Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer

Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast 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 Research Article Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer Sisi Gong, Zhijun Liao, Meie Wang, Fen Lian, Ruirui Tong, Rongfu Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4365055/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Triple-negative breast cancer (TNBC) is recognized for its aggressive nature, lack of effective diagnosis and treatment, and generally poor prognosis. The objective of this study was to investigate the metabolic changes in TNBC using metabolomics approaches and to explore underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were firstly explored between 18 TNBC and 21 healthy controls (HC) by liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly altered metabolites (DMs). Subsequently, the receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was subsequently conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways—tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis—were enriched, explaining the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as critical components. In summary, this study highlights metabolic biomarkers that could potentially be utilized for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data provides a validated and in-depth understanding of TNBC metabolism. Triple-negative breast cancer Metabolomics Transcriptomics Biomarker Pathways Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction The intricate nature of breast cancer (BC), characterized by its diverse subtypes, is particularly highlighted by the clinical challenges associated with Triple Negative Breast Cancer (TNBC). Accounting for an estimated 10–15% of all BC cases, TNBC is distinguished by its aggressive cellular behavior, increased likelihood of recurrence, and generally poorer prognostic outcomes [ 1 , 2 ]. The hallmark of TNBC is its absence of estrogen and progesterone receptors, in addition to a minimal expression of the human epidermal growth factor receptor 2 (HER2) [ 3 ], which significantly diminishes the efficacy of standard hormone therapies and HER2-targeted treatments. This situation underscores the critical need for the development of novel diagnostic and therapeutic strategies that are tailored specifically to address the unique challenges of TNBC. Advancements in the fields of metabolomics and transcriptomics herald new vistas for elucidating the intricate molecular perturbations characteristic of oncogenesis, with the potential to facilitate the identification of novel biomarkers and therapeutic avenues. Metabolomics, in particular, has emerged as the preeminent technology for the advancement of early diagnosis and the refinement of precision medicine. This approach enables the comprehensive quantification and characterization of low-molecular-weight molecules within biological systems, thereby illuminating potential diagnostic biomarkers and mirroring the underlying biochemical activities and states of cells and tissues [ 4 ]. Through the analysis of metabolite profiles from serum, tissue, and cell samples, researchers have identified metabolic disturbances in TNBC patients, including alterations in the glycerophospholipid metabolism pathway, fatty acid metabolism, the tricarboxylic acid (TCA) cycle, and glutathathione biosynthesis pathway [ 5 – 8 ]. However, the results across different biological specimens show significant disparities, highlighting challenges in the credibility and reproducibility of diagnostic biomarkers. This is mainly because the identification of disrupted metabolic pathways in TNBC largely relies on changes in metabolite levels, with only a few biomarkers being validated through other omics approaches. Systems biology focuses on the biological significance of metabolites, advocating for the integration of metabolomics with other omics technologies to elucidate the complex networks of molecular pathways involved in tumorigenesis [ 9 ]. Transcriptomics, which interprets the functional components of the genome, contributes valuable insights into the unique biological responses to diseases. The fusion of metabolomics and transcriptomics data has propelled cancer research forward, leveraging advancements in systems biology and bioinformatics [ 10 – 12 ]. Yet, the application of this integrated approach remains underutilized in TNBC research, indicating a significant area for further exploration. Consequently, the elucidation of the specific aberrant metabolic pathways contributing to the pathogenesis of TNBC necessitates the implementation of a meticulously designed research methodology, underpinned by an integrated analytical framework. The objective of the present investigation is to harness the capabilities of integrated omics technologies to discern differentially expressed metabolites and genes, thereby shedding light on the metabolic pathways that diverge in TNBC from those in healthy control (HC) subjects. By undertaking exhaustive analyses through both metabolomics and transcriptomics, this study endeavors to enhance our comprehension of the metabolic deviations and gene expression alterations characteristic of TNBC. This endeavor aims to lay the groundwork for the identification of novel biomarkers and to foster a deeper understanding of the underlying pathophysiological mechanisms of TNBC. 2. Materials and Methods 2.1. Chemical and Materials Methanol and acetonitrile of high performance liquid chromatography (HPLC) grade were procured from Fisher Scientific (Loughborough, UK). Similarly, formic acid, also of HPLC grade, was acquired from TCI (Shanghai, China). The procurement of ammonium acetate, adhering to HPLC grade standards, was facilitated through Sigma-Aldrich (Shanghai, China). The 2-chloro-L-phenylalanine was obtained from Aladdin (Shanghai, China).Furthermore, distilled water was filtered through the Milli-Q system (Millipore, Bedford, USA). 2.2. Study design and Sample Collection This investigation was conducted at the Second Affiliated Hospital of Fujian Medical University over the period of 2021 to 2022, receiving ethical endorsement from the hospital's Ethics Committee under the reference number 2021[168]. Prior to the procurement of blood specimens, informed consent was duly acquired in written form from all the 51 subjects who were recruited for participation in this study. The research design was bifurcated into two distinct phases: the preliminary discovery phase, which comprised 18 individuals diagnosed with TNBC alongside 21 HC participants, and the subsequent validation phase, which included 7 TNBC patients and 5 control subjects. The diagnostic criterion for TNBC was strictly aligned with the international consensus, identifying patients based on the absence of estrogen receptor, progesterone receptor, and HER2 expression. The control cohort consisted of healthy volunteers, age-matched and with no prior history of breast disease, whose health status was rigorously verified through comprehensive physical exams. Blood specimens were procured from fasting participants, subsequently deposited into tubes specifically engineered for serum segregation. Following a centrifugation process at 3000 rpm for a duration of 5 minutes at a temperature of 4 ℃, the serum was successfully isolated. Immediate post-isolation, the serum samples were expeditiously transferred to a refrigeration unit maintained at −80°C, thereby preserving them for future metabolomics analyses. 2.3. Sample Preparation Commence by thawing the experimental specimens at an ambient temperature of 4 ℃, then subject them to vortex mixing for a duration of one minute to ensure a uniform mixture. With meticulous precision, transfer 100 µL of the specimen into a 2 mL centrifuge tube. Subsequently, introduce 400 µL of a methanol solution, preserved at a temperature of -20 ℃, into the tube and subject it to vortex mixing for another minute to ensure thorough mixing. The mixture is then centrifuged at 12,000 rpm for 10 minutes at a temperature of 4 ℃, a step designed to precipitate proteins. Upon the completion of centrifugation, carefully collect the supernatant and subject it to evaporation under a centrifugal vacuum to achieve dryness. Subsequently, with exactitude, add 150 µL of an 80% methanol-water solution containing 2-chloro-L-phenylalanine (concentration of 4 ppm), maintained at 4 ℃, to reconstitute the specimen. Thereafter, collect the supernatant, filter it through a 0.22 µm membrane, and transfer the filtrate into a vial prepared for liquid chromatography-mass spectrometry (LC-MS) analysis. In a parallel experimental setup, pooled quality control (QC) samples were meticulously prepared by amalgamating equal volumes of all serum supernatants. These QC samples played a pivotal role in the evaluation of the stability and consistency of the overall experimental outcomes. To facilitate the equilibration of the analytical column, the pooled QC sample was initially introduced into the system via five consecutive injections at the commencement of the analytical batch. To ensure the accuracy and reliability of the analytical workflow, it was imperative that the QC sample be injected subsequent to every six serum sample injections throughout the entirety of the analytical procedure, thereby guaranteeing the maintenance of stringent analytical standards. 2.4. UHPLC–MS based metabolome profiling Chromatographic separations were performed on a Vanquish ultra-high performance liquid chromatography (UHPLC) System (Thermo Fisher Scientific, USA), employing an ACQUITY UPLC® HSS T3 column (150×2.1 mm, 1.8 µm, Waters, Milford, MA, USA) for the analysis. The metabolomic analyses were performed in both electrospray ionization positive (ESI+) and negative (ESI−) ion modes. For ESI+, the mobile phases were composed of A2 (0.1% formic acid in water) and B2 (0.1% formic acid in acetonitrile), with the elution gradient meticulously structured as follows: from 0 to 1 minute, the composition was maintained at 2% B2; from 1 to 9 minutes, it was gradually increased from 2% to 50% B2; from 9 to 12 minutes, it was further increased from 50% to 98% B2; from 12 to 13.5 minutes, it was held constant at 98% B2; from 13.5 to 14 minutes, it was rapidly decreased from 98% to 2% B2; and finally, from 14 to 20 minutes, it was maintained at 2% B2. In the ESI- mode, the mobile phases comprised A3 (ammonium formate at 5 mM) and B3 (acetonitrile), with the elution conditions set as follows: from 0 to 1 minute, the composition was at 2% B3; from 1 to 9 minutes, it was increased from 2% to 50% B3; from 9 to 12 minutes, it was raised from 50% to 98% B3; from 12 to 13.5 minutes, it remained at 98% B3; from 13.5 to 14 minutes, it was decreased from 98% to 2% B3; and from 14 to 17 minutes, it was kept at 2% B3. The column oven temperature was uniformly maintained at 40°C, with a flow rate of 0.25 mL/min and an injection volume of 2 μL. Throughout the duration of the experiment, all pre-treated serum samples were preserved at 4°C. Metabolite detection was facilitated through a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA), which was equipped with an ESI ion source and operated in both MS1 and MS/MS (Full MS-ddMS2 mode, data-dependent MS/MS) acquisition modes. The operational parameters were meticulously defined, with sheath gas pressure set at 30 arb, auxiliary gas flow at 10 arb, spray voltages calibrated at 3.50 kV for ESI(+) and -2.50 kV for ESI(−), capillary temperature at 325℃, MS1 scan range from m/z 81 to 1000, MS1 resolving power at 60000 FWHM, eight data-dependent scans per cycle, MS/MS resolving power at 15000 FWHM, normalized collision energy at 30%, and dynamic exclusion time set to automatic. 2.5. Metabolomics data analysis The transformation of raw data into mzXML format was accomplished utilizing MSConvert, a component of the ProteoWizard software suite (version 3.0.8789)[13]. This preliminary step facilitated subsequent analytical processes. The feature detection, retention time correction, and alignment of the data were executed through the application of XCMS. Subsequently, advanced multivariate statistical analyses, namely principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were conducted using Simca-P14.0 software. These analyses served to delineate distinct groups and pinpoint biomarkers indicative of TNBC. To ascertain the robustness of the model, a permutation test encompassing 200 random permutations was employed, evaluating the OPLS-DA model based on its R2 (explained variance) and Q2 (predictive ability) parameters. The identification of discriminating metabolites was facilitated by the OPLS-DA model through the implementation of the variable importance on projection (VIP) strategy, whereby only metabolites exhibiting a VIP value in excess of 1 were deemed to possess statistical significance in the classification of TNBC. Following this, a nonparametric univariate statistical analysis was conducted, employing the Mann-Whitney U test (p < 0.05) in conjunction with fold change (FC) values ≤ 0.67 or ≥ 1.5 to discern differential metabolites (DMs). The evaluation of the DMs' predictive capacity was undertaken through receiver operating characteristic (ROC) curve analysis, which leveraged the area under the ROC curve (AUC) as an indicator of the overall test efficacy. The optimum AUC, sensitivity, and specificity were determined by maximizing the Youden index, calculated as sensitivity + specificity - 1[4]. This analytical process was executed utilizing SPSS software (version 22.0). The initial identification of DMs was predicated on the verification of accurate molecular weight (< 30 ppm). This was followed by an analysis based on precise mass numbers and high-resolution target MS/MS spectra, in conjunction with the fragmentation laws of various metabolites. The exploration for potential structures of differential metabolites was conducted through database searches (including METLIN, HMDB, and MassBank) and literature reviews, thereby accruing information on candidate metabolites. Furthermore, Metabolite Set Enrichment Analysis (MSEA) was performed via MetaboAnalyst 6.0 (https://metascape.org/gp/index.html), aimed at elucidating metabolic pathways distinctly altered in TNBC patients in comparison to HC subjects. 2.6. Transcriptomics analysis In the investigation of TNBC, three pertinent datasets from the Gene Expression Omnibus (GEO) database were meticulously selected for analysis: GSE65194, encompassing 55 TNBC tissue samples alongside 11 samples of healthy breast tissue derived from mammoplasty procedures; GSE45827, comprising 11 TNBC and 5 healthy breast tissue samples; and GSE36295, containing 41 TNBC tissues as well as 11 samples of normal tissue. The identification of differentially expressed genes (DEGs) contrasting the TNBC group with the group of normal breast tissues was executed utilizing the GEO2R analytical tool, adhering to stringent cutoff criteria of an absolute log2 FC greater than 2 and an adjusted p-value less than 0.05. This initial analysis facilitated the generation of volcano plots and Venn diagrams, accessible via (http://www.bioinformatics.com.cn/), to discern DEGs consistently observed across the trio of datasets. Subsequent to the identification of shared DEGs, a comprehensive examination of the biological processes (BP), molecular functions (MF), cellular components (CC), and implicated pathways was conducted. This examination was facilitated through gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, employing the Database for Annotation, Visualization, and Integrated Discovery (DAVID, version 12.0) as the analytical platform. This multifaceted approach aimed to elucidate the underlying molecular mechanisms and potential pathophysiological pathways relevant to TNBC, thereby contributing valuable insights into the biological characterization of this aggressive breast cancer subtype 2.7. Joint analysis of metabolomics and transcriptomics An integrative analysis was undertaken to explore the synergistic relationship between DMs and DEGs, as identified through comprehensive metabolomic and transcriptomic investigations. This endeavor was facilitated by employing the Joint-Pathway Analysis module available within the MetaboAnalyst 6.0 platform, aimed at constructing a detailed metabolic pathway enrichment diagram. The analysis leveraged the total number of identified metabolites to evaluate the relevance and significance of each pathway, with pathways demonstrating a P-value less than 0.05 being deemed significantly enriched. In parallel, the KEGG database served as a pivotal resource for elucidating potential genes implicated within these significantly enriched pathways. The utilization of Cytoscape software version 3.9.1, in conjunction with the Metscape plugin, facilitated the elucidation of the intricate connections and interdependencies between metabolites and genes, thereby enabling the visualization of compound networks. 2.8. Validation of the expression of hub DEGs Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/) represents a sophisticated interactive web service dedicated to the analysis of RNA sequencing expression data, incorporating 9,736 tumor and 8,587 normal samples derived from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects [14]. Concurrently, UALCAN (http://ualcan.path.uab.edu) emerges as an extensive, intuitive web portal tailored for the analysis of cancer OMICS data. This portal not only facilitates gene expression analysis predicated on clinical data from TCGA but also extends its functionality to include protein expression analysis leveraging data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset [14, 15]. Furthermore, the Human Protein Atlas (HPA) database (https://www.proteinatlas.org) provides an invaluable open-access repository of immunohistochemical images, documenting a broad spectrum of immune response observations across both neoplastic and normal tissues [16]. Employing the comprehensive datasets available within these repositories, a detailed comparative analysis of the mRNA and protein expressions of key hub genes in breast cancer versus normal breast tissue was conducted, with immunohistochemistry serving as the foundational analytical technique. The open-access status of these databases obviates the necessity for ethical approval, thereby negating the requirement for formal authorization from a local ethics committee. 