A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms

A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms Fei Long, XingYu Pu, Xin Wang, DongXue Ma, ShanHu Gao, Jun Shi, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4380755/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Feb, 2025 Read the published version in Journal of Ovarian Research → Version 1 posted 11 You are reading this latest preprint version Abstract Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. Exosomes, nanosized extracellular vesicles found in the blood, have been proposed as promising biomarkers for liquid biopsies. In this study we recruited 37 OC patients, 22 benign ovarian tumor (BE) patients, and 46 control (CON) patients. Plasma exosomes were purified from blood samples and sensitive thermal separation probe-based mass spectrometry analysis using a global untargeted metabolic profiling strategy was employed to characterize the metabolite fingerprints. Uniform manifold approximation and projection (UMAP) analysis demonstrated a distinct separation of exosomes among the three groups. We screened for diagnostic biomarkers from plasma exosome metabolites using seven machine learning algorithms, including artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM). DT (0.98), RF (0.92), and ANN (0.89) were given the highest AUC values for the OC-CON comparison. RF (0.93), NB (0.88), and SVM (0.81) were given the highest AUCs for the OC-BE comparison. A total of 18, 138, and 157 metabolic features exhibited significant differences (FC= 1.5, p < 0.01, q < 0.01) in the OC vs BE, BE vs CON, and OC vs CON, comparisons, respectively. Notably, the quantities of 9-octadecenamide and 1,4-methanobenzocyclodecene were significantly elevated, while maltol showed a significant reduction in the OC group compared to the BE group. When comparing the OC group to the CON group, the concentrations of 4-amino-furazan-3-carboxylic acid 2-hydroxy-4-methoxybenzaldehyde, N-phenylethyl, and 4-morpholineethanamine were significantly elevated, while the remaining metabolites, including hydrazine and pyridine sulfonamide, were reduced, in the OC group. The metabolites showing different abundancies are associated with cancer-related mutations, immune responses, and metabolic reprogramming. We demonstrate that the RF algorithm, combined with sensitive thermal separation probe-based mass spectrometry analysis of plasma exosomes, can effectively identify OC patients with good accuracy. Thus, our study has shortlisted a set of potential biomarkers in plasma exosomes, and the proposed approach could serve as a routine prescreening tool for ovarian cancer. metabolomics machine learning metabolic pathways plasma exosomes ovarian cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction 1.1 Ovarian cancer According to the World Health Organization (WHO), in 2018, ovarian cancer (OC) ranked as the second most common malignancy among gynecological diseases, following cervical cancer, with an estimated 184,799 fatalities, which constitute approximately 6.6% of all cancer-related deaths. Due to its asymptomatic early stages(Bray et al., 2018), often OC is not diagnosed until it reaches an advanced stage, leading to a high fatality rate, earning it the moniker “silent killer”(Stewart et al., 2019); OC is the deadliest malignant tumor(Brett M et al., 2017) among all female reproductive diseases. Thus, it is necessary to understand the pathophysiology of OC and discover a more robust diagnostics tool. Liquid biopsy, as a non-invasive technique for cancer diagnosis, represents a novel and promising approach to cancer detection and monitoring(Roy et al., 2021). 1.2 Extracellular vesicles (EVs) The definitive diagnostic approach for cancer patients remains biopsies. Although there have been notable advances in the utilization of circulating tumor cells and circulating tumor DNA for diagnosing OC, issues remain for its early detection and disease progression monitoring(Chang et al., 2019). Furthermore, the same issues apply to currently available clinical biomarkers. As a minimally invasive method, liquid biopsy offers the advantage of early diagnosis and real-time therapeutic response monitoring. This technique, which captures and detects tumor-related biomarkers in body fluids, shows significant promise for diagnostic application. Extracellular vesicles (EVs), characterized by a bilayer phospholipid membrane structure, are secreted by most cells and have potential as liquid biopsy biomarkers. They can be isolated from a variety of bodily fluids, including blood. Studies have revealed that the intracellular trafficking of exosome cargo depends not only on the endocytic pathway but also on the biosynthetic secretory pathway for their release. This suggests that the contents of exosomes can reflect the pathological and physiological states of the parental cells or source organs(Mashouri et al., 2019; Teruel-Montoya et al., 2019; Mohamed et al., 2020; Xia et al., 2020). EVs derived from OC tissue carry many tumor-related biomarkers, such as proteins, lipids, and nucleic acids, that are associated with disease progression. It has been demonstrated that EVs derived from OC patients' plasma exhibit elevated levels of TGFβ1 and melanoma-associated antigen 3 (MAGE3)(Magdalena Derbis, 2012), compared to those from patients with benign disease, providing evidence of their potential as biomarkers for differentiating benign and malignant OC diseases. Furthermore, investigations have found an association of EV biomarkers with tumor stage and prognosis(Menay et al., 2017), indicating their potential roles in cancer diagnosis. Despite increasing research on OC EVs there is a lack of studies investigating metabolomics-based biomarkers. 1.3 The application of metabolomics to OC Metabolic alterations occur downstream of genetic and proteomic regulation. Conversely, metabolic dysregulation can lead to gene overexpression or silencing(Arneth, 2019), which ultimately impacts metabolites. Therefore, the metabolome, the most proximate phenotype among all ‘omes’, provides valuable insights into the biological responses to both internal and external perturbations, including growth, disease, genetic modifications, and environmental effects. In this context, analysis of metabolic patterns is a better strategy for monitoring dynamic changes in biological states. Most studies use nuclear magnetic resonance (NMR) and gas or liquid chromatography combined with mass spectrometry (GC–MS or LC–MS) as analytical platforms for metabolic profiling. MS has higher sensitivity than NMR and is undoubtably the preferred choice for analyzing trace amounts of exosome samples. GC exhibits a significant advantage over LC in terms of database completeness and is less prone to matrix effects and ion suppression by co-eluting compounds(Gowda and Djukovic, 2014; Mastrangelo et al., 2015), resulting in greater chromatographic resolution. However, there are some issues with the detection of polar, thermolabile, and non-volatile metabolites which require chemical derivatization prior to analysis(Poojary and Passamonti, 2016). The Agilent G4381A ChromatoProbe (a Thermal Separation Probe; TSP) can be mounted on Agilent standard shunt/no-shunt or multi-mode injection ports in conjunction with the GC-MS system to quickly separate and identify metabolites in complex samples. This setup not only enables direct detection of metabolites but is also faster for scarcely sample preparation and higher sensitivity for trace constituent, making it an effective solution for to the need for chemical derivatization. Although there have been previous metabolomic studies on OC, there has been a lack of investigation of the exosome metabolome related to OC. In the present study, we employed a sensitive TSP-based mass spectrometry analysis and combined it with machine learning algorithms to identify meatabolite changes among OC, BE and CON plasma exosomes. 2. Experimental section 2.1 Study design and subject s The clinical study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Chongqing Hospitals (Chongqing, China). All participants were recruited from the Second Affiliated Hospital of Chongqing Medical University and provided informed consent prior to participation in the trial from July 2020 to December 2021. The patients were recruited according to the following inclusion and exclusion criteria: 1) Participants with severe chronic diseases such as hypertension, infectious disease, diabetes, metabolic disorders, or a diagnosis of malignancy other than ovarian cancer were excluded from this study to minimize recruitment bias; 2) the control group (n=46) included women with uterine fibroids or endometrioma without any ovarian lesion; 3) the benign ovarian tumor group (n=22) included women with teratoma and ovarian cyst, and 4) the ovarian cancer group (n=37) were included based on levels of preoperative serum markers, including Carbohydrate Antigen (CA125 > 35 U/mL) and Human Epididymis Protein (HE4 > 70 pmol/L in pre-menopausal patients, or HE4 >140 pmol/L in post-menopausal patients), and the imaging modality of sonography for evaluation of an adnexal mass. Details of the clinical samples, including the age, BMI, tumor site, histological type, and stage of ovary cancer, are provided in Table 1 . 2.2 Blood collection Peripheral blood samples (5 mL) were collected in EDTA tubes and delivered to the laboratory in an insulated ice box within 2 h. The samples were centrifuged at 1,000 g for 10 min to remove the blood cells and the supernatant plasma was stored at -80 °C until EV isolation. 