Metabolomics and transcriptomics joint analysis reveals altered amino acid metabolism in esophageal squamous cell carcinoma

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Objectives This study aims to explore the altered metabolic pathways for ESCC through joint-pathway analysis of differentially expressed metabolites and genes. Methods Differentially expressed metabolites in ESCC were collected from published tissue-based metabolomics studies. Differentially expressed genes in ESCC were obtained using bioinformatic analysis of online ESCC transcriptome data. Then, joint-pathway analysis was performed to explore the altered metabolic pathways in ESCC. Immunohistochemistry (IHC) staining and arginine-deprivation experiments were conducted to verified the key enzymes in metabolic pathway and their potential function in ESCC. Results A total of 9 tissue-based metabolomics studies revealed 495 differentially expressed metabolites in ESCC. Enrichment analysis of the 69 high-frequency metabolites, defined as reported by over 2 studies, showed that the top enriched pathways were urea cycle, arginine and proline metabolism and ammonia recycling. Besides, bioinformatic analysis of a dataset (GSE53625) showed 2679 differentially expressed genes in ESCC. Joint-pathway analysis illustrated that the top 5 significantly altered metabolic pathways were glycerolipid metabolism, ascorbate and aldarate metabolism, histidine metabolism, arginine and proline metabolism, and linoleic acid metabolism. IHC staining and arginine-deprivation experiments revealed the up-regulating of arginine transporter (CAT1) and characteristic of arginine-dependent proliferation in ESCC. Conclusions This study revealed the altered amino acid metabolism, especially arginine and proline metabolism, as the most significant metabolic characteristic in ESCC. However, further functional study is needed. esophageal squamous cell carcinoma metabolic pathway joint-pathway analysis amino acid metabolism arginine and proline metabolism. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Esophageal cancer is an extremely dangerous form of cancer with an aggressive nature and high mortality rate. It is ranked 6th among the leading causes of cancer-related deaths worldwide and is the 8th most prevalent form of cancer globally. The 5-year survival rate is only around 15%-25%(Domper Arnal et al., 2015 ), and this highlights the challenging aspect of treating this disease. Esophageal cancer has two main pathological types: adenocarcinoma (EAC) and squamous cell carcinoma (ESCC), with the latter accounting for 80% of all esophageal cancer cases(Xi et al., 2022 ). ESCC has a discouraging prognosis and a high fatality rate mainly due to its difficult detection in the early stages. It is typically identified at later disease stages by enhanced thoracic computerized tomography (CT) and gastroscopy(Baba et al., 2018 ). Although surgical resection, radiotherapy, and chemotherapy are the primary clinical treatments for ESCC, their efficacy is limited, and they often have severe adverse effects(Yang et al., 2020 ). As a result, it is crucial to explore new therapeutic options and targets, particularly those focused on future research and development. It is well-known that metabolic reprogramming is one of the hallmarks of cancer(Ward and Thompson, 2012 ), and emerging evidence has revealed that tumor cells undergo metabolic reprogramming to fuel their proliferation and differentiation(Sun et al., 2019 ). As a result, identifying therapeutic targets or biomarkers for cancer based on altered metabolome has emerged as a promising strategy(Martinez-Outschoorn et al., 2017 ). Metabolomics is the study of small molecule metabolites, typically less than 1000, in a biological system such as cell, tissue, organ, or organism. There are three primary analytical platforms for metabolomics, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance spectroscopy (NMR), and each with their unique analytical range. Unlike other "omics" approaches, metabolomics can provide a snapshot of changes at the biochemical level, making it a highly sensitive tool for identifying pathological variants(Griffin and Shockcor, 2004 ) , (Schmidt et al., 2021 ) , (Nicholson et al., 2002 ). While several metabolomics studies have investigated the metabolomic profile of ESCC(Xu et al., 2013 ; Zhang et al., 2012 ), the results reported by each study are limited by the analytical coverage of the platforms utilized, such as LC-MS, GC-MS, and NMR. As a consequence, the number of differentially expressed metabolites reported in each study is relatively small. Furthermore, the results of these studies have sometimes been contradictory, making it challenging to determine the precise changes in metabolite levels associated with ESCC. Thus, integrating different metabolomics studies, carefully analyzing and weighting the results from each, is critical for identifying the metabolic processes that are truly altered in ESCC. By doing so, researchers can provide effective targets for the treatment of ESCC and enhance the understanding of its pathogenesis. A multi-molecule level approach that systematically combines genes and metabolites can provide a new direction for disease research, as studying biomolecular changes at a single level is insufficient for systems biology research(Hasin et al., 2017 ; Yan et al., 2018 ). Controversial results have emerged in recent years regarding multi-omics studies on ESCC, due to the lack of strict inclusion criteria such as sample size, sample type, clinical information, metabolomics testing methods, and other factors. Thus, it is essential to systematically review and select appropriate studies for multi-omics analysis of ESCC, in order to explore the molecular features and potential targets. This study collected differentially expressed metabolites from published studies and obtained differentially expressed genes through bioinformatic analysis of online data. Joint-pathway analysis of gene and metabolite was utilized to investigate metabolic alterations and identify potential therapeutic targets in ESCC. Key enzymes in the feature metabolic pathway were validated by immunohistochemistry (IHC) staining. These findings may offer promising biomarkers and therapeutic strategies for ESCC. 2. Materials and Methods 2.1. Collection of differentially expressed metabolites and pathway analysis To collect tissue-based metabolomics studies on ESCC, a literature search was conducted on PubMed ( https://pubmed.ncbi.nlm.nih.gov/ ), using the following inclusion criteria: 1) based on metabolomics, 2) ESCC tissue samples, and 3) complete metabolomics results and patient clinical information. A total of 9 metabolomics studies met the criteria, and 495 unique differential metabolites were obtained after removing duplicates. High-frequency metabolites refers to metabolites that appeared in two or more studies with consistent or inconsistent trends. Enrichment analysis was performed using online software MetaboAnalyst ( https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml ). This method utilizes SMPDB, which included 99 metabolite sets based on normal human metabolic pathways, as the metabolite set library for enrichment analysis. 2.2. Bioinformatics analysis of ESCC transcriptome Based on sample size and clinical information integrity, microarray data, which was deposited in Gene Expression Omnibus (GEO) under accession number GSE53625 (Agilent-038314 CBC Homo sapiens lncRNA + mRNA microarray V2.0), were processed as described previously(Li et al., 2021 ). In briefly, the probe sets in GSE53625 were re-annotated by mapping all sequences provided in GPL18109 annotation file to human genome (hg38) using SeqMap(Qu et al., 2022 ). Probes that were mapped to protein-coding transcripts were remained. Average value was used for the genes with multiple probes. Differentially expression analysis between cancer and normal tissues was performed using R package limma (version 3.6.3). KEGG pathway enrichment analysis of differential genes was conducted using online tool "bioinformatics network analysis" from online software DAVID ( https://david.ncifcrf.gov/ ). 2.3. Joint-pathway analysis of differential metabolite and gene To gain a comprehensive understanding of metabolic reprogramming in esophageal squamous cell carcinoma (ESCC), we employed the joint-pathway analysis method from Metaboanalyst ( https://www.metaboanalyst.ca/ ) to perform integrative analysis of differentially expressed metabolites and genes in ESCC. This method utilizes hypergeometric testing for enrichment analysis, degree centrality for topology measurement, and combine p values (unweighted) for integration of results. 2.4. Immunohistochemistry (IHC) staining ESCC tissue samples were collected from 119 patients recruited after histopathologic confirmation of ESCC and radical resection at Zhejiang Cancer Hospital, China, from May 2010 to December 2012. The clinical stages of ESCC patients were determined based on the American Joint Committee on Cancer 8th edition staging system. This study was approved by the Institutional Ethical Review Board of Zhejiang Cancer Hospital and all patients were informed about and gave their consent for the study before surgery. Detailed clinical information is presented in the table (Supplemental file3: Table S9) . The IHC staining procedure was processed as described previously(Zhu et al., 2020 ). In briefly, the tissue microarray slides were deparaffinized in xylene and gradient ethanol. Slides were immunohistochemically stained according to the manufacturer's instructions. Antibodies for identification of protein expression of CAT1 (Affinity Biosciences, Jiangsu, China; Cat#:DF13433) at a dilution of 1:200, ASS1 (Affinity Biosciences, Jiangsu, China; Cat#:BF0242) at a dilution of 1:500, and ODC1 (Affinity Biosciences, Jiangsu, China; Cat#:DF6712) at a dilution of 1:200 were used in this study. The IHC staining results were reviewed by a pathologist, and the staining score was defined as multiplying the percentage of positive cells by staining intensity. 