2.9. Kaplan-Meier plotter database analysis The Kaplan-Meier plotter database (www.kmplot.com) was deployed to elucidate the association between mRNA levels of each pivotal DEG and the prognostic outcomes of patients afflicted with TNBC. To this end, patient samples were stratified into two distinct groups predicated upon the median expression level of each gene, delineating cohorts with high versus low expression, thereby facilitating a rigorous evaluation of the prognostic relevance attributed to each gene. Notably, the platform autonomously computes the hazard ratios (HR) accompanied by 95% confidence intervals (CI) and Log rank P values, thereby streamlining the analytical process. 3. Results 3.1. General Characteristics of Study Participants In the current investigation, the cohort comprised exclusively female subjects, with an established homogeneity in age demographics across all study groups. To minimize the potential confounding impact of variables such as age, homogeneity within each group was rigorously evaluated utilizing the Kruskal-Wallis test. The participant pool included a total of 51 individuals: the discovery set consisted of 18 patients diagnosed with TNBC (age 47 [range 27-59] years) and 21 HC (age, 46 [range 33-66] years), whereas the validation set encompassed 7 TNBC patients (age 51 [range 36-58] years) and 5 HC (age, 50 [range 39-63] years). Analysis revealed no significant disparities in baseline characteristics among the groups, thereby reinforcing the internal validity of the study findings. 3.2. The metabolomics analysis for TNBC and HC serum samples 3.2.1. Reliability of the analytical method In this study, multivariate statistical analyses were initially utilized to construct metabolic profiles for the entirety of the samples under study. The reliability of this analytical method was rigorously evaluated through the systematic repetition of analyses on QC samples across all sample runs. Subsequently, the PCA score plots for the samples within the discovery dataset were examined. Notably, all QC samples ( Figure 1 , yellow) exhibited a pronounced clustering in both ESI+ and ESI- modes. This observation unequivocally confirms the analytical system's stability and reproducibility. 3.2.2. Differential Metabolite Screening As shown in Figure 1 , the PCA scores plots exhibited well distinguishable patterns between TNBC and HC samples, implying some remarkable differences existed in the serum endogenous metabolites between the two different groups. Building on this initial finding, OPLS-DA analyses was applied to further pinpoint these metabolic discrepancies. The results ( Figure 2A, B ) demonstrated a clear division between the two groups, with impressive R 2 Y and Q 2 values of 0.984 and 0.878 in ESI+ mode, and 0.995 and 0.834 in ESI- mode, respectively. Subsequently, the results from 100 permutation tests revealed that the permuted R 2 and Q 2 values on the left side were consistently lower than the original values on the right side, indicating no overfitting of the model (Figure 2C, D) . Furthermore, the intercept of Q 2 being below zero further supports the model's reliability and validity [17]. In this work, subsequent to the application of predefined criteria, a total of 22 DMs was identified as potential biomarkers for differentiating between TNBC and HC specimens. The comparative analysis elucidated that within the TNBC cohort, there were 13 metabolites exhibiting up-regulation and 9 demonstrating down-regulation in contrast to the HC group. The concentration profiles of these 22 DMs were systematically represented in a heat map ( Supplementary Figure 1 ), while comprehensive details encompassing retention time (RT), mass-to-charge ratio (m/z), adduct ion, FC p-value, VIP, and mean decrease accuracy were listed in Table 1 . Table 1 Serum differential metabolites detected by UHPLC-MS between TNBC and HC subjects. No. Metabolites Rt(s) mz formula KEGG Adduct ion FC P VIP AUC sensitivity specificity Youden Index 1 Isonicotinic acid 600.2 122.0217 C6H5NO2 C07446 [M-H]- 0.54 0.036 1.464 / / / / 2 Ergothioneine 102.7 230.096 C9H16N3O2S C05570 [M+H]+ 0.54 0.006 1.546 0.828 0.905 0.667 0.571 3 Glutaric acid 86.1 131.0329 C5H8O4 C00489 [M-H]- 0.55 0.005 1.555 / / / / 4 Urocanic acid 139.1 137.0345 C6H6N2O2 C00785 [M-H]- 0.58 0.003 1.730 / / / / 5 Acetylcholine chloride 33 180.9729 C7H16NO2. Cl C08201 [M-H]- 0.58 0.005 1.767 / / / / 6 2,3-Butanediol 152.6 154.9901 C4H10O2S2 C00265 [M+H]+ 0.59 0.011 1.036 / / / / 7 5'-Methylthioadenosine 897.2 297.2429 C11H15N5O3S C00170 [M-H]- 0.6 0.024 1.705 / / / / 8 N-Acetyl-D-tryptophan 397.1 246.1238 C13H14N2O3 C03137 [M+H]+ 0.61 0.001 1.715 0.865 0.905 0.778 0.683 9 9(S)-HPOT 855.1 293.2108 C18H30O4 C16321 [M+H]+ 0.65 0.006 1.429 / / / / 10 4-Hydroxyphenylacetaldehyde 95.1 136.048 C8H8O2 C03765 [M+H]+ 1.5 0.000 1.851 0.841 0.833 0.905 0.738 11 7-Methylguanine 139.6 166.0724 C6H7N5O C02242 [M+H]+ 1.53 0.000 2.406 0.992 1 0.952 0.952 12 Thymidine 539.7 242.1759 C10H14N2O5 C00214 [M-H]- 1.58 0.000 1.987 0.847 0.833 0.762 0.595 13 Oxalacetic acid 83.1 130.9993 C4H4O5 C00036 [M-H]- 1.59 0.029 1.572 / / / / 14 CMP-3-deoxy-D-manno-octulosonate 773 542.1068 C17H26N3O15P C04121 [M-H]- 1.62 0.006 1.421 / / / / 15 Oxoglutaric acid 75.8 145.0137 C5H6O5 C00026 [M-H]- 1.66 0.000 2.237 0.865 0.778 0.952 0.73 16 Bilirubin 917.5 585.2655 C33H36N4O6 C00486 [M+H]+ 1.7 0.000 1.731 0.852 0.778 0.857 0.635 17 Thymine 430.5 125.0347 C5H6N2O2 C00178 [M-H]- 1.71 0.047 1.288 / / / / 18 L-Valine 135.2 118.087 C5H11NO2 C00183 [M+H]+ 1.73 0.015 1.018 / / / / 19 Arachidic acid 917.3 311.2954 C20H40O2 C06425 [M-H]- 1.75 0.003 1.581 / / / / 20 (S)-4-Hydroxymandelate 245.5 151.0336 C8H8O4 C03198 [M+H]+ 1.85 0.007 1.121 / / / / 21 Pipecolic acid 96.9 129.0654 C6H11NO2 C00408 [M+H]+ 2.14 0.000 1.646 0.907 0.944 0.762 0.706 22 L-Methionine 137.4 148.0426 C5H11NO2S C00073 [M-H]- 2.49 0.000 2.285 0.886 0.833 0.905 0.738 3.2.3. Evaluating and validating the diagnostic ability of metabolites To ascertain the diagnostic potential of specific metabolites, ROC analysis was employed to assess the diagnostic accuracy of individual metabolites. This analysis yielded that 9 DMs manifested statistically significant diagnostic capabilities, as evidenced by their respective values of AUC, sensitivity, specificity, and Youden index, which were listed in Table 1 . Notably, metabolites such as 7-methylguanine, pipecolic acid, L-methionine, oxoglutaric acid, bilirubin, thymidine, and 4-hydroxyphenylacetaldehyde possessed predictive value for TNBC in serum samples obtained from HC subjects. In contrast, N-acetyl-D-tryptophan and ergothioneine showed negative predictive value. Among these metabolites, 7-methylguanine in serum samples exhibited the highest efficacy in distinguishing TNBC patients from healthy controls, demonstrated by its outstanding diagnostic metrics: an AUC of 0.992, sensitivity of 100%, specificity of 95.2%, and a Youden index of 0.952. To corroborate the results obtained from the initial discovery set, serum samples were procured from 7 individuals diagnosed with TNBC and 5 HC. These samples underwent analysis employing identical UHPLC–MS procedures as those utilized for the discovery cohort. The preliminary phase involved a comparative analysis of the mean peak areas of 7-methylguanine between TNBC patients and healthy individuals across both cohorts. Findings demonstrated a significant elevation of 7-methylguanine levels in the serum samples of TNBC patients in both cohorts (p < 0.01; Figure 3A ), indicating a consistent elevation of this metabolite in the context of TNBC. Subsequently, to ascertain the diagnostic utility of 7-methylguanine within a clinical setting, ROC curves were generated based on the relative peak areas of metabolites derived from the validation sample cohort. Within this validation cohort, 7-methylguanine exhibited an AUC of 0.971, with a sensitivity of 85.7% and specificity of 100%, corresponding to a Youden index of 0.857. These metrics closely paralleled those observed within the discovery set ( Figure 3B) , reinforcing the potential of 7-methylguanine as a robust biomarker for TNBC. 3.2.4. Metabolite Set Enrichment Analysis (MSEA) The findings indicate that TNBC is characterized by distinct metabolite profiles, implying alterations in metabolic biological networks. To delineate the disrupted metabolic pathways, informed by the altered set of DMs, comprehensive enrichment and pathway analyses were undertaken. The analyses revealed that the most significantly enriched pathways in TNBC patients include the malate-aspartate shuttle, alanine metabolism, spermidine and spermine biosynthesis, urea cycle, ammonia recycling, TCA cycle, gluconeogenesis, and aspartate metabolism, all of which demonstrated statistical significance (p-values < 0.05) as depicted in the bar chart in Supplementary Figure 2. 3.3. The transcriptomics analysis for TNBC and HC tissue samples 3.3.1. Identification of differentially expressed genes in TNBC In this study, three GEO datasets were scrutinized: GSE65194, GSE45827, and GSE36295. To ensure the dataset’s quality is reliable, a rigorous analytical approach was employed using the GEO2R tool, with selection criteria set at an absolute log fold change (|logFC|) exceeding 2 and an adjusted p-value below 0.05. This analysis yielded a discovery of 1,561 up-regulated and 1,035 down-regulated DEGs in the GSE65194 dataset ( Figure 4A ), 1,533 up-regulated and 1,047 down-regulated DEGs in GSE45827 ( Figure 4B ), and 77 up-regulated along with 137 down-regulated DEGs in GSE36295 (Figure 4C ). Subsequent to the identification of DEGs within each dataset, an online Venn diagram tool was employed to intersect and visualize the DEGs across the three datasets, facilitating the identification of common DEGs. This analysis revealed a total of 160 DEGs demonstrating uniform expression trends across the datasets, encompassing 57 genes that were up-regulated and 103 that were down-regulated, as depicted in Figure 4D . 3.3.2. Gene ontology and KEGG enrichment functional analysis of overlapping DEGs To examine the biological categorization of the 160 common DEGs, functional and pathway enrichment analyses were executed utilizing the DAVID database. These investigations comprised GO enrichment analysis and KEGG pathways, which disclosed associations of the DEGs with 39 GO terms including BP, CC and MF, in addition to 2 significant pathways, as list in Supplementary Table S1. The threshold for deeming results statistically significant was established at a False Discovery Rate (FDR) below 0.05. As depicted in Figure 5A , the GO analysis explicitly highlighted that DEGs pertaining to BP were notably concentrated in areas such as cell division, mitotic spindle organization, and bacterial response and so on. For CC, a significant enrichment was observed in structures including the midbody, spindle, and condensed chromosome outer kinetochore. Furthermore, changes in MF were mainly enriched in microtubule binding. Regarding the KEGG pathway analysis, the DEGs were predominantly enriched in the PPAR signaling pathway and tyrosine metabolism ( Figure 5B ) 3.4. Integrative analysis of metabolomics and transcriptomics data To advance the systematic exploration of TNBC, a comprehensive biological pathway analysis was performed by linking important 22 DMs and the 160 DEGs through shared metabolic pathways with the Joint Pathway Analysis module on MetaboAnalyst 6.0. Our analysis unveiled three pathways of notable perturbation: tyrosine metabolism, phenylalanine metabolism, and glycolysis or gluconeogenesis, each characterized by p-values < 0.05 and impact≥ 0.5 ( Figure 6A, table 2 ). Central DEGs linked to these pathways were enumerated in Table 3. To better understand the metabolite mechanism and gene dys-regulation, DMs and DEGs were introduced into the Metscape plug-in of the Cytoscape 3.7.1 database to collect the compound–reaction–enzyme–gene network in combination with the top three enriched pathways ( Figure 6B ). Consequently, these investigations bolster the validity of the metabolites, genes, and pathways selected for this study. Table 2 Joint analysis pathways of differential metabolites and genes No Pathway name Match status P value Impact 1 Tyrosine metabolism 6/88 0.001 0.345 2 Phenylalanine metabolism 3/21 0.002 0.600 3 Glycolysis or Gluconeogenesis 4/61 0.008 0.117 Table 3 Related differentially expressed genes by joint-pathway analysis Gene Enriched pathway Function MAOA Tyrosine metabolism, Phenylalanine metabolism, monoamine oxidase A ADH1B Tyrosine metabolism, Glycolysis or Gluconeogenesis, "alcohol dehydrogenase 1B (class I), beta polypeptide" ADH1C Tyrosine metabolism, Glycolysis or Gluconeogenesis, "alcohol dehydrogenase 1C (class I), gamma polypeptide" AOC3 Tyrosine metabolism, Phenylalanine metabolism amine oxidase copper containing 3 TAT Tyrosine metabolism, Phenylalanine metabolism tyrosine aminotransferase PCK1 Glycolysis or Gluconeogenesis phosphoenolpyruvate carboxykinase 1 3.5. Verifications of six hub genes expression Upon integrating the outcomes derived from metabolomics and transcriptomics datasets, this study identified MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1 as potential key players in the pathogenesis of TNBC. The validation of RNA and protein expression levels of these e hub DEGs was conducted utilizing online tumor and normal clinical samples from the GEPIA and UALCAN platforms. Utilizing the GEPIA platform, we assessed the mRNA expression levels of six pivotal genes in a dataset comprising 135 TNBC specimens and 291 normal breast tissue specimens. This dataset was collated from the comprehensive resources of TCGA and GTEx database. This examination revealed a statistically significant reduction in the expression of these genes in TNBC in comparison to normal samples (p < 0.05, Figure 7) . Subsequent verification of protein expression levels through the UALCAN cancer database corroborated these findings, demonstrating a significant decrease in their expression within TNBC tissues relative to normal tissues (p < 0.05, Figure 8 ). These observations were further substantiated by immunohistochemical analyses sourced from the HPA database. Specifically, as depicted in Figure 9 , the expression levels of MAOA, ADH1B, ADH1C, AOC3, and PCK1 were found to be downregulated in breast cancer tissues compared to normal tissues. 3.6. The survival analysis of hub genes in TNBC The Kaplan-Meier Plotter, an accessible online analytical tool, was utilized to conduct survival analyses predicated on gene expression levels, thereby evaluating the prognostic relevance of key genes. This analysis divided TNBC patient samples into dichotomous groups based on median mRNA expression levels of each gene, delineating cohorts with high versus low expression. Notably, AOC3 and PCK1 were identified as genes significantly associated with poor overall survival (OS). Results showed that overexpression of AOC3 (HR 95%CI =3.56 (1.62-7.8), log-rank P =0.00073) and PCK1 (HR 95%CI =2.86 (1.19-6.85), log-rank P =0.04) were associated with unfavorable OS of TNBC patients ( Figure 10 ). Consequently, this evidence supports the hypothesis that AOC3 and PCK1 may function as potential biomarkers for prognostication in TNBC patient populations. Based on these results, it is hypothesized that AOC3 and PCK1 may serve as potential biomarkers for predicting the prognosis of TNBC patients. Discussion TNBC is recognized as the most fatal subtype of BC characterized by low overall survival (OS) rates and high rates of invasion and metastasis, posing an unmet medical challenge [18]. Clinical tumor markers such as carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3) are frequently used in BC diagnosis; nevertheless, their specificity and accuracy fall short of clinical standards [19]. Currently, there are no reliable biomarkers specifically for TNBC, highlighting a critical gap in diagnostic tools. Metabolic reprogramming, a hallmark of cancer, presents new prospects for cancer detection, prognosis, and treatment [20, 21]. It has been proved that, metabolic dysregulation is linked to therapy response and clinical outcome across various cancer types and may impact the tumorigenesis, progression, and prognosis of BC via pathways related to angiogenesis, anti-apoptosis, mitogenesis, chronic inflammation, increased visceral fat reserves, and other cancer-associated adipokines. [22-25]. This study aims to identify more reliable and specific serum markers for diagnosing TNBC using a metabolomic approach. While metabolomics has been applied in numerous studies to discover novel biomarkers for TNBC, relying solely on this method does not fully elucidate TNBC pathophysiology. Therefore, this research also incorporates pathway and network analyses, integrating metabolomics and transcriptomics data to deepen our understanding of the interactions between selected metabolites and genes within dysregulated pathways. The current investigation employed an untargeted metabolomics strategy, leveraging ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) and multivariate statistical analysis, to identify metabolites that exhibit altered levels in TNBC relative to HC. This comprehensive metabolomics analysis revealed 13 metabolites that were up-regulated and 9 metabolites that were down-regulated in TNBC. Notably, among these metabolites, 7-methylguanine emerged as a potential consistent biomarker for TNBC, as demonstrated through ROC analysis. Furthermore, metabolite set enrichment analysis illuminated several disrupted metabolic pathways critical to TNBC pathophysiology, including