2.3 Isolation of EVs from plasma To isolate the plasma EVs, we followed a modified differential ultracentrifugation method based on a previously described procedure(Luo et al., 2020). PBS was filtered through a 0.22 μm PVDF filter before all EV isolation and characterization experiments. The resulting plasma were centrifuged at 5,000 g (4°C) for 30 min to further eliminate cell debris. Next, the supernatant was diluted to a total of 12 mL and centrifuged at 100,000 g (4°C) for 90 min to isolate total EV pellets, which were resuspended in 12 ml PBS for centrifugation again at 100,000 g (4°C) for 90 min. The EVs were aliquoted and stored at -80℃ prior to verification of the isolated EVs and metabolomic analysis. 2.4 Transmission Electron Microscopy (TEM) analysis of EVs Aliquots (4 μL) of EVs were dropped onto the carbon film copper grid and air-dried inside a fume hood overnight for TEM analysis to observe the morphology and ultrastructure of EVs. The grids were examined with a Hitachi-7500 transmission electron microscope (Hitachi, Tokyo, Japan) at an acceleration voltage of 80 kV. 2.5 Nanoparticle Tracking Analysis (NTA) of EVs NTA was performed to analyze the EV particle size and density distribution. Fresh EV samples (5 μL) were diluted in fresh phosphate-buffered saline (PBS) to a concentration between 1x10 7 - 1x10 9 particles/mL, and vortexed for 1 min to resuspend aggregated pellets. Then, the EV sizes were measured with a Nanosight NS300 (Malvern Panalytical Ltd., Malvern, UK) and the size distribution of EVs calculated with NanoSight NTA version 3.2 software. The resulting data was displayed in the form of a curve where the abscissa and the ordinate values represent the EV particle size and concentration respectively. 2.6 Western blot (WB) analysis of EVs WB analysis followed a previously published method(Luo et al., 2020). A 12% sodium dodecyl sulphate polyacrylamide gel was used for EV separation. Primary antibodies were added after blocking with 8% BSA, and the membrane was incubated at 4°C overnight. The primary antibodies used were CD63 (Abcam, 1:1000) and TSG101 (Abcam, 1:5000). Protein bands were visualized after electrophoresis separation with the GeneGnome XRQ analysis system (Syngene, UK). 2.7 Bicinchonininc acid (BCA) analysis The protein concentration of EVs was measured with a BCA assay, and a protein standard curve, and used to adjust the loading concentration of EV samples for GC-MS analysis. The BCA assay was performed according to the manufacturer’s instructions (Beyotime, China). 2.8 Thermal Separation Probe (TSP) and GC-inlet The TSP (Agilent, US) extraction approach was implemented to measure trace amounts of metabolites in plasma EV samples with a single extraction step. EV samples (60 µL, 10 8-10 particles per mL) were transferred into micro-vials (MicroV TSProbe Vial: 32 mm × 12 mm, Agilent, USA) and dried in a SpeedVac for 3 h. The micro-vials were inserted directly into the GC inlet with temperature programming (Multimode inlet, Agilent, USA) using the TSP apparatus. 2.9 Gas chromatography-mass spectrometry (GC-MS) analysis An Agilent 5977A GC/MSD was used to detect trace metabolites in EV samples. The GC-inlet was set at in the splitless mode with a flow rate of 1 mL/min helium carrier, and the inlet temperature initially at 120℃ and held for 0.1 min, then ramped the inlet temperature to 275℃ at a rate of 100 ℃. The volatile compounds were then separated on a DB-FFAP capillary column (30 m × 250 μm id × 0.25 μm, Agilent, CA, USA) and detected by mass spectrometry (Agilent 7890B-5977A) with electron impact ionization via electron emission at 70 eV. The GC-MS parameters were set following a previously published protocol(Han et al., 2019). The temperature was set at 250°C, 230°C, and 150°C for the auxiliary, MS quadrupole, and MS source respectively. The mass range was detected between 30 μm to 550 μm in trace ion detection mode, the threshold was 150, and the scan speed was set at 2.8 scans/sec. The oven temperature program was as follows: (1) 70°C hold for 2 min; (2) increase to 150°C at 50°C/min then hold for 3 min; (3) increase to 190°C at 15°C/min then hold for 5 min; and (4) reach 240°C at 20°C/min then hold for 5 min. The whole process took 21.767 minutes. 2.10 Quality control To minimize the batch variability, an equal volume of EVs derived from plasma from OC, BE, and CON groups was pooled together to prepare the quality control (QC) samples, and blank samples comprised empty micro-vials solely containing an internal standard. For each sample batch analysis, the first three samples analyzed were blanks, and then the QC samples were incorporated as the first, middle, and last EV sample for the GC-MS batch. 2.11 Metabolite identification, quantification and normalization The chromatographic peaks were deconvoluted and identified using Automated Mass Spectral Deconvolution & Identification System software. The metabolites were identified if they matched both the in-house MFC library spectra >90% and their respective GC retention time within a 30-second window. The identification of remaining compounds used a commercial NIST mass spectral library. The relative concentration of metabolites was calculated using the MassOmics R-based script from the peak height of the most abundant fragmented iron mass within a predetermined retention time. The background contamination and any carryover from the previous analysis was subtracted using blank samples. The relative concentration of identified compounds was first normalized to the internal standard (d4-alanine). Median centring of QC samples was implemented to adjust for batch effects. Dilution correction was achieved using the albumen concentration measured with the BCA assay. 2.12 Machine learning development and classification Since different machine learning models may predict or rank different classification results, seven machine learning methods were performed to build the most appropriate binary classifier for identifying the category of samples. Classifiers included artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM). First, Total ion chromatography (TIC) fragments and identified metabolites were corrected with a data rectification procedure encompassing standard internal, QC samples, and protein concentration. Subsequently, TIC fragment and metabolite datasets were scaled by log 2 transformation and a z-score normalization algorithm. The normalized data were randomly split into the training and testing datasets. The significant features were selected by recursive feature elimination (RFE) methods with the training dataset. The seven supervised machine learning models with the selected metabolite features were trained to build efficient classifiers using the R-library packages, including Caret, neuralnet, e1071, kknn, and C50. To further internally validate the model, a stratified 5-fold cross-validation method was used to tune the hyper-parameter of each model using the testing datasets. Confusion matrix and receiver operator characteristic (ROC) curves were used to evaluate the performance of classifiers and provided a calculating foundation of metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, which were defined as follows: Accuracy = (TP+TN) / (TP+FP+TN+FN); Sensitivity = TP / (TP + FN); Specificity = TN / (TN + FP); Positive predictive value = TP / (TP + FP); Negative predictive value = TN / (TN + FN). TP, true positive; TN, true negative; FP, false positive; FN, false negative. 2.13 Statistical analysis The non-parametric Kruskal-Wallis H test was performed to evaluate the differences in the clinical characteristics of three groups: control, benign ovarian tumors, and ovarian cancer. Principal component analysis (PCA) was plotted to illustrate the overall relationship between the three groups using the FactoMineR R-package. The ROC curve was implemented to determine the predictability of the classifier using the POCR R-package. The upSet diagram and heatmap were constructed using UpSetR and ggplot2 R-packages respectively. Metabolic pathways were amalysed using Metaboanalyst (https://www.metaboanalyst.ca/MetaboAnalyst/Secure/process/FilterView.xhtml) based on the KEGG metabolic framework, and chord plots connecting metabolites and their participating metabolic pathways were reconstructed via the GOplot R-package. The significant metabolic pathways were determined by P-values < 0.05 and corresponding FDR values < 0.2 using the q-value R-package. 3. Results and Discussion 3.1 Clinical characteristics of study participants Plasma EVs obtained from participants with ovarian cancer (OC, n = 37), benign tumor (BE, n = 22), and normal controls (CON, n = 46) were isolated in order to identify potential biomarker candidates. The clinical characteristics of the patients are summarized in Table 1 . The P-value for age was less than 0.001, indicating a statistically significant age difference among the participant groups. Conversely, there was no statistically significant difference in terms of BMI (P-value=0.618). The majority of OC cases (89.2%) were classified as high-grade serous. Among patients with malignant tumors, there were 9, 1, 24, and 3 patients in stages I, II, II, and IV respectively. Table 1 Clinical characteristics of study subjects 3.2 Isolation and evolution of EVs The EVs derived from the OC, BE, and CON participants were isolated from 5 mL of plasma using differential ultracentrifugation. TEM, NTA and western blotting were used to validate the quality of the isolated EVs from plasma ( Figure 1 ). TEM showed that the vesicles were spherical membrane structure with a size range of 50-150 nm ( Figure 1A ). Additionally, the size distribution analyzed by NTA showed that the median diameter of EVs were 126.5 nm between 100 and 500 nm, which is consistent with reported studies(Cocucci and Meldolesi, 2015) ( Figure 1B ). The membrane proteins CD63 and TSG101 (common EV markers) were highly enriched in EVs from patients detected by western blotting ( Figure 1C ). 