2.5. Arginine-deprivation experiments Human ESCC KYSE150 and KYSE30 cell lines were purchased from Nanjing Kebai Biotechnology Co., Ltd. (Nanjing, China) in 2016, and authenticated by a short tandem repeat (STR) report by Shanghai biowing biotechnology Co., Ltd (Nanjing, China) in 2019. The cells were cultured in RPMI 1640 (Gibco, Thermo Fisher Scientific, USA) supplemented with 10% fetal bovine serum (FBS) and 100 U/mL penicillin/100 µg/mL streptomycin at 37°C under 5% CO 2 . CCK8 assay Cells were seeded into 96-well plates at a density of 3000 cells per well and incubated at 37°C and 5% CO 2 with costumed (without arginine) RPMI 1640 Medium (Coolaber Technology Co., Ltd., Beijing, China) adding a series of concentrations (0, 0.25, 0.5, 1.0, 1.5, 2.0 mM) of arginine. CCK8 assay (APExBIO Technology LLC, USA) was performed according to the product's protocol at a series of time points (24, 48, 72, and 96 hours). Each experiment consisted of five replicates and was repeated at least three times. Clone formation assay Cells were seeded in 6-well plate at a density of 1000 cells per well, and incubated under the same conditions as described above. After 3 weeks, the cells were fixed with 4% polyformaldehyde for 20 minutes, and stained with 1% crystal violet, and the numbers of colonies containing more than 50 cells were counted microscopically. The experiment was performed in triplicate and repeated three times. 3. Results 3.1. Differentially expressed metabolites from 9 ESCC tissue-based metabolomics studies With the keywords of "ESCC", "esophageal squamous cell carcinoma", and "metabolomics ", a total of 58 related articles retrieved. After systematically review, in which studies of non-ESCC tissue-based research or with incomplete information were excluded, a total of 9 eligible metabolomics studies(Chen et al., 2020 ; Chen et al., 2021 ; Sun et al., 2019 ; Tokunaga et al., 2018 ; Wang et al., 2013 ; Wu et al., 2009 ; Xu et al., 2022 ; Yang et al., 2022 ; Zang et al., 2021 ) was remained ( Table 1 ). The 9 studies included the three primary analytical platforms for metabolomics (NMR, GC-MS, LC-MS). Besides, mass spectrometry imaging (MSI), capillary electrophoresis-mass spectrometry (CE-MS) were also used ( Fig. 1 A ) . After collecting differential metabolites from the 9 studies, there were 740 differential metabolites including duplicates (Supplemental file1: Table S1 ) . After removing exogenous compounds and duplicate metabolites, 495 unique differential metabolites were remained, including 327 up-regulated and 168 down-regulated ones. In terms of high frequency metabolite, there was a total of 69 metabolites reported in over two studies, of which 11 had inconsistent change trends and 58 had consistent trend ( Fig. 1 B, Table 2 , Supplemental file1: Table S2 ) . Enrichment analysis of 495 differential metabolites revealed a total of 42 significant metabolic pathways (FDR < 0.05) ( Fig. 1 C, Supplemental file1: Table S3 ) , and the top 5 were apartate metabolism; glycine and serine metabolism; urea cycle; nicotinate and nicotinamide metabolism; glutamate metabolism. Enrichment analysis of 327 up-regulated metabolites revealed 18 significantly enriched metabolic pathways and the top 5 metabolic pathways were aspartate metabolism; glycine and serine metabolism; nicotinate and nicotinamide metabolism; purine metabolism; methionine metabolism ( Fig. 1 D, Supplemental file1: Table S4) . While pathway enrichment analysis of 168 down-regulated metabolites showed that there were 47 significantly enriched metabolic pathways and the top 5 included warburg effect; citric acid cycle; gluconeogenesis; galactose metabolism; purine metabolism ( Fig. 1 E, Supplemental file1: Table S5) . Furthermore, Enrichment analysis of 69 high-frequency metabolites, there were a total of 19 metabolic pathways were significantly enriched, and the top 5 pathways were urea cycle; arginine and proline metabolism; ammonia recycling; aspartate metabolism; glycine and serine metabolism ( Fig. 1 F, Supplemental file1: Table S6) . Table 1 Nine eligible ESCC tissue-based metabolomics research articles Studies Journal Year Platform a Paired b Sample c Cmpd d DOI Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry J Chromatogr B Analyt Technol Biomed Life Sci. 2009 GC-MS Yes Tissue (tumor = 20 and normal = 20) 20 DOI: 10.1016/j.jchromb.2009.07.039 1H-NMR based metabonomic profiling of human esophageal cancer tissue Mol Cancer. • • • . 2013 NMR No Tissue (tumor = 89 and normal = 26) 42 DOI: 10.1186/1476-4598-12-25 Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry Int J Oncol. 2018 CE-TOFMS Yes Tissue (tumor = 35 and normal = 35) 110 DOI: 10.3892/ijo.2018.4340 Spatially resolved metabolomics to discover tumor-associated metabolic alterations Proc Natl Acad Sci USA. 2019 AFADESI-MSI Yes Tissue (tumor = 256 and normal = 256) 27 DOI: 10.1073/pnas.1808950116 Metabolomic Characterization Reveals ILF2 and ILF3 Affected Metabolic Adaptions in Esophageal Squamous Cell Carcinoma Front Mol Biosci. 2021 CE-MS/LC-MS Yes Tissue (tumor = 28 and normal = 28) 112 DOI: 10.3389/fmolb.2021.721990 Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma J Pharm Biomed Anal. 2021 UPLC/MS No Tissue tumor = 141 and normal = 70) 41 DOI: 10.1016/j.jpba.2021.113937 Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma Front Oncol. 2020 LC-MS Yes Tissue (tumor = 23 and normal = 23) 26 DOI: 10.3389/fonc.2020.01545 Untargeted metabolomics analysis of esophageal squamous cell cancer progression J Transl Med. 2022 LC-MS/MS No Tissue (tumor = 60 and normal = 15) 145 DOI: 10.1186/s12967-022-03311-z Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy Biomed Res Int. . 2022 HPLC-TOF-MS/MS Yes Tissue (tumor = 210 and normal = 210) 269 DOI: 10.1155/2022/7819235 a : Metabolomics detection platform used in the study: GC-MS(gas chromatography-mass spectrometry), NMR(1H nuclear magnetic resonance), CE-TOFMS(capillary electrophoresis time-of-flight mass spectrometry), AFADESI-MSI(airflow-assisted desorption electrospray ionization mass spectrometry imaging), CE-MS/LC-MS(capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry), UPLC-MS(ultra-high-performance liquid chromatography coupled with high resolution mass), LC-MS(liquid chromatography-mass spectrometry), LC-MS/MS(liquid chromatography with tandem mass spectrometry), HPLC-TOF-MS/MS(high-performance liquid chromatography time-of-flight mass spectrometry with tandem mass spectrometry). b : Whether the tissues used in the study were paired, “yes” means paired, “no” means unpaired. c : The number of ESCC tissue as well as normal esophageal tissue used in the study. d : The result of the number of differential metabolites in the metabolomics studies. Table 2 Summary of the high-frequency metabolites a Metabolites identified by > 2studies Studies b Up/down regulation Metabolites identified by > 2studies Studies b Up/down regulation Beta-Alanine 2 Up D-Phenyllactic acid 2 Up Creatine 3 Down Gluconic acid 4 Up Deoxyguanosine 2 Up Glutamine 3 Down Glycerophosphocholine 3 Up L-Kynurenine 4 Up Citric acid 2 Down L-Leucine 2 Up GABA 2 Up L-Methionine 3 Up Glutathione 2 Up 4-Hydroxyproline 2 Up Guanosine 2 Up Myristic acid 3 Up Fumaric acid 2 Down N-Acetyl-L-aspartic acid 2 Up Glutamate 4 Up L-Valine 4 Up Hypoxanthine 3 Up Citrulline 2 Up L-Tyrosine 5 Up L-Tryptophan 4 Up Phenylalanine 7 Up S-Adenosylmethionine 2 Up L-Proline 3 Up N'-Formylkynurenine 2 Up L-Threonine 3 Up ADP 2 Down L-Isoleucine 5 Up N-Acetyl-glucosamine 1-phosphate 2 Up L-Histidine 3 Up Guanosine monophosphate 3 Up L-Lysine 2 Up Putrescine 2 Up L-Serine 2 Up CDP 2 Down L-Lactic acid 3 Up Phosphorylcholine 2 Up L-Aspartic acid 3 Up Palmitoylethanolamide 2 Up Ornithine 2 Up Hypogeic acid 2 Up Palmitic acid 2 Up N8-Acetylspermidine 2 Up Palmitoylcarnitine 3 Up Ophthalmic acid 2 Down Pyruvic acid 2 Down LysoPC(18:2(9Z,12Z)/0:0) 2 Up Uracil 3 Up gamma-Glutamylglutamic acid 2 Up N-alpha-Acetyl-L-lysine 2 Up N-Acetyl-L-methionine 3 Up L-Arginine 2 Up Ricinoleic acid 2 Up Adenosine triphosphate 2 Down Creatinine 2 Down Aminoadipic acid 2 Up/down Arachidonic acid 2 Up/down 3 Up/down Dihydroxyacetone phosphate 3 Up/down Glucose 2 Up/down Glycine 4 Up/down l-Asparagine 5 Up/down LysoPC(24:1(15Z)/0:0) 2 Up/down Myo-inositol 3 Up/down N-Acetylputrescine 3 Up/down Phosphocreatine 3 Up/down a : High-frequency metabolites: metabolites identified by > 2 studies, with or without consistent trends. b : Number of studies reporting this differential metabolite. 3.2. Differentially expressed genes in ESCC Differentially expressed genes (DEG) were defined as adjusted P value less than 0.05 and |log 2 FC| > 1. A total of 2679 differentially expressed genes, including 1080 up-regulated and 1599 down-regulated, were obtained ( Fig. 2 A, Supplemental file2: Table S7) . Heatmap plotting showed that these DEGs could significantly distinguish between ESCC cancer tissues and normal tissues ( Fig. 2 B ). Pathway enrichment analysis of DEGs was performed using DAVID ( https://david.ncifcrf.gov/ ), and a total of 17 pathways was significantly (FDR < 0.05) enriched. There was a total of 253 metabolic genes, suggesting a metabolic reprogramming in ESCC ( Fig. 2 C, Supplemental file2: Table S8) . Interestingly, the arginine and proline metabolism pathway was ranked high, with 16 differential genes and an enrichment ratio of 2.27 ( Fig. 2 D ) . This pathway was also enriched in the differential metabolite analysis, further supporting the importance of the arginine and proline metabolism pathway in ESCC. 3.3. Joint-pathway analysis of differentially expressed metabolites and genes Joint-pathway analysis revealed that there were 12 significantly enriched metabolic pathways (FDR < 0.05) ( Table 3 ) , with the top 5 being: glycerolipid metabolism; ascorbate and aldarate metabolism; histidine metabolism; arginine and proline metabolism; linoleic acid metabolism. All the pathways, except for mucin type O-glycan biosynthesis, had hits from both metabolite and gene. Among the 12 pathways, there were 7 ones related to amino acid metabolism, indicating a potential role of amino acid metabolism in ESCC. Of them, altered arginine and proline metabolism had 12 metabolites hits, including L-arginine, creatine, 4-aminobutanoate, putrescine, S-adenosyl-L-methionine, spermidine, spermine, D-proline, hydroxyproline, L-glutamate, ornithine, pyruvate, while had 16 genes hits, including ARG1, NOS2, GATM, CKMT2, CKMT1A, ALDH2, ALDH9A1, ALDH3A2, ALDH7A1, MAOA, MAOB, L3HYPDH, PYCR1, P4HA3, P4HA1, ODC1. Table 3 Joint-pathway analysis of genes and metabolites Pathway name Total a Hits (cmpd) b Hits (gene) c FDR d Impact Glycerolipid metabolism 35 3 18 3.01E-05 1.18 Ascorbate and aldarate metabolism 13 1 6 2.29E-04 1.00 Histidine metabolism 32 6 10 9.22E-04 0.81 Arginine and proline metabolism 78 12 16 1.22E-03 