and can, therefore, be found in in a variety of matrices, including: 1) infected cells and/or their microenvironment, 2) blood, 3) breath, 4) skin, 5) urine, 6) feces, and/or 7) saliva [ 13 – 17 ]. Research indicates that the VOCs in body cover a range of chemical families: acids, alcohols, ketones, aldehydes, amines, N-heterocycles, O-heterocycles, sulfur compounds, and hydrocarbons [ 13 , 18 ]. Intriguing questions have been raised from the VOCs of different bodily fluids, such as: Are some VOCs present in the blood but not in the excreta? Are the same types of compounds present in different human matrices? These questions have no clear answer. In response to these challenges of volatolomics in the clinical translational applications of GC, our study constructed a comprehensive chromatography-based analytical procedure, composed of solid-phase microextraction and gas chromatography-ion mobility spectrometry. Coupled with human multi-matrix, this procedure achieved qualitative and signal response analysis of 84 VOCs in blood, feces and urine. In addition, we used a marker importance assessment framework, taking unique advantage of four machine learning algorithms, which successfully identified a biomarker panel of VOCs for non-invasive diagnosis of GC. METHODS 2.1 Patient recruitment This study was reported following the Standards for Reporting of Diagnostic Accuracy (STARD) reporting guidelines (Fig. 1 ). All participants gave informed consent and all studies were approved by the ethical committees of the Ethics Committee Board of the Huadong Hospital Affiliated to Fudan University (KY 2023K127) and registered in the Chinese Clinical Trial Registry (ChiCTR2300073117). All methods were performed in accordance with the relevant guidelines and regulations. The potentially eligible participants undergoing gastroscopy or colonoscopy were recruited from Huadong Hospital Affiliated to Fudan University in Shanghai, from July 2023 to February 2024. All participants were recruited simultaneously and consecutively throughout the study to reduce potential biases. Specifically, inclusion criteria included confirmation of adenocarcinoma through pathology for GC patients and normal gastroscopy results for healthy controls. Exclusion criteria included patients who: (1) received chemotherapy, radiation therapy, or had token neoadjuvant therapy; (2) had chronic inflammatory diseases; (3) suffered from any respiratory diseases; (4) suffer from Helicobacter pylori , or (5) were previously diagnosed with other malignancies. In this exploratory study, we finally enrolled a total of 63 patients diagnosed with GC and 65 controls. No adverse events were observed during sample collection. Clinical assessment data, pathological diagnoses, and demographic information of participants were collected from the most recent clinical evaluation conducted closest to the date of sample collection (Table 1 ). Table 1 Patient baseline characteristics. Continuous variables are presented as means ± SD. Categorical variables are presented as numbers (%). Characteristic GC ( N = 63) Control ( N = 65) P values Demographic data Age, years, mean ± SD 64.7 ± 10.0 58.4 ± 12.2 0.53 Age ≥ 60 32 (50.8) 29 (44.6) 0.79 male 44 (69.8) 37 (58.4) 0.20 BMI, kg/m 2 , mean ± SD 22.2 ± 3.3 24.5 ± 5.0 0.02 BMI ≥ 25 5 (7.9) 10 (15.4) 0.13 Pathological data Adenocarcinoma 63 (100.0) Gastric polyp 8 (12.3) Healthy 57 (87.3) Tumor diameter 2 39 (61.9) N > 1 30 (47.6) M > 0 21 (33.3) N: number; SD: standard deviation. BMI: body mass index. T = size of the tumor and its spread in nearby tissue, N = cancer’s spread in nearby lymph nodes; M = Metastasis (cancer’s spread to any other part of body). P values are from ANOVA or Pearson’s chi-square test. 2.2 Sample collection and processing Peripheral blood, urine and fecal samples were collected at Huadong Hospital, Fudan University. A total of 24 peripheral blood samples were obtained with 9 samples collected from healthy controls (HC) and 15 samples from patients diagnosed with GC. The blood samples were centrifuged at 3,000 rpm for 10 minutes at 4°C, and 1 mL of the supernatant serum was carefully transferred polypropylene vials. In addition to the serum samples, 126 urine samples were collected, comprising 63 samples from HC and 63 samples from GC. Each subject provided approximately 8 mL of urine, collected in sterile containers. Furthermore, 51 fecal samples were collected, with 34 samples obtained from HC and 17 samples from GC. Each sample comprised 2–4 g of feces stored in a 10 mL Supelco vial. Upon collection and homogenized, all sample was stored in a -80 ℃ refrigerator within 3 h and retained until needed for the experiments. 2.3 Analyses of volatile profiles A manual solid-phase microextraction (SPME) holder equipped with carboxen/polydimethylsiloxane (CAR/PDMS) fibers was used to extract analytes from all samples. The SPME fiber was inserted into the vial and exposed to the headspace gas to enrich compounds. For the serum, 1 mL of sample was incubated at 55 ℃ for 5 minutes. For the urine, 2 mL sample with 0.5 g of aspartic acid powder incubated for 5 minutes at 60°C. For the feces, 0.5 g sample with 1mL ultrapure water and 0.5 g aspartic acid powder was incubated at 60 ℃ for 5 minutes. Following the SPME procedure, analytes of sample were extracted and introduced into the gas chromatography-ion mobility spectrometry (GC-IMS, “FlavorSpec” brand, Dortmund, Germany) using a heated syringe through the GC injection port at 80°C. GC-IMS first pre-separates the analytes by GC, then IMS achieved a secondary separation based on the mass of the molecular ion of the substance to be measured and the one-dimensional collision cross-sectional area. The GC-IMS system was equipped with an RTX column, which was used under isothermal conditions at temperature of 80°C. Nitrogen served as the carrier gas, with the following gradient: 0 min: 2 mL/min; 1 min: 2 mL/min; 8 min: 100 mL/min; 10 min: 150 mL/min; 15 min: 150 mL/min. Analytes were then ionized under the positive-ion mode in the IMS chamber using a tritium (3H) source. Ionized analytes then entered the 98 mm long IMS drift tube, where an electric field (strength: 500 V/cm) was applied. The ionization chamber and the drift tube were programmed at 45°C. The drift gas was set at a constant flow rate of 150 mL/min counter-current of the analyte ion flow. Each IMS spectrum was acquired as the average of six scans. In this analytical process, each sample generated data consisting of retention index, drift time, and peak intensity. Two-dimensional signal peak characterization can be conducted utilizing the retention index (RI) from the GC and the drift time (Dt) from the IMS, where each data point corresponds to a unique signal peak. The VOC signal peaks of each bile sample were characterized by retention index, migration time and peak strength. 2.4 Statistical analysis and predictive models The signal peak intensity underwent normalization to obtain a standardized response. No data were excluded from analysis and missing data were filled with the mean values. Differences in the VOC levels between GC and controls were assessed using Wilcoxon Mann − Whitney U test. The supervised model of orthogonal partial least-squares discriminant analysis (OPLS-DA) was applied to describe the global metabolic changes between cancer and control groups. For diagnostic modeling, machine learning classification models were employed. 70% of the samples from dataset were allocated to the training set. Subsequently, four machine learning classification algorithms—Random Forest Classifier, XGBoost, Logistic Regression and Linear SVC—were trained. This training implemented a recursive feature elimination method under stratified fivefold cross-validation conditions (RFECV), as implemented in the Python RFE package. These four algorithms encapsulate diverse classification criteria within the realm of machine learning [ 19 , 20 ]. The RFECV algorithm utilized all compounds to construct an initial model on the training set, ranking each compound based on its significance in effectively categorizing GC and controls. This iterative process involves eliminating the least significant compound in each round until identifying a subset that yields the highest sensitivity. RESULTS 3.1 Metabolic consistency of GC serum, feces and urine Volatolomics based on SPME-GC-IMS was employed to profile serum, feces and urine samples from GC patients, aiming to explore the comprehensive metabolic dysregulation in gastric cancer. A total of 44, 83, and 84 VOCs peaks were detected in serum, feces and urine samples from GC patients, respectively (Fig. 2 (A) and (B), Table S1 ). Notably, the numbers of dysregulated metabolic peaks, determined through the Wilcoxon Mann − Whitney U test with Benjamini − Hochberg-based FDR adjustment ( P < 0.05), were 6, 7 and 8 in serum, feces and urine samples, respectively (Figure S1 -3). We further compared the dysregulated metabolic peaks shared by each matrix evaluated the consistency of metabolic profiles between three sample types. However, only 2-octanone was shared among all matrices (Fig. 2 (C)). Moreover, very few of these shared metabolic peaks exhibited consistent up- or down-regulation trends across three sample types. Among them, short-chain fatty acids such as 2-methylbutanoic acid, 3-methylbutanoic acid and hexanoic acid, along with aldehyde like hexanal were up-regulated in GC feces and urine samples, while 3-methylbutanoic acid and (Z)-4-heptenal were down-regulated in serum types. These findings clearly demonstrated that the metabolic dysregulation in patients was dependent on the sample type, indicating that metabolite biomarkers discovered in the bloodstream may not be consistent with those discovered in excreta matrices. 