3.3 Overall changes in metabolite profiles Alterations in cellular metabolism have been reported in numerous human cancers and are thought to reflect the metabolic demands related to cancer development(Zhong et al., 2022). In this study we performed comprehensive metabolic profiling of plasma exosome samples collected from 105 women diagnosed either with OC, BE, or controls (CON). Uniform manifold approximation and projection (UMAP) was employed utilizing 388 chromatographic peaks identified in exosomes. The results demonstrated a distinct separation of exosomes among the three groups, as depicted in Figure 2A . Furthermore, we undertook pairwise comparisons of the metabolite profiles detected in the OC, BE, and CON groups. A total of 18, 138, and 157 metabolites exhibited significant differences in abundance (FC= 1.5, p < 0.01, q < 0.01) in the comparisons between OC vs BE, BE vs CON, and OC vs CON, respectively ( Figure 2B ). These differences were visualized through a heat map and volcano plots in Figures 2C-E . In particular, in the comparison between the OC and BE groups, the quantities of 9-octadecenamide and 1,4-methanobenzocyclodecene were significantly elevated in OC group, while maltol showed a significant reduction. The concentrations of 4-amino-furazan-3-carboxylic acid 2-hydroxy-4-methoxybenzaldehyde, N-phenylethyl, and 4-morpholineethanamine were significantly higher in the OC group than in the CON group, while the remaining metabolites, including hydrazine and pyridine sulfonamide, were lower. Interestingly, there were no significant differences in the metabolite profile between the BE and CON groups. Several of the identified metabolites have been previously implicated in cancer pathogenesis. For instance, Spencer and Kisby demonstrated that hydrazine induces DNA damage, which consequently leads to mutations and unregulated mitosis(Spencer and Kisby, 2021). Similarly, another study reported that maltol substantially inhibited tumor growth through the enhancement of immune function, induction of apoptosis, and inhibition of angiogenesis(李伟, 2020). Our findings are consistent with those of Bachmayr et al , who observed alterations in the blood metabolites of OC patients; specifically, reductions in lipids and amino acids were directly associated with tumor metabolism and facilitated cellular proliferation and growth(Bachmayr-Heyda et al.). Collectively, these metabolites showing differential abundancies are linked to cancer-related mutations, immune responses, and metabolic reprogramming, thus indicating their potential utility as diagnostic biomarkers for OC. 3.4 Metabolic Pathway Enrichment Analysis To gain further insights into the biological role of the identified metabolites, we conducted a pathway enrichment analysis using the KEGG metabolic network database ( Figure 3A ). The results of the predicted pathway analysis revealed that only the butanoate metabolism pathway was upregulated in exosomes derived from the OC group. In contrast, nearly all other metabolic pathways, including carbohydrate metabolism, lipid amino acid metabolism, cofactor metabolism, vitamin metabolism, and xenobiotic biodegradation, were found to be downregulated in the OC group. Furthermore, the metabolites with differential abundance were then mapped to 17 significant metabolic pathways including glycolysis, gluconeogenesis, and the the metabolism of glyoxylate, dicarboxylate, butanoate, glutathione, glycerophospholipid, pyruvate, glycine, serine, threonine, tyrosine, phenylalanine, tryptophan, nicotinate, nicotinamide, propanoate, ubiquinone, and bile acids as illustrated by the Sankey diagram in Figure 3B . Other studies have found similar metabolic alterations in OC. Specifically, Zhong et al . observed an upregulation of glutathione metabolism in women diagnosed with this malignancy(Zhong et al., 2022). Elevated glutathione metabolism has been shown to accompany tumor growth, presumably to counteract the increased oxidative stress arising from accelerated metabolic rates(Huang et al.; Gamcsik et al., 2012; Zhong et al., 2022). Both Denkert et al . revealed elevated levels of amino acid intermediates, such as glycine, in OC tissues(Denkert et al., 2006). The increase in amino acid metabolism suggests that substantial pools of intermediate nutrients are being mobilized for molecular assembly in cancer cells. Thus, this research provides novel insight into the metabolite phenotype of plasma exomes that discriminates OC from BE and normal ovarian pathophysiology. 3.5 Machine learning algorithms for disease prediction In order to determine which metabolites can be used to discriminate the OC group from the BE and CON groups, we used binary classification algorithms including artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM). Prior to modelling, the entire dataset was shuffled randomly. To avoid overfitting, the model was built using split data, 75 % of the training and 25 % of the test set. As a result of applying these the algorithms to the dataset, a panel of metabolites in the plasma exosomes was identified. This included 1,4-methanobenzocyclodecene, 9-octadecenamide, methyl stearate, 4-morpholineethanamine, decanoic acid, aminophenylacetylene, lacthydrazide, pentanone, 2,4-ditert butylphenol, phenol, 3,5-dimethoxy, phenpropionic acid and silacyclopentane. The contributions of the nominated metabolites to the performance of each machine learning algorithm were ranked in Supplementary Figure 1 . The area under the curve (AUC) of biomarker signatures discriminating the OC from the CON and BE groups are displayed in Figure 4 . DT (0.98), RF (0.92), and ANN (0.89) resulted in the highest AUC values for the OC-CON comparison. Meanwhile, for the OC-CON comparison, the DT classifier displayed the best discrimination power (AUC value of 0.98) based on the top-ranking features. For the OC-BE comparison, RF (0.93), NB (0.88), and SVM (0.81) gave the highest AUC values. To further assess the performance of the top-ranking features, an independent plasma exosome dataset (n=18) was recruited to validate the models. We observed that the external validation of the independent exosome dataset yielded AUC values of 0.81 and 0.75 for the OC-BE and OC-CON comparisons respectively ( Supplementary Table 1 ). Notably, RF provided the overall best performance for both model tests and the validation because it is a machine-learning approach for binary classification and can handle smaller datasets well(Wu et al., 2021). LR appeared to give the worst performance, likely due to underfitting resulting from a smaller dataset(Iddamalgoda et al., 2016). Together with the pros and cons of the different machine learning algorithms ( Supplementary Table 2 ), we determined that RF was an appropriate model for our relatively small dataset and binary research question. 4. Conclusion This study is the first identification of diagnostic biomarkers from plasma exosome metabolites in OC patients using machine learning algorithms. The profile of ovarian exosome metabolites was characterized by metabolites associated with the metabolism of carbohydrates, amino acids, and lipids. We demonstrated that the RF Model, combined with sensitive thermal separation probe-based mass spectrometry analysis of plasma exosomes, can effectively identify OC patients with high accuracy. Thus, our study has shortlisted a set of potential biomarkers in plasma exosomes, and the proposed approach could serve as a routine prescreening tool for OC. Declarations Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. Ethics statement The studies involving human participants were reviewed and approved by the Research Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University, China (202164). The participants provided their written informed consent to participate in this study. Author contributions FL contributed to the sample and data collection, XP, and XW performed the statistical analysis, interpreted the results, and wrote the manuscript. DM contributed to data collection and interpreting the results, SG and JS contributed to interpreting the results and wrote the manuscript. XZ, RR, LW and ZC contributed to the samples collection. RC commented on the experimental design and revised the manuscript. T-LH and YY devised the original laboratory study. T-LH interpreted the results, supported the writing of the manuscript, directed the project, guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version. Funding This study was supported by the Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (2023KFKT002), Chongqing Science & Technology Commission (cstc2021jcyj-msxmX0213), Chongqing Municipal Education Commission (KJZD-K202100407), and Senior Medical Talents Program of Chongqing for Young and Middle-aged【2022】15, and the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University. Acknowledgements We thank all collaborators for data collection. We also thank all the study participants. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Supplementary material The Supplementary Material for this article can be found online at: References Arneth, B. (2019). Tumor Microenvironment. Medicina 56(1). doi: 10.3390/medicina56010015. Bachmayr-Heyda, A., Aust, S., Auer, K., Meier, S.M., Schmetterer, K.G., Dekan, S., et al. Integrative Systemic and Local Metabolomics with Impact on Survival in High-Grade Serous Ovarian Cancer. (1557-3265 (Electronic)). 