0.90 Linoleic acid metabolism 17 3 8 2.76E-03 2.00 Valine, leucine and isoleucine biosynthesis 12 6 2 5.11E-03 1.55 Phenylalanine metabolism 21 4 7 6.25E-03 1.55 Mucin type O-glycan biosynthesis 22 0 8 7.79E-03 0.62 Glutathione metabolism 56 8 12 7.79E-03 0.85 Arginine biosynthesis 27 8 4 8.14E-03 1.23 beta-Alanine metabolism 44 7 9 1.82E-02 0.86 Nitrogen metabolism 10 2 4 2.15E-02 0.78 a : The number of total of genes and metabolites in the pathway. b : The number of metabolite hits in the joint-pathway analysis. c : The number of gene hits in the joint-pathway analysis. d : False discovery rate. 3.4. Up-regulated arginine transporter CAT1 and down-regulated succinic acid synthetase1 (ASS1) in ESCC To investigating the potential mechanism for the accumulation of arginine in ESCC, arginine transporter CAT1, which is responsible for uptake arginine extracellularly, as well as succinic acid synthetase1 (ASS1), which mediates biosynthesis of arginine from the urea cycle, were included in the study. Representative images of IHC staining of ESCC tissue microarray were shown in Fig. 3 . CAT1 was significantly up-regulated in ESCC tissues compared to normal esophageal tissues (mean IHC score: 7.36 vs. 4.86, p = 0.03) ( Fig. 3 A ) ; while ASS1 was shown significantly down-regulated in ESCC tissues compared to normal ones (mean IHC score, 8.00 vs. 4.53; p = 0.01) ( Fig. 3 B ) . These findings suggest that the elevated expression of the amino acid transporter CAT1 may be involved in the up-regulation of arginine in ESCC. 3.5. Arginine influences ESCC cell proliferation The CCK8 assay revealed that the proliferative capacity of KYSE30 cells was relatively low in the control group (0 mM arginine), and showed a significant increase when arginine was added to the medium (p < 0.01). However, the proliferative ability of KYSE30 cells displayed growth that was independent of concentration ( Fig. 4 A ) . Similar trends were observed in KYSE150 cell ( Fig. 4 B ) . The clone-formation assay results showed that there were no KYSE30 cell clones in the control group (0 mM arginine). When different concentrations of arginine were added to the medium, KYSE30 cell clones were formed. The clone formation was independent of concentration, which is consistent with that in CCK8 experiment ( Fig. 4 C ) . These findings suggest that within a certain range of concentrations, arginine can enhance the proliferation ability of ESCC cells, which provides valuable insights into the potential utility of manipulating arginine levels as a therapeutic strategy for treating ESCC. 4. Discussion Integrative metabolomics and genomics become a popular strategy in cancer research, which facilitate in discovering biomarkers and understanding the molecular mechanisms of carcinogenesis. Since a single metabolomics study with a single analytical platform is hardly able to cover the whole metabolic profile of a disease, systemic reviewing of the published metabolic studies is a convenient way to collect the available differentially expressed metabolites and analyze the metabolism features for a disease. In fact, Li et al. performed similar study, in which seven metabolomics articles and six ESCC mRNA datasets were used for joint-pathway analysis for ESCC(Li et al., 2017 ). However, only two of the seven studies reviewed in their study were tissue-based metabolomics research article, and the others were from plasma/serum/other fluid -based metabolomics. Metabolite pool in plasma is obviously different from that in tumor. Therefore, the discover from their study can hardly represent the real metabolism features of ESCC. Recently, there is growing number of studies from ESCC tissue-based metabolomics studies available, and it is worthy to conducted a systemic review of ESCC tissue-based metabolomics study to investigate the big metabolic landscape of ESCC. Thus, we performed this study, and screened out 9 relevant articles between 2009 and 2022. Different analytical tool platforms, including NMR, GC-MS, LC-MS and CE-MS, were used in metabolomics for ESCC. Each platform has its own advantages and limitations, and the reliability of the results obtained from different platforms might have inconsistent result. NMR is non-invasive, rapid, and can detect metabolites in vivo, but has lower sensitivity and limited dynamic range. LC-MS and GC-MS offer improved sensitivity and resolution, while CE-MS is often used for metabolite profiling due to its high sensitivity(Liu and Zhong, 2019 ). Combinations of multiple analytical techniques, including GC-MS, NMR, and LC-MS, have been widely used to improve the sensitivity, specificity, and selectivity of metabolite detection in recent years(Gao and Xu, 2015 ). The 9 studies reviewed in this study covered NMR, LC-MS, GC-MS and CE-MS, the combination of multiple analytical techniques provided a comprehensive overview of the differentially expressed metabolites in ESCC. This study revealed that there was a total of 495 unique differential metabolites, 58 high-frequency metabolites with consistent trends. Based on the results of collected differential metabolites, especially high-frequency metabolites, and the pathway enrichment analysis, dysregulated amino acid metabolism was the most significant metabolic feature in ESCC. The high-frequency metabolite table showed 19 amino acids was reported to altered in ESCC. Most of amino acids, such as L-arginine, glutamate, L-proline, L-aspartic acid, were significantly accumulated in ESCC tissue compared to normal tissue, which indicating an increased uptake of amino acids in ESCC. Glutamine was the only down-regulated amino acid reported in ESCC, and it might be caused by the factor that consumption of arginine was much higher than its absorption from extracellular environment. While glycine and asparagine had inconsistent change trends in different studies. Additionally, the abnormal amino acid metabolism observed in ESCC may serve as a potential biomarker for diagnosis and monitoring of the disease. Targeting amino acid metabolism pathways could be a promising therapeutic strategy, as inhibitors of enzymes involved in amino acid metabolism have shown promising results in inhibiting tumor growth and improving survival. Our previous study(Chen et al., 2021 )[22] revealed a significant alteration in amino acid metabolism, such as tryptophan metabolism, was significantly up-regulated in ESCC, and its corresponding amino acid transporters, such as SLC7A5, SLC1A5 and SLC16A10, were evidently over-expressed in ESCC. Some studies have shown that, amino acid transporters, such as SLC7A5 and SLC1A5, are over-expressed in several tumors and essential for cancer cell growth(Wang and Zou, 2020 ). And the pharmacologic inhibition and knockdown/knockout of these transporters can significantly suppress the proliferation of cancer cells(Kanai, 2022 ). Therefore, nutritional interventions that target specific amino acids, such as arginine or glutamine, may also be beneficial for patients with ESCC. Based on the results from enrichment analysis of high-frequency metabolites and joint-pathway analysis, arginine and proline metabolism was illustrated to be a significantly dysregulated pathway in ESCC. Among the top 5 significantly enriched pathways of the high-frequency metabolites, the urea cycle, which was found deregulation in several cancers to maximize the body nitrogen incorporation into tumor growth(Keshet et al., 2018 ), is included in arginine and proline metabolism. The ammonia recycling exists down stream of arginine and proline metabolism to recover ammonia and keep the balance of nitrogen metabolism in the body, which performs a similar function to the urea cycle. Both of the aspartate metabolism and glycine and serine metabolism have more or less intersection with arginine and proline metabolism through transamination. All of these results hint at the importance of altered arginine and proline metabolism in ESCC. Arginine, as an important amino acid that plays a critical role in cellular metabolism and immune function(Szefel et al., 2019 ), was found up-regulated in ESCC in this study. Existing literature reports, altered arginine and proline metabolism has been observed in various tumors, particularly those with chemo resistance and poor prognosis. Arginine is obtained by cells through two pathways under normal physiological conditions: production via the ornithine cycle by ASS1(Szlosarek, 2014 ) and transport into cells via the CAT1(Satriano, 2004 ) ( Fig. 5 ) . In this study, IHC staining was performed and the result verified the up-regulating expression of CAT1 and the down-regulating expression of ASS1 in ESCC, which implied that CAT1 might be the main cause of increased arginine uptake in ESCC, and targeting this transporter might be a potential therapeutic strategy for this type of cancer. Other reference reported that circulating arginine promotes tumor growth(Poillet-Perez et al., 2018 ). And this study further supported the concept with CCK8 and clone-formation assays demonstrating that arginine enhances the capacity of proliferation in ESCC cell lines. Therefore, blocking arginine uptake through CAT1 or other means could be a viable therapeutic strategy for ESCC. However, more research is needed to fully understand the role of arginine in ESCC growth and to determine the best approach for targeting arginine and proline metabolism in cancer therapy. The ornithine is first synthesized from glutamine via glutaminase (GLS), pyrroline-5-carboxylate synthase (P5CS) and ornithine aminotransferase (OAT), or be generated from proline via proline oxidase (PO). Ornithine then enters the urea cycle (shown in the blue area). The enzyme ornithine carbamoyltransferase (OCT) converts the ornithine to citrulline and the argininosuccinate synthetase1 (ASS1) combines citrulline with aspartate to generate argininosuccinate. After that, the enzyme argininosuccinate lyase (ASL) will remove fumaric acid from argininosuccinate to generate arginine. Extracellular arginine can be transported into cells by the cationic amino acid transporters (CAT1) as well. Arginine then converted into ornithine and urea by arginase I/II (ARGI/II). Subsequently, ornithine can be recycled back into arginine through the urea cycle or further converted to polyamines in spermidine and spermine metabolism through ornithine decarboxylase (ODC1) (showed yellow area). Among the above metabolites, up-regulated differentially expressed metabolites show