3.2 Metabolic profiles of GC feces and urine To gain insights into metabolic contribution of human excreta matrices, we further applied the OPLS-DA supervised model to characterize the overall metabolic changes between the cancer and control groups. However, the OPLS-DA algorithm failed to construct significant PLS models on the VOCs dataset of fecal samples, due to insufficient meaningful information in the dataset. Conversely, the OPLS-DA model based on VOCs of urine successfully distinguished GC samples from those of the control group, indicating distinct metabolic profiles between them. Nevertheless, OPLS-DA model characteristics were R2Y = 0.28 and Q2Y = 0.15 (Figure S4), suggesting that most VOCs metabolic features in urine samples were considered noise. Further efforts were needed to eliminate this noise and confirm the sensitivity of metabolic features. It is still noteworthy that patients with GC exhibit systemic metabolic abnormalities, the VOCs metabolic changes in urine were greater than those in fecal samples, which indicates that detecting metabolic signatures through humoral transport may aid in noninvasive screening for GC. 3.3 Diagnostic Potential of VOCs profiles in metabolites for GC Next, we employed the RFECV model to train classification models based on VOCs profiles in metabolites of urine and fecal samples. This model facilitated the best performance of the algorithm while minimizing the interference of less significant compounds [ 21 ]. We prioritized sensitivity(recall) to maximize the proportion of actual GC cases correctly identified, and favored models capable of generating predictions using relatively few compounds. Here, we tested the ability of our algorithms to detect all GC without regard to TME status. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves(Figure 3 ). As expected, models utilizing urine volatiles consistently exhibited better predictive performance. The sensitivity of the model utilizing urine volatiles for GC identification ranges from 72–76%, whereas the model based on feces volatiles exhibits poorer performance, with a sensitivity of only 65–70%. The resulting model diagnostic performance (area under the curve (AUC), sensitivity, accuracy and F1 Score), along with the key compounds used as predictors, were presented in Table 2 . Table 2 Diagnostic performance and key predictors of gastric cancer. XGBoost Logistic Regression Linear SVC Random Forest Sensitivity 0.75 0.76 0.73 0.72 Accuracy 0.76 0.78 0.72 0.73 Precision 0.78 0.74 0.72 0.72 F1 Score 0.75 0.76 0.73 0.72 AUC 0.85 0.82 0.78 0.79 1 Acetophenone Acetophenone Acetophenone Acetophenone 2 Hexanal-D 2-Octanone 2-Octanone Hexanal-D 3 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-one 6-Methyl-5-hepten-2-one 4 Cyclohexanone Cyclohexanone Cyclohexanone Cyclohexanone 5 Decanoic acid Decanoic acid Decanoic acid Myrcene 6 Heptanal 3-Methylvaleric acid 2-Undecanone Decanoic acid 7 3-Methylbutanoic acid 1-Octen-3-one 2-Hexanone-M Heptanal 8 β-Citronellol 1-Phenylethylacetate (E)-2-Pentenal 2-Undecanone 9 Cyclohexen-2-one-D 3-Methylbutanoic acid Citronellal Nonanal-D 10 Benzoic acid Myrcene 1-Phenylethylacetate α-Phellandrene 11 Camphene Camphene Camphene Camphene 12 α-Phellandrene 3-Methylbutanoic acid 3-Methylbutanoic acid 13 Decanoic acid Nonanal-M 14 Methional We also compared the diagnostic performance of the biomarkers in urine with typical serum antigen biomarkers (obtained from the patients' clinical assessments), such as carbohydrate antigen 19 − 9 (CA19-9), carcinoembryonic antigen (CEA) and carbohydrate antigen 72 − 4 (CA72-4) [ 22 , 23 ]. The serum biomarkers only attained an AUC of 0.68, a sensitivity of 0.74, an accuracy of 0.63, a specificity of 0.70 and a F1 score of 0.69). Overall, the results of our predictive models suggest the presence of volatile signatures in urine can reliably identify participants with GC, crucially, that these VOCs exhibit higher detection accuracy compared to traditional blood markers. 3.4 Biomarker selection for GC prediction To underscore compounds with potential diagnostic significance, our analysis focused on those consistently identified as crucial predictors of infection status across four distinct machine learning algorithms. In general prediction without considering GC staging status, the six most significant urine volatiles for model accuracy were acetophenone, 6-methyl-5-hepten-2-one, cyclohexanone, decanoic acid, camphene and 3-methylbutanoic acid. To ensure these VOCs were not associated with a confounding demographic variable between the comparison groups, linear regression models were performed for each of the 6 VOCs. There were no significant differences in the concentration of these 6 VOCs between participants of different genders, ages and BMI (Table S2). Four of the compounds presented in Table 2 , 2 -undecanone, heptanal, nonanal and 6-methyl-5-hepten-2-one [ 24 – 27 ], were notable in that they have previously been reported as biomarkers in diagnostic prediction models for GC. Relevant studies have shown that compared to healthy individuals, cancer patients exhibit abnormal aldehyde metabolism. This may be due to changes in the composition of cancer cell membrane lipids. An increase in the concentration of unsaturated fatty acids could promote the production of certain aldehydes through lipid peroxidation [ 28 ]. Similar to aldehydes, the regulation of ketone bodies differs between normal and tumor tissues. In many cancers, the production of ketones begins with a typical mechanism of increasing long-chain fatty acid (LCFA) oxidation [ 29 ]. In addition, other VOCs have previously been shown to be abnormal in various metabolites in patients with cancer or other disease states. The relevant research findings were detailed in Table 3 . Consistent with these studies, we observed abnormal levels of ketone bodies, aldehydes and short-chain fatty acids in blood, urine and fecal samples from cancer patients, suggesting that these metabolites were likely the result of dysregulated systemic metabolic circulations. Table 3 Potential VOCs biomarkers for cancer based on literature review. Marker of Detected Markers Sample Type Reference Gastric cancer 2-propenenitrile; 2-butoxy-ethanol; furfural; 6-methyl-5-hepten-2-one; isoprene; styrene; 6-methyl-5-hepten-2-one; 2-ethyl-1 hexanol; nonanal Breath [ 24 ] Gastric cancer 2-propenenitrile; furfural; 6-methyl-5-hepten-2-one Breath [ 25 ] Gastric cancer 2;3-butanediol; Hexadecane; 3;8-dimethyl- undecane; 1;3-dioxolan-2-one Breath [ 37 ] Lung cancer 2-metylbutylacetat; 3-methyl-1-butanol; ethylbenzol; heptanal; hexanal; iso-propylamin; n-dodecane; cyclohexanone Breath [ 38 ] Lung cancer 2-hydroxyacetaldehyde; isoprene; pentanal; butyric acid; toluene; 2;5-dimethylfuran; cyclohexanone; hexanal; heptanal; acetophenone; propylcyclohexane; octanal; nonanal; decanal; 2;2-dimethyldecane; acetaldehyde Breath [ 39 ] Pancreatic ductal adenocarcinoma 2-pentanone; hexanal; 3-hexanone; p-cymene Urine [ 40 ] Bladder cancer hexanal; benzaldehyde; butyrophenone; 3-hydroxyanthranilic acid; benzoic acid; trans -3-hexanoic acid; cis -3-hexanoic acid; 2-butanone; 2-pentanone; 2;3-butanedione; 4-heptanone; dimethyl disulphide; 2-propanol; acetic acid; piperitone; thujone Urine [ 41 ] Colorectal cancer 3-methylbutanoic acid; propan‐2‐ol; hexan‐2‐one and ethyl 3-methyl‐ butanoate Feces [ 42 ] Breast cancer ethanol; heptane; 2-propenoic acid; butyl ester; 6-methyl-5-Hepten-2-one Breath [ 43 ] DISCUSSION Our study reveals that gastric cancer induces significant alterations in human volatile emissions. Notably, VOCs in urine constitutes a distinct disease signature that reliably predicting human cancer. The resulting single urine test demonstrated a sensitivity of 76% and an accuracy of 78% in diagnosing gastric cancer. Furthermore, our