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Analytica Chimica Acta 900 , 21-35. doi: 10.1016/j.aca.2015.10.001. Menay, F., Herschlik, L., De Toro, J., Cocozza, F., Tsacalian, R., Gravisaco, M.J., et al. (2017). Exosomes Isolated from Ascites of T-Cell Lymphoma-Bearing Mice Expressing Surface CD24 and HSP-90 Induce a Tumor-Specific Immune Response. Frontiers in Immunology 8. doi: 10.3389/fimmu.2017.00286. Mohamed, A.S., Hosney, M., Bassiony, H., Hassanein, S.S., Soliman, A.M., Fahmy, S.R., et al. (2020). Sodium pentobarbital dosages for exsanguination affect biochemical, molecular and histological measurements in rats. Scientific Reports 10(1). doi: 10.1038/s41598-019-57252-7. Poojary, M.M., and Passamonti, P. (2016). Improved conventional and microwave-assisted silylation protocols for simultaneous gas chromatographic determination of tocopherols and sterols: Method development and multi-response optimization. Journal of Chromatography A 1476 , 88-104. doi: 10.1016/j.chroma.2016.10.064. Roy, D., Pascher, A., Juratli, M.A., and Sporn, J.C. (2021). The Potential of Aptamer-Mediated Liquid Biopsy for Early Detection of Cancer. International Journal of Molecular Sciences 22(11). doi: 10.3390/ijms22115601. Spencer, P.S., and Kisby, G.E. (2021). Role of Hydrazine-Related Chemicals in Cancer and Neurodegenerative Disease. Chemical Research in Toxicology 34(9) , 1953-1969. doi: 10.1021/acs.chemrestox.1c00150. Stewart, C., Ralyea, C., and Lockwood, S. (2019). Ovarian Cancer: An Integrated Review. Seminars in Oncology Nursing 35(2) , 151-156. doi: 10.1016/j.soncn.2019.02.001. Teruel-Montoya, R., Luengo-Gil, G., Vallejo, F., Yuste, J.E., Bohdan, N., García-Barberá, N., et al. (2019). Differential miRNA expression profile and proteome in plasma exosomes from patients with paroxysmal nocturnal hemoglobinuria. Scientific Reports 9(1). doi: 10.1038/s41598-019-40453-5. Wu, Z., Zhu, M., Kang, Y., Leung, E.L.-H., Lei, T., Shen, C., et al. (2021). Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets. Briefings in Bioinformatics 22(4). doi: 10.1093/bib/bbaa321. Xia, Y., Rao, L., Yao, H., Wang, Z., Ning, P., and Chen, X. (2020). Engineering Macrophages for Cancer Immunotherapy and Drug Delivery. Advanced Materials 32(40). doi: 10.1002/adma.202002054. Zhong, X., Ran, R., Gao, S., Shi, M., Shi, X., Long, F., et al. (2022). Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer. Frontiers in Oncology 12. doi: 10.3389/fonc.2022.916375. 李伟 (2020). 5-羟甲基糠醛和麦芽酚的肝保护及抗肿瘤药理活性研究. 博士后, 中国农业科学院. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.tif SupplementaryTable1.docx SupplementaryTable2.docx Cite Share Download PDF Status: Published Journal Publication published 12 Feb, 2025 Read the published version in Journal of Ovarian Research → Version 1 posted Editorial decision: Revision requested 23 Jul, 2024 Reviews received at journal 22 Jul, 2024 Reviews received at journal 18 Jul, 2024 Reviews received at journal 04 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers agreed at journal 29 Jun, 2024 Reviewers agreed at journal 22 Jun, 2024 Reviewers invited by journal 10 May, 2024 Editor assigned by journal 08 May, 2024 Submission checks completed at journal 07 May, 2024 First submitted to journal 07 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4380755","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":301754592,"identity":"2a295392-7438-4d92-96b1-fb4f7f4b09d2","order_by":0,"name":"Fei Long","email":"","orcid":"","institution":"State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Long","suffix":""},{"id":301754593,"identity":"6c6cfa1e-d52c-4b3e-bc43-c603125fe842","order_by":1,"name":"XingYu Pu","email":"","orcid":"","institution":"State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"XingYu","middleName":"","lastName":"Pu","suffix":""},{"id":301754594,"identity":"cf712f4c-14d0-4f08-b4d3-1a38a879bcd1","order_by":2,"name":"Xin Wang","email":"","orcid":"","institution":"State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wang","suffix":""},{"id":301754595,"identity":"562a9c11-5f7d-4662-8383-fd99056c431d","order_by":3,"name":"DongXue Ma","email":"","orcid":"","institution":"State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical 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Zhong","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"XiaoCui","middleName":"","lastName":"Zhong","suffix":""},{"id":301754599,"identity":"d0a0b2f2-fc8a-43bc-ab88-ef31dc47f304","order_by":7,"name":"Rui Ran","email":"","orcid":"","institution":"Department of Obstetrics and Gynaecology, The Second Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Ran","suffix":""},{"id":301754600,"identity":"97f6a385-7088-4cd5-bbca-efe189959d71","order_by":8,"name":"LianLian Wang","email":"","orcid":"","institution":"Department of Reproductive Center, The First Affiliated Hospital of Chongqing Medical 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Research Institute, University of Otago","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"D","lastName":"Cannon","suffix":""},{"id":301754604,"identity":"f5c04672-539e-4c05-ab18-3eab569fa86a","order_by":12,"name":"Ting-Li Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACZiB+YHCAgYG9ASpygIAOHpCWBJAWHphSglpARAJImUQCkVrs2XkPv0gouCNvLvn44a2bbQxyfDcSGD8X4HUYX5pFgsEzw52z04ytc9sYjCVvJDBLz8CrhcfMIMHgMOOG2zls0kAtiRtuJLAx8xChxX7DzTNgLfXEaDF+ANQCNJwHrCXBgKCWwzxmQGWHkzecAfol55yE4cwzD5ul8Wlh7z9j/OHDn8O2G44ffng7p8xGnu948sHP+LQAAZsEjCUBRgyMDfg1AJPMByQto2AUjIJRMAowAQBgZUqcXpDFygAAAABJRU5ErkJggg==","orcid":"","institution":"State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Ting-Li","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-05-07 06:43:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4380755/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4380755/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13048-025-01590-w","type":"published","date":"2025-02-12T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56683244,"identity":"e6dfee06-a4b7-4125-9c18-c3d7bffeed77","added_by":"auto","created_at":"2024-05-17 18:34:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":621216,"visible":true,"origin":"","legend":"\u003cp\u003eThe characterization of EVs. \u003cstrong\u003e(A)\u003c/strong\u003e Representative electron microscopy micrographs of EVs isolated from plasma (indicated by arrows), bar = 200 nm. \u003cstrong\u003e(B)\u003c/strong\u003e NTA analysis to determine the size distribution and number of EVs. \u003cstrong\u003e(C)\u003c/strong\u003eWestern blotting validated exosomes through CD63 and TSG101.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/d92ff6a1e9c0ec586c7b1b62.jpg"},{"id":56682599,"identity":"897cefe2-0595-4a11-ad40-911d2f41c07c","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1610152,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolites in the exosome samples from study participants.\u003cstrong\u003e (A) \u003c/strong\u003eUMAP clustering of all participant samples colored according to the participant group; each point in the plot represents a participant. Red dots represent the benign tumor group (n = 22), green dots indicate the control group (n =46), and blue dots represent samples derived from the ovarian cancer group (n = 39). \u003cstrong\u003e(B)\u003c/strong\u003e Upset plot and Venn diagram of metabolites with differential abundance (p \u0026lt; 0.05). The individual or connected dots represent the various intersections of metabolites that were either unique to, or shared among, comparisons.\u003cstrong\u003e (C) \u003c/strong\u003eHeat Map showing the profile of metabolites in plasma exosome samples and their metabolic classifications. The relative concentration of metabolites in the samples are expressed on a log\u003csub\u003e2\u003c/sub\u003e scale. The red block indicates that the metabolite level in the divisor array is higher than that in the divisor array, while the blue block indicates that the metabolite level in the divisor array is lower than that in the divisor array. Only metabolites with both p-value and q-value less than 0.01 in the logistic regression adjusted for age and BMI are displayed.\u003cstrong\u003e (D-\u003c/strong\u003e \u003cstrong\u003eE) \u003c/strong\u003eVolcano plot of the metabolites with differential abundance (p \u0026lt; 0.05, FC \u0026gt; 1.5). Red dots indicate upregulation and blue dots indicate downregulation.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/8dfbd64229434f735c75805a.jpg"},{"id":56682600,"identity":"e2389bf0-b405-45e2-8035-0e9eb3c41b34","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":807613,"visible":true,"origin":"","legend":"\u003cp\u003eDifferent metabolic pathways and metabolic networks in ovarian cancer (OC) and control (CON) groups. \u003cstrong\u003e(A)\u003c/strong\u003e Activities of metabolic pathways in exosomes derived from the plasma of the OC and CON groups. Red dots and lines represent the metabolic activities in exosomes from the CON group that were adjusted to 0. Blue triangles represent metabolic activities in exosomes from the OC group relative to the CON group. The metabolic activities are expressed on a log\u003csub\u003e2\u003c/sub\u003e scale. The triangle size indicates the number of identified metabolites in the pathway. Only the metabolic pathways with a significant p-value less than 0.01 (Logistic regression with age as a confounding factor) are plotted. \u003cstrong\u003e(B)\u003c/strong\u003e A Sankey diagram displaying how the metabolites with differential abundance connect to their participating metabolic pathways.