red, down-regulated differentially expressed metabolites show blue and non-differentially expressed metabolites show grey. Notably, spermidine and spermine biosynthesis, as a downstream metabolic reaction to arginine and proline metabolism, also shown significant alterations in the results of differential metabolite enrichment analysis, 8 out of 18 metabolites in this pathway were differential metabolites, 7 of which showed up-regulation, including ornithine, pyrophosphate, S-adenosylmethionine, spermine, spermidine, putrescine, and 5 were high-frequency metabolites, including adenosine triphosphate, ornithine, S-adenosylmethionine, spermidine, putrescine. In this study, polyamines, including spermidine, spermine, and putrescine, are up-regulated metabolites and IHC staining results indicated the up-regulation of ODC1, a key enzyme involved in polyamine synthesis ( Fig. 3 A, Fig. 3 B ). Studies reported that, polyamines are essential for normal cell growth and their depletion results in cytostasis. Dysregulation of polyamine metabolism is common in many cancers, such as prostate cancer, colorectal cancer and ovarian cancer(Du and Han, 2021 ; Holbert et al., 2022 ). Elevated polyamine levels are necessary for transformation and tumor progression and targeting polyamine metabolism with inhibitors such as difluoromethylornithine (DFMO), inhibitor of ODC1(Casero et al., 2018 ), has shown promising results in phase I trials for various cancers. These findings highlight the metabolic specificity of polyamine synthesis in ESCC and suggest the potential feasibility of polyamine metabolic inhibitors in ESCC treatment, which should be further explored. In conclusion, the joint-pathway analysis of differential genes and differential metabolites explored a relatively wide metabolic landscape for ESCC, and revealed the amino acid metabolism pathways, such as arginine and proline metabolism pathway and polyamine metabolism, as the potential targets for ESCC. However, further functional studies are needed for investigating the potential clinical significance of these metabolic targets in ESCC. Declarations This research was supported by grants from the National Natural Science Foundation of China (No. 81672315,81302840), from the Medical and the Health Science Project of Zhejiang Province (2022KY622), from the Zhejiang Provincial Natural Science Foundation of China (LY23H010002), Key R&D Program Projects in Zhejiang Province (2018C04009), from the Medical and the Health Science Project of Zhejiang Province (2020KY487). 5.Data availability statement The datasets generated for this study can be found in the GEO/GSE53625/ https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53625. 6.Ethics statement The studies involving human participants were reviewed and approved by the Research Ethics Committee of Zhejiang Cancer Hospital, China. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. 7.Author contributions Yang Chen, Zhongjian Chen, and Weimin Mao conceived, designed the study, interpreted the data, wrote the first draft of the manuscript, and contributed to the final version of the manuscript. Yang Chen, Huan Yang, Xiancong Huang and Ruting Wang performed the experiment, conducted the bioinformatics analysis. All authors approved the submitted version of this manuscript. 8.Funding This research was supported by grants from the National Natural Science Foundation of China (No. 81672315,81302840), from the Medical and the Health Science Project of Zhejiang Province (2022KY622), from the Zhejiang Provincial Natural Science Foundation of China (LY23H010002), Key R&D Program Projects in Zhejiang Province (2018C04009), from the Medical and the Health Science Project of Zhejiang Province (2020KY487). 9.Acknowledgements We thank Biobank in Zhejiang Cancer Hospital for providing all the samples in the study. 10. Conflict of interest statement The authors declare that there is no conflict of interests, we do not have any possible conflicts of interest. References Baba, Y., Yoshida, N., Kinoshita, K., Iwatsuki, M., Yamashita, Y.I., Chikamoto, A., Watanabe, M. and Baba, H. (2018) Clinical and Prognostic Features of Patients With Esophageal Cancer and Multiple Primary Cancers: A Retrospective Single-institution Study. Ann Surg 267, 478-483. Casero, R.A., Jr., Murray Stewart, T. and Pegg, A.E. (2018) Polyamine metabolism and cancer: treatments, challenges and opportunities. Nat Rev Cancer 18, 681-695. Chen, Z., Dai, Y., Huang, X., Chen, K., Gao, Y., Li, N., Wang, D., Chen, A., Yang, Q., Hong, Y., Zeng, S. and Mao, W. (2020) Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma. Front Oncol 10, 1545. Chen, Z., Gao, Y., Huang, X., Yao, Y., Chen, K., Zeng, S. and Mao, W. (2021) Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma. J Pharm Biomed Anal 197, 113937. Domper Arnal, M.J., Ferrandez Arenas, A. and Lanas Arbeloa, A. (2015) Esophageal cancer: Risk factors, screening and endoscopic treatment in Western and Eastern countries. World J Gastroenterol 21, 7933-43. Du, T. and Han, J. (2021) Arginine Metabolism and Its Potential in Treatment of Colorectal Cancer. Front Cell Dev Biol 9, 658861. Gao, P. and Xu, G. (2015) Mass-spectrometry-based microbial metabolomics: recent developments and applications. Anal Bioanal Chem 407, 669-80. Griffin, J.L. and Shockcor, J.P. (2004) Metabolic profiles of cancer cells. Nat Rev Cancer 4, 551-61. Hasin, Y., Seldin, M. and Lusis, A. (2017) Multi-omics approaches to disease. Genome Biol 18, 83. Holbert, C.E., Cullen, M.T., Casero, R.A., Jr. and Stewart, T.M. (2022) Polyamines in cancer: integrating organismal metabolism and antitumour immunity. Nat Rev Cancer 22, 467-480. Kanai, Y. (2022) Amino acid transporter LAT1 (SLC7A5) as a molecular target for cancer diagnosis and therapeutics. Pharmacol Ther 230, 107964. Keshet, R., Szlosarek, P., Carracedo, A. and Erez, A. (2018) Rewiring urea cycle metabolism in cancer to support anabolism. Nat Rev Cancer 18, 634-645. Li, C., Wang, Q., Ma, J., Shi, S., Chen, X., Yang, H. and Han, J. (2017) Integrative Pathway Analysis of Genes and Metabolites Reveals Metabolism Abnormal Subpathway Regions and Modules in Esophageal Squamous Cell Carcinoma. Molecules 22 . Li, Y., Xu, F., Chen, F., Chen, Y., Ge, D., Zhang, S. and Lu, C. (2021) Transcriptomics based multi-dimensional characterization and drug screen in esophageal squamous cell carcinoma. EBioMedicine 70, 103510. Liu, Q.T. and Zhong, X.Y. (2019) [Application of metabolomics in neonatal clinical practice]. Zhongguo Dang Dai Er Ke Za Zhi 21, 942-948. Martinez-Outschoorn, U.E., Peiris-Pages, M., Pestell, R.G., Sotgia, F. and Lisanti, M.P. (2017) Cancer metabolism: a therapeutic perspective. Nat Rev Clin Oncol 14, 113. Nicholson, J.K., Connelly, J., Lindon, J.C. and Holmes, E. (2002) Metabonomics: a platform for studying drug toxicity and gene function. Nat Rev Drug Discov 1, 153-61. Poillet-Perez, L., Xie, X., Zhan, L., Yang, Y., Sharp, D.W., Hu, Z.S., Su, X., Maganti, A., Jiang, C., Lu, W., Zheng, H., Bosenberg, M.W., Mehnert, J.M., Guo, J.Y., Lattime, E., Rabinowitz, J.D. and White, E. (2018) Autophagy maintains tumour growth through circulating arginine. Nature 563, 569-573. Qu, Y., Feng, J., Wu, X., Bai, L., Xu, W., Zhu, L., Liu, Y., Xu, F., Zhang, X., Yang, G., Lv, J., Chen, X., Shi, G.H., Wang, H.K., Cao, D.L., Xiang, H., Li, L., Tan, S., Gan, H.L., Sun, M.H., Qiu, J., Zhang, H., Zhao, J.Y., Ye, D. and Ding, C. (2022) A proteogenomic analysis of clear cell renal cell carcinoma in a Chinese population. Nat Commun 13, 2052. Satriano, J. (2004) Arginine pathways and the inflammatory response: interregulation of nitric oxide and polyamines: review article. Amino Acids 26, 321-9. Schmidt, D.R., Patel, R., Kirsch, D.G., Lewis, C.A., Vander Heiden, M.G. and Locasale, J.W. (2021) Metabolomics in cancer research and emerging applications in clinical oncology. CA Cancer J Clin 71, 333-358. Sun, C., Li, T., Song, X., Huang, L., Zang, Q., Xu, J., Bi, N., Jiao, G., Hao, Y., Chen, Y., Zhang, R., Luo, Z., Li, X., Wang, L., Wang, Z., Song, Y., He, J. and Abliz, Z. (2019) Spatially resolved metabolomics to discover tumor-associated metabolic alterations. Proc Natl Acad Sci U S A 116, 52-57. Szefel, J., Danielak, A. and Kruszewski, W.J. (2019) Metabolic pathways of L-arginine and therapeutic consequences in tumors. Adv Med Sci 64, 104-110. Szlosarek, P.W. (2014) Arginine deprivation and autophagic cell death in cancer. Proc Natl Acad Sci U S A 111, 14015-6. Tokunaga, M., Kami, K., Ozawa, S., Oguma, J., Kazuno, A., Miyachi, H., Ohashi, Y., Kusuhara, M. and Terashima, M. (2018) Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. Int J Oncol 52, 1947-1958. Wang, L., Chen, J., Chen, L., Deng, P., Bu, Q., Xiang, P., Li, M., Lu, W., Xu, Y., Lin, H., Wu, T., Wang, H., Hu, J., Shao, X., Cen, X. and Zhao, Y.L. (2013) 1H-NMR based metabonomic profiling of human esophageal cancer tissue. Mol Cancer 12, 25. Wang, W. and Zou, W. (2020) Amino Acids and Their Transporters in T Cell Immunity and Cancer Therapy. Molecular Cell 80, 384-395. Ward, P.S. and Thompson, C.B. (2012) Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell 21, 297-308. Wu, H., Xue, R., Lu, C., Deng, C., Liu, T., Zeng, H., Wang, Q. and Shen, X. (2009) Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 877, 3111-7. Xi, Y., Lin, Y., Guo, W., Wang, X., Zhao, H., Miao, C., Liu, W., Liu, Y., Liu, T., Luo, Y., Fan, W., Lin, A., Chen, Y., Sun, Y., Ma, Y., Niu, X., Zhong, C., Tan, W., Zhou, M., Su, J., Wu, C. and Lin, D. (2022) Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma. Signal Transduct Target Ther 7, 53. Xu, J., Cao, W., Shao, A., Yang, M., Andoh, V., Ge, Q., Pan, H.W. and Chen, K.P. (2022) Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy. Biomed Res Int 2022, 7819235. Xu, J., Chen, Y., Zhang, R., Song, Y., Cao, J., Bi, N., Wang, J., He, J., Bai, J., Dong, L., Wang, L., Zhan, Q. and Abliz, Z. (2013) Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers. Mol Cell Proteomics 12, 1306-18. Yan, J., Risacher, S.L., Shen, L. and Saykin, A.J. (2018) Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 19, 1370-1381. Yang, T., Hui, R., Nouws, J., Sauler, M., Zeng, T. and Wu, Q. (2022) Untargeted metabolomics analysis of esophageal squamous cell cancer progression. J Transl Med 20, 127. Yang, Y.M., Hong, P., Xu, W.W., He, Q.Y. and Li, B. (2020) Advances in targeted therapy for esophageal cancer. Signal Transduct Target Ther 5, 229. Zang, B., Wang, W., Wang, Y., Li, P., Xia, T., Liu, X., Chen, D., Piao, H.L., Qi, H. and