analysis identifies several key compounds crucial for predictive modeling, underscoring their importance for further exploration as potential biomarkers in developing a robust, non-invasive volatiles-based diagnostic method for gastric cancer. In studies focusing on urinary volatile compounds, more than 800 compounds have been detected, with common chemical categories including organic oxides, organic disulfides, and phenols [ 30 – 32 ]. Our research findings have identified specific VOCs associated with GC, namely: acetophenone, 6-methyl-5-hepten-2-one, cyclohexanone, camphene, decanoic acid and 3-methylbutanoic acid. These compounds may indicate dysfunction in several metabolic pathways. Specifically, overexpression of fatty acid synthase in human malignancies, including gastric cancer, leads to increased de novo synthesis of fatty acids, which correlates with poor prognosis. Fatty acid metabolism supports tumor initiation and disease progression through processes such as energy generation (β-oxidation), membrane biosynthesis, energy storage and production, and the generation of signaling intermediates. Notably, ketones are highly represented in cancer and some kidney diseases, being the more abundant in cancer. Research suggests that ketones may act as chemical inducers, stimulating the migration of epithelial cancer cells and promoting the growth of primary tumors. Acetone and other ketone bodies are believed to serve as alternative energy sources, allowing sustained abnormal tumor growth [ 33 – 35 , 29 ]. Given the increasing evidence of an association between these VOCs in excretion and metabolic dysregulation in malignancies, the full understanding of this relationship remains incomplete [ 12 , 36 ]. It is postulated that volatiles originate from the body's metabolism when produced naturally, and are released directly into the circulation through cell membranes, subsequently redistributing throughout various matrices within the body. Although we acknowledge that metabolic dysregulation varies depending on the sample type, the abnormality of these volatiles in multiple human matrices has two primary implications. Firstly, the simultaneous presence of most VOCs in blood, urine and feces serves as evidence of metabolic circulation in the body. Secondly, if tumors are indeed the source of these VOCs in various matrices, their re-excretion through the body's circulation may result in a reduced detection level of certain VOCs or a change in their distribution across different matrices. Several limitations exist in the present study. it is important to acknowledge the clinical and analytical limitations imposed by the single-center design of our study. We are planning a larger multicenter study to conclusively determine the accuracy of the test. It is crucial to carefully assess the reproducibility of results obtained using different equipment across different laboratories or over an extended period in a central laboratory. Furthermore, we did not assess the effect of tumor stage, histology and location on volatiles due to underpowering. Further research is needed to elucidate the mechanisms underlying the production and dysregulation of abnormal metabolites. This will improve our understanding of confounding factors and refine VOCs testing protocols. We must emphasize that this study represents the first attempt to analyze volatile compounds across three human matrices, including blood, feces and urine. While our sample sizes enabled the analysis of differences, the limited number of blood samples precluded robust diagnostic modeling and equivalence analyses, and caution should be exercised when inferring direct correlations between relative VOC markers. Also, this study does not obviate the need for more invasive diagnostic procedures in clinical decision-making, individuals with abnormal urine test results may still require further confirmatory investigations, including endoscopy, to identify the underlying pathology. In summary, our study marks the first attempt to diagnose GC patients based on VOCs emissions from human metabolites. We have demonstrated that non-invasive volatiles derived from short-chain fatty acids, ketones and others, can enhance the diagnosis of GC. This work represents a significant step forward in non-invasive screening and diagnosis for GC, providing new reliable resources for early detection of the disease. Declarations Ethics approval and consent to participate All participants gave informed consent and all studies were approved by the ethical committees of the Ethics Committee Board of the Huadong Hospital Affiliated to Fudan University (KY 2023K127). Consent for publication Not applicable. Availability of data and materials The datasets used during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Authors' contributions L.B. and Y.Z. conceptualized the study. T.S. developed the methodology, and created the visualizations. T.S. and W.Y.F. conducted the investigation and wrote the original draft of the manuscript. L.B. provided supervision throughout the project. All authors reviewed and edited the manuscript. Acknowledgements Not applicable. Authors' information T.S. (Tao Sha): Dr. Tao Sha is an attending physician in the Emergency Department at Huadong hospital affiliated to Fudan University. His primary research focus is on the application of machine learning in the medical field. He is currently leading a research team dedicated to developing models that use metabolic products of bacteria and tumors as diagnostic markers for diseases. W.Y.F. (Wenyan Fei): MD,Wenyan Fei is an undergraduate student in the Clinical Medicine (eight-year program) at the School of Basic Medical Sciences, Fudan University. Her main research interests include gastrointestinal tumors and tumor glycobiology. Y.Z. (Yun Zhao): MMED,Dr. Yun Zhao is the head of the Emergency Department at Huadong Hospital affiliated to Fudan University, specializing in the diagnosis and treatment of acute and critical illnesses. L.B. (Lin Bai): MMED,Dr. Lin Bai is the associate chief physician in the Emergency Department at Huadong Hospital affiliated to Fudan University. 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J Breath Res. 2014;8(1):014001. doi: 10.1088/1752-7155/8/1/014001 . Zhang Y, editor. Support Vector Machine Classification Algorithm and Its Application. Information Computing and Applications; 2012 2012//; Berlin, Heidelberg: Springer Berlin Heidelberg. Breiman L. Random Forests. Machine Learning. 2001;45(1):5–32. doi: 10.1023/A:1010933404324 . Bifarin OO, Gaul DA, Sah S, Arnold RS, Ogan K, Master VA et al. Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics. Journal of Proteome Research. 2021;20(7):3629–41. doi: 10.1021/acs.jproteome.1c00213 . Akagi J, Takai E, Tamori Y, Nakagawa K, Ogawa M. CA19-9 epitope a possible marker for MUC-1/Y protein. Int J Oncol. 2001;18(5):1085–91. doi: 10.3892/ijo.18.5.1085 . Gantuya B, Oyuntsetseg K, Bolor D, Erdene-Ochir Y, Sanduijav R, Davaadorj D et al. Evaluation of serum markers for gastric cancer and its precursor diseases among high incidence and mortality rate of gastric cancer area. Gastric Cancer. 2019;22(1):104–12. doi: 10.1007/s10120-018-0844-8 . Amal H, Leja M, Broza YY, Tisch U, Funka K, Liepniece-Karele I et al. Geographical variation in the exhaled volatile organic compounds. J Breath Res. 2013;7(4):047102. doi: 10.1088/1752-7155/7/4/047102 . Xu Zq, Broza YY, Ionsecu R, Tisch U, Ding L, Liu H et al. A nanomaterial-based breath test for distinguishing gastric cancer from benign gastric conditions. Br J Cancer. 2013;108(4):941–50. doi: 10.1038/bjc.2013.44 . Kumar S, Huang J, Abbassi-Ghadi N, Mackenzie HA, Veselkov KA, Hoare JM et al. Mass Spectrometric Analysis of Exhaled Breath for the Identification of Volatile Organic Compound Biomarkers in Esophageal and Gastric Adenocarcinoma. Ann Surg. 2015;262(6):981–90. doi: 10.1097/sla.0000000000001101 . Leiherer A, Ślefarska D, Leja M, Heinzle C, Mündlein A, Kikuste I et al. The Volatilomic Footprints of Human HGC-27 and CLS-145 Gastric Cancer Cell Lines. Frontiers in molecular biosciences. 2020;7:607904. doi: 10.3389/fmolb.2020.607904 . Dinavahi SS, Bazewicz CG, Gowda R, Robertson GP. Aldehyde Dehydrogenase Inhibitors for Cancer Therapeutics. Trends Pharmacol Sci. 2019;40(10):774–89. doi: 10.1016/j.tips.2019.08.002 . Chung J, Akter S, Han S, Shin Y, Choi TG, Kang I et al. Diagnosis by Volatile Organic Compounds in Exhaled Breath in Exhaled Breath from Patients with Gastric and Colorectal Cancers. Int J Mol Sci. 2022;24(1). doi: 10.3390/ijms24010129 . Llambrich M, Brezmes J, Cumeras R. The untargeted urine volatilome for biomedical applications: methodology and volatilome database. Biological Procedures Online. 