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/452df562d5a59fef77c8b463.jpg"},{"id":56682605,"identity":"264639b1-3118-492c-95bf-d20fd80a2b3f","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4824878,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant metabolites were identified by receiver operator characteristic (ROC) analysis using the seven machine learning algorithms ANN, DT, KNN, LR, NB, RF, and SVM. ROC curve analysis and the corresponding area under the curve (AUC; median with 95% confidence interval) of each model for OC vs BE (\u003cstrong\u003eA, B\u003c/strong\u003e) comparisons, or OC vs CON (\u003cstrong\u003eC, D\u003c/strong\u003e) comparisons. ANN artificial neural network, DT decision tree, KNN K nearest neighbor, LR logistics regression, NB naïve bayes, RF random forest, SVM support vector machine.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/f9df0905c3fc081c640ab6fd.jpg"},{"id":56682602,"identity":"bca9efcf-47db-4419-bc67-1f28930b9103","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":842646,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of study approaches and main findings. Plasma exosomes were purified from blood samples obtained from both ovarian cancer (OC) patients and non-malignant patients using ultracentrifugation. The global untargeted metabolite fingerprint of plasma-derived exosomes was characterized using a sensitive thermal separation probe (TSP)-based mass spectrometry analysis. To screen potential diagnostic biomarkers for ovarian cancer, we employed seven machine learning algorithms, namely Artificial Neural Network (ANN), Decision Tree (DT), k-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), and Support Vector Machine (SVM). Particularly, RF model combined with TSP mass spectrometry analysis of plasma exosomes demonstrated a remarkable capability to identify OC patients accurately. Notably, metabolites contributing to this discrimination included hydrazine, maltol, 4-morpholineethanamine, and methyl stearate. These identified metabolites are associated with cancer phenotypes, such as cancer-related mutations, immune responses, and metabolic reprogramming.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/4d1c277bef6a356023347324.jpg"},{"id":76487597,"identity":"b7b73a2d-c497-402a-aeaf-af8f704b6a06","added_by":"auto","created_at":"2025-02-17 16:09:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9814728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/76f72364-a69e-47c3-bec6-378efc8e973b.pdf"},{"id":56682604,"identity":"427e2d64-edb2-4ac5-9d1c-6af5b85dfc44","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":787018,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/7ff5451842525669de83b77e.tif"},{"id":56682606,"identity":"ac628278-16bd-4c63-af66-5b9719ac734f","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15984,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/61bc4dacc89fd5c08eadfc62.docx"},{"id":56682603,"identity":"05d7bc58-303f-40ff-8e05-aa001db71389","added_by":"auto","created_at":"2024-05-17 18:26:55","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":20165,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4380755/v1/fcc7967530dacfc26b85cce3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms","fulltext":[{"header":"1. Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Ovarian cancer\u003c/h2\u003e \u003cp\u003eAccording to the World Health Organization (WHO), in 2018, ovarian cancer (OC) ranked as the second most common malignancy among gynecological diseases, following cervical cancer, with an estimated 184,799 fatalities, which constitute approximately 6.6% of all cancer-related deaths. Due to its asymptomatic early stages(Bray et al., 2018), often OC is not diagnosed until it reaches an advanced stage, leading to a high fatality rate, earning it the moniker \u0026ldquo;silent killer\u0026rdquo;(Stewart et al., 2019); OC is the deadliest malignant tumor(Brett M et al., 2017) among all female reproductive diseases. Thus, it is necessary to understand the pathophysiology of OC and discover a more robust diagnostics tool. Liquid biopsy, as a non-invasive technique for cancer diagnosis, represents a novel and promising approach to cancer detection and monitoring(Roy et al., 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Extracellular vesicles (EVs)\u003c/h2\u003e \u003cp\u003eThe definitive diagnostic approach for cancer patients remains biopsies. Although there have been notable advances in the utilization of circulating tumor cells and circulating tumor DNA for diagnosing OC, issues remain for its early detection and disease progression monitoring(Chang et al., 2019). Furthermore, the same issues apply to currently available clinical biomarkers. As a minimally invasive method, liquid biopsy offers the advantage of early diagnosis and real-time therapeutic response monitoring. This technique, which captures and detects tumor-related biomarkers in body fluids, shows significant promise for diagnostic application.\u003c/p\u003e \u003cp\u003eExtracellular vesicles (EVs), characterized by a bilayer phospholipid membrane structure, are secreted by most cells and have potential as liquid biopsy biomarkers. They can be isolated from a variety of bodily fluids, including blood. Studies have revealed that the intracellular trafficking of exosome cargo depends not only on the endocytic pathway but also on the biosynthetic secretory pathway for their release. This suggests that the contents of exosomes can reflect the pathological and physiological states of the parental cells or source organs(Mashouri et al., 2019; Teruel-Montoya et al., 2019; Mohamed et al., 2020; Xia et al., 2020). EVs derived from OC tissue carry many tumor-related biomarkers, such as proteins, lipids, and nucleic acids, that are associated with disease progression. It has been demonstrated that EVs derived from OC patients' plasma exhibit elevated levels of TGFβ1 and melanoma-associated antigen 3 (MAGE3)(Magdalena Derbis, 2012), compared to those from patients with benign disease, providing evidence of their potential as biomarkers for differentiating benign and malignant OC diseases. Furthermore, investigations have found an association of EV biomarkers with tumor stage and prognosis(Menay et al., 2017), indicating their potential roles in cancer diagnosis. Despite increasing research on OC EVs there is a lack of studies investigating metabolomics-based biomarkers.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e1.3 The application of metabolomics to OC\u003c/h3\u003e\n\u003cp\u003eMetabolic alterations occur downstream of genetic and proteomic regulation. Conversely, metabolic dysregulation can lead to gene overexpression or silencing(Arneth, 2019), which ultimately impacts metabolites. Therefore, the metabolome, the most proximate phenotype among all \u0026lsquo;omes\u0026rsquo;, provides valuable insights into the biological responses to both internal and external perturbations, including growth, disease, genetic modifications, and environmental effects. In this context, analysis of metabolic patterns is a better strategy for monitoring dynamic changes in biological states.\u003c/p\u003e \u003cp\u003eMost studies use nuclear magnetic resonance (NMR) and gas or liquid chromatography combined with mass spectrometry (GC\u0026ndash;MS or LC\u0026ndash;MS) as analytical platforms for metabolic profiling. MS has higher sensitivity than NMR and is undoubtably the preferred choice for analyzing trace amounts of exosome samples. GC exhibits a significant advantage over LC in terms of database completeness and is less prone to matrix effects and ion suppression by co-eluting compounds(Gowda and Djukovic, 2014; Mastrangelo et al., 2015), resulting in greater chromatographic resolution. However, there are some issues with the detection of polar, thermolabile, and non-volatile metabolites which require chemical derivatization prior to analysis(Poojary and Passamonti, 2016). The Agilent G4381A ChromatoProbe (a Thermal Separation Probe; TSP) can be mounted on Agilent standard shunt/no-shunt or multi-mode injection ports in conjunction with the GC-MS system to quickly separate and identify metabolites in complex samples. This setup not only enables direct detection of metabolites but is also faster for scarcely sample preparation and higher sensitivity for trace constituent, making it an effective solution for to the need for chemical derivatization.\u003c/p\u003e \u003cp\u003eAlthough there have been previous metabolomic studies on OC, there has been a lack of investigation of the exosome metabolome related to OC. In the present study, we employed a sensitive TSP-based mass spectrometry analysis and combined it with machine learning algorithms to identify meatabolite changes among OC, BE and CON plasma exosomes.