Ma, Y. (2021) Metabolomic Characterization Reveals ILF2 and ILF3 Affected Metabolic Adaptions in Esophageal Squamous Cell Carcinoma. Front Mol Biosci 8, 721990. Zhang, J., Bowers, J., Liu, L., Wei, S., Gowda, G.A., Hammoud, Z. and Raftery, D. (2012) Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods. PLoS One 7, e30181. Zhu, G., Li, X., Li, J., Zhou, W., Chen, Z., Fan, Y., Jiang, Y., Zhao, Y., Sun, G. and Mao, W. (2020) Arsenic trioxide (ATO) induced degradation of Cyclin D1 sensitized PD-1/PD-L1 checkpoint inhibitor in oral and esophageal squamous cell carcinoma. J Cancer 11, 6516-6529. Additional Declarations No competing interests reported. Supplementary Files Supplementalfile1.xlsx Supplementalfile2.xlsx Supplementalfile3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3117927","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":213954032,"identity":"74eeee49-e2ae-4084-9fd9-3d3459ff4a96","order_by":0,"name":"Yang Chen","email":"","orcid":"","institution":"Department of Medical Oncology, the Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310022","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Chen","suffix":""},{"id":213954036,"identity":"01c09d89-8405-4745-ad0b-0133178af84c","order_by":1,"name":"Huan Yang","email":"","orcid":"","institution":"Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Yang","suffix":""},{"id":213954039,"identity":"b71eb6a0-b581-461b-8061-779f7198b8ab","order_by":2,"name":"Xiancong Huang","email":"","orcid":"","institution":"Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiancong","middleName":"","lastName":"Huang","suffix":""},{"id":213954042,"identity":"1e4ddc25-5a6a-4833-8fb6-9279c7741535","order_by":3,"name":"Ruting Wang","email":"","orcid":"","institution":"Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruting","middleName":"","lastName":"Wang","suffix":""},{"id":213954045,"identity":"69b781d2-a469-4925-bbc8-07a022c8e0f7","order_by":4,"name":"Weimin Mao","email":"","orcid":"","institution":"Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Mao","suffix":""},{"id":213954049,"identity":"c8d651fc-9435-4d88-9c0d-0b4ed47c022d","order_by":5,"name":"Zhongjian Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACCRDBY8PA2ECiljSStTAcJsFd/LObjz38IXPenrn98AOGj3tqgSIE7JO4cyzdQILnNjNjT5oB44xnx4EiB/BrMZDIMZMw4LnNxjiDh4GZ58AxoEgCIS353yQSeM7xkKIlh03iAM8BCaiWGsJaJG6kmUk28CQbgPxycMaBAzwSNwho4Z+R/EzyZ4+dvWH74YcPPhyok+OfQUALGDD2MDAYNjAwHABGEA8R6kHgBwODPIRVR6SOUTAKRsEoGEkAAHqVPMO/qceNAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Zhongjian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2023-06-28 02:14:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3117927/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3117927/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39384549,"identity":"77562328-6b8b-4052-85ec-d4f8d60dd4cf","added_by":"auto","created_at":"2023-06-30 17:48:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6461878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed metabolites from 9 ESCC tissue-based metabolomics studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eFlow chart of the article reviewing for ESCC tissue-based metabolomics.\u003cstrong\u003e (B)\u003c/strong\u003e Summary of the differentially expressed metabolites from 9 ESCC tissue-based metabolomics studies. Enrichment analysis of the total of 495 differential metabolites (\u003cstrong\u003eC\u003c/strong\u003e), of 327 up-regulated differential metabolites (D), of 168 down-regulated differential metabolites (\u003cstrong\u003eE\u003c/strong\u003e), and of 69 high frequency differential metabolites (\u003cstrong\u003eF\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/dc40f8c2d77d500f03396da7.jpg"},{"id":39382832,"identity":"37a8f91e-1f0a-492d-99af-53c8d12d84ce","added_by":"auto","created_at":"2023-06-30 17:32:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8335236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes in ESCC from GSE53625 dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Volcano plot for differential genes. (\u003cstrong\u003eB\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eHeatmap plotting with the differential genes, C: cancer, N: normal. (\u003cstrong\u003eC) \u003c/strong\u003ePathway enrichment result. (\u003cstrong\u003eD\u003c/strong\u003e) Pathway analysis result. (\u003cstrong\u003eE\u003c/strong\u003e)\u003cstrong\u003e \u003c/strong\u003eHeatmap with differential metabolic genes belong to arginine and proline metabolism, C: cancer, N: normal.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/84a335da94868989572e9795.jpg"},{"id":39382831,"identity":"45286fa8-ca33-4673-bc9c-40409906c6a8","added_by":"auto","created_at":"2023-06-30 17:32:46","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14951351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIHC staining showed the up-regulated arginine transporter (CAT1), down-regulated succinic acid synthetase1 (ASS1) in ESCC, and up-regulated ornithine decarboxylase (ODC1) in ESCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eThe bar graph of the IHC score of the CAT1 (\u003cstrong\u003eA\u003c/strong\u003e), ASS1 (\u003cstrong\u003eB\u003c/strong\u003e), and ODC1 (\u003cstrong\u003eC\u003c/strong\u003e) between cancer and normal tissue, and representative images of IHC staining at a magnification of 40x and 200x.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/90823cecfc076cf711e099a5.jpg"},{"id":39382830,"identity":"85fd38ca-8593-4d75-933e-ba0396da4b75","added_by":"auto","created_at":"2023-06-30 17:32:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1017587,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eArginine influences ESCC cell proliferation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCells of KYSE30 \u003cstrong\u003e(A) \u003c/strong\u003eand KYSE150 \u003cstrong\u003e(B) \u003c/strong\u003ewere cultured in media containing different concentrations of arginine (including 0.00, 0.25, 0.50, 1.00, 1.50, 2.00mM) with 6 technical replicates, and CCK8 assay were performed at 24h, 28h, 72h and 96h. \u003cstrong\u003e(C)\u003c/strong\u003e KYSE30 cells were used for clone formation assay, different concentrations of arginine (including 0.00, 0.25, 0.50, 1.00, 1.50, 2.00mM) with 3 technical replicates were used.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/f380bd0783b3744fa26df475.jpg"},{"id":39382826,"identity":"60b0beca-84e3-451a-aeec-c8cfeef2206a","added_by":"auto","created_at":"2023-06-30 17:32:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1046107,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram for arginine and proline metabolic pathway in ESCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ornithine is first synthesized from glutamine via glutaminase (GLS), pyrroline-5-carboxylate synthase (P5CS) and ornithine aminotransferase (OAT), or be generated from proline via proline oxidase (PO). Ornithine then enters the urea cycle (shown in the blue area). The enzyme ornithine carbamoyltransferase (OCT) converts the ornithine to citrulline and the argininosuccinate synthetase1 (ASS1) combines citrulline with aspartate to generate argininosuccinate. After that, the enzyme argininosuccinate lyase (ASL) will remove fumaric acid from argininosuccinate to generate arginine. Extracellular arginine can be transported into cells by the cationic amino acid transporters (CAT1) as well. Arginine then converted into ornithine and urea by arginase I/II (ARGI/II). Subsequently, ornithine can be recycled back into arginine through the urea cycle or further converted to polyamines in spermidine and spermine metabolism through ornithine decarboxylase (ODC1) (showed yellow area). Among the above metabolites, up-regulated differentially expressed metabolites show red, down-regulated differentially expressed metabolites show blue and non-differentially expressed metabolites show grey.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/ce6a622b907f7a17741d555b.jpg"},{"id":46163493,"identity":"946d2da8-ce8e-4622-b59f-f8c169a48618","added_by":"auto","created_at":"2023-11-09 14:44:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1298967,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/0aaaf7ff-8120-46b3-afa2-2c71ee62e76f.pdf"},{"id":39383609,"identity":"a70a7531-d67f-4603-bf04-16644c364dc0","added_by":"auto","created_at":"2023-06-30 17:40:45","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":90615,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/388e4e5c5458be4ae9220617.xlsx"},{"id":39382829,"identity":"d90475b5-7a23-44ec-8fae-3ba8e53202de","added_by":"auto","created_at":"2023-06-30 17:32:45","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":226398,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/0d1f9a1ad5c9fd3a1de71e7e.xlsx"},{"id":39382825,"identity":"f9006ba5-1dcf-452e-aeb7-483b0434bc49","added_by":"auto","created_at":"2023-06-30 17:32:45","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10982,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3117927/v1/6ad49144dcdf6ff42e4a9f17.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomics and transcriptomics joint analysis reveals altered amino acid metabolism in esophageal squamous cell carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEsophageal cancer is an extremely dangerous form of cancer with an aggressive nature and high mortality rate. It is ranked 6th among the leading causes of cancer-related deaths worldwide and is the 8th most prevalent form of cancer globally. The 5-year survival rate is only around 15%-25%(Domper Arnal et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and this highlights the challenging aspect of treating this disease. Esophageal cancer has two main pathological types: adenocarcinoma (EAC) and squamous cell carcinoma (ESCC), with the latter accounting for 80% of all esophageal cancer cases(Xi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). ESCC has a discouraging prognosis and a high fatality rate mainly due to its difficult detection in the early stages. It is typically identified at later disease stages by enhanced thoracic computerized tomography (CT) and gastroscopy(Baba et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although surgical resection, radiotherapy, and chemotherapy are the primary clinical treatments for ESCC, their efficacy is limited, and they often have severe adverse effects(Yang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result, it is crucial to explore new therapeutic options and targets, particularly those focused on future research and development.