2022;24(1). doi: 10.1186/s12575-022-00184-w . Outeiro-Pinho G, Barros-Silva D, Aznar E, Sousa A-I, Vieira-Coimbra M, Oliveira J et al. MicroRNA-30a-5pme: a novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma in tissue and urine samples. J Exp Clin Cancer Res. 2020;39(1). doi: 10.1186/s13046-020-01600-3 . McFarlane M, Millard A, Hall H, Savage R, Constantinidou C, Arasaradnam R et al. Urinary volatile organic compounds and faecal microbiome profiles in colorectal cancer. Colorectal Dis. 2019;21(11):1259–69. doi: 10.1111/codi.14739 . Janfaza S, Khorsand B, Nikkhah M, Zahiri J. Digging deeper into volatile organic compounds associated with cancer. Biol Methods Protoc. 2019;4(1):bpz014. doi: 10.1093/biomethods/bpz014 . Zhou Y, Niu C, Li Y, Gao B, Zheng J, Guo X et al. Fatty acid synthase expression and esophageal cancer. Mol Biol Rep. 2012;39(10):9733–9. doi: 10.1007/s11033-012-1838-y . van Vorstenbosch R, Cheng HR, Jonkers D, Penders J, Schoon E, Masclee A et al. Systematic Review: Contribution of the Gut Microbiome to the Volatile Metabolic Fingerprint of Colorectal Neoplasia. Metabolites. 2022;13(1). doi: 10.3390/metabo13010055 . Arasaradnam RP, Covington JA, Harmston C, Nwokolo CU. Review article: next generation diagnostic modalities in gastroenterology–gas phase volatile compound biomarker detection. Aliment Pharmacol Ther. 2014;39(8):780–9. doi: 10.1111/apt.12657 . Tong H, Wang Y, Li Y, Liu S, Chi C, Liu D et al. Volatile organic metabolites identify patients with gastric carcinoma, gastric ulcer, or gastritis and control patients. Cancer Cell Int. 2017;17(1):108. doi: 10.1186/s12935-017-0475-x . Handa H, Usuba A, Maddula S, Baumbach JI, Mineshita M, Miyazawa T. Exhaled breath analysis for lung cancer detection using ion mobility spectrometry. PLoS One. 2014;9(12):e114555. doi: 10.1371/journal.pone.0114555 . Wang P, Huang Q, Meng S, Mu T, Liu Z, He M et al. Identification of lung cancer breath biomarkers based on perioperative breathomics testing: A prospective observational study. EClinicalMedicine. 2022;47:101384. doi: 10.1016/j.eclinm.2022.101384 . Wen Q, Myridakis A, Boshier PR, Zuffa S, Belluomo I, Parker AG et al. A Complete Pipeline for Untargeted Urinary Volatolomic Profiling with Sorptive Extraction and Dual Polar and Nonpolar Column Methodologies Coupled with Gas Chromatography Time-of-Flight Mass Spectrometry. Anal Chem. 2023;95(2):758–65. doi: 10.1021/acs.analchem.2c02873 . Weber CM, Cauchi M, Patel M, Bessant C, Turner C, Britton LE et al. Evaluation of a gas sensor array and pattern recognition for the identification of bladder cancer from urine headspace. Analyst. 2011;136(2):359–64. doi: 10.1039/c0an00382d . Bond A, Greenwood R, Lewis S, Corfe B, Sarkar S, O'Toole P et al. Volatile organic compounds emitted from faeces as a biomarker for colorectal cancer. Aliment Pharmacol Ther. 2019;49(8):1005–12. doi: 10.1111/apt.15140 . Barash O, Zhang W, Halpern JM, Hua QL, Pan YY, Kayal H et al. Differentiation between genetic mutations of breast cancer by breath volatolomics. Oncotarget. 2015;6(42):44864–76. doi: 10.18632/oncotarget.6269 . Additional Declarations No competing interests reported. Supplementary Files SIVolatileorganiccompoundsinurinerevealsdistinctdiagnosticsignaturesforgastriccancer0511.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4609159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329610573,"identity":"d623a85c-b789-4412-8fd1-ac088253239b","order_by":0,"name":"Tao Sha","email":"","orcid":"","institution":"Huadong hospital affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Sha","suffix":""},{"id":329610574,"identity":"c7dd85f8-efd3-4aa4-85f2-2b575d6389cb","order_by":1,"name":"Wenyan Fei","email":"","orcid":"","institution":"Shanghai Medical college of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Wenyan","middleName":"","lastName":"Fei","suffix":""},{"id":329610578,"identity":"b791ae18-a648-401e-a518-f83d1f93e21c","order_by":2,"name":"Yun Zhao","email":"","orcid":"","institution":"Huadong hospital affiliated to Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhao","suffix":""},{"id":329610580,"identity":"9638453f-c5dd-46d3-a308-bb34f3fafcc5","order_by":3,"name":"Lin Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACAwbGxgMJDDb8DAwJID4zUVoagFrSJBtI0MLAcICB4TAJWszZDzcceLjjvITB8eRnDxgqrBMb2M8ewKvFsiex4UDimdsSBmeemRswnElPbODJS8DvsAMgLW236wxuJJhJMLYdTmyQ4DHAr+X8Q5CWcxIGN9K/STD+I0bLDbAtB4BacoC2NBChxXIG2JZkCckzb8okEo6lG7fx5ODXYs6f/vDhzzY7Cb7j6dskPtRYy/azn8GvBRUkADEbCepHwSgYBaNgFOAAAG5UTN6nDTPMAAAAAElFTkSuQmCC","orcid":"","institution":"Huadong hospital affiliated to Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Bai","suffix":""}],"badges":[],"createdAt":"2024-06-20 04:55:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4609159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4609159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61184332,"identity":"2a789489-d8ec-4946-9348-24e086c7a887","added_by":"auto","created_at":"2024-07-26 17:12:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49858,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant recruitment.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4609159/v1/55efd044bdd97071681ac6e4.png"},{"id":61183508,"identity":"af06e402-e28d-43a2-8898-3f3d224fc550","added_by":"auto","created_at":"2024-07-26 17:04:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAberrant metabolic signatures in three human matrices. \u003c/strong\u003eA, Schematic diagram depicting the collection procedure of the multiple human matrices sample from participants; B, Venn diagram of 178 metabolites of in three human matrices; C, Comparisons of signal intensity of VOC in human serum, feces and urine in patients with GC and healthy controls. Mann-Whitney \u003cem\u003eU\u003c/em\u003e test with Bonferroni adjusted \u003cem\u003eP\u003c/em\u003evalue indicated on top.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4609159/v1/a56ccd8cba59171bd2ae35ac.png"},{"id":61184333,"identity":"eb525a3e-1000-42af-a3ce-5ecda7fbf441","added_by":"auto","created_at":"2024-07-26 17:12:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74579,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssessment of the diagnostic performance of urine VOCs through machine learning models. \u003c/strong\u003eA, RFECV model-based biomarker selection for gastric cancer prediction; B, Receiver operating characteristic (ROC) curves for gastric cancer, validated through five-fold cross-validation.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4609159/v1/73324ba41b2d1f8b37a83979.png"},{"id":62846504,"identity":"88a0ef99-fecf-4f63-977b-710a2c734964","added_by":"auto","created_at":"2024-08-20 07:32:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4609159/v1/42c1d8c2-d2e4-47a4-9219-8da5840f5ae3.pdf"},{"id":61183510,"identity":"ab8d4818-d26b-4cbf-ab9d-d0deb284a6a2","added_by":"auto","created_at":"2024-07-26 17:04:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1877035,"visible":true,"origin":"","legend":"","description":"","filename":"SIVolatileorganiccompoundsinurinerevealsdistinctdiagnosticsignaturesforgastriccancer0511.docx","url":"https://assets-eu.researchsquare.com/files/rs-4609159/v1/c3d32d283ff34fac431feaba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Volatile organic compounds in urine reveals distinct diagnostic signatures for gastric cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGastric cancer (GC) is the leading cause of cancer-related mortality worldwide. Early diagnosis and treatment are critical for improving survival [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Detecting and diagnosing GC at an early stage remain challenging due to the absence of clear clinical signs and distinctive biomarkers. Annual endoscopic examination has been proven to substantially reduce cancer-specific mortality. Nevertheless, implementing this invasive method presents various challenges. High costs, potential risks of complications such as bleeding and perforation, and depending heavily on skills of endoscopist have limited its acceptability and application in population screening [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Therefore, there is an urgent clinical need for a highly accurate and non-invasive tool for GC screening.\u003c/p\u003e \u003cp\u003eA variety of analytical methods have been developed for disease diagnosis, including fecal occult blood test, and (bio)compounds used as markers for disease screening and therapeutic management, such as faecal calprotectin, but accuracy is far from optimal [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Metabolomics is a method of studying the metabolic activities within organisms by analyzing the composition and changes of metabolites in biological systems. Due to the influence of various factors such as gene expression, environmental factors, and lifestyle, metabolic activities are subject to change. Therefore, different physiological and pathological states typically exhibit specific metabolic profiles. Metabolomics provides a comprehensive approach to understanding metabolic activities within organisms, thus holding potential value in disease diagnosis and monitoring.