\u003c/p\u003e"},{"header":"2. Experimental section","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study design and subject\u003c/strong\u003es\u003c/p\u003e\n\u003cp\u003eThe clinical study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee of the Chongqing Hospitals (Chongqing, China). All participants were recruited from the Second Affiliated Hospital of Chongqing Medical University and provided informed consent prior to participation in the trial from July 2020 to December 2021. The patients were recruited according to the following inclusion and exclusion criteria: 1) Participants with severe chronic diseases such as hypertension, infectious disease, diabetes, metabolic disorders, or a diagnosis of malignancy other than ovarian cancer were excluded from this study to minimize recruitment bias; 2) the control group (n=46) included women with uterine fibroids or endometrioma without any ovarian lesion; 3) the benign ovarian tumor group (n=22) included women with teratoma and ovarian cyst, and 4) the ovarian cancer group (n=37) were included based on levels of preoperative serum markers, including Carbohydrate Antigen (CA125 \u0026gt; 35 U/mL) and Human Epididymis Protein (HE4 \u0026gt; 70 pmol/L in pre-menopausal patients, or HE4 \u0026gt;140 pmol/L in post-menopausal patients), and the imaging modality of sonography for evaluation of an adnexal mass. Details of the clinical samples, including the age, BMI, tumor site, histological type, and stage of ovary cancer, are provided in\u003cstrong\u003e\u0026nbsp;Table 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Blood collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood samples (5 mL) were collected in EDTA tubes and delivered to the laboratory in an insulated ice box within 2 h. The samples were centrifuged at 1,000 g for 10 min to remove the blood cells and the supernatant plasma was stored at -80 \u0026deg;C until EV isolation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Isolation of EVs from plasma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo isolate the plasma EVs, we followed a modified differential ultracentrifugation method based on a previously described procedure(Luo et al., 2020). PBS was filtered through a 0.22 \u0026mu;m PVDF filter before all EV isolation and characterization experiments. The resulting plasma were centrifuged at 5,000 g (4\u0026deg;C) for 30 min to further eliminate cell debris. Next, the supernatant was diluted to a total of 12 mL and centrifuged at 100,000 g (4\u0026deg;C) for 90 min to isolate total EV pellets, which were resuspended in 12 ml PBS for centrifugation again at 100,000 g (4\u0026deg;C) for 90 min. The EVs were aliquoted and stored at -80℃ prior to verification of the isolated EVs and metabolomic analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Transmission Electron Microscopy (TEM) analysis of EVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAliquots (4 \u0026mu;L)\u0026nbsp;of EVs were\u0026nbsp;dropped onto the carbon film copper grid and air-dried\u0026nbsp;inside a fume hood overnight for TEM analysis to observe the morphology and ultrastructure of EVs. The grids were examined with a Hitachi-7500 transmission electron microscope (Hitachi, Tokyo, Japan) at an acceleration voltage of 80 kV.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Nanoparticle Tracking Analysis (NTA) of EVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNTA was performed to analyze the EV particle size and density distribution. Fresh EV samples (5 \u0026mu;L) were diluted in fresh phosphate-buffered saline (PBS) to a concentration between 1x10\u003csup\u003e7\u003c/sup\u003e - 1x10\u003csup\u003e9\u003c/sup\u003e particles/mL, and vortexed for 1 min to resuspend aggregated pellets. Then, the EV sizes were measured with a Nanosight NS300 (Malvern Panalytical Ltd., Malvern, UK) and the size distribution of EVs calculated with NanoSight NTA version 3.2 software. The resulting data was displayed in the form of a curve where the abscissa and the ordinate values represent the EV particle size and concentration respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Western blot (WB) analysis of EVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWB analysis followed a previously published method(Luo et al., 2020). A\u0026nbsp;12% sodium dodecyl sulphate polyacrylamide gel was used for EV separation. Primary antibodies were added after blocking with 8% BSA, and the membrane was incubated at 4\u0026deg;C overnight.\u0026nbsp;The primary antibodies used were\u0026nbsp;CD63 (Abcam, 1:1000) and TSG101 (Abcam, 1:5000). Protein bands were visualized after electrophoresis separation with the GeneGnome XRQ analysis system (Syngene, UK).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Bicinchonininc acid (BCA) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The protein concentration of EVs was measured with a BCA assay, and a protein standard curve, and used to adjust the loading concentration of EV samples for GC-MS analysis. The BCA assay was performed according to the manufacturer\u0026rsquo;s instructions (Beyotime, China).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Thermal Separation Probe (TSP) and GC-inlet\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TSP (Agilent, US) extraction approach was implemented to measure trace amounts of metabolites in plasma EV samples with a single extraction step. EV samples (60 \u0026micro;L, 10\u003csup\u003e8-10\u003c/sup\u003e particles per mL) were transferred into micro-vials (MicroV TSProbe Vial: 32 mm\u0026nbsp;\u0026times;\u0026nbsp;12 mm, Agilent, USA) and dried in a SpeedVac for 3 h. The micro-vials were inserted directly into the GC inlet with temperature programming (Multimode inlet, Agilent, USA) using the TSP apparatus.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Gas chromatography-mass spectrometry (GC-MS) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn Agilent 5977A GC/MSD was used to detect trace metabolites in EV samples. The GC-inlet was set at in the splitless mode with a flow rate of 1 mL/min helium carrier, and the inlet temperature initially at 120℃ and held for 0.1 min, then ramped the inlet temperature to 275℃ at a rate of 100 ℃. The volatile compounds were then separated on a DB-FFAP capillary column (30 m \u0026times; 250 \u0026mu;m id \u0026times; 0.25 \u0026mu;m, Agilent, CA, USA) and detected by mass spectrometry (Agilent 7890B-5977A) with electron impact ionization via electron emission at 70 eV. The GC-MS parameters were set following a previously published protocol(Han et al., 2019). The temperature was set at 250\u0026deg;C, 230\u0026deg;C, and 150\u0026deg;C for the auxiliary, MS quadrupole, and MS source respectively. The mass range was detected between 30 \u0026mu;m to 550 \u0026mu;m in trace ion detection mode, the threshold was 150, and the scan speed was set at 2.8 scans/sec. The oven temperature program was as follows: (1) 70\u0026deg;C hold for 2 min; (2) increase to 150\u0026deg;C at 50\u0026deg;C/min then hold for 3 min; (3) increase to 190\u0026deg;C at 15\u0026deg;C/min then hold for 5 min; and (4) reach 240\u0026deg;C at 20\u0026deg;C/min then hold for 5 min. The whole process took 21.767 minutes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Quality control\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo minimize the batch variability, an equal volume of EVs derived from plasma from OC, BE, and CON groups was pooled together to prepare the quality control (QC) samples, and blank samples comprised empty micro-vials solely containing an internal standard. For each sample batch analysis, the first three samples analyzed were blanks, and then the QC samples were incorporated as the first, middle, and last EV sample for the GC-MS batch.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Metabolite identification, quantification and normalization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe chromatographic peaks were deconvoluted and identified using Automated Mass Spectral Deconvolution \u0026amp; Identification System software. The metabolites were identified if they matched both the in-house MFC library spectra \u0026gt;90% and their respective GC retention time within a 30-second window. The identification of remaining compounds used a commercial NIST mass spectral library.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relative concentration of metabolites was calculated using the MassOmics R-based script from the peak height of the most abundant fragmented iron mass within a predetermined retention time. The background contamination and any carryover from the previous analysis was subtracted using blank samples. The relative concentration of identified compounds was first normalized to the internal standard (d4-alanine). Median centring of QC samples was implemented to adjust for batch effects. Dilution correction was achieved using the albumen concentration measured with the BCA assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Machine learning development and classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince different machine learning models may predict or rank different classification results, seven machine learning methods were performed to build the most appropriate binary classifier for identifying the category of samples. Classifiers included artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Na\u0026iuml;ve Bayes (NB), random forest (RF), and support vector machine (SVM). First, Total ion chromatography (TIC) fragments and identified metabolites were corrected with a data rectification procedure encompassing standard internal, QC samples, and protein concentration. Subsequently, TIC fragment and metabolite datasets were scaled by log\u003csub\u003e2\u003c/sub\u003e transformation and a z-score normalization algorithm. The normalized data were randomly split into the training and testing datasets. The significant features were selected by recursive feature elimination (RFE) methods with the training dataset. The seven supervised machine learning models with the selected metabolite features were trained to build efficient classifiers using the R-library packages, including Caret, neuralnet, e1071, kknn, and C50. To further internally validate the model, a stratified 5-fold cross-validation method was used to tune the hyper-parameter of each model using the testing datasets. Confusion matrix and receiver operator characteristic (ROC) curves were used to evaluate the performance of classifiers and provided a calculating foundation of metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, which were defined as follows:\u003c/p\u003e\n\u003cp\u003eAccuracy = (TP+TN) / (TP+FP+TN+FN);\u003c/p\u003e\n\u003cp\u003eSensitivity = TP / (TP + FN);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecificity = TN / (TN + FP);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePositive predictive value = TP / (TP + FP);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNegative predictive value = TN / (TN + FN).