the malate-aspartate shuttle and the TCA cycle. These pathways play essential roles in cellular energy metabolism, suggesting their significant involvement in the metabolic reprogramming characteristic of TNBC. To enhance our comprehension of the underlying mechanisms of TNBC, we analyzed data amalgamated from three distinct GEO datasets. This comprehensive analysis included 26 samples of normal breast tissues and 101 samples of TNBC tissues. From this, we identified a total of 160 DEGs, consisting of 103 genes that were down-regulated and 57 that were up-regulated in TNBC tissues. Functional enrichment analysis of these DEGs highlighted their significant involvement in cell proliferation processes, such as cell division, mitotic spindle organization, chromosome segregation, and positive regulation of chromosome segregation, all of which align with the hallmark rapid proliferation characteristics of TNBC cells. Furthermore, KEGG pathway enrichment analysis revealed that the DEGs were predominantly associated with the PPAR signaling pathway and tyrosine metabolism. This suggests a critical role for these genes in regulating fatty acid and amino acid energy metabolism within TNBC cells. The integrative analysis of metabolomics and transcriptomics datasets has significantly advanced our understanding of the interplay between metabolic dysregulation and gene expression alterations in TNBC. This analysis has illuminated key pathways, including tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis, underlining the complex biological landscape of TNBC that transcends simple genomic alterations. The perturbation of these pathways likely mirrors the adaptive oncogenic processes characteristic of TNBC, presenting potential targets for therapeutic intervention. Importantly, the analysis delineated 2 DMs (4-hydroxyphenylacetaldehyde and oxalacetic acid) and 6 DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) as integral components of these pathways. Further validation through the GEPIA, UALCAN and HPA databases revealed a consistent pattern of expression for these hub DEGs at both the RNA and protein levels, reinforcing their pivotal role in the pathophysiology of TNBC. The perturbation of tyrosine and phenylalanine metabolism has been associated with various pathologies, including gastroesophageal malignancies[26], non-small cell lung cancer[10], and BC[27]. Research conducted by Christofk et al. [27] highlighted that invasive BC cells, in the face of amino acid deprivation, harness the process of extracellular matrix internalization and lysosomal degradation as a means to procure amino acids. This adaptive mechanism plays a crucial role in supporting cellular proliferation and enhancing migration capabilities, evidencing a metabolic dependency on phenylalanine and tyrosine. In our investigation, we identified that 4-hydroxyphenylacetaldehyde, along with the genes MAOA, AOC3, and TAT, were significantly enriched in the metabolic pathways of tyrosine and phenylalanine. This observation intimated the potential reliance of TNBC cells on these metabolic pathways as a mechanism to drive tumorigenesis. The MAOA gene encodes the enzyme monoamine oxidase-A, present in both peripheral tissues and the central nervous system, and is crucial for breaking down monoamines such as norepinephrine (NE), epinephrine, and dopamine [28]. Recent findings have shown that different cancer types exhibit unique patterns of MAOA regulation and functionality. Overexpression of MAOA has been identified in glioma [29], classical Hodgkin lymphomas [30], and prostate cancer[31]. Conversely, a trend towards decreased MAOA expression has been observed in pancreatic ductal adenocarcinoma [32], hepatocellular carcinoma (HCC) [28], and gastric cancer[33]. Notably, prior research has consistently reported a marked decrease in MAOA expression in invasive BC compared to noncancerous cells [34] and normal breast tissue [35], corroborating the observations presented in our study. A recent report from Wang et al has shed light on the role of NE, derived from tyrosine, in modulating inflammatory immune responses within the tumor microenvironment through interactions with beta-adrenergic receptors (β-ARs), thereby influencing tumor cell invasion and migration [6]. Suppressing the effects of the NK cell-enriched environment and lessening the antitumor effect can be achieved by chemical sympathectomy or blocking the β-AR signaling pathway [10]. Additionally, it has been demonstrated that MAOA may affect the emergence and progression of cancers by deteriorating the neurotransmitters downstream, specifically NE, in cases of pancreatic and liver cancer. Our study reveals MAOA's involvement in the metabolism of tyrosine and phenylalanine, suggesting a disruption in amino acid metabolism in TNBC patients. Furthermore, a decrease in MAOA expression was observed in the TNBC cohort relative to the control group at both the mRNA and protein levels. This leads us to hypothesize that TNBC may exhibit elevated NE levels, potentially activating immune cells for antitumor responses, a hypothesis that warrants further investigation. The AOC3 gene is responsible for encoding amine oxidase copper-containing 3, a membrane-bound adhesion protein, also known as vascular adhesion protein 1 (VAP1) [36]. This multifunctional molecule, primarily found in vascular endothelium and pericytes, plays a crucial role in facilitating leukocyte anchoring and trafficking to inflammatory tissues [37]. Research suggests that VAP-1 contributes to the adherence of tumor-infiltrating lymphocytes to various carcinomas, aiding in the destruction of cancer cells [38]. Evidence from several studies suggests a different role of AOC3, wherein it has been implicated in promoting the development of cancers such as melanoma and lymphoma [39], yet paradoxically, and its expression is decreased in certain aggressive cancer forms, including prostate and colorectal cancers [38, 39]. Our studies, incorporating GEPIA and CPTAC data, indicate a significant reduction of AOC3 in TNBC, implying the reduction of AOC3 may be linked to the tumor aggressiveness. Additionally, our research has elucidated an association between low expression of AOC3 and poor prognostic outcomes in TNBC, thereby proposing its utility as a prognostic biomarker. This proposition is further corroborated by proteomic analyses from Shaheed et al[40] comparing neoplastic breast tissue to benign counterparts, where a discernible reduction in AOC3 expression was observed, intimating at its prognostic relevance in BC. The precise mechanisms underpinning the relationship between low AOC3 expression levels and unfavorable prognostic outcomes remain elusive. Nonetheless, one potential mechanism to consider is the impact on tumor immunity. AOC3 supports lymphocyte adherence to endothelial cells, promoting lymphocyte aggregation within tumor vesicles. This triggers a local immune response by activating tumor-infiltrating lymphocytes, potentially inhibiting tumor growth [41]. The absence or reduction of AOC3 expression in TNBC could theoretically attenuate local immune responses, thereby contributing to a worse prognosis. It is imperative that further investigative endeavors are undertaken to elucidate and substantiate this hypothesized linkage. The TAT gene plays an indispensable role in the biosynthesis of tyrosine aminotransferase, a liver-specific mitochondrial enzyme that plays a crucial role in converting tyrosine into harmless molecules. These molecules are then either expelled through the renal pathway or utilized in metabolic processes to generate energy. It has been reported that the mutations in the TAT gene result in an enzyme deficiency, causing a harmful accumulation of tyrosine and its derivatives [42]. This buildup can damage vital organs such as the liver, kidneys, and nervous system, as well as other tissues, by disrupting their normal functions. One evidence has shown that reduced expression of TAT is observed in HCC, implicating a contributory role in the pathogenesis of this malignancy. Further in vitro analyses have indicated that TAT is instrumental in mediating apoptotic pathways and exhibiting anti-oncogenic effects, highlighting a significant association with the development and progression of HCC [42]. Our work has revealed a marked decrease in TAT levels and an increase in 4-hydroxyphenylacetaldehyde, a tyrosine metabolite, in patients with TNBC compared to the control group. This observation not only intimated a perturbation in tyrosine metabolism within TNBC but also intimated that the downregulation of TAT may contribute to progression of TNBC. Cancer cells are characterized by a significant reprogramming of cellular energy metabolism, a phenomenon predominantly illustrated by the Warburg effect[21].This phenomenon, observed even in oxygen-rich environments, is marked by a significantly increase in glucose uptake, enhanced glycolysis, and augmented production of lactic acid within tumor cells [43, 44]. Concurrently, gluconeogenesis, the synthesis of glucose from non-carbohydrate sources like glucogenic amino acids, pyruvate, lactate, and glycerol, is typically suppressed due to the preferential activation of the glycolysis pathway in cancer cells[45]. In our investigation, we observed an enrichment of oxaloacetic acid and PCK1 during the metabolic processes of glycolysis and gluconeogenesis. This observation indicates a critical involvement of these components in facilitating the interconnected pathways of energy metabolism within cancer cells, suggesting their potential roles in metabolic reprogramming associated with oncogenesis. The PCK1 gene, localized on the chromosomal region 20q13.31 in humans, demonstrates variable expression in various tumor types, manifesting overexpression in colorectal and melanoma malignancies while exhibiting underexpression in HCC and renal cell carcinoma [46, 47]. Research by Bian et al. has demonstrated that enhancing the stability of the PCK1-encoded protein can increase gluconeogenesis, decrease glycolysis, and suppress the proliferation of cancer cells [47]. As a pivotal enzyme in gluconeogenesis, PCK1 catalyzes the conversion of oxaloacetic acid into phosphoenolpyruvate. Our findings indicate a notable increase in oxaloacetate levels accompanied by reduced PCK1 expression TNBC patients, indicating an inhibition of the gluconeogenesis. Additionally, our work identified a significant association between PCK1 overexpression and decreased OS in TNBC patients (P < 0.05), aligning with the outcomes of prior research[43]. Our findings represent a groundbreaking contribution towards identifying potential biomarkers for TNBC. Nevertheless, these promising findings are tempered by certain limitations inherent in our study, such as the relatively small sample size, and the imperative for subsequent validation across larger and more varied cohorts. Moreover, the analytic procedures employed necessitate rigorous replication and standardization prior to their integration into clinical application. In conclusion, our research underlines the utility of combining metabolomic and transcriptomic analyses to provide a more comprehensive view of the complexities of TNBC. It unveils potential diagnostic biomarkers and therapeutic targets, offering promising avenues for revolutionizing TNBC management. The imperative for future investigations to validate these findings and extend the omics-based methodology to additional cancer subtypes is clear. Such endeavors are crucial for propelling the field of personalized medicine forward in the realm of oncology, potentially enhancing patient outcomes through more tailored and effective treatment strategies. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affiliated Hospital of Fujian Medical University (Approval number: 2021[168]). Written informed consent was obtained from individual or guardian participants. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare no competing financial interests. Funding This research was funded by the National Natural Science Foundation of China [NSFC 62072107] and the Natural Science Foundation of Fujian Province [2021J01278]. Authors’ Contributions Study concept and design: SG and RH. Acquisition of data: JH, SC, XC, XD, LL and YZ. Analysis and interpretation of data: SG and QW. Drafting of the manuscript: SG and CF. Critical revision of the manuscript for important intellectual content: all the authors. Acknowledgments We extend our heartfelt gratitude to all the patients and healthy volunteers who participated in this research, acknowledging their invaluable contribution. Our special thanks are directed towards the staff of the Department of Laboratory Medicine at the Second Affiliated Hospital of Fujian Medical University for their assistance. Additionally, we express our appreciation to the reviewers and the editor for their insightful feedback and constructive comments, which have significantly enriched this work. Supplementary data Supplementary data associated with this article can be found in Supplementary Table S1 and Supplementary Figures. References J. Ma, C. Chen, S. Liu et al , Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis, Cancer Gene Ther, 29(11)(2022) 1578-1589. Q. Yuan, L. Zheng, Y. Liao et al , Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis, World J Surg Oncol, 19(1)(2021). R. Bissanum, R. Kamolphiwong, R. Navakanitworakul et al , Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer, Translational Cancer Research, 11(9)(2022) 3039-3049. X. Lin, S. He, S. Wu et al , Diagnostic biomarker panels of osteoarthritis: UPLC-QToF/MS-based serum metabolic profiling, Peerj, 11(2023). L. Li, X. Zheng, Q. Zhou et al , Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population, Scientific reports, 10(1)(2020) 370. Y. Yamashita, S. Nishiumi, S. Kono et al , Differences in elongation of very long chain fatty acids and fatty acid metabolism between triple-negative and hormone receptor-positive breast cancer, BMC Cancer, 17(1)(2017) 589. A. Beatty, L.S. Fink, T. Singh et al , Metabolite Profiling Reveals the Glutathione Biosynthetic Pathway as a Therapeutic Target in Triple-Negative Breast Cancer, Mol Cancer Ther, 17(1)(2018) 264-275. N.J. Lanning, J.P. Castle, S.J. Singh et al , Metabolic profiling of triple-negative breast cancer cells reveals metabolic vulnerabilities, Cancer & metabolism, 5(1)(2017) 6. E.P. Resurreccion, K.-w. Fong, The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer, Metabolites, 12(6)(2022). H.-S. Lee, C. Ruiying, L. Zeyun et al , A comprehensive analysis of metabolomics and transcriptomics in non-small cell lung cancer, PLoS One, 15(5)(2020). Y. Peng, D. Yin, X. Li et al , Integration of transcriptomics and metabolomics reveals a novel gene signature guided by FN1 associated with immune response in oral squamous cell carcinoma tumorigenesis, J Cancer Res Clin Oncol, 149(9)(2023) 6097-6113. B. Du, F. Zhang, Q. Zhou et al , Joint analysis of the metabolomics and transcriptomics uncovers the dysregulated network and develops the diagnostic model of high-risk neuroblastoma, Sci Rep, 13(1)(2023). C.A. Smith, E.J. Want, G. O'Maille et al , XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification, Anal Chem, 78(3)(2006) 779-787. M. Zhang, H. Chen, M. Wang et al , Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer, Biosci Rep, 40(2)(2020). D. Chandrashekar, B. Bashel, S. Balasubramanya et al , UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses, Neoplasia, 19(8)(2017) 649-658. B. Ying, W. Xu, Y. Nie et al , HSPA8 Is a New Biomarker of Triple Negative Breast Cancer Related to Prognosis and Immune Infiltration, Dis Markers, 2022(2022) 8446857. S. Gong, Q. Wang, J. Huang et al , LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer, Methods, 222(2024) 100-111. Y. Gong, P. Ji, Y.S. Yang et al , Metabolic-Pathway-Based Subtyping of Triple-Negative Breast Cancer Reveals Potential Therapeutic Targets, Cell Metab, 33(1)(2021) 51-64 e59. K.S.S. Sai Baba, M.A. Rehman, J. Pradeep Kumar et al , Serum Human Epididymis Protein-4 (HE4) - A novel Approach to Differentiate Malignant Frombenign Breast Tumors, Asian Pac J Cancer Prev, 22(8)(2021) 2509-2507. U. Martinez-Outschoorn, M. Peiris-Pagés, R. Pestell et al , Cancer metabolism: a therapeutic perspective, Nat Rev Clin Oncol 14(1)(2017) 11-31. Z. Li, C. Sun, Z. Qin, Metabolic reprogramming of cancer-associated fibroblasts and its effect on cancer cell reprogramming, Theranostics, 11(17)(2021) 8322-8336. J. Li, K. Liu, Z. Ji et al , Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection, Cancer Sci, 114(2)(2023) 680-689. S. Nunes, J. Sousa, F. Silva et al , Peripheral Blood Serum NMR Metabolomics Is a Powerful Tool to Discriminate Benign and Malignant Ovarian Tumors, Metabolites, 13(9)(2023). S. Wu, W. Xue, H. Yu et al , Serum uric acid levels and health outcomes in CKD: a prospective cohort study, Nephrol Dial Transplant, 10.1093/ndt/gfad201(2023). X. Zhang, X. Kang, L. Jin et al , ABCC9, NKAPL, and TMEM132C are potential diagnostic and prognostic markers in triple‐negative breast cancer, Cell Biol Int, 44(10)(2020) 2002-2010. T. Wiggins, S. Kumar, S.R. Markar et al , Tyrosine, Phenylalanine, and Tryptophan in Gastroesophageal Malignancy: A Systematic Review, Cancer Epidemiol Biomarkers Prev, 24(1)(2015) 32-38. H. Christofk, M. Nazemi, B. Yanes et al , The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism, PLoS Biol, 22(1)(2024). Y.Y. Pang, J.D. Li, L. Gao et al , The clinical value and potential molecular mechanism of the downregulation of MAOA in hepatocellular carcinoma tissues, Cancer Medicine, 9(21)(2020) 8004-8019. S. Kushal, W. Wang, V.P. Vaikari et al , Monoamine oxidase A (MAO A) inhibitors decrease glioma progression, Oncotarget, 7(12)(2016) 13842-13853. P. Li, I. Siddiqi, A. Mottok et al , Monoamine oxidase A is highly expressed in classical Hodgkin lymphoma, J Pathol, 243(2)(2017) 220-229. S. Gaur, M. Gross, C. Liao et al , Effect of Monoamine oxidase A (MAOA) inhibitors on androgen-sensitive and castration-resistant prostate cancer cells, Prostate, 79(6)(2019) 667-677. V.O. Oria, P. Bronsert, A.R. Thomsen et al , Proteome Profiling of Primary Pancreatic Ductal Adenocarcinomas Undergoing Additive Chemoradiation Link ALDH1A1 to Early Local Recurrence and Chemoradiation Resistance, Transl Oncol, 11(6)(2018) 1307-1322. Y. Wang, S. Wang, Q. Yang et al , Norepinephrine Enhances Aerobic Glycolysis and May Act as a Predictive Factor for Immunotherapy in Gastric Cancer, Journal Of Immunology Research, 2021(2021) 1-13. Y. Ren, H. Jiang, D. Ma et al , Parkin degrades estrogen-related receptors to limit the expression of monoamine oxidases, Hum Mol Genet, 20(6)(2011) 1074-1083. A. Daniel, A. Gaviglio, T. Knutson et al , Progesterone receptor-B enhances estrogen responsiveness of breast cancer cells via scaffolding PELP1- and estrogen receptor-containing transcription complexes, Oncogene, 34(4)(2015) 506-515. S.