\u003c/p\u003e \u003cp\u003eIt is well-known that metabolic reprogramming is one of the hallmarks of cancer(Ward and Thompson, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and emerging evidence has revealed that tumor cells undergo metabolic reprogramming to fuel their proliferation and differentiation(Sun et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As a result, identifying therapeutic targets or biomarkers for cancer based on altered metabolome has emerged as a promising strategy(Martinez-Outschoorn et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Metabolomics is the study of small molecule metabolites, typically less than 1000, in a biological system such as cell, tissue, organ, or organism. There are three primary analytical platforms for metabolomics, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance spectroscopy (NMR), and each with their unique analytical range. Unlike other \"omics\" approaches, metabolomics can provide a snapshot of changes at the biochemical level, making it a highly sensitive tool for identifying pathological variants(Griffin and Shockcor, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(Schmidt et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e(Nicholson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile several metabolomics studies have investigated the metabolomic profile of ESCC(Xu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the results reported by each study are limited by the analytical coverage of the platforms utilized, such as LC-MS, GC-MS, and NMR. As a consequence, the number of differentially expressed metabolites reported in each study is relatively small. Furthermore, the results of these studies have sometimes been contradictory, making it challenging to determine the precise changes in metabolite levels associated with ESCC. Thus, integrating different metabolomics studies, carefully analyzing and weighting the results from each, is critical for identifying the metabolic processes that are truly altered in ESCC. By doing so, researchers can provide effective targets for the treatment of ESCC and enhance the understanding of its pathogenesis.\u003c/p\u003e \u003cp\u003eA multi-molecule level approach that systematically combines genes and metabolites can provide a new direction for disease research, as studying biomolecular changes at a single level is insufficient for systems biology research(Hasin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Controversial results have emerged in recent years regarding multi-omics studies on ESCC, due to the lack of strict inclusion criteria such as sample size, sample type, clinical information, metabolomics testing methods, and other factors. Thus, it is essential to systematically review and select appropriate studies for multi-omics analysis of ESCC, in order to explore the molecular features and potential targets.\u003c/p\u003e \u003cp\u003eThis study collected differentially expressed metabolites from published studies and obtained differentially expressed genes through bioinformatic analysis of online data. Joint-pathway analysis of gene and metabolite was utilized to investigate metabolic alterations and identify potential therapeutic targets in ESCC. Key enzymes in the feature metabolic pathway were validated by immunohistochemistry (IHC) staining. These findings may offer promising biomarkers and therapeutic strategies for ESCC.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Collection of differentially expressed metabolites and pathway analysis\u003c/h2\u003e \u003cp\u003eTo collect tissue-based metabolomics studies on ESCC, a literature search was conducted on PubMed (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), using the following inclusion criteria: 1) based on metabolomics, 2) ESCC tissue samples, and 3) complete metabolomics results and patient clinical information. A total of 9 metabolomics studies met the criteria, and 495 unique differential metabolites were obtained after removing duplicates. High-frequency metabolites refers to metabolites that appeared in two or more studies with consistent or inconsistent trends.\u003c/p\u003e \u003cp\u003eEnrichment analysis was performed using online software MetaboAnalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/MetaboAnalyst/home.xhtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This method utilizes SMPDB, which included 99 metabolite sets based on normal human metabolic pathways, as the metabolite set library for enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Bioinformatics analysis of ESCC transcriptome\u003c/h2\u003e \u003cp\u003eBased on sample size and clinical information integrity, microarray data, which was deposited in Gene Expression Omnibus (GEO) under accession number GSE53625 (Agilent-038314 CBC Homo sapiens lncRNA\u0026thinsp;+\u0026thinsp;mRNA microarray V2.0), were processed as described previously(Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In briefly, the probe sets in GSE53625 were re-annotated by mapping all sequences provided in GPL18109 annotation file to human genome (hg38) using SeqMap(Qu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Probes that were mapped to protein-coding transcripts were remained. Average value was used for the genes with multiple probes. Differentially expression analysis between cancer and normal tissues was performed using R package \u003cem\u003elimma\u003c/em\u003e (version 3.6.3). KEGG pathway enrichment analysis of differential genes was conducted using online tool \"bioinformatics network analysis\" from online software DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Joint-pathway analysis of differential metabolite and gene\u003c/h2\u003e \u003cp\u003eTo gain a comprehensive understanding of metabolic reprogramming in esophageal squamous cell carcinoma (ESCC), we employed the joint-pathway analysis method from Metaboanalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to perform integrative analysis of differentially expressed metabolites and genes in ESCC. This method utilizes hypergeometric testing for enrichment analysis, degree centrality for topology measurement, and combine p values (unweighted) for integration of results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Immunohistochemistry (IHC) staining\u003c/h2\u003e \u003cp\u003eESCC tissue samples were collected from 119 patients recruited after histopathologic confirmation of ESCC and radical resection at Zhejiang Cancer Hospital, China, from May 2010 to December 2012. The clinical stages of ESCC patients were determined based on the American Joint Committee on Cancer 8th edition staging system. This study was approved by the Institutional Ethical Review Board of Zhejiang Cancer Hospital and all patients were informed about and gave their consent for the study before surgery. Detailed clinical information is presented in the table \u003cb\u003e(Supplemental file3: Table S9)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe IHC staining procedure was processed as described previously(Zhu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In briefly, the tissue microarray slides were deparaffinized in xylene and gradient ethanol. Slides were immunohistochemically stained according to the manufacturer's instructions. Antibodies for identification of protein expression of CAT1 (Affinity Biosciences, Jiangsu, China; Cat#:DF13433) at a dilution of 1:200, ASS1 (Affinity Biosciences, Jiangsu, China; Cat#:BF0242) at a dilution of 1:500, and ODC1 (Affinity Biosciences, Jiangsu, China; Cat#:DF6712) at a dilution of 1:200 were used in this study. The IHC staining results were reviewed by a pathologist, and the staining score was defined as multiplying the percentage of positive cells by staining intensity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Arginine-deprivation experiments\u003c/h2\u003e \u003cp\u003eHuman ESCC KYSE150 and KYSE30 cell lines were purchased from Nanjing Kebai Biotechnology Co., Ltd. (Nanjing, China) in 2016, and authenticated by a short tandem repeat (STR) report by Shanghai biowing biotechnology Co., Ltd (Nanjing, China) in 2019. The cells were cultured in RPMI 1640 (Gibco, Thermo Fisher Scientific, USA) supplemented with 10% fetal bovine serum (FBS) and 100 U/mL penicillin/100 \u0026micro;g/mL streptomycin at 37\u0026deg;C under 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCCK8 assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCells were seeded into 96-well plates at a density of 3000 cells per well and incubated at 37\u0026deg;C and 5% CO\u003csub\u003e2\u003c/sub\u003e with costumed (without arginine) RPMI 1640 Medium (Coolaber Technology Co., Ltd., Beijing, China) adding a series of concentrations (0, 0.25, 0.5, 1.0, 1.5, 2.0 mM) of arginine. CCK8 assay (APExBIO Technology LLC, USA) was performed according to the product's protocol at a series of time points (24, 48, 72, and 96 hours). Each experiment consisted of five replicates and was repeated at least three times.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClone formation assay\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCells were seeded in 6-well plate at a density of 1000 cells per well, and incubated under the same conditions as described above. After 3 weeks, the cells were fixed with 4% polyformaldehyde for 20 minutes, and stained with 1% crystal violet, and the numbers of colonies containing more than 50 cells were counted microscopically. The experiment was performed in triplicate and repeated three times.