\u003c/p\u003e \u003cp\u003eVolatile metabolomics, as a significant subfield of metabolomics, entails the chemical profiling of volatile organic compounds (VOCs) involved in biological processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Among this strategy, only a few volatile biomarkers have been proposed for clinical use, e.g., nitric oxide (a biomarker for asthma) and carbon monoxide (a biomarker for \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection) [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These biomarkers can only be determined through breathing, which presents significant technical challenges such as standardized sample collection and storage, thereby limiting the information they can offer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This makes the analysis of bodily fluid volatiles clinically significant.\u003c/p\u003e \u003cp\u003eDisease-specific VOCs are produced mainly through changes in specific biochemical pathways in the body [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Following their production, VOCs are emitted and can, therefore, be found in in a variety of matrices, including: 1) infected cells and/or their microenvironment, 2) blood, 3) breath, 4) skin, 5) urine, 6) feces, and/or 7) saliva [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Research indicates that the VOCs in body cover a range of chemical families: acids, alcohols, ketones, aldehydes, amines, N-heterocycles, O-heterocycles, sulfur compounds, and hydrocarbons [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Intriguing questions have been raised from the VOCs of different bodily fluids, such as: Are some VOCs present in the blood but not in the excreta? Are the same types of compounds present in different human matrices? These questions have no clear answer.\u003c/p\u003e \u003cp\u003eIn response to these challenges of volatolomics in the clinical translational applications of GC, our study constructed a comprehensive chromatography-based analytical procedure, composed of solid-phase microextraction and gas chromatography-ion mobility spectrometry. Coupled with human multi-matrix, this procedure achieved qualitative and signal response analysis of 84 VOCs in blood, feces and urine. In addition, we used a marker importance assessment framework, taking unique advantage of four machine learning algorithms, which successfully identified a biomarker panel of VOCs for non-invasive diagnosis of GC.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient recruitment\u003c/h2\u003e \u003cp\u003e This study was reported following the Standards for Reporting of Diagnostic Accuracy (STARD) reporting guidelines (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All participants gave informed consent and all studies were approved by the ethical committees of the Ethics Committee Board of the Huadong Hospital Affiliated to Fudan University (KY 2023K127) and registered in the Chinese Clinical Trial Registry (ChiCTR2300073117). All methods were performed in accordance with the relevant guidelines and regulations. The potentially eligible participants undergoing gastroscopy or colonoscopy were recruited from Huadong Hospital Affiliated to Fudan University in Shanghai, from July 2023 to February 2024. All participants were recruited simultaneously and consecutively throughout the study to reduce potential biases. Specifically, inclusion criteria included confirmation of adenocarcinoma through pathology for GC patients and normal gastroscopy results for healthy controls. Exclusion criteria included patients who: (1) received chemotherapy, radiation therapy, or had token neoadjuvant therapy; (2) had chronic inflammatory diseases; (3) suffered from any respiratory diseases; (4) suffer from \u003cem\u003eHelicobacter pylori\u003c/em\u003e, or (5) were previously diagnosed with other malignancies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this exploratory study, we finally enrolled a total of 63 patients diagnosed with GC and 65 controls. No adverse events were observed during sample collection. Clinical assessment data, pathological diagnoses, and demographic information of participants were collected from the most recent clinical evaluation conducted closest to the date of sample collection (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003e\u003cb\u003ePatient baseline characteristics.\u003c/b\u003e Continuous variables are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Categorical variables are presented as numbers (%).\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 \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGC (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;63)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographic data\u003c/b\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\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.7\u0026thinsp;\u0026plusmn;\u0026thinsp;10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (69.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePathological data\u003c/b\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\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (100.0)\u003c/p\u003e \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\u003eGastric polyp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \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\u003eHealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (87.3)\u003c/p\u003e \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\u003e\u003cb\u003eTumor diameter\u003c/b\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\u003e\u0026lt;\u0026thinsp;4 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (44.4)\u003c/p\u003e \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\u003e4\u0026ndash;6 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (39.7)\u003c/p\u003e \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\u003e\u0026ge;\u0026thinsp;6 cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (15.9)\u003c/p\u003e \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\u003e\u003cb\u003eTNM staging\u003c/b\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\u003eT\u0026thinsp;\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (61.9)\u003c/p\u003e \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\u003eN\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (47.6)\u003c/p\u003e \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\u003eM\u0026thinsp;\u0026gt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (33.3)\u003c/p\u003e \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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eN: number; SD: standard deviation. BMI: body mass index. T\u0026thinsp;=\u0026thinsp;size of the tumor and its spread in nearby tissue, N\u0026thinsp;=\u0026thinsp;cancer\u0026rsquo;s spread in nearby lymph nodes; M\u0026thinsp;=\u0026thinsp;Metastasis (cancer\u0026rsquo;s spread to any other part of body).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eP\u003c/em\u003e values are from ANOVA or Pearson\u0026rsquo;s chi-square test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection and processing\u003c/h2\u003e \u003cp\u003ePeripheral blood, urine and fecal samples were collected at Huadong Hospital, Fudan University. A total of 24 peripheral blood samples were obtained with 9 samples collected from healthy controls (HC) and 15 samples from patients diagnosed with GC. The blood samples were centrifuged at 3,000 rpm for 10 minutes at 4\u0026deg;C, and 1 mL of the supernatant serum was carefully transferred polypropylene vials. In addition to the serum samples, 126 urine samples were collected, comprising 63 samples from HC and 63 samples from GC. Each subject provided approximately 8 mL of urine, collected in sterile containers. Furthermore, 51 fecal samples were collected, with 34 samples obtained from HC and 17 samples from GC. Each sample comprised 2\u0026ndash;4 g of feces stored in a 10 mL Supelco vial. Upon collection and homogenized, all sample was stored in a -80 ℃ refrigerator within 3 h and retained until needed for the experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analyses of volatile profiles\u003c/h2\u003e \u003cp\u003eA manual solid-phase microextraction (SPME) holder equipped with carboxen/polydimethylsiloxane (CAR/PDMS) fibers was used to extract analytes from all samples. The SPME fiber was inserted into the vial and exposed to the headspace gas to enrich compounds. For the serum, 1 mL of sample was incubated at 