-J. Chang, H.-P. Tu, Y.-C.C. Lai et al , Increased Vascular Adhesion Protein 1 (VAP-1) Levels Are Associated with Alternative M2 Macrophage Activation and Poor Prognosis for Human Gliomas, Diagnostics, 10(5)(2020). M. Salmi, S.J.A. Jalkanen, r. signaling, Vascular Adhesion Protein-1: A Cell Surface Amine Oxidase in Translation, Antioxid Redox Signal, 30(3)(2019) 314-332. J. Chen, J. Xi, Y. Tian et al , Identification, prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody‐based assays on tissue specimens, Proteomics, 13(15)(2013) 2268-2277. S.T. Ward, C.J. Weston, E.L. Shepherd et al , Evaluation of serum and tissue levels of VAP-1 in colorectal cancer, BMC Cancer, 16(1)(2016). S. Shaheed, N. Rustogi, A. Scally et al , Identification of stage-specific breast markers using quantitative proteomics, J Proteome Res, 12(12)(2013) 5696-5708. W. Sun, J. Choi, Y. Cha et al , Evaluation of the Expression of Amine Oxidase Proteins in Breast Cancer, Int J Mol Sci, 18(12)(2017). L. Fu, S.-S. Dong, Y.-W. Xie et al , Down-regulation of tyrosine aminotransferase at a frequently deleted region 16q22 contributes to the pathogenesis of hepatocellular carcinoma, Hepatology, 51(5)(2010) 1624-1634. B. Alshehri, Prognostic significance and expression pattern of glucose related genes in breast cancer: A comprehensive computational biology approach, Saudi J Biol Sci, 31(1)(2024). M. Shah, V. Shrivastava, S. Sofi et al , Chlorogenic Acid Restores Ovarian Functions in Mice with Letrozole-Induced Polycystic Ovarian Syndrome Via Modulation of Adiponectin Receptor, Biomedicines, 11(3)(2023). I. Yustisia, R. Amriani, H. Cangara et al , High expression of FBP1 and LDHB in fibroadenomas and invasive breast cancers, Breast Disease, 40(4)(2021) 251-256. Z. Wang, C. Dong, Gluconeogenesis in Cancer: Function and Regulation of PEPCK, FBPase, and G6Pase, Trends in Cancer, 5(1)(2019) 30-45. X.-l. Bian, H.-z. Chen, P.-b. Yang et al , Nur77 suppresses hepatocellular carcinoma via switching glucose metabolism toward gluconeogenesis through attenuating phosphoenolpyruvate carboxykinase sumoylation, Nature Communications, 8(1)(2017). Supplementary Files SupplementaryFigures.docx SupplementaryTableS1.xlsx Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2024 Read the published version in Journal of Translational Medicine → Version 1 posted Reviewers agreed at journal 26 May, 2024 Reviewers invited by journal 26 May, 2024 Editor assigned by journal 06 May, 2024 First submitted to journal 03 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. 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-4365055","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":306792186,"identity":"0d6e3f27-9940-46d7-8c91-785a953b84e3","order_by":0,"name":"Sisi Gong","email":"","orcid":"","institution":"Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sisi","middleName":"","lastName":"Gong","suffix":""},{"id":306792187,"identity":"c35ddaba-d674-4e4b-bbb0-87649a5ad907","order_by":1,"name":"Zhijun Liao","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhijun","middleName":"","lastName":"Liao","suffix":""},{"id":306792188,"identity":"2272027e-7cd1-4d4b-b1da-fbfd9485d96e","order_by":2,"name":"Meie Wang","email":"","orcid":"","institution":"Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meie","middleName":"","lastName":"Wang","suffix":""},{"id":306792189,"identity":"3369f232-5028-4d33-892c-d7ea89ef9aeb","order_by":3,"name":"Fen Lian","email":"","orcid":"","institution":"Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fen","middleName":"","lastName":"Lian","suffix":""},{"id":306792190,"identity":"c8f81ca2-789c-4eec-aedf-95101a0dd5d7","order_by":4,"name":"Ruirui Tong","email":"","orcid":"","institution":"Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruirui","middleName":"","lastName":"Tong","suffix":""},{"id":306792191,"identity":"8509c3b9-34ec-400e-a304-31d37c0948cb","order_by":5,"name":"Rongfu Huang","email":"","orcid":"","institution":"Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Rongfu","middleName":"","lastName":"Huang","suffix":""},{"id":306792192,"identity":"91808168-d869-4231-be03-d6cd2d6f1e52","order_by":6,"name":"Chun mei Fan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3RMQuCQBTA8QPhWo5cnxg29AUMIfo4vqWWgkYHCcU4h4oa+xiOjUpgy9XsaN+gtqZI58KzreF+8/15d/cIUZQ/RPUoTfEFlt4Jw9L1fHnShRzLOx07xuYU2aXI5YlFZoPhgXqYFFNu3FZai4sRQU3GwLFFxj0MKNHjtducaNvcZACWcQ55gcceAXFJJFOuE5PZ4HSrKQUKSmyYy5LZyGQuYFAgXyDXWiXO8JAC7quEtEvqT34EUH1yFoErciZ9S39XrzJYVquMb4+n51t6vG1OPrDfjiuKoihfvQHVUk4eN+aiuwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-1942-8436","institution":"Second Affiliated Hospital of Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Chun","middleName":"mei","lastName":"Fan","suffix":""}],"badges":[],"createdAt":"2024-05-03 15:51:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4365055/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4365055/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12967-024-05843-y","type":"published","date":"2024-11-11T15:57:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57902914,"identity":"015486f2-29b6-4174-a27d-bc6965a6617d","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171774,"visible":true,"origin":"","legend":"\u003cp\u003ePCA score plots of samples in the discovery set and QCs (HC: green; TNBC patients: red; QC: yellow) in (\u003cstrong\u003eA\u003c/strong\u003e) positive and (\u003cstrong\u003eB\u003c/strong\u003e) negative electrospray ionization mode\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/b036537815e866c9f35b193e.png"},{"id":57903510,"identity":"76b06118-81a1-4531-9925-2f6b173fd4ed","added_by":"auto","created_at":"2024-06-07 09:19:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":47787,"visible":true,"origin":"","legend":"\u003cp\u003eThe OPLS-DA score plots of the two groups revealed the clustering of samples in the discover set and their corresponding permutation tests. OPLS-DA score plots for HC (green, n = 21) and TNBC (red, n = 18) in (\u003cstrong\u003eA\u003c/strong\u003e) ESI+ mode and (\u003cstrong\u003eB\u003c/strong\u003e) ESI- mode. The corresponding validation plots for putative features with 100 permutation tests in (\u003cstrong\u003eC\u003c/strong\u003e) ESI+ mode and (\u003cstrong\u003eD\u003c/strong\u003e) ESI- mode.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/ea6158a6d1e72d2d736e7234.png"},{"id":57903509,"identity":"bb22b71b-8175-41ab-8453-71133f2e6b35","added_by":"auto","created_at":"2024-06-07 09:19:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121907,"visible":true,"origin":"","legend":"\u003cp\u003eValidation the diagnostic efficacy of 7-Methylguanine for discriminating between TNBC and HC groups across discovery set and validation sets. \u003cstrong\u003e(A)\u003c/strong\u003e Comparative analysis of 7-methylguanine levels(****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 in the discovery set; ##\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01 in the validation set). \u003cstrong\u003e(B)\u003c/strong\u003e ROC curve analysis for diagnostic accuracy.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/f09b5efc728cb45393957f95.png"},{"id":57902910,"identity":"8b37e35e-6079-47e3-ae84-28d776640804","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88652,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of overlapping DEGs. Volcano plots for DEGs in TNBC and normal tissues based on data from GEO datasets\u003cstrong\u003e (A) \u003c/strong\u003eGSE65194,\u003cstrong\u003e (B) \u003c/strong\u003eGSE45827 and \u003cstrong\u003e(C) \u003c/strong\u003eGSE36295. \u003cstrong\u003e(D)\u003c/strong\u003e Venn diagrams of the DEGs from the three data sets. Different colors in the figure mean different data sets.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/6d37feb76cc3a5a1dc2dc57e.png"},{"id":57902921,"identity":"ecb514f1-8df2-418f-a9be-95bfcc51b521","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64914,"visible":true,"origin":"","legend":"\u003cp\u003eDAVID analysis of the overlapping DEGs. \u003cstrong\u003e(A) \u003c/strong\u003eGO and\u003cstrong\u003e (B)\u003c/strong\u003e KEGG enrichment analyses of the common DEGs. The size of the node reflects the count of genes enriched in terms, and the color shows the P value, the redder the color, the more significant it is. DAVID, Database for Annotation, Visualization, and Integrated Discovery; DEGs, differentially expressed genes; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/06c69ffe959f9886632aec2d.png"},{"id":57903511,"identity":"2d8bed1a-e4b6-4556-bf25-6c6e5dffef6a","added_by":"auto","created_at":"2024-06-07 09:19:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":69219,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated transcriptomics and metabolomics analyses of TNBC metabolic pathways. (A) Metabolic pathway enrichment plot. (B) The compound–reaction–enzyme–gene network of the key metabolites and genes. Significant overexpression in red, significant downexpression in blue.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/4500aecf6eb3252dfdf1802f.png"},{"id":57902916,"identity":"236f8c35-574f-400c-a2d2-ef7a50f93bcd","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":78871,"visible":true,"origin":"","legend":"\u003cp\u003eSignificantly expressed six genes in TNBC samples compared to normal samples. (A)MAOA, (B)ADH1B, (C)ADH1C, (D)AOC3, (E)TAT and (F)PCK1 have notable low mRNA expression in TNBC specimen compared to normal specimen (*p \u0026lt; 0.05). Red color refers to tumor tissues and grey color refers to normal samples\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/e18dfc4321a2099efc944379.png"},{"id":57902922,"identity":"cfb53f62-0049-414d-9ef9-4631a43ea892","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":82255,"visible":true,"origin":"","legend":"\u003cp\u003eThe protein expression of(A)MAOA, (B)ADH1B, (C)ADH1C, (D)AOC3, (E)TAT and (F)PCK1 in normal tissues and breast cancer tissues based on subclasses analyzed by UALCAN cancer database. Z-values show standard deviations for the specified cancer type from the median across samples. Values for the Log2 Spectral count ratio obtained from CPTAC were first normalized within each sample profile and then across samples.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/e0906a491ddd0e332e09145e.png"},{"id":57902919,"identity":"ef834c4f-7956-4a06-999f-28774112a716","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":143086,"visible":true,"origin":"","legend":"\u003cp\u003eThe representative immunohistochemistry (IHC) images of MAOA, ADH1B, ADH1C, AOC3 and PCK1 in BC and normal tissues were extracted from the HPA database. In each set, tumor tissue sections were displayed on the upper side and normal tissue sections were displayed on the lower side\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/a18cdc2a3ec0812a25ebc8fe.png"},{"id":57902918,"identity":"0ee4fda4-4a4d-4895-932c-9fad19b0c1bd","added_by":"auto","created_at":"2024-06-07 09:11:30","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":93907,"visible":true,"origin":"","legend":"\u003cp\u003eOverall survival (OS) data evaluating the prognostic value of (A)MAOA, (B)ADH1B, (C)ADH1C, (D)AOC3, (E)TAT and (F)PCK1 in TNBC patients using Kaplan-Meier plotter.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/db61537bf831b04e57a5919a.png"},{"id":69274986,"identity":"b0dc2781-6e38-4a33-ab97-d7cd53b84a97","added_by":"auto","created_at":"2024-11-18 16:42:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1889523,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/2daeab8d-a2c8-4784-936d-f1b3d7d533fe.pdf"},{"id":57902923,"identity":"35a4e856-9eed-4179-8cdb-9d4ab61e8474","added_by":"auto","created_at":"2024-06-07 09:11:31","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":106215,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/db8589c1c00623514798557f.docx"},{"id":57903512,"identity":"ec98343c-bacd-46a4-a36a-c9599083dd70","added_by":"auto","created_at":"2024-06-07 09:19:30","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":11273,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4365055/v1/a3acc9ccd939defb6a0c150c.xlsx"}],"financialInterests":"","formattedTitle":"Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe intricate nature of breast cancer (BC), characterized by its diverse subtypes, is particularly highlighted by the clinical challenges associated with Triple Negative Breast Cancer (TNBC). Accounting for an estimated 10\u0026ndash;15% of all BC cases, TNBC is distinguished by its aggressive cellular behavior, increased likelihood of recurrence, and generally poorer prognostic outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The hallmark of TNBC is its absence of estrogen and progesterone receptors, in addition to a minimal expression of the human epidermal growth factor receptor 2 (HER2) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which significantly diminishes the efficacy of standard hormone therapies and HER2-targeted treatments. This situation underscores the critical need for the development of novel diagnostic and therapeutic strategies that are tailored specifically to address the unique challenges of TNBC.\u003c/p\u003e \u003cp\u003eAdvancements in the fields of metabolomics and transcriptomics herald new vistas for elucidating the intricate molecular perturbations characteristic of oncogenesis, with the potential to facilitate the identification of novel biomarkers and therapeutic avenues. Metabolomics, in particular, has emerged as the preeminent technology for the advancement of early diagnosis and the refinement of precision medicine. This approach enables the comprehensive quantification and characterization of low-molecular-weight molecules within biological systems, thereby illuminating potential diagnostic biomarkers and mirroring the underlying biochemical activities and states of cells and tissues [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Through the analysis of metabolite profiles from serum, tissue, and cell samples, researchers have identified metabolic disturbances in TNBC patients, including alterations in the glycerophospholipid metabolism pathway, fatty acid metabolism, the tricarboxylic acid (TCA) cycle, and glutathathione biosynthesis pathway [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the results across different biological specimens show significant disparities, highlighting challenges in the credibility and reproducibility of diagnostic biomarkers. This is mainly because the identification of disrupted metabolic pathways in TNBC largely relies on changes in metabolite levels, with only a few biomarkers being validated through other omics approaches. Systems biology focuses on the biological significance of metabolites, advocating for the integration of metabolomics with other omics technologies to elucidate the complex networks of molecular pathways involved in tumorigenesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Transcriptomics, which interprets the functional components of the genome, contributes valuable insights into the unique biological responses to diseases. The fusion of metabolomics and transcriptomics data has propelled cancer research forward, leveraging advancements in systems biology and bioinformatics [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Yet, the application of this integrated approach remains underutilized in TNBC research, indicating a significant area for further exploration.\u003c/p\u003e \u003cp\u003eConsequently, the elucidation of the specific aberrant metabolic pathways contributing to the pathogenesis of TNBC necessitates the implementation of a meticulously designed research methodology, underpinned by an integrated analytical framework. The objective of the present investigation is to harness the capabilities of integrated omics technologies to discern differentially expressed metabolites and genes, thereby shedding light on the metabolic pathways that diverge in TNBC from those in healthy control (HC) subjects. By undertaking exhaustive analyses through both metabolomics and transcriptomics, this study endeavors to enhance our comprehension of the metabolic deviations and gene expression alterations characteristic of TNBC. This endeavor aims to lay the groundwork for the identification of novel biomarkers and to foster a deeper understanding of the underlying pathophysiological mechanisms of TNBC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Chemical and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMethanol and acetonitrile of high performance liquid chromatography (HPLC) grade were procured from Fisher Scientific (Loughborough, UK). Similarly, formic acid, also of HPLC grade, was acquired from TCI (Shanghai, China). The procurement of ammonium acetate, adhering to HPLC grade standards, was facilitated through Sigma-Aldrich (Shanghai, China). The 2-chloro-L-phenylalanine was obtained from Aladdin (Shanghai, China).Furthermore, distilled water was filtered through the Milli-Q system (Millipore, Bedford, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Study design and Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation was conducted at the Second Affiliated Hospital of Fujian Medical University over the period of 2021 to 2022, receiving ethical endorsement from the hospital's Ethics Committee under the reference number 2021[168]. Prior to the procurement of blood specimens, informed consent was duly acquired in written form from all the 51 subjects who were recruited for participation in this study. The research design was bifurcated\u0026nbsp;into two distinct phases: the preliminary discovery phase, which comprised 18 individuals diagnosed with TNBC alongside 21 HC participants, and the subsequent validation phase, which included 7 TNBC patients and 5 control subjects. The diagnostic criterion for TNBC was strictly aligned with the international consensus, identifying patients based on the absence of estrogen receptor, progesterone receptor, and HER2 expression. The control cohort consisted of healthy volunteers, age-matched and with no prior history of breast disease, whose health status was rigorously verified through comprehensive physical exams.