\u003c/p\u003e\n\u003cp\u003eTP, true positive; TN, true negative; FP, false positive; FN, false negative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.13 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe non-parametric Kruskal-Wallis H test was performed to evaluate the differences in the clinical characteristics of three groups: control, benign ovarian tumors, and ovarian cancer. Principal component analysis (PCA) was plotted to illustrate the overall relationship between the three groups using the FactoMineR R-package. The ROC curve was implemented to determine the predictability of the classifier using the POCR R-package. The upSet diagram and heatmap were constructed using UpSetR and ggplot2 R-packages respectively. Metabolic pathways were amalysed using Metaboanalyst (https://www.metaboanalyst.ca/MetaboAnalyst/Secure/process/FilterView.xhtml) based on the KEGG metabolic framework, and chord plots connecting metabolites and their participating metabolic pathways were reconstructed via the GOplot R-package. The significant metabolic pathways were determined by P-values \u0026lt; 0.05 and corresponding FDR values \u0026lt; 0.2 using the q-value R-package.\u003c/p\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Clinical characteristics of study participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlasma EVs obtained from participants with ovarian cancer (OC, n = 37), benign tumor (BE, n = 22), and normal controls (CON, n = 46) were isolated in order to identify potential biomarker candidates. The clinical characteristics of the patients are summarized in \u003cstrong\u003eTable 1\u003c/strong\u003e. The P-value for age was less than 0.001, indicating a statistically significant age difference among the participant groups. Conversely, there was no statistically significant difference in terms of BMI (P-value=0.618). The majority of OC cases (89.2%) were classified as high-grade serous. Among patients with malignant tumors, there were 9, 1, 24, and 3 patients in stages I, II, II, and IV respectively.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1\u003c/strong\u003e Clinical characteristics of study subjects\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"658\" height=\"709\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Isolation and evolution of EVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe EVs derived from the OC, BE, and CON participants were isolated from 5 mL of plasma using differential ultracentrifugation. TEM, NTA and western blotting were used to validate the quality of the isolated EVs from plasma (\u003cstrong\u003eFigure 1\u003c/strong\u003e). TEM showed that the vesicles were spherical membrane structure with a size range of 50-150 nm (\u003cstrong\u003eFigure 1A\u003c/strong\u003e). Additionally, the size distribution analyzed by NTA showed that the median diameter of EVs were 126.5 nm between 100 and 500 nm, which is consistent with reported studies(Cocucci and Meldolesi, 2015)\u0026nbsp;(\u003cstrong\u003eFigure 1B\u003c/strong\u003e). The membrane proteins CD63 and TSG101 (common EV markers) were highly enriched in EVs from patients detected by western blotting (\u003cstrong\u003eFigure 1C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Overall changes in metabolite profiles\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlterations in cellular metabolism have been reported in numerous human cancers and are thought to reflect the metabolic demands related to cancer development(Zhong et al., 2022). In this study we performed comprehensive metabolic profiling of plasma exosome samples collected from 105 women diagnosed either with OC, BE, or controls (CON). Uniform manifold approximation and projection (UMAP) was employed utilizing 388 chromatographic peaks identified in exosomes. The results demonstrated a distinct separation of exosomes among the three groups, as depicted in \u003cstrong\u003eFigure 2A\u003c/strong\u003e. Furthermore, we undertook pairwise comparisons of the metabolite profiles detected in the OC, BE, and CON groups. A total of 18, 138, and 157 metabolites exhibited significant differences in abundance (FC= 1.5, p \u0026lt; 0.01, q \u0026lt; 0.01) in the comparisons between OC vs BE, BE vs CON, and OC vs CON, respectively (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). These differences were visualized through a heat map and volcano plots in\u003cstrong\u003e\u0026nbsp;Figures 2C-E\u003c/strong\u003e. In particular, in the comparison between the OC and BE groups, the quantities of 9-octadecenamide and 1,4-methanobenzocyclodecene were significantly elevated in OC group, while maltol showed a significant reduction. The concentrations of 4-amino-furazan-3-carboxylic acid 2-hydroxy-4-methoxybenzaldehyde, N-phenylethyl, and 4-morpholineethanamine were significantly higher in the OC group than in the CON group, while the remaining metabolites, including hydrazine and pyridine sulfonamide, were lower. Interestingly, there were no significant differences in the metabolite profile between the BE and CON groups. Several of the identified metabolites have been previously implicated in cancer pathogenesis. For instance, Spencer and Kisby demonstrated that hydrazine induces DNA damage, which consequently leads to mutations and unregulated mitosis(Spencer and Kisby, 2021). Similarly, another study reported that maltol substantially inhibited tumor growth through the enhancement of immune function, induction of apoptosis, and inhibition of angiogenesis(李伟, 2020). Our findings are consistent with those of Bachmayr\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, who observed alterations in the blood metabolites of OC patients; specifically, reductions in lipids and amino acids were directly associated with tumor metabolism and facilitated cellular proliferation and growth(Bachmayr-Heyda et al.). Collectively, these metabolites showing differential abundancies are linked to cancer-related mutations, immune responses, and metabolic reprogramming, thus indicating their potential utility as diagnostic biomarkers for OC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Metabolic Pathway Enrichment Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo gain further insights into the biological role of the identified metabolites, we conducted a pathway enrichment analysis using the KEGG metabolic network database (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). The results of the predicted pathway analysis revealed that only the butanoate metabolism pathway was upregulated in exosomes derived from the OC group. In contrast, nearly all other metabolic pathways, including carbohydrate metabolism, lipid amino acid metabolism, cofactor metabolism, vitamin metabolism, and xenobiotic biodegradation, were found to be downregulated in the OC group. Furthermore, the metabolites with differential abundance were then mapped to 17 significant metabolic pathways including glycolysis, gluconeogenesis, and the the metabolism of glyoxylate, dicarboxylate, butanoate, glutathione, glycerophospholipid, pyruvate, glycine, serine, threonine, tyrosine, phenylalanine, tryptophan, nicotinate, nicotinamide, propanoate, ubiquinone, and bile acids as illustrated by the Sankey diagram in \u003cstrong\u003eFigure 3B\u003c/strong\u003e. Other studies have found similar metabolic alterations in OC. Specifically, Zhong \u003cem\u003eet al\u003c/em\u003e. observed an upregulation of glutathione metabolism in women diagnosed with this malignancy(Zhong et al., 2022). Elevated glutathione metabolism has been shown to accompany tumor growth, presumably to counteract the increased oxidative stress arising from accelerated metabolic rates(Huang et al.; Gamcsik et al., 2012; Zhong et al., 2022). Both Denkert\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e. revealed elevated levels of amino acid intermediates, such as glycine, in OC tissues(Denkert et al., 2006). The increase in amino acid metabolism suggests that substantial pools of intermediate nutrients are being mobilized for molecular assembly in cancer cells. Thus, this research provides novel insight into the metabolite phenotype of plasma exomes that discriminates OC from BE and normal ovarian pathophysiology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Machine learning algorithms for disease prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to determine which metabolites can be used to discriminate the OC group from the BE and CON groups, we used binary classification algorithms including artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Na\u0026iuml;ve Bayes (NB), random forest (RF), and support vector machine (SVM). Prior to modelling, the entire dataset was shuffled randomly. To avoid overfitting, the model was built using split data, 75 % of the training and 25 % of the test set.