55 ℃ for 5 minutes. For the urine, 2 mL sample with 0.5 g of aspartic acid powder incubated for 5 minutes at 60\u0026deg;C. For the feces, 0.5 g sample with 1mL ultrapure water and 0.5 g aspartic acid powder was incubated at 60 ℃ for 5 minutes. Following the SPME procedure, analytes of sample were extracted and introduced into the gas chromatography-ion mobility spectrometry (GC-IMS, \u0026ldquo;FlavorSpec\u0026rdquo; brand, Dortmund, Germany) using a heated syringe through the GC injection port at 80\u0026deg;C. GC-IMS first pre-separates the analytes by GC, then IMS achieved a secondary separation based on the mass of the molecular ion of the substance to be measured and the one-dimensional collision cross-sectional area. The GC-IMS system was equipped with an RTX column, which was used under isothermal conditions at temperature of 80\u0026deg;C. Nitrogen served as the carrier gas, with the following gradient: 0 min: 2 mL/min; 1 min: 2 mL/min; 8 min: 100 mL/min; 10 min: 150 mL/min; 15 min: 150 mL/min. Analytes were then ionized under the positive-ion mode in the IMS chamber using a tritium (3H) source. Ionized analytes then entered the 98 mm long IMS drift tube, where an electric field (strength: 500 V/cm) was applied. The ionization chamber and the drift tube were programmed at 45\u0026deg;C. The drift gas was set at a constant flow rate of 150 mL/min counter-current of the analyte ion flow. Each IMS spectrum was acquired as the average of six scans.\u003c/p\u003e \u003cp\u003eIn this analytical process, each sample generated data consisting of retention index, drift time, and peak intensity. Two-dimensional signal peak characterization can be conducted utilizing the retention index (RI) from the GC and the drift time (Dt) from the IMS, where each data point corresponds to a unique signal peak. The VOC signal peaks of each bile sample were characterized by retention index, migration time and peak strength.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis and predictive models\u003c/h2\u003e \u003cp\u003eThe signal peak intensity underwent normalization to obtain a standardized response. No data were excluded from analysis and missing data were filled with the mean values. Differences in the VOC levels between GC and controls were assessed using Wilcoxon Mann\u0026thinsp;\u0026minus;\u0026thinsp;Whitney \u003cem\u003eU\u003c/em\u003e test. The supervised model of orthogonal partial least-squares discriminant analysis (OPLS-DA) was applied to describe the global metabolic changes between cancer and control groups. For diagnostic modeling, machine learning classification models were employed. 70% of the samples from dataset were allocated to the training set. Subsequently, four machine learning classification algorithms\u0026mdash;Random Forest Classifier, XGBoost, Logistic Regression and Linear SVC\u0026mdash;were trained. This training implemented a recursive feature elimination method under stratified fivefold cross-validation conditions (RFECV), as implemented in the Python RFE package. These four algorithms encapsulate diverse classification criteria within the realm of machine learning [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The RFECV algorithm utilized all compounds to construct an initial model on the training set, ranking each compound based on its significance in effectively categorizing GC and controls. This iterative process involves eliminating the least significant compound in each round until identifying a subset that yields the highest sensitivity.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Metabolic consistency of GC serum, feces and urine\u003c/h2\u003e \u003cp\u003eVolatolomics based on SPME-GC-IMS was employed to profile serum, feces and urine samples from GC patients, aiming to explore the comprehensive metabolic dysregulation in gastric cancer. A total of 44, 83, and 84 VOCs peaks were detected in serum, feces and urine samples from GC patients, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A) and (B), Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Notably, the numbers of dysregulated metabolic peaks, determined through the Wilcoxon Mann\u0026thinsp;\u0026minus;\u0026thinsp;Whitney \u003cem\u003eU\u003c/em\u003e test with Benjamini\u0026thinsp;\u0026minus;\u0026thinsp;Hochberg-based FDR adjustment (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), were 6, 7 and 8 in serum, feces and urine samples, respectively (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-3). We further compared the dysregulated metabolic peaks shared by each matrix evaluated the consistency of metabolic profiles between three sample types. However, only 2-octanone was shared among all matrices (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (C)). Moreover, very few of these shared metabolic peaks exhibited consistent up- or down-regulation trends across three sample types. Among them, short-chain fatty acids such as 2-methylbutanoic acid, 3-methylbutanoic acid and hexanoic acid, along with aldehyde like hexanal were up-regulated in GC feces and urine samples, while 3-methylbutanoic acid and (Z)-4-heptenal were down-regulated in serum types. These findings clearly demonstrated that the metabolic dysregulation in patients was dependent on the sample type, indicating that metabolite biomarkers discovered in the bloodstream may not be consistent with those discovered in excreta matrices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Metabolic profiles of GC feces and urine\u003c/h2\u003e \u003cp\u003eTo gain insights into metabolic contribution of human excreta matrices, we further applied the OPLS-DA supervised model to characterize the overall metabolic changes between the cancer and control groups. However, the OPLS-DA algorithm failed to construct significant PLS models on the VOCs dataset of fecal samples, due to insufficient meaningful information in the dataset. Conversely, the OPLS-DA model based on VOCs of urine successfully distinguished GC samples from those of the control group, indicating distinct metabolic profiles between them. Nevertheless, OPLS-DA model characteristics were R2Y\u0026thinsp;=\u0026thinsp;0.28 and Q2Y\u0026thinsp;=\u0026thinsp;0.15 (Figure S4), suggesting that most VOCs metabolic features in urine samples were considered noise. Further efforts were needed to eliminate this noise and confirm the sensitivity of metabolic features. It is still noteworthy that patients with GC exhibit systemic metabolic abnormalities, the VOCs metabolic changes in urine were greater than those in fecal samples, which indicates that detecting metabolic signatures through humoral transport may aid in noninvasive screening for GC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Diagnostic Potential of VOCs profiles in metabolites for GC\u003c/h2\u003e \u003cp\u003eNext, we employed the RFECV model to train classification models based on VOCs profiles in metabolites of urine and fecal samples. This model facilitated the best performance of the algorithm while minimizing the interference of less significant compounds [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We prioritized sensitivity(recall) to maximize the proportion of actual GC cases correctly identified, and favored models capable of generating predictions using relatively few compounds. Here, we tested the ability of our algorithms to detect all GC without regard to TME status. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves(Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As expected, models utilizing urine volatiles consistently exhibited better predictive performance. The sensitivity of the model utilizing urine volatiles for GC identification ranges from 72\u0026ndash;76%, whereas the model based on feces volatiles exhibits poorer performance, with a sensitivity of only 65\u0026ndash;70%. The resulting model diagnostic performance (area under the curve (AUC), sensitivity, accuracy and F1 Score), along with the key compounds used as predictors, were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic performance and key predictors of gastric cancer.