\u003c/p\u003e\n\u003cp\u003eBlood specimens were procured from fasting participants, subsequently deposited into tubes specifically engineered for serum segregation. Following a centrifugation process at 3000 rpm for a duration of 5 minutes at a temperature of 4\u0026nbsp;℃, the serum was successfully isolated. Immediate post-isolation, the serum samples were expeditiously transferred to a refrigeration unit maintained at −80°C, thereby preserving them for future metabolomics analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Sample Preparation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCommence by thawing the experimental specimens at an ambient temperature of 4\u0026nbsp;℃, then subject them to vortex mixing for a duration of one minute to ensure a uniform mixture. With meticulous precision, transfer 100 µL of the specimen into a 2 mL centrifuge tube. Subsequently, introduce 400 µL of a methanol solution, preserved at a temperature of -20\u0026nbsp;℃, into the tube and subject it to vortex mixing for another minute to ensure thorough mixing. The mixture is then centrifuged at 12,000 rpm for 10 minutes at a temperature of 4\u0026nbsp;℃, a step designed to precipitate proteins. Upon the completion of centrifugation, carefully collect the supernatant and subject it to evaporation under a centrifugal vacuum to achieve dryness. Subsequently, with exactitude, add 150 µL of an 80% methanol-water solution containing 2-chloro-L-phenylalanine (concentration of 4 ppm), maintained at 4\u0026nbsp;℃, to reconstitute the specimen. Thereafter, collect the supernatant, filter it through a 0.22 µm membrane, and transfer the filtrate into a vial prepared for liquid chromatography-mass spectrometry (LC-MS) analysis.\u003c/p\u003e\n\u003cp\u003eIn a parallel experimental setup, pooled quality control (QC) samples were meticulously prepared by amalgamating equal volumes of all serum supernatants. These QC samples played a pivotal role in the evaluation of the stability and consistency of the overall experimental outcomes. To facilitate the equilibration of the analytical column, the pooled QC sample was initially introduced into the system via five consecutive injections at the commencement of the analytical batch. To ensure the accuracy and reliability of the analytical workflow, it was imperative that the QC sample be injected subsequent to every six serum sample injections throughout the entirety of the analytical procedure, thereby guaranteeing the maintenance of stringent analytical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4.\u0026nbsp; \u0026nbsp;\u0026nbsp;UHPLC–MS based metabolome profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChromatographic separations were performed on a Vanquish ultra-high performance liquid chromatography (UHPLC) System (Thermo Fisher Scientific, USA), employing an ACQUITY UPLC® HSS T3 column (150×2.1 mm, 1.8 µm, Waters, Milford, MA, USA) for the analysis.\u003c/p\u003e\n\u003cp\u003eThe metabolomic analyses were performed in both electrospray ionization positive (ESI+) and negative (ESI−) ion modes. For ESI+, the mobile phases were composed of A2 (0.1% formic acid in water) and B2 (0.1% formic acid in acetonitrile), with the elution gradient meticulously structured as follows: from 0 to 1 minute, the composition was maintained at 2% B2; from 1 to 9 minutes, it was gradually increased from 2% to 50% B2; from 9 to 12 minutes, it was further increased from 50% to 98% B2; from 12 to 13.5 minutes, it was held constant at 98% B2; from 13.5 to 14 minutes, it was rapidly decreased from 98% to 2% B2; and finally, from 14 to 20 minutes, it was maintained at 2% B2. In the ESI- mode, the mobile phases comprised A3 (ammonium formate at 5 mM) and B3 (acetonitrile), with the elution conditions set as follows: from 0 to 1 minute, the composition was at 2% B3; from 1 to 9 minutes, it was increased from 2% to 50% B3; from 9 to 12 minutes, it was raised from 50% to 98% B3; from 12 to 13.5 minutes, it remained at 98% B3; from 13.5 to 14 minutes, it was decreased from 98% to 2% B3; and from 14 to 17 minutes, it was kept at 2% B3. The column oven temperature was uniformly maintained at 40°C, with a flow rate of 0.25 mL/min and an injection volume of 2 μL. Throughout the duration of the experiment, all pre-treated serum samples were preserved at 4°C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMetabolite detection was facilitated through a Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA), which was equipped with an ESI ion source and operated in both MS1 and MS/MS (Full MS-ddMS2 mode, data-dependent MS/MS) acquisition modes. The operational parameters were meticulously defined, with sheath gas pressure set at 30 arb, auxiliary gas flow at 10 arb, spray voltages calibrated at 3.50 kV for ESI(+) and -2.50 kV for ESI(−), capillary temperature at 325℃, MS1 scan range from m/z 81 to 1000, MS1 resolving power at 60000 FWHM, eight data-dependent scans per cycle, MS/MS resolving power at 15000 FWHM, normalized collision energy at 30%, and dynamic exclusion time set to automatic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Metabolomics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003edata analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transformation of raw data into mzXML format was accomplished utilizing MSConvert, a component of the ProteoWizard software suite (version 3.0.8789)[13]. This preliminary step facilitated subsequent analytical processes. The feature detection, retention time correction, and alignment of the data were executed through the application of XCMS. Subsequently, advanced multivariate statistical analyses, namely principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), were conducted using Simca-P14.0 software. These analyses served to delineate distinct groups and pinpoint biomarkers indicative of TNBC. To ascertain the robustness of the model, a permutation test encompassing 200 random permutations was employed, evaluating the OPLS-DA model based on its R2 (explained variance) and Q2 (predictive ability) parameters. The identification of discriminating metabolites was facilitated by the OPLS-DA model through the implementation of the variable importance on projection (VIP) strategy, whereby only metabolites exhibiting a VIP value in excess of 1 were deemed to possess statistical significance in the classification of TNBC. Following this, a nonparametric univariate statistical analysis was conducted, employing the Mann-Whitney U test (p \u0026lt; 0.05) in conjunction with fold change (FC) values\u0026nbsp;≤\u0026nbsp;0.67 or\u0026nbsp;≥\u0026nbsp;1.5 to discern differential metabolites (DMs).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The evaluation of the DMs' predictive capacity was undertaken through receiver operating characteristic (ROC) curve analysis, which leveraged the area under the ROC curve (AUC) as an indicator of the overall test efficacy. The optimum AUC, sensitivity, and specificity were determined by maximizing the Youden index, calculated as sensitivity + specificity - 1[4]. This analytical process was executed utilizing SPSS software (version 22.0).\u003c/p\u003e\n\u003cp\u003eThe initial identification of DMs was predicated on the verification of accurate molecular weight (\u0026lt; 30 ppm). This was followed by an analysis based on precise mass numbers and high-resolution target MS/MS spectra, in conjunction with the fragmentation laws of various metabolites. The exploration for potential structures of differential metabolites was conducted through database searches (including METLIN, HMDB, and MassBank) and literature reviews, thereby accruing information on candidate metabolites.\u003c/p\u003e\n\u003cp\u003eFurthermore, Metabolite Set Enrichment Analysis (MSEA) was performed via MetaboAnalyst 6.0 (https://metascape.org/gp/index.html), aimed at elucidating metabolic pathways distinctly altered in TNBC patients in comparison to HC subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6.\u0026nbsp; \u0026nbsp;\u0026nbsp;Transcriptomics analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the investigation of TNBC, three pertinent datasets from the Gene Expression Omnibus (GEO) database were meticulously selected for analysis: GSE65194, encompassing 55 TNBC tissue samples alongside 11 samples of healthy breast tissue derived from mammoplasty procedures; GSE45827, comprising 11 TNBC and 5 healthy breast tissue samples; and GSE36295, containing 41 TNBC tissues as well as 11 samples of normal tissue. The identification of differentially expressed genes (DEGs) contrasting the TNBC group with the group of normal breast tissues was executed utilizing the GEO2R analytical tool, adhering to stringent cutoff criteria of an absolute log2 FC greater than 2 and an adjusted p-value less than 0.05. This initial analysis facilitated the generation of volcano plots and Venn diagrams, accessible via (http://www.bioinformatics.com.cn/), to discern DEGs consistently observed across the trio of datasets.\u003c/p\u003e\n\u003cp\u003eSubsequent to the identification of shared DEGs, a comprehensive examination of the biological processes (BP), molecular functions (MF), cellular components (CC), and implicated pathways was conducted. This examination was facilitated through gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, employing the Database for Annotation, Visualization, and Integrated Discovery (DAVID, version 12.0) as the analytical platform. This multifaceted approach aimed to elucidate the underlying molecular mechanisms and potential pathophysiological pathways relevant to TNBC, thereby contributing valuable insights into the biological characterization of this aggressive breast cancer subtype\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7.\u0026nbsp; \u0026nbsp;\u0026nbsp;Joint analysis of metabolomics and transcriptomics\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn integrative analysis was undertaken to explore the synergistic relationship between DMs and DEGs, as identified through comprehensive metabolomic and transcriptomic investigations. This endeavor was facilitated by employing the Joint-Pathway Analysis module available within the MetaboAnalyst 6.0 platform, aimed at constructing a detailed metabolic pathway enrichment diagram. The analysis leveraged the total number of identified metabolites to evaluate the relevance and significance of each pathway, with pathways demonstrating a P-value less than 0.05 being deemed significantly enriched. In parallel, the KEGG database served as a pivotal resource for elucidating potential genes implicated within these significantly enriched pathways. The utilization of Cytoscape software version 3.9.1, in conjunction with the Metscape plugin, facilitated the elucidation of the intricate connections and interdependencies between metabolites and genes, thereby enabling the visualization of compound networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8.\u0026nbsp; \u0026nbsp;\u0026nbsp;Validation of the expression of hub DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/) represents a sophisticated interactive web service dedicated to the analysis of RNA sequencing expression data, incorporating 9,736 tumor and 8,587 normal samples derived from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects\u0026nbsp;[14]. Concurrently, UALCAN (http://ualcan.path.uab.edu) emerges as an extensive, intuitive web portal tailored for the analysis of cancer OMICS data. This portal not only facilitates gene expression analysis predicated on clinical data from TCGA but also extends its functionality to include protein expression analysis leveraging data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset\u0026nbsp;[14, 15]. Furthermore, the Human Protein Atlas (HPA) database (https://www.proteinatlas.org) provides an invaluable open-access repository of immunohistochemical images, documenting a broad spectrum of immune response observations across both neoplastic and normal tissues\u0026nbsp;[16]. Employing the comprehensive datasets available within these repositories, a detailed comparative analysis of the mRNA and protein expressions of key hub genes in breast cancer versus normal breast tissue was conducted, with immunohistochemistry serving as the foundational analytical technique. The open-access status of these databases obviates the necessity for ethical approval, thereby negating the requirement for formal authorization from a local ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9.\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eKaplan-Meier plotter database analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier plotter database (www.kmplot.com) was deployed to elucidate the association between mRNA levels of\u0026nbsp;each\u0026nbsp;pivotal DEG\u0026nbsp;and the prognostic outcomes of patients afflicted with\u0026nbsp;TNBC. To this end, patient samples were stratified into two distinct groups predicated upon the median expression level of each gene, delineating cohorts with high versus low expression, thereby facilitating a rigorous evaluation of the prognostic relevance attributed to each gene. Notably, the platform autonomously computes the hazard ratios (HR) accompanied by 95% confidence intervals (CI) and Log rank P values, thereby streamlining the analytical process.\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;General Characteristics of Study Participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the current investigation, the cohort comprised exclusively female subjects, with an established homogeneity in age demographics across all study groups. To minimize the potential confounding impact of variables such as age, homogeneity within each group was rigorously evaluated utilizing the Kruskal-Wallis test. The participant pool included a total of 51 individuals: the discovery set consisted of 18 patients diagnosed with TNBC (age 47 [range 27-59] years) and 21 HC (age, 46 [range 33-66] years), whereas the validation set encompassed 7 TNBC patients (age 51 [range 36-58] years) and 5 HC (age, 50 [range 39-63] years). Analysis revealed no significant disparities in baseline characteristics among the groups, thereby reinforcing the internal validity of the study findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;The metabolomics analysis for TNBC and HC serum samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Reliability of the analytical method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this\u0026nbsp;study, multivariate statistical analyses were initially utilized to construct metabolic profiles for the entirety of the samples under study.\u0026nbsp;The reliability of this analytical method\u0026nbsp;was rigorously evaluated through the systematic repetition of analyses on QC samples across all sample runs. Subsequently, the PCA score plots for the samples within the discovery dataset were examined. Notably, all QC samples (\u003cstrong\u003eFigure 1\u003c/strong\u003e, yellow) exhibited a pronounced clustering in both ESI+ and ESI- modes. This observation unequivocally confirms the analytical system\u0026apos;s stability and reproducibility.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Differential\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMetabolite Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e, the PCA scores plots exhibited\u0026nbsp;well\u0026nbsp;distinguishable patterns\u0026nbsp;between TNBC and HC samples, implying some remarkable differences existed in the serum endogenous metabolites between the two different groups.\u0026nbsp;Building on this initial finding, OPLS-DA analyses was applied to further pinpoint these metabolic discrepancies. The results (\u003cstrong\u003eFigure 2A, B\u003c/strong\u003e) demonstrated a clear division between the two groups, with impressive \u003cem\u003eR\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eY\u003c/em\u003e and \u003cem\u003eQ\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values of 0.984 and 0.878 in ESI+ mode, and 0.995 and 0.834\u0026nbsp;in ESI- mode, respectively. Subsequently, the results from 100 permutation tests revealed that the permuted \u003cem\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e and\u003cem\u003e\u0026nbsp;Q\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e values on the left side were consistently lower than the original values on the right side, indicating no overfitting of the model \u003cstrong\u003e(Figure 2C, D)\u003c/strong\u003e. Furthermore, the intercept of \u003cem\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e being below zero further supports the model\u0026apos;s reliability and validity\u0026nbsp;[17].