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Differentially expressed metabolites from 9 ESCC tissue-based metabolomics studies\u003c/h2\u003e \u003cp\u003eWith the keywords of \"ESCC\", \"esophageal squamous cell carcinoma\", and \"metabolomics \", a total of 58 related articles retrieved. After systematically review, in which studies of non-ESCC tissue-based research or with incomplete information were excluded, a total of 9 eligible metabolomics studies(Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tokunaga et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) was remained \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe 9 studies included the three primary analytical platforms for metabolomics (NMR, GC-MS, LC-MS). Besides, mass spectrometry imaging (MSI), capillary electrophoresis-mass spectrometry (CE-MS) were also used \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. After collecting differential metabolites from the 9 studies, there were 740 differential metabolites including duplicates \u003cb\u003e(Supplemental file1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. After removing exogenous compounds and duplicate metabolites, 495 unique differential metabolites were remained, including 327 up-regulated and 168 down-regulated ones. In terms of high frequency metabolite, there was a total of 69 metabolites reported in over two studies, of which 11 had inconsistent change trends and 58 had consistent trend \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplemental file1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eEnrichment analysis of 495 differential metabolites revealed a total of 42 significant metabolic pathways (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, \u003cb\u003eSupplemental file1: Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e)\u003c/b\u003e, and the top 5 were apartate metabolism; glycine and serine metabolism; urea cycle; nicotinate and nicotinamide metabolism; glutamate metabolism. Enrichment analysis of 327 up-regulated metabolites revealed 18 significantly enriched metabolic pathways and the top 5 metabolic pathways were aspartate metabolism; glycine and serine metabolism; nicotinate and nicotinamide metabolism; purine metabolism; methionine metabolism \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, \u003cb\u003eSupplemental file1: Table S4)\u003c/b\u003e. While pathway enrichment analysis of 168 down-regulated metabolites showed that there were 47 significantly enriched metabolic pathways and the top 5 included warburg effect; citric acid cycle; gluconeogenesis; galactose metabolism; purine metabolism \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, \u003cb\u003eSupplemental file1: Table S5)\u003c/b\u003e. Furthermore, Enrichment analysis of 69 high-frequency metabolites, there were a total of 19 metabolic pathways were significantly enriched, and the top 5 pathways were urea cycle; arginine and proline metabolism; ammonia recycling; aspartate metabolism; glycine and serine metabolism \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF, \u003cb\u003eSupplemental file1: Table S6)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNine eligible ESCC tissue-based metabolomics research articles\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJournal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlatform\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePaired\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCmpd\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJ Chromatogr B Analyt Technol Biomed Life Sci.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGC-MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;20 and normal\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jchromb.2009.07.039\u003c/span\u003e\u003cspan address=\"10.1016/j.jchromb.2009.07.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1H-NMR based metabonomic profiling of human esophageal cancer tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMol Cancer.\u003c/em\u003e\u0026bull; \u0026bull; \u0026bull;\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;89 and normal\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1476-4598-12-25\u003c/span\u003e\u003cspan address=\"10.1186/1476-4598-12-25\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eInt J Oncol.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCE-TOFMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;35 and normal\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ijo.2018.4340\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2018.4340\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpatially resolved metabolomics to discover tumor-associated metabolic alterations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eProc Natl Acad Sci USA.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAFADESI-MSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;256 and normal\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.1808950116\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1808950116\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolomic Characterization Reveals ILF2 and ILF3 Affected Metabolic Adaptions in Esophageal Squamous Cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFront Mol Biosci.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCE-MS/LC-MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;28 and normal\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmolb.2021.721990\u003c/span\u003e\u003cspan address=\"10.3389/fmolb.2021.721990\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJ Pharm Biomed Anal.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUPLC/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003etumor\u0026thinsp;=\u0026thinsp;141 and normal\u0026thinsp;=\u0026thinsp;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jpba.2021.113937\u003c/span\u003e\u003cspan address=\"10.1016/j.jpba.2021.113937\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFront Oncol.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC-MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;23 and normal\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2020.01545\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2020.01545\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUntargeted metabolomics analysis of esophageal squamous cell cancer progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJ Transl Med.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLC-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;60 and normal\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12967-022-03311-z\u003c/span\u003e\u003cspan address=\"10.1186/s12967-022-03311-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBiomed Res Int.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHPLC-TOF-MS/MS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003cp\u003e(tumor\u0026thinsp;=\u0026thinsp;210 and normal\u0026thinsp;=\u0026thinsp;210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDOI:\u003c/p\u003e \u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2022/7819235\u003c/span\u003e\u003cspan address=\"10.1155/2022/7819235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e: Metabolomics detection platform used in the study: GC-MS(gas chromatography-mass spectrometry), NMR(1H nuclear magnetic resonance), CE-TOFMS(capillary electrophoresis time-of-flight mass spectrometry), AFADESI-MSI(airflow-assisted desorption electrospray ionization mass spectrometry imaging), CE-MS/LC-MS(capillary electrophoresis-mass spectrometry and liquid chromatography-mass spectrometry), UPLC-MS(ultra-high-performance liquid chromatography coupled with high resolution mass), LC-MS(liquid chromatography-mass spectrometry), LC-MS/MS(liquid chromatography with tandem mass spectrometry), HPLC-TOF-MS/MS(high-performance liquid chromatography time-of-flight mass spectrometry with tandem mass spectrometry).\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e: Whether the tissues used in the study were paired, \u0026ldquo;yes\u0026rdquo; means paired, \u0026ldquo;no\u0026rdquo; means unpaired.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e: The number of ESCC tissue as well as normal esophageal tissue used in the study.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e: The result of the number of differential metabolites in the metabolomics studies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the high-frequency metabolites\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003cp\u003eidentified by \u0026gt;\u0026thinsp;2studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudies\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003cp\u003eregulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003cp\u003eidentified by \u0026gt;\u0026thinsp;2studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStudies\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003cp\u003eregulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeta-Alanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD-Phenyllactic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGluconic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeoxyguanosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophosphocholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL-Kynurenine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL-Leucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGABA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL-Methionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutathione\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4-Hydroxyproline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuanosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyristic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFumaric acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN-Acetyl-L-aspartic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL-Valine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypoxanthine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCitrulline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Tyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL-Tryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS-Adenosylmethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Proline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN'-Formylkynurenine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Threonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eADP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Isoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN-Acetyl-glucosamine 1-phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Histidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuanosine monophosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePutrescine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Serine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Lactic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhosphorylcholine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Aspartic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalmitoylethanolamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrnithine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHypogeic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalmitic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN8-Acetylspermidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalmitoylcarnitine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOphthalmic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyruvic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLysoPC(18:2(9Z,12Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUracil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003egamma-Glutamylglutamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-alpha-Acetyl-L-lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN-Acetyl-L-methionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Arginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRicinoleic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenosine triphosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCreatinine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAminoadipic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArachidonic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDihydroxyacetone phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003el-Asparagine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLysoPC(24:1(15Z)/0:0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyo-inositol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN-Acetylputrescine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhosphocreatine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUp/down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e: High-frequency metabolites: metabolites identified by \u0026gt;\u0026thinsp;2 studies, with or without consistent trends.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e: Number of studies reporting this differential metabolite.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Differentially expressed genes in ESCC\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEG) were defined as adjusted P value less than 0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 1. A total of 2679 differentially expressed genes, including 1080 up-regulated and 1599 down-regulated, were obtained \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cb\u003eSupplemental file2: Table S7)\u003c/b\u003e. Heatmap plotting showed that these DEGs could significantly distinguish between ESCC cancer tissues and normal tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePathway enrichment analysis of DEGs was performed using DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and a total of 17 pathways was significantly (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) enriched. There was a total of 253 metabolic genes, suggesting a metabolic reprogramming in ESCC \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, \u003cb\u003eSupplemental file2: Table S8)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eInterestingly, the arginine and proline metabolism pathway was ranked high, with 16 differential genes and an enrichment ratio of 2.27 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. This pathway was also enriched in the differential metabolite analysis, further supporting the importance of the arginine and proline metabolism pathway in ESCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Joint-pathway analysis of differentially expressed metabolites and genes\u003c/h2\u003e \u003cp\u003eJoint-pathway analysis revealed that there were 12 significantly enriched metabolic pathways (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, with the top 5 being: glycerolipid metabolism; ascorbate and aldarate metabolism; histidine metabolism; arginine and proline metabolism; linoleic acid metabolism. All the pathways, except for mucin type O-glycan biosynthesis, had hits from both metabolite and gene. Among the 12 pathways, there were 7 ones related to amino acid metabolism, indicating a potential role of amino acid metabolism in ESCC. Of them, altered arginine and proline metabolism had 12 metabolites hits, including L-arginine, creatine, 4-aminobutanoate, putrescine, S-adenosyl-L-methionine, spermidine, spermine, D-proline, hydroxyproline, L-glutamate, ornithine, pyruvate, while had 16 genes hits, including ARG1, NOS2, GATM, CKMT2, CKMT1A, ALDH2, ALDH9A1, ALDH3A2, ALDH7A1, MAOA, MAOB, L3HYPDH, PYCR1, P4HA3, P4HA1, ODC1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eJoint-pathway analysis of genes and metabolites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathway name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHits\u003c/p\u003e \u003cp\u003e(cmpd)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHits\u003c/p\u003e \u003cp\u003e(gene)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFDR\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerolipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.01E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAscorbate and aldarate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.29E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.22E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine and proline metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinoleic acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.76E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine, leucine and isoleucine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.11E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.25E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMucin type O-glycan biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.79E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutathione metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.79E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine biosynthesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.14E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebeta-Alanine metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNitrogen metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.15E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003e: The number of total of genes and metabolites in the pathway.\u003c/p\u003e \u003cp\u003e \u003csup\u003eb\u003c/sup\u003e: The number of metabolite hits in the joint-pathway analysis.\u003c/p\u003e \u003cp\u003e \u003csup\u003ec\u003c/sup\u003e: The number of gene hits in the joint-pathway analysis.\u003c/p\u003e \u003cp\u003e \u003csup\u003ed\u003c/sup\u003e: False discovery rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.4. Up-regulated arginine\u003c/b\u003e transporter \u003cb\u003eCAT1 and down-regulated succinic acid synthetase1 (ASS1) in ESCC\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTo investigating the potential mechanism for the accumulation of arginine in ESCC, arginine transporter CAT1, which is responsible for uptake arginine extracellularly, as well as succinic acid synthetase1 (ASS1), which mediates biosynthesis of arginine from the urea cycle, were included in the study. Representative images of IHC staining of ESCC tissue microarray were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. CAT1 was significantly up-regulated in ESCC tissues compared to normal esophageal tissues (mean IHC score: 7.36 vs. 4.86, p\u0026thinsp;=\u0026thinsp;0.03) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e; while ASS1 was shown significantly down-regulated in ESCC tissues compared to normal ones (mean IHC score, 8.00 vs. 4.53; p\u0026thinsp;=\u0026thinsp;0.01) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. These findings suggest that the elevated expression of the amino acid transporter CAT1 may be involved in the up-regulation of arginine in ESCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Arginine influences ESCC cell proliferation\u003c/h2\u003e \u003cp\u003eThe CCK8 assay revealed that the proliferative capacity of KYSE30 cells was relatively low in the control group (0 mM arginine), and showed a significant increase when arginine was added to the medium (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, the proliferative ability of KYSE30 cells displayed growth that was independent of concentration \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Similar trends were observed in KYSE150 cell \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe clone-formation assay results showed that there were no KYSE30 cell clones in the control group (0 mM arginine). When different concentrations of arginine were added to the medium, KYSE30 cell clones were formed. The clone formation was independent of concentration, which is consistent with that in CCK8 experiment \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. These findings suggest that within a certain range of concentrations, arginine can enhance the proliferation ability of ESCC cells, which provides valuable insights into the potential utility of manipulating arginine levels as a therapeutic strategy for treating ESCC.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIntegrative metabolomics and genomics become a popular strategy in cancer research, which facilitate in discovering biomarkers and understanding the molecular mechanisms of carcinogenesis. Since a single metabolomics study with a single analytical platform is hardly able to cover the whole metabolic profile of a disease, systemic reviewing of the published metabolic studies is a convenient way to collect the available differentially expressed metabolites and analyze the metabolism features for a disease. In fact, Li et al. performed similar study, in which seven metabolomics articles and six ESCC mRNA datasets were used for joint-pathway analysis for ESCC(Li et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, only two of the seven studies reviewed in their study were tissue-based metabolomics research article, and the others were from plasma/serum/other fluid -based metabolomics. Metabolite pool in plasma is obviously different from that in tumor. Therefore, the discover from their study can hardly represent the real metabolism features of ESCC. Recently, there is growing number of studies from ESCC tissue-based metabolomics studies available, and it is worthy to conducted a systemic review of ESCC tissue-based metabolomics study to investigate the big metabolic landscape of ESCC. Thus, we performed this study, and screened out 9 relevant articles between 2009 and 2022.\u003c/p\u003e \u003cp\u003eDifferent analytical tool platforms, including NMR, GC-MS, LC-MS and CE-MS, were used in metabolomics