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXGBoost\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLogistic Regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLinear SVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRandom Forest\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1 Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcetophenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcetophenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcetophenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAcetophenone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHexanal-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-Octanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2-Octanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHexanal-D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6-Methyl-5-hepten-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6-Methyl-5-hepten-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6-Methyl-5-hepten-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6-Methyl-5-hepten-2-one\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyclohexanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCyclohexanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCyclohexanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCyclohexanone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMyrcene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeptanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-Methylvaleric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2-Undecanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDecanoic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-Methylbutanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-Octen-3-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2-Hexanone-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHeptanal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ-Citronellol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1-Phenylethylacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(E)-2-Pentenal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2-Undecanone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyclohexen-2-one-D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3-Methylbutanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCitronellal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNonanal-D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenzoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyrcene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1-Phenylethylacetate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eα-Phellandrene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamphene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCamphene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCamphene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCamphene\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eα-Phellandrene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3-Methylbutanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3-Methylbutanoic acid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecanoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNonanal-M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\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 \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMethional\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\u003eWe also compared the diagnostic performance of the biomarkers in urine with typical serum antigen biomarkers (obtained from the patients' clinical assessments), such as carbohydrate antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9), carcinoembryonic antigen (CEA) and carbohydrate antigen 72\u0026thinsp;\u0026minus;\u0026thinsp;4 (CA72-4) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The serum biomarkers only attained an AUC of 0.68, a sensitivity of 0.74, an accuracy of 0.63, a specificity of 0.70 and a F1 score of 0.69). Overall, the results of our predictive models suggest the presence of volatile signatures in urine can reliably identify participants with GC, crucially, that these VOCs exhibit higher detection accuracy compared to traditional blood markers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Biomarker selection for GC prediction\u003c/h2\u003e \u003cp\u003eTo underscore compounds with potential diagnostic significance, our analysis focused on those consistently identified as crucial predictors of infection status across four distinct machine learning algorithms. In general prediction without considering GC staging status, the six most significant urine volatiles for model accuracy were acetophenone, 6-methyl-5-hepten-2-one, cyclohexanone, decanoic acid, camphene and 3-methylbutanoic acid. To ensure these VOCs were not associated with a confounding demographic variable between the comparison groups, linear regression models were performed for each of the 6 VOCs. There were no significant differences in the concentration of these 6 VOCs between participants of different genders, ages and BMI (Table S2).\u003c/p\u003e \u003cp\u003eFour of the compounds presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-undecanone, heptanal, nonanal and 6-methyl-5-hepten-2-one [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], were notable in that they have previously been reported as biomarkers in diagnostic prediction models for GC. Relevant studies have shown that compared to healthy individuals, cancer patients exhibit abnormal aldehyde metabolism. This may be due to changes in the composition of cancer cell membrane lipids. An increase in the concentration of unsaturated fatty acids could promote the production of certain aldehydes through lipid peroxidation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similar to aldehydes, the regulation of ketone bodies differs between normal and tumor tissues. In many cancers, the production of ketones begins with a typical mechanism of increasing long-chain fatty acid (LCFA) oxidation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, other VOCs have previously been shown to be abnormal in various metabolites in patients with cancer or other disease states. The relevant research findings were detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Consistent with these studies, we observed abnormal levels of ketone bodies, aldehydes and short-chain fatty acids in blood, urine and fecal samples from cancer patients, suggesting that these metabolites were likely the result of dysregulated systemic metabolic circulations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential VOCs biomarkers for cancer based on literature review.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker of\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetected Markers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-propenenitrile; 2-butoxy-ethanol; furfural; 6-methyl-5-hepten-2-one; isoprene; styrene; 6-methyl-5-hepten-2-one; 2-ethyl-1 hexanol; nonanal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-propenenitrile; furfural; 6-methyl-5-hepten-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2;3-butanediol; Hexadecane; 3;8-dimethyl- undecane; 1;3-dioxolan-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-metylbutylacetat; 3-methyl-1-butanol; ethylbenzol; heptanal; hexanal; iso-propylamin; n-dodecane; cyclohexanone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-hydroxyacetaldehyde; isoprene; pentanal; butyric acid; toluene; 2;5-dimethylfuran; cyclohexanone; hexanal; heptanal; acetophenone; propylcyclohexane; octanal; nonanal; decanal; 2;2-dimethyldecane; acetaldehyde\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePancreatic ductal adenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2-pentanone; hexanal; 3-hexanone; p-cymene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBladder cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehexanal; benzaldehyde; butyrophenone; 3-hydroxyanthranilic acid; benzoic acid; \u003cem\u003etrans\u003c/em\u003e-3-hexanoic acid; \u003cem\u003ecis\u003c/em\u003e-3-hexanoic acid; 2-butanone; 2-pentanone; 2;3-butanedione; 4-heptanone; dimethyl disulphide; 2-propanol; acetic acid; piperitone; thujone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eColorectal cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3-methylbutanoic acid; propan‐2‐ol; hexan‐2‐one and ethyl 3-methyl‐ butanoate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eethanol; heptane; 2-propenoic acid; butyl ester; 6-methyl-5-Hepten-2-one\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study reveals that gastric cancer induces significant alterations in human volatile emissions. Notably, VOCs in urine constitutes a distinct disease signature that reliably predicting human cancer. The resulting single urine test demonstrated a sensitivity of 76% and an accuracy of 78% in diagnosing gastric cancer. Furthermore, our analysis identifies several key compounds crucial for predictive modeling, underscoring their importance for further exploration as potential biomarkers in developing a robust, non-invasive volatiles-based diagnostic method for gastric cancer.