\u003c/p\u003e\n\u003cp\u003eIn this work, subsequent to the application of predefined criteria, a total of 22 DMs was identified as potential biomarkers for differentiating between TNBC and HC specimens. The comparative analysis elucidated that within the TNBC cohort, there were 13 metabolites exhibiting up-regulation and 9 demonstrating down-regulation in contrast to the HC group. The concentration profiles of these 22 DMs were systematically represented in a heat map (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figure 1\u003c/strong\u003e), while comprehensive details encompassing retention time (RT), mass-to-charge ratio (m/z), adduct ion, FC p-value, VIP, and mean decrease accuracy were listed in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Serum differential metabolites detected by UHPLC-MS between TNBC and HC subjects.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"749\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\" valign=\"top\"\u003e\n \u003cp\u003eMetabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"top\"\u003e\n \u003cp\u003eRt(s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\" valign=\"top\"\u003e\n \u003cp\u003emz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\" valign=\"top\"\u003e\n \u003cp\u003eformula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\" valign=\"top\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\"\u003e\n \u003cp\u003eAdduct ion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\" valign=\"top\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"top\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"top\"\u003e\n \u003cp\u003eVIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003especificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003eYouden Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eIsonicotinic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e600.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e122.0217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC6H5NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC07446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.464\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eErgothioneine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e102.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e230.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC9H16N3O2S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC05570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.546\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eGlutaric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e131.0329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC5H8O4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.555\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eUrocanic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e139.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e137.0345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC6H6N2O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eAcetylcholine chloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e180.9729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC7H16NO2. Cl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC08201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.005\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.767\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003e2,3-Butanediol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e152.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e154.9901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC4H10O2S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.036\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003e5\u0026apos;-Methylthioadenosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e897.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e297.2429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC11H15N5O3S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.024\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.705\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eN-Acetyl-D-tryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e397.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e246.1238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC13H14N2O3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC03137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.715\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003e9(S)-HPOT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e855.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e293.2108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC18H30O4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC16321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.429\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003e4-Hydroxyphenylacetaldehyde\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e136.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC8H8O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC03765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.851\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003e7-Methylguanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e139.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e166.0724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC6H7N5O\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC02242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e2.406\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eThymidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e539.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e242.1759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC10H14N2O5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.595\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eOxalacetic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e83.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e130.9993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC4H4O5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.572\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eCMP-3-deoxy-D-manno-octulosonate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e542.1068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC17H26N3O15P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC04121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.421\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eOxoglutaric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e75.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e145.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC5H6O5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e2.237\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eBilirubin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e917.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e585.2655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC33H36N4O6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.731\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eThymine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e430.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e125.0347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC5H6N2O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.047\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.288\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eL-Valine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e135.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e118.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC5H11NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.015\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.018\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eArachidic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e917.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e311.2954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC20H40O2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC06425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.003\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.581\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003e(S)-4-Hydroxymandelate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e245.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e151.0336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC8H8O4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC03198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.007\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.121\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\" valign=\"bottom\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003ePipecolic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e96.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e129.0654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC6H11NO2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M+H]+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e1.646\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"3.8821954484605086%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.072289156626507%\"\u003e\n \u003cp\u003eL-Methionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e137.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.362784471218206%\"\u003e\n \u003cp\u003e148.0426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.512717536813922%\"\u003e\n \u003cp\u003eC5H11NO2S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.42570281124498%\"\u003e\n \u003cp\u003eC00073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.5595716198125835%\" valign=\"bottom\"\u003e\n \u003cp\u003e[M-H]-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.417670682730924%\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e2.285\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.087014725568943%\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.49665327978581%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.764390896921017%\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.157965194109773%\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Evaluating and validating the diagnostic ability of metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ascertain the diagnostic potential of specific metabolites, ROC analysis was employed to assess the diagnostic accuracy of individual metabolites. This analysis yielded that 9 DMs manifested statistically significant diagnostic capabilities, as evidenced by their respective values of AUC, sensitivity, specificity, and Youden index, which were listed in \u003cstrong\u003eTable 1\u003c/strong\u003e. Notably, metabolites such as 7-methylguanine, pipecolic acid, L-methionine, oxoglutaric acid, bilirubin, thymidine, and 4-hydroxyphenylacetaldehyde possessed predictive value for TNBC in serum samples obtained from HC subjects. In contrast, N-acetyl-D-tryptophan and ergothioneine showed negative predictive value. Among these metabolites, 7-methylguanine in serum samples exhibited the highest efficacy in distinguishing TNBC patients from healthy controls, demonstrated by its outstanding diagnostic metrics: an AUC of 0.992, sensitivity of 100%, specificity of 95.2%, and a Youden index of 0.952.\u003c/p\u003e\n\u003cp\u003eTo corroborate the results obtained from the initial discovery set, serum samples were procured from 7 individuals diagnosed with TNBC and 5 HC. These samples underwent analysis employing identical UHPLC\u0026ndash;MS procedures as those utilized for the discovery cohort. The preliminary phase involved a comparative analysis of the mean peak areas of 7-methylguanine between TNBC patients and healthy individuals across both cohorts. Findings demonstrated a significant elevation of 7-methylguanine levels in the serum samples of TNBC patients in both cohorts (p \u0026lt; 0.01; \u003cstrong\u003eFigure 3A\u003c/strong\u003e), indicating a consistent elevation of this metabolite in the context of TNBC. Subsequently, to ascertain the diagnostic utility of 7-methylguanine within a clinical setting, ROC curves were generated based on the relative peak areas of metabolites derived from the validation sample cohort. Within this validation cohort, 7-methylguanine exhibited an AUC of 0.971, with a sensitivity of 85.7% and specificity of 100%, corresponding to a Youden index of 0.857. These metrics closely paralleled those observed within the discovery set (\u003cstrong\u003eFigure 3B)\u003c/strong\u003e, reinforcing the potential of 7-methylguanine as a robust biomarker for TNBC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Metabolite Set Enrichment Analysis (MSEA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe findings indicate that TNBC is characterized by distinct metabolite profiles, implying alterations in metabolic biological networks. To delineate the disrupted metabolic pathways, informed by the altered set of DMs, comprehensive enrichment and pathway analyses were undertaken. The analyses revealed that the most significantly enriched pathways in TNBC patients include the malate-aspartate shuttle, alanine metabolism, spermidine and spermine biosynthesis, urea cycle, ammonia recycling, TCA cycle, gluconeogenesis, and aspartate metabolism, all of which demonstrated statistical significance (p-values \u0026lt; 0.05) as depicted in the bar chart in \u003cstrong\u003eSupplementary Figure 2.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp; \u0026nbsp;\u0026nbsp;The transcriptomics analysis for TNBC and HC tissue samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Identification of differentially expressed genes in TNBC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, three GEO datasets were scrutinized: GSE65194, GSE45827, and GSE36295. To ensure the dataset\u0026rsquo;s quality is reliable, a rigorous analytical approach was employed using the GEO2R tool, with selection criteria set at an absolute log fold change (|logFC|) exceeding 2 and an adjusted p-value below 0.05. This analysis yielded a discovery of 1,561 up-regulated and 1,035 down-regulated DEGs in the GSE65194 dataset (\u003cstrong\u003eFigure 4A\u003c/strong\u003e), 1,533 up-regulated and 1,047 down-regulated DEGs in GSE45827 (\u003cstrong\u003eFigure 4B\u003c/strong\u003e), and 77 up-regulated along with 137 down-regulated DEGs in GSE36295 \u003cstrong\u003e(Figure 4C\u003c/strong\u003e). Subsequent to the identification of DEGs within each dataset, an online Venn diagram tool was employed to intersect and visualize the DEGs across the three datasets, facilitating the identification of common DEGs. This analysis revealed a total of 160 DEGs demonstrating uniform expression trends across the datasets, encompassing 57 genes that were up-regulated and 103 that were down-regulated, as depicted in \u003cstrong\u003eFigure 4D\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Gene ontology and KEGG enrichment functional analysis of overlapping DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the biological categorization of the 160 common DEGs, functional and pathway enrichment analyses were executed utilizing the DAVID database. These investigations comprised GO enrichment analysis and KEGG pathways, which disclosed associations of the DEGs with 39 GO terms including BP, CC and MF, in addition to 2 significant pathways, as list in \u003cstrong\u003eSupplementary Table S1.\u003c/strong\u003e The threshold for deeming results statistically significant was established at a False Discovery Rate (FDR) below 0.05. As depicted in \u003cstrong\u003eFigure 5A\u003c/strong\u003e, the GO analysis explicitly highlighted that DEGs pertaining to BP were notably concentrated in areas such as cell division, mitotic spindle organization, and bacterial response and so on. For CC, a significant enrichment was observed in structures including the midbody, spindle, and condensed chromosome outer kinetochore. Furthermore, changes in MF were mainly enriched in microtubule binding. Regarding the KEGG pathway analysis, the DEGs were predominantly enriched in the PPAR signaling pathway and tyrosine metabolism (\u003cstrong\u003eFigure 5B\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Integrative analysis of metabolomics and transcriptomics data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo advance the systematic exploration of TNBC, a comprehensive biological pathway analysis was performed by linking important 22 DMs and the 160 DEGs through shared metabolic pathways with the Joint Pathway Analysis module on MetaboAnalyst 6.0. Our analysis unveiled three pathways of notable perturbation: tyrosine metabolism, phenylalanine metabolism, and glycolysis or gluconeogenesis, each characterized by p-values \u0026lt; 0.05 and impact\u0026ge;\u0026nbsp;0.5 (\u003cstrong\u003eFigure 6A, table 2\u003c/strong\u003e). Central DEGs linked to these pathways were enumerated in \u003cstrong\u003eTable 3.