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a result of applying these the algorithms to the dataset, a panel of metabolites in the plasma exosomes was identified. This included 1,4-methanobenzocyclodecene, 9-octadecenamide, methyl stearate, 4-morpholineethanamine, decanoic acid, aminophenylacetylene, lacthydrazide, pentanone, 2,4-ditert butylphenol, phenol, 3,5-dimethoxy, phenpropionic acid and silacyclopentane. The contributions of the nominated metabolites to the performance of each machine learning algorithm were ranked in\u003cstrong\u003e\u0026nbsp;Supplementary Figure 1\u003c/strong\u003e. The area under the curve (AUC) of biomarker signatures discriminating the OC from the CON and BE groups are displayed in \u003cstrong\u003eFigure 4\u003c/strong\u003e. DT (0.98), RF (0.92), and ANN (0.89) resulted in the highest AUC values for the OC-CON comparison. Meanwhile, for the OC-CON comparison, the DT classifier displayed the best discrimination power (AUC value of 0.98) based on the top-ranking features. For the OC-BE comparison, RF (0.93), NB (0.88), and SVM (0.81) gave the highest AUC values. To further assess the performance of the top-ranking features, an independent plasma exosome dataset (n=18) was recruited to validate the models. We observed that the external validation of the independent exosome dataset yielded AUC values of 0.81 and 0.75 for the OC-BE and OC-CON comparisons respectively (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e). Notably, RF provided the overall best performance for both model tests and the validation because it is a machine-learning approach for binary classification and can handle smaller datasets well(Wu et al., 2021). LR appeared to give the worst performance, likely due to underfitting resulting from a smaller dataset(Iddamalgoda et al., 2016). Together with the pros and cons of the different machine learning algorithms (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e), we determined that RF was an appropriate model for our relatively small dataset and binary research question.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study is the first identification of diagnostic biomarkers from plasma exosome metabolites in OC patients using machine learning algorithms. The profile of ovarian exosome metabolites was characterized by metabolites associated with the metabolism of carbohydrates, amino acids, and lipids. We demonstrated that the RF Model, combined with sensitive thermal separation probe-based mass spectrometry analysis of plasma exosomes, can effectively identify OC patients with high accuracy. Thus, our study has shortlisted a set of potential biomarkers in plasma exosomes, and the proposed approach could serve as a routine prescreening tool for OC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Research Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University, China (202164). The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFL contributed to the sample and data collection, XP, and XW performed the statistical analysis, interpreted the results, and wrote the manuscript. DM contributed to data collection and interpreting the results, SG and JS contributed to interpreting the results and wrote the manuscript. XZ, RR, LW and ZC contributed to the samples collection. RC commented on the experimental design and revised the manuscript. T-LH and YY devised the original laboratory study. T-LH interpreted the results, supported the writing of the manuscript, directed the project, guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (2023KFKT002), Chongqing Science \u0026amp; Technology Commission (cstc2021jcyj-msxmX0213), Chongqing Municipal Education Commission (KJZD-K202100407), and Senior Medical Talents Program of Chongqing for Young and Middle-aged【2022】15, and the Kuanren Talents Program of the Second Affiliated Hospital of Chongqing Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all collaborators for data collection. We also thank all the study participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublisher’s note\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary material\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Supplementary Material for this article can be found online at:\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArneth, B. (2019). Tumor Microenvironment. \u003cem\u003eMedicina\u003c/em\u003e 56(1). doi: 10.3390/medicina56010015.\u003c/li\u003e\n\u003cli\u003eBachmayr-Heyda, A., Aust, S., Auer, K., Meier, S.M., Schmetterer, K.G., Dekan, S., et al. Integrative Systemic and Local Metabolomics with Impact on Survival in High-Grade Serous Ovarian Cancer. (1557-3265 (Electronic)).\u003c/li\u003e\n\u003cli\u003eBray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., and Jemal, A. (2018). 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Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer. \u003cem\u003eFrontiers in Oncology\u003c/em\u003e 12. doi: 10.3389/fonc.2022.916375.\u003c/li\u003e\n\u003cli\u003e李伟 (2020). \u003cem\u003e5-羟甲基糠醛和麦芽酚的肝保护及抗肿瘤药理活性研究.\u003c/em\u003e 博士后, 中国农业科学院.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"metabolomics, machine learning, metabolic pathways, plasma, exosomes, ovarian cancer","lastPublishedDoi":"10.21203/rs.3.rs-4380755/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4380755/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOvarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. Exosomes, nanosized extracellular vesicles found in the blood, have been proposed as promising biomarkers for liquid biopsies. In this study we recruited 37 OC patients, 22 benign ovarian tumor (BE) patients, and 46 control (CON) patients. Plasma exosomes were purified from blood samples and sensitive thermal separation probe-based mass spectrometry analysis using a global untargeted metabolic profiling strategy was employed to characterize the metabolite fingerprints. Uniform manifold approximation and projection (UMAP) analysis demonstrated a distinct separation of exosomes among the three groups. We screened for diagnostic biomarkers from plasma exosome metabolites using seven machine learning algorithms, including artificial neural network (ANN), decision tree (DT), K nearest neighbor (KNN), logistics regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM). DT (0.98), RF (0.92), and ANN (0.89) were given the highest AUC values for the OC-CON comparison. RF (0.93), NB (0.88), and SVM (0.81) were given the highest AUCs for the OC-BE comparison. A total of 18, 138, and 157 metabolic features exhibited significant differences (FC= 1.5, p \u0026lt; 0.01, q \u0026lt; 0.01) in the OC vs BE, BE vs CON, and OC vs CON, comparisons, respectively. Notably, the quantities of 9-octadecenamide and 1,4-methanobenzocyclodecene were significantly elevated, while maltol showed a significant reduction in the OC group compared to the BE group. When comparing the OC group to the CON group, the concentrations of 4-amino-furazan-3-carboxylic acid 2-hydroxy-4-methoxybenzaldehyde, N-phenylethyl, and 4-morpholineethanamine were significantly elevated, while the remaining metabolites, including hydrazine and pyridine sulfonamide, were reduced, in the OC group. The metabolites showing different abundancies are associated with cancer-related mutations, immune responses, and metabolic reprogramming. We demonstrate that the RF algorithm, combined with sensitive thermal separation probe-based mass spectrometry analysis of plasma exosomes, can effectively identify OC patients with good accuracy. Thus, our study has shortlisted a set of potential biomarkers in plasma exosomes, and the proposed approach could serve as a routine prescreening tool for ovarian cancer.\u003c/p\u003e","manuscriptTitle":"A Metabolic Fingerprint of Ovarian Cancer: A Novel Diagnostic Strategy Employing Plasma Exosome-Based Metabolomics and Machine Learning Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-17 18:26:50","doi":"10.21203/rs.3.rs-4380755/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-23T15:04:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T18:25:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-18T10:36:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-04T19:00:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12165272386260542558015838267496085803","date":"2024-07-02T19:53:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52300311054828451041011462068494184088","date":"2024-06-29T18:13:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"200807039142423944756300327510283077263","date":"2024-06-22T05:11:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-10T20:15:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-09T00:41:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-07T07:23:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2024-05-07T06:41:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50b3ced6-9e38-4782-b8ae-4bd5181a727a","owner":[],"postedDate":"May 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:02:52+00:00","versionOfRecord":{"articleIdentity":"rs-4380755","link":"https://doi.org/10.1186/s13048-025-01590-w","journal":{"identity":"journal-of-ovarian-research","isVorOnly":false,"title":"Journal of Ovarian Research"},"publishedOn":"2025-02-12 15:57:40","publishedOnDateReadable":"February 12th, 2025"},"versionCreatedAt":"2024-05-17 18:26:50","video":"","vorDoi":"10.1186/s13048-025-01590-w","vorDoiUrl":"https://doi.org/10.1186/s13048-025-01590-w","workflowStages":[]},"version":"v1","identity":"rs-4380755","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4380755","identity":"rs-4380755","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}