\u003c/p\u003e \u003cp\u003eIn studies focusing on urinary volatile compounds, more than 800 compounds have been detected, with common chemical categories including organic oxides, organic disulfides, and phenols [\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our research findings have identified specific VOCs associated with GC, namely: acetophenone, 6-methyl-5-hepten-2-one, cyclohexanone, camphene, decanoic acid and 3-methylbutanoic acid. These compounds may indicate dysfunction in several metabolic pathways. Specifically, overexpression of fatty acid synthase in human malignancies, including gastric cancer, leads to increased de novo synthesis of fatty acids, which correlates with poor prognosis. Fatty acid metabolism supports tumor initiation and disease progression through processes such as energy generation (β-oxidation), membrane biosynthesis, energy storage and production, and the generation of signaling intermediates. Notably, ketones are highly represented in cancer and some kidney diseases, being the more abundant in cancer. Research suggests that ketones may act as chemical inducers, stimulating the migration of epithelial cancer cells and promoting the growth of primary tumors. Acetone and other ketone bodies are believed to serve as alternative energy sources, allowing sustained abnormal tumor growth [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the increasing evidence of an association between these VOCs in excretion and metabolic dysregulation in malignancies, the full understanding of this relationship remains incomplete [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is postulated that volatiles originate from the body's metabolism when produced naturally, and are released directly into the circulation through cell membranes, subsequently redistributing throughout various matrices within the body. Although we acknowledge that metabolic dysregulation varies depending on the sample type, the abnormality of these volatiles in multiple human matrices has two primary implications. Firstly, the simultaneous presence of most VOCs in blood, urine and feces serves as evidence of metabolic circulation in the body. Secondly, if tumors are indeed the source of these VOCs in various matrices, their re-excretion through the body's circulation may result in a reduced detection level of certain VOCs or a change in their distribution across different matrices.\u003c/p\u003e \u003cp\u003eSeveral limitations exist in the present study. it is important to acknowledge the clinical and analytical limitations imposed by the single-center design of our study. We are planning a larger multicenter study to conclusively determine the accuracy of the test. It is crucial to carefully assess the reproducibility of results obtained using different equipment across different laboratories or over an extended period in a central laboratory. Furthermore, we did not assess the effect of tumor stage, histology and location on volatiles due to underpowering. Further research is needed to elucidate the mechanisms underlying the production and dysregulation of abnormal metabolites. This will improve our understanding of confounding factors and refine VOCs testing protocols. We must emphasize that this study represents the first attempt to analyze volatile compounds across three human matrices, including blood, feces and urine. While our sample sizes enabled the analysis of differences, the limited number of blood samples precluded robust diagnostic modeling and equivalence analyses, and caution should be exercised when inferring direct correlations between relative VOC markers. Also, this study does not obviate the need for more invasive diagnostic procedures in clinical decision-making, individuals with abnormal urine test results may still require further confirmatory investigations, including endoscopy, to identify the underlying pathology.\u003c/p\u003e \u003cp\u003eIn summary, our study marks the first attempt to diagnose GC patients based on VOCs emissions from human metabolites. We have demonstrated that non-invasive volatiles derived from short-chain fatty acids, ketones and others, can enhance the diagnosis of GC. This work represents a significant step forward in non-invasive screening and diagnosis for GC, providing new reliable resources for early detection of the disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave informed consent and all studies were approved by the ethical committees of the Ethics Committee Board of the Huadong Hospital Affiliated to Fudan University (KY 2023K127).\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used during the current study are available from the corresponding author on reasonable request.\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\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.B. and Y.Z. conceptualized the study. T.S. developed the methodology, and created the visualizations. T.S. and W.Y.F. conducted the investigation and wrote the original draft of the manuscript. L.B. provided supervision throughout the project. All authors reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.S.\u0026nbsp;(Tao\u0026nbsp;Sha): Dr. Tao\u0026nbsp;Sha\u0026nbsp;is an attending physician in the Emergency Department at Huadong hospital affiliated to Fudan University. His primary research focus is on the application of machine learning in the medical field. He is currently leading a research team dedicated to developing models that use metabolic products of bacteria and tumors as diagnostic markers for diseases.\u003c/p\u003e\n\u003cp\u003eW.Y.F.\u0026nbsp;(Wenyan\u0026nbsp;Fei):\u0026nbsp;MD,Wenyan\u0026nbsp;Fei\u0026nbsp;is an undergraduate student in the Clinical Medicine (eight-year program) at the School of Basic Medical Sciences, Fudan University. Her main research interests include gastrointestinal tumors and tumor glycobiology.\u003c/p\u003e\n\u003cp\u003eY.Z.\u0026nbsp;(Yun\u0026nbsp;Zhao):\u0026nbsp;MMED,Dr. Yun\u0026nbsp;Zhao\u0026nbsp;is the head of the Emergency Department at Huadong Hospital affiliated\u0026nbsp;to\u0026nbsp;Fudan University, specializing in the diagnosis and treatment of acute and critical illnesses.\u003c/p\u003e\n\u003cp\u003eL.B. (Lin Bai): MMED,Dr. Lin Bai is the associate chief physician in the Emergency Department at Huadong Hospital affiliated to Fudan University. 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Oncotarget. 2015;6(42):44864\u0026ndash;76. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.6269\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.6269\" 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":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, Volatolomics, Machine learning, Urine, Noninvasive diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-4609159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4609159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eGastric cancer (GC) remains a significant contributor to cancer-related mortality, underscoring the critical necessity for specific biomarkers to enable early diagnosis and prognosis. Analyzing volatile organic compounds (VOCs) in vivo offers a promising non-invasive approach for assessing metabolic processes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 201 metabolic samples were acquired from 63 GC patients and 65 healthy controls. Employing solid-phase microextraction and gas chromatography-ion mobility spectrometry-based analytical procedures, we conducted qualitative and signal response analysis of VOCs in blood, feces and urine. Volatolomics was comprehensively investigated across multiple human matrices, and a machine learning-based marker importance assessment framework was employed to evaluate diagnostic biomarkers of GC. Furthermore, a single urine test diagnostic method was established to assess the sensitivity and accuracy of VOCs in diagnosing GC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe underscored the specific VOCs alterations in human matrices, with particular emphasis on serum, feces and urine. We confirmed the dysregulation of GC metabolism during tumor development, as evidenced by VOCs such as short-chain fatty acids and ketones. Our developed urine-based VOCs targeted assay demonstrated superior diagnostic efficacy (AUC\u0026thinsp;=\u0026thinsp;0.85, accuracy\u0026thinsp;=\u0026thinsp;0.76, precision\u0026thinsp;=\u0026thinsp;0.78, sensitivity\u0026thinsp;=\u0026thinsp;0.75, F1 score\u0026thinsp;=\u0026thinsp;0.75) compared to conventional serum markers (AUC\u0026thinsp;=\u0026thinsp;0.68, accuracy\u0026thinsp;=\u0026thinsp;0.63, precision\u0026thinsp;=\u0026thinsp;0.70, sensitivity\u0026thinsp;=\u0026thinsp;0.72, F1 score\u0026thinsp;=\u0026thinsp;0.69).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eUrine VOCs testing enhances GC detection efficacy and represents a novel strategy for cancer diagnosis. The confirmed robustness and precision underscore its potential for clinical translation.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eChiCTR, ChiCTR2300073117. 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