\u003c/strong\u003e To better understand the metabolite mechanism and gene dys-regulation, DMs and DEGs were introduced into the Metscape plug-in of the Cytoscape 3.7.1 database to collect the compound\u0026ndash;reaction\u0026ndash;enzyme\u0026ndash;gene network in combination with the top three enriched pathways (\u003cstrong\u003eFigure 6B\u003c/strong\u003e). Consequently, these investigations bolster the validity of the metabolites, genes, and pathways selected for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eJoint analysis pathways of differential metabolites and genes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.776173285198556%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.82310469314079%\"\u003e\n \u003cp\u003ePathway name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\"\u003e\n \u003cp\u003eMatch status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.592057761732853%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03610108303249%\"\u003e\n \u003cp\u003eImpact\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.776173285198556%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.82310469314079%\"\u003e\n \u003cp\u003eTyrosine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\"\u003e\n \u003cp\u003e6/88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.592057761732853%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03610108303249%\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.776173285198556%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.82310469314079%\"\u003e\n \u003cp\u003ePhenylalanine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\"\u003e\n \u003cp\u003e3/21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.592057761732853%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03610108303249%\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.776173285198556%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.82310469314079%\"\u003e\n \u003cp\u003eGlycolysis or Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.772563176895307%\"\u003e\n \u003cp\u003e4/61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.592057761732853%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.03610108303249%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eRelated differentially expressed genes by joint-pathway analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eEnriched pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003eFunction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003eMAOA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eTyrosine metabolism, Phenylalanine metabolism,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003emonoamine oxidase A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003eADH1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eTyrosine metabolism, Glycolysis or Gluconeogenesis,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003e\u0026quot;alcohol dehydrogenase 1B (class I), beta polypeptide\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003eADH1C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eTyrosine metabolism, Glycolysis or Gluconeogenesis,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003e\u0026quot;alcohol dehydrogenase 1C (class I), gamma polypeptide\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003eAOC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eTyrosine metabolism, Phenylalanine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003eamine oxidase copper containing 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003eTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eTyrosine metabolism, Phenylalanine metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003etyrosine aminotransferase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.262798634812286%\"\u003e\n \u003cp\u003ePCK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.70648464163823%\"\u003e\n \u003cp\u003eGlycolysis or Gluconeogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.03071672354949%\"\u003e\n \u003cp\u003ephosphoenolpyruvate carboxykinase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Verifications of six hub genes expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon integrating the outcomes derived from metabolomics and transcriptomics datasets, this study identified MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1 as potential key players in the pathogenesis of TNBC. The validation of RNA and protein expression levels of these e hub DEGs was conducted utilizing online tumor and normal clinical samples from the GEPIA and UALCAN platforms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUtilizing the GEPIA platform, we assessed the mRNA expression levels of six pivotal genes in a dataset comprising 135 TNBC specimens and 291 normal breast tissue specimens. This dataset was collated from the comprehensive resources of TCGA and GTEx database. This examination revealed a statistically significant reduction in the expression of these genes in TNBC in comparison to normal samples (p \u0026lt; 0.05, \u003cstrong\u003eFigure 7)\u003c/strong\u003e. Subsequent verification of protein expression levels through the UALCAN cancer database corroborated these findings, demonstrating a significant decrease in their expression within TNBC tissues relative to normal tissues (p \u0026lt; 0.05, \u003cstrong\u003eFigure 8\u003c/strong\u003e). These observations were further substantiated by immunohistochemical analyses sourced from the HPA database. Specifically, as depicted in \u003cstrong\u003eFigure 9\u003c/strong\u003e, the expression levels of MAOA, ADH1B, ADH1C, AOC3, and PCK1 were found to be downregulated in breast cancer tissues compared to normal tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6.\u0026nbsp; \u0026nbsp;\u0026nbsp;The survival analysis of hub genes in TNBC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier Plotter, an accessible online analytical tool, was utilized to conduct survival analyses predicated on gene expression levels, thereby evaluating the prognostic relevance of key genes. This analysis divided TNBC patient samples into dichotomous groups based on median mRNA expression levels of each gene, delineating cohorts with high versus low expression. Notably, AOC3 and PCK1 were identified as genes significantly associated with poor overall survival (OS). Results showed that overexpression of AOC3 (HR 95%CI =3.56 (1.62-7.8), log-rank P =0.00073) and PCK1 (HR 95%CI =2.86 (1.19-6.85), log-rank P =0.04) were associated with unfavorable OS of TNBC patients (\u003cstrong\u003eFigure 10\u003c/strong\u003e). Consequently, this evidence supports the hypothesis that AOC3 and PCK1 may function as potential biomarkers for prognostication in TNBC patient populations. Based on these results, it is hypothesized that AOC3 and PCK1 may serve as potential biomarkers for predicting the prognosis of TNBC patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTNBC is recognized as the most fatal subtype of BC characterized by low overall survival (OS) rates and high rates of invasion and metastasis, posing\u0026nbsp;an unmet\u0026nbsp;medical challenge\u0026nbsp;[18]. Clinical tumor markers such as carcinoembryonic antigen (CEA) and cancer antigen 15-3 (CA15-3) are frequently used in BC diagnosis; nevertheless, their specificity and accuracy fall short of clinical standards\u0026nbsp;[19]. Currently, there are no reliable biomarkers specifically for TNBC, highlighting a critical gap in diagnostic tools.\u0026nbsp;Metabolic reprogramming, a hallmark of cancer, presents new\u0026nbsp;prospects for cancer detection, prognosis, and treatment\u0026nbsp;[20, 21].\u0026nbsp;It has been proved that, metabolic dysregulation is linked to therapy response and clinical outcome\u0026nbsp;across various\u0026nbsp;cancer\u0026nbsp;types\u0026nbsp;and may impact the\u0026nbsp;tumorigenesis, progression, and prognosis of BC via pathways related to angiogenesis, anti-apoptosis, mitogenesis, chronic inflammation, increased visceral fat reserves, and other cancer-associated adipokines.\u0026nbsp;[22-25].\u0026nbsp;This study aims to identify more reliable and specific serum markers for diagnosing TNBC using a metabolomic approach. While metabolomics has been applied in numerous studies to discover novel biomarkers for TNBC, relying solely on this method does not fully elucidate TNBC pathophysiology. Therefore, this research also incorporates pathway and network analyses, integrating metabolomics and transcriptomics data to deepen our understanding of the interactions between selected metabolites and genes within dysregulated pathways.\u003c/p\u003e\n\u003cp\u003eThe current investigation employed an untargeted metabolomics strategy, leveraging ultra-high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS) and multivariate statistical analysis, to identify metabolites that exhibit altered levels in TNBC relative to HC. This comprehensive metabolomics analysis revealed 13 metabolites that were up-regulated and 9 metabolites that were down-regulated in TNBC. Notably, among these metabolites, 7-methylguanine emerged as a potential consistent biomarker for TNBC, as demonstrated through ROC analysis. Furthermore, metabolite set enrichment analysis illuminated several disrupted metabolic pathways critical to TNBC pathophysiology, including the malate-aspartate shuttle and the TCA cycle. These pathways play essential roles in cellular energy metabolism, suggesting their significant involvement in the metabolic reprogramming characteristic of TNBC.\u003c/p\u003e\n\u003cp\u003eTo enhance our comprehension of the underlying mechanisms of TNBC, we analyzed data amalgamated from three distinct GEO datasets. This comprehensive analysis included 26 samples of normal breast tissues and 101 samples of TNBC tissues. From this, we identified a total of 160 DEGs, consisting of 103 genes that were down-regulated and 57 that were up-regulated in TNBC tissues. Functional enrichment analysis of these DEGs highlighted their significant involvement in cell proliferation processes, such as cell division, mitotic spindle organization, chromosome segregation, and positive regulation of chromosome segregation, all of which align with the hallmark rapid proliferation characteristics of TNBC cells. Furthermore, KEGG pathway enrichment analysis revealed that the DEGs were predominantly associated with the PPAR signaling pathway and tyrosine metabolism. This suggests a critical role for these genes in regulating fatty acid and amino acid energy metabolism within TNBC cells.\u003c/p\u003e\n\u003cp\u003eThe integrative analysis of metabolomics and transcriptomics datasets has significantly advanced our understanding of the interplay between metabolic dysregulation and gene expression alterations in TNBC. This analysis has illuminated key pathways, including tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis, underlining the complex biological landscape of TNBC that transcends simple genomic alterations. The perturbation of these pathways likely mirrors the adaptive oncogenic processes characteristic of TNBC, presenting potential targets for therapeutic intervention. Importantly, the analysis delineated 2 DMs (4-hydroxyphenylacetaldehyde and oxalacetic acid) and 6 DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) as integral components of these pathways.\u0026nbsp;Further validation through the GEPIA, UALCAN and HPA databases revealed a consistent pattern of expression for these hub DEGs at both the RNA and protein levels, reinforcing their pivotal role in the pathophysiology of TNBC.\u003c/p\u003e\n\u003cp\u003eThe perturbation of tyrosine and phenylalanine metabolism has been associated with various pathologies, including gastroesophageal malignancies[26], non-small cell lung cancer[10], and BC[27]. Research conducted by Christofk et al.\u0026nbsp;[27]\u0026nbsp;highlighted that invasive BC cells, in the face of amino acid deprivation, harness the process of extracellular matrix internalization and lysosomal degradation as a means to procure amino acids. This adaptive mechanism plays a crucial role in supporting cellular proliferation and enhancing migration capabilities, evidencing a metabolic dependency on phenylalanine and tyrosine.\u0026nbsp;In our investigation, we identified that 4-hydroxyphenylacetaldehyde, along with the genes MAOA, AOC3, and TAT, were significantly enriched in the metabolic pathways of tyrosine and phenylalanine. This observation intimated\u0026nbsp;the potential reliance of\u0026nbsp;TNBC\u0026nbsp;cells on these metabolic pathways as a mechanism to drive tumorigenesis.\u003c/p\u003e\n\u003cp\u003eThe MAOA gene encodes the enzyme monoamine oxidase-A, present in both peripheral tissues and the central nervous system, and is crucial for breaking down monoamines such as norepinephrine (NE), epinephrine, and dopamine\u0026nbsp;[28]. Recent findings have shown that different cancer types exhibit unique patterns of MAOA regulation and functionality. Overexpression of MAOA has been identified in glioma\u0026nbsp;[29], classical Hodgkin lymphomas\u0026nbsp;[30], and prostate cancer[31]. Conversely, a trend towards decreased MAOA expression has been observed in pancreatic ductal adenocarcinoma\u0026nbsp;[32], hepatocellular carcinoma (HCC)\u0026nbsp;[28], and gastric cancer[33]. Notably, prior research has consistently reported a marked decrease in MAOA expression in invasive BC compared to noncancerous cells\u0026nbsp;[34]\u0026nbsp;and normal breast tissue\u0026nbsp;[35], corroborating the observations presented in our study. A recent report from Wang et al has shed light on the role of NE, derived from tyrosine, in modulating inflammatory immune responses within the tumor microenvironment through interactions with beta-adrenergic receptors (β-ARs), thereby influencing tumor cell invasion and migration [6]. Suppressing the effects of the NK cell-enriched environment and lessening the antitumor effect can be achieved by chemical sympathectomy or blocking the β-AR signaling pathway [10]. Additionally, it has been demonstrated that MAOA may affect the emergence and progression of cancers by deteriorating the neurotransmitters downstream, specifically NE, in cases of pancreatic and liver cancer. Our study reveals MAOA's involvement in the metabolism of tyrosine and phenylalanine, suggesting a disruption in amino acid metabolism in TNBC patients. Furthermore, a decrease in MAOA expression was observed in the TNBC cohort relative to the control group at both the mRNA and protein levels. This leads us to hypothesize that TNBC may exhibit elevated NE levels, potentially activating immune cells for antitumor responses, a hypothesis that warrants further investigation.\u003c/p\u003e\n\u003cp\u003eThe AOC3 gene is responsible for encoding amine oxidase copper-containing 3, a membrane-bound adhesion protein, also known as vascular adhesion protein 1 (VAP1)\u0026nbsp;[36]. This multifunctional molecule, primarily found in vascular endothelium and pericytes, plays a crucial role in facilitating leukocyte anchoring and trafficking to inflammatory tissues\u0026nbsp;[37]. Research suggests that VAP-1 contributes to the adherence of tumor-infiltrating lymphocytes to various carcinomas, aiding in the destruction of cancer cells\u0026nbsp;[38]. Evidence from several studies suggests a different role of AOC3, wherein it has been implicated in promoting\u0026nbsp;the development of cancers such as melanoma and lymphoma\u0026nbsp;[39], yet paradoxically, and its expression is decreased in certain aggressive cancer forms, including prostate and colorectal cancers\u0026nbsp;[38, 39]. Our studies, incorporating GEPIA and CPTAC data, indicate a significant reduction of AOC3 in TNBC, implying the reduction of AOC3 may be linked to the tumor aggressiveness. Additionally, our research has elucidated an association between low expression of AOC3 and poor prognostic outcomes in TNBC, thereby proposing its utility as a prognostic biomarker. This proposition is further corroborated by proteomic analyses from\u0026nbsp;Shaheed et al[40]\u0026nbsp;comparing neoplastic breast tissue to benign counterparts, where a discernible reduction in AOC3 expression was observed, intimating at its prognostic relevance in BC.\u003c/p\u003e\n\u003cp\u003eThe precise mechanisms underpinning the relationship between low AOC3 expression levels and unfavorable prognostic outcomes remain elusive. Nonetheless, one potential mechanism to consider is the impact on tumor immunity. AOC3 supports lymphocyte adherence to endothelial cells, promoting lymphocyte aggregation within tumor vesicles. This triggers a local immune response by activating tumor-infiltrating lymphocytes, potentially inhibiting tumor growth\u0026nbsp;[41]. The absence or reduction of AOC3 expression in TNBC could theoretically attenuate local immune responses, thereby contributing to a worse prognosis. It is imperative that further investigative endeavors are undertaken to elucidate and substantiate this hypothesized linkage.\u003c/p\u003e\n\u003cp\u003eThe TAT gene plays an indispensable role in the biosynthesis of tyrosine aminotransferase, a liver-specific mitochondrial enzyme that plays a crucial role in converting tyrosine into harmless molecules. These molecules are then either expelled through the renal pathway or utilized in metabolic processes to generate energy. It has been reported that the mutations in the TAT gene result in an enzyme deficiency, causing a harmful accumulation of tyrosine and its derivatives\u0026nbsp;[42]. This buildup can damage vital organs such as the liver, kidneys, and nervous system, as well as other tissues, by disrupting their normal functions. One evidence has shown that reduced expression of TAT is observed in HCC, implicating a contributory role in the pathogenesis of this malignancy. Further in vitro analyses have indicated that TAT is instrumental in mediating apoptotic pathways and exhibiting anti-oncogenic effects, highlighting a significant association with the development and progression of HCC\u0026nbsp;[42]. Our work has revealed a marked decrease in TAT levels and an increase in 4-hydroxyphenylacetaldehyde, a tyrosine metabolite, in patients with TNBC compared to the control group. This observation not only intimated a perturbation in tyrosine metabolism within TNBC but also intimated that the downregulation of TAT may contribute to progression of TNBC.\u003c/p\u003e\n\u003cp\u003eCancer cells are characterized by a significant reprogramming of cellular energy metabolism, a phenomenon predominantly illustrated by the Warburg effect[21].This phenomenon, observed even in oxygen-rich environments, is marked by a significantly increase in glucose uptake, enhanced glycolysis, and augmented production of lactic acid within tumor cells\u0026nbsp;[43, 44]. Concurrently, gluconeogenesis, the synthesis of glucose from non-carbohydrate sources like glucogenic amino acids, pyruvate, lactate, and glycerol, is typically suppressed due to the preferential activation of the glycolysis pathway in cancer cells[45]. In our investigation, we observed an enrichment of oxaloacetic acid and PCK1 during the metabolic processes of glycolysis and gluconeogenesis. This observation indicates a critical involvement of these components in facilitating the interconnected pathways of energy metabolism within cancer cells, suggesting their potential roles in metabolic reprogramming associated with oncogenesis.\u003c/p\u003e\n\u003cp\u003eThe PCK1 gene, localized on the chromosomal region 20q13.31 in humans, demonstrates variable expression in various tumor types, manifesting overexpression in colorectal and melanoma malignancies while exhibiting underexpression in HCC and renal cell carcinoma\u0026nbsp;[46, 47]. Research by Bian et al. has demonstrated that enhancing the stability of the PCK1-encoded protein can increase gluconeogenesis, decrease glycolysis, and suppress the proliferation of cancer cells\u0026nbsp;[47].\u003c/p\u003e\n\u003cp\u003eAs a pivotal enzyme in gluconeogenesis, PCK1 catalyzes the conversion of oxaloacetic acid into phosphoenolpyruvate. Our findings indicate a notable increase in oxaloacetate levels accompanied by reduced PCK1 expression TNBC patients, indicating an inhibition of the gluconeogenesis. Additionally, our work identified a significant association between PCK1 overexpression and decreased OS in TNBC patients (P \u0026lt; 0.05), aligning with the outcomes of prior research[43].\u003c/p\u003e\n\u003cp\u003eOur findings represent a groundbreaking contribution towards identifying potential biomarkers for TNBC. Nevertheless, these promising findings are tempered by certain limitations inherent in our study, such as the relatively small sample size, and the imperative for subsequent validation across larger and more varied cohorts. Moreover, the analytic procedures employed necessitate rigorous replication and standardization prior to their integration into clinical application.\u003c/p\u003e\n\u003cp\u003eIn conclusion, our research underlines the utility of combining metabolomic and transcriptomic analyses to provide a more comprehensive view of the complexities of TNBC. It unveils potential diagnostic biomarkers and therapeutic targets, offering promising avenues for revolutionizing TNBC management. The imperative for future investigations to validate these findings and extend the omics-based methodology to additional cancer subtypes is clear. Such endeavors are crucial for propelling the field of personalized medicine forward in the realm of oncology, potentially enhancing patient outcomes through more tailored and effective treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of\u0026nbsp;the Second Affiliated Hospital of Fujian Medical University\u0026nbsp;(Approval number: 2021[168]). Written informed consent was obtained from individual or guardian participants.\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\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research was funded by the National Natural Science Foundation of China [NSFC 62072107] and the Natural Science Foundation of Fujian Province [2021J01278].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: SG and RH. Acquisition of data: JH, SC, XC, XD, LL and YZ. Analysis and interpretation of data: SG and QW. Drafting of the manuscript: SG and CF. Critical revision of the manuscript for important intellectual content: all the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe extend our heartfelt gratitude to all the patients and healthy volunteers who participated in this research, acknowledging their invaluable contribution. Our special thanks are directed towards the staff of the Department of Laboratory Medicine at the Second Affiliated Hospital of Fujian Medical University for their assistance. Additionally, we express our appreciation to the reviewers and the editor for their insightful feedback and constructive comments, which have significantly enriched this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data associated with this article can be found in Supplementary Table S1 and Supplementary Figures.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJ. Ma, C. Chen, S. Liu\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis, Cancer Gene Ther, 29(11)(2022) 1578-1589.\u003c/li\u003e\n \u003cli\u003eQ. Yuan, L. Zheng, Y. Liao\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis, World J Surg Oncol, 19(1)(2021).\u003c/li\u003e\n \u003cli\u003eR. Bissanum, R. Kamolphiwong, R. Navakanitworakul\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Integrated bioinformatic analysis of potential biomarkers of poor prognosis in triple-negative breast cancer, Translational Cancer Research, 11(9)(2022) 3039-3049.\u003c/li\u003e\n \u003cli\u003eX. Lin, S. He, S. Wu\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Diagnostic biomarker panels of osteoarthritis: UPLC-QToF/MS-based serum metabolic profiling, Peerj, 11(2023).\u003c/li\u003e\n \u003cli\u003eL. Li, X. Zheng, Q. Zhou\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Metabolomics-Based Discovery of Molecular Signatures for Triple Negative Breast Cancer in Asian Female Population, Scientific reports, 10(1)(2020) 370.\u003c/li\u003e\n \u003cli\u003eY. Yamashita, S. Nishiumi, S. Kono\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Differences in elongation of very long chain fatty acids and fatty acid metabolism between triple-negative and hormone receptor-positive breast cancer, BMC Cancer, 17(1)(2017) 589.\u003c/li\u003e\n \u003cli\u003eA. Beatty, L.S. Fink, T. Singh\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Metabolite Profiling Reveals the Glutathione Biosynthetic Pathway as a Therapeutic Target in Triple-Negative Breast Cancer, Mol Cancer Ther, 17(1)(2018) 264-275.\u003c/li\u003e\n \u003cli\u003eN.J. Lanning, J.P. Castle, S.J. Singh\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Metabolic profiling of triple-negative breast cancer cells reveals metabolic vulnerabilities, Cancer \u0026amp; metabolism, 5(1)(2017) 6.\u003c/li\u003e\n \u003cli\u003eE.P. Resurreccion, K.-w. Fong, The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer, Metabolites, 12(6)(2022).\u003c/li\u003e\n \u003cli\u003eH.-S. Lee, C. Ruiying, L. Zeyun\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, A comprehensive analysis of metabolomics and transcriptomics in non-small cell lung cancer, PLoS One, 15(5)(2020).\u003c/li\u003e\n \u003cli\u003eY. Peng, D. Yin, X. Li\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Integration of transcriptomics and metabolomics reveals a novel gene signature guided by FN1 associated with immune response in oral squamous cell carcinoma tumorigenesis, J Cancer Res Clin Oncol, 149(9)(2023) 6097-6113.\u003c/li\u003e\n \u003cli\u003eB. Du, F. Zhang, Q. Zhou\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Joint analysis of the metabolomics and transcriptomics uncovers the dysregulated network and develops the diagnostic model of high-risk neuroblastoma, Sci Rep, 13(1)(2023).\u003c/li\u003e\n \u003cli\u003eC.A. Smith, E.J. Want, G. O\u0026apos;Maille\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification, Anal Chem, 78(3)(2006) 779-787.\u003c/li\u003e\n \u003cli\u003eM. Zhang, H. Chen, M. Wang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Bioinformatics analysis of prognostic significance of COL10A1 in breast cancer, Biosci Rep, 40(2)(2020).\u003c/li\u003e\n \u003cli\u003eD. Chandrashekar, B. Bashel, S. Balasubramanya\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses, Neoplasia, 19(8)(2017) 649-658.\u003c/li\u003e\n \u003cli\u003eB. Ying, W. Xu, Y. Nie\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, HSPA8 Is a New Biomarker of Triple Negative Breast Cancer Related to Prognosis and Immune Infiltration, Dis Markers, 2022(2022) 8446857.\u003c/li\u003e\n \u003cli\u003eS. Gong, Q. Wang, J. Huang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer, Methods, 222(2024) 100-111.\u003c/li\u003e\n \u003cli\u003eY. Gong, P. Ji, Y.S. Yang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Metabolic-Pathway-Based Subtyping of Triple-Negative Breast Cancer Reveals Potential Therapeutic Targets, Cell Metab, 33(1)(2021) 51-64 e59.\u003c/li\u003e\n \u003cli\u003eK.S.S. Sai Baba, M.A. Rehman, J. Pradeep Kumar\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Serum Human Epididymis Protein-4 (HE4) - A novel Approach to Differentiate Malignant Frombenign Breast Tumors, Asian Pac J Cancer Prev, 22(8)(2021) 2509-2507.\u003c/li\u003e\n \u003cli\u003eU. Martinez-Outschoorn, M. Peiris-Pag\u0026eacute;s, R. Pestell\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Cancer metabolism: a therapeutic perspective, Nat Rev Clin Oncol 14(1)(2017) 11-31.\u003c/li\u003e\n \u003cli\u003eZ. Li, C. Sun, Z. Qin, Metabolic reprogramming of cancer-associated fibroblasts and its effect on cancer cell reprogramming, Theranostics, 11(17)(2021) 8322-8336.\u003c/li\u003e\n \u003cli\u003eJ. Li, K. Liu, Z. Ji\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Serum untargeted metabolomics reveal metabolic alteration of non-small cell lung cancer and refine disease detection, Cancer Sci, 114(2)(2023) 680-689.\u003c/li\u003e\n \u003cli\u003eS. Nunes, J. Sousa, F. Silva\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Peripheral Blood Serum NMR Metabolomics Is a Powerful Tool to Discriminate Benign and Malignant Ovarian Tumors, Metabolites, 13(9)(2023).\u003c/li\u003e\n \u003cli\u003eS. Wu, W. Xue, H. Yu\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Serum uric acid levels and health outcomes in CKD: a prospective cohort study, Nephrol Dial Transplant, 10.1093/ndt/gfad201(2023).\u003c/li\u003e\n \u003cli\u003eX. Zhang, X. Kang, L. Jin\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, ABCC9, NKAPL, and TMEM132C are potential diagnostic and prognostic markers in triple‐negative breast cancer, Cell Biol Int, 44(10)(2020) 2002-2010.\u003c/li\u003e\n \u003cli\u003eT. Wiggins, S. Kumar, S.R. Markar\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Tyrosine, Phenylalanine, and Tryptophan in Gastroesophageal Malignancy: A Systematic Review, Cancer Epidemiol Biomarkers Prev, 24(1)(2015) 32-38.\u003c/li\u003e\n \u003cli\u003eH. Christofk, M. Nazemi, B. Yanes\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, The extracellular matrix supports breast cancer cell growth under amino acid starvation by promoting tyrosine catabolism, PLoS Biol, 22(1)(2024).\u003c/li\u003e\n \u003cli\u003eY.Y. Pang, J.D. Li, L. Gao\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, The clinical value and potential molecular mechanism of the downregulation of MAOA in hepatocellular carcinoma tissues, Cancer Medicine, 9(21)(2020) 8004-8019.\u003c/li\u003e\n \u003cli\u003eS. Kushal, W. Wang, V.P. Vaikari\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Monoamine oxidase A (MAO A) inhibitors decrease glioma progression, Oncotarget, 7(12)(2016) 13842-13853.\u003c/li\u003e\n \u003cli\u003eP. Li, I. Siddiqi, A. Mottok\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Monoamine oxidase A is highly expressed in classical Hodgkin lymphoma, J Pathol, 243(2)(2017) 220-229.\u003c/li\u003e\n \u003cli\u003eS. Gaur, M. Gross, C. Liao\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Effect of Monoamine oxidase A (MAOA) inhibitors on androgen-sensitive and castration-resistant prostate cancer cells, Prostate, 79(6)(2019) 667-677.\u003c/li\u003e\n \u003cli\u003eV.O. Oria, P. Bronsert, A.R. Thomsen\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Proteome Profiling of Primary Pancreatic Ductal Adenocarcinomas Undergoing Additive Chemoradiation Link ALDH1A1 to Early Local Recurrence and Chemoradiation Resistance, Transl Oncol, 11(6)(2018) 1307-1322.\u003c/li\u003e\n \u003cli\u003eY. Wang, S. Wang, Q. Yang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Norepinephrine Enhances Aerobic Glycolysis and May Act as a Predictive Factor for Immunotherapy in Gastric Cancer, Journal Of Immunology Research, 2021(2021) 1-13.\u003c/li\u003e\n \u003cli\u003eY. Ren, H. Jiang, D. Ma\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Parkin degrades estrogen-related receptors to limit the expression of monoamine oxidases, Hum Mol Genet, 20(6)(2011) 1074-1083.\u003c/li\u003e\n \u003cli\u003eA. Daniel, A. Gaviglio, T. Knutson\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Progesterone receptor-B enhances estrogen responsiveness of breast cancer cells via scaffolding PELP1- and estrogen receptor-containing transcription complexes, Oncogene, 34(4)(2015) 506-515.\u003c/li\u003e\n \u003cli\u003eS.-J. Chang, H.-P. Tu, Y.-C.C. Lai\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Increased Vascular Adhesion Protein 1 (VAP-1) Levels Are Associated with Alternative M2 Macrophage Activation and Poor Prognosis for Human Gliomas, Diagnostics, 10(5)(2020).\u003c/li\u003e\n \u003cli\u003eM. Salmi, S.J.A. Jalkanen, r. signaling, Vascular Adhesion Protein-1: A Cell Surface Amine Oxidase in Translation, Antioxid Redox Signal, 30(3)(2019) 314-332.\u003c/li\u003e\n \u003cli\u003eJ. Chen, J. Xi, Y. Tian\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Identification, prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody‐based assays on tissue specimens, Proteomics, 13(15)(2013) 2268-2277.\u003c/li\u003e\n \u003cli\u003eS.T. Ward, C.J. Weston, E.L. Shepherd\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Evaluation of serum and tissue levels of VAP-1 in colorectal cancer, BMC Cancer, 16(1)(2016).\u003c/li\u003e\n \u003cli\u003eS. Shaheed, N. Rustogi, A. Scally\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Identification of stage-specific breast markers using quantitative proteomics, J Proteome Res, 12(12)(2013) 5696-5708.\u003c/li\u003e\n \u003cli\u003eW. Sun, J. Choi, Y. Cha\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Evaluation of the Expression of Amine Oxidase Proteins in Breast Cancer, Int J Mol Sci, 18(12)(2017).\u003c/li\u003e\n \u003cli\u003eL. Fu, S.-S. Dong, Y.-W. Xie\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Down-regulation of tyrosine aminotransferase at a frequently deleted region 16q22 contributes to the pathogenesis of hepatocellular carcinoma, Hepatology, 51(5)(2010) 1624-1634.\u003c/li\u003e\n \u003cli\u003eB. Alshehri, Prognostic significance and expression pattern of glucose related genes in breast cancer: A comprehensive computational biology approach, Saudi J Biol Sci, 31(1)(2024).\u003c/li\u003e\n \u003cli\u003eM. Shah, V. Shrivastava, S. Sofi\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Chlorogenic Acid Restores Ovarian Functions in Mice with Letrozole-Induced Polycystic Ovarian Syndrome Via Modulation of Adiponectin Receptor, Biomedicines, 11(3)(2023).\u003c/li\u003e\n \u003cli\u003eI. Yustisia, R. Amriani, H. Cangara\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, High expression of FBP1 and LDHB in fibroadenomas and invasive breast cancers, Breast Disease, 40(4)(2021) 251-256.\u003c/li\u003e\n \u003cli\u003eZ. Wang, C. Dong, Gluconeogenesis in Cancer: Function and Regulation of PEPCK, FBPase, and G6Pase, Trends in Cancer, 5(1)(2019) 30-45.\u003c/li\u003e\n \u003cli\u003eX.-l. Bian, H.-z. Chen, P.-b. Yang\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e, Nur77 suppresses hepatocellular carcinoma via switching glucose metabolism toward gluconeogenesis through attenuating phosphoenolpyruvate carboxykinase sumoylation, Nature Communications, 8(1)(2017).\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Triple-negative breast cancer, Metabolomics, Transcriptomics, Biomarker, Pathways","lastPublishedDoi":"10.21203/rs.3.rs-4365055/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4365055/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTriple-negative breast cancer (TNBC) is recognized for its aggressive nature, lack of effective diagnosis and treatment, and generally poor prognosis. The objective of this study was to investigate the metabolic changes in TNBC using metabolomics approaches and to explore underlying mechanisms through integrated analysis with transcriptomics. In this study, serum untargeted metabolic profiles were firstly explored between 18 TNBC and 21 healthy controls (HC) by liquid chromatography-mass spectrometry (LC-MS), identifying a total of 22 significantly altered metabolites (DMs). Subsequently, the receiver operating characteristic analysis revealed that 7-methylguanine could serve as a potential biomarker for TNBC in both the discovery and validation sets. Additionally, transcriptomic datasets were retrieved from the GEO database to identify differentially expressed genes (DEGs) between TNBC and normal tissues. An integrative analysis of the DMs and DEGs was subsequently conducted, uncovering potential molecular mechanisms underlying TNBC. Notably, three pathways\u0026mdash;tyrosine metabolism, phenylalanine metabolism, and glycolysis/gluconeogenesis\u0026mdash;were enriched, explaining the energy metabolism disorders in TNBC. Within these pathways, two DMs (4-hydroxyphenylacetaldehyde and oxaloacetic acid) and six DEGs (MAOA, ADH1B, ADH1C, AOC3, TAT, and PCK1) were identified as critical components. In summary, this study highlights metabolic biomarkers that could potentially be utilized for the diagnosis and screening of TNBC. The comprehensive analysis of metabolomics and transcriptomics data provides a validated and in-depth understanding of TNBC metabolism.\u003c/p\u003e","manuscriptTitle":"Comprehensive analysis of the metabolomics and transcriptomics uncovers the dysregulated network and potential biomarkers of Triple Negative Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 09:11:25","doi":"10.21203/rs.3.rs-4365055/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-26T04:39:09+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-26T04:27:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-06T15:40:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2024-05-03T11:51:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a519a7c8-dbdd-40de-b884-94fd68fdcea9","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T16:01:54+00:00","versionOfRecord":{"articleIdentity":"rs-4365055","link":"https://doi.org/10.1186/s12967-024-05843-y","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2024-11-11 15:57:30","publishedOnDateReadable":"November 11th, 2024"},"versionCreatedAt":"2024-06-07 09:11:25","video":"","vorDoi":"10.1186/s12967-024-05843-y","vorDoiUrl":"https://doi.org/10.1186/s12967-024-05843-y","workflowStages":[]},"version":"v1","identity":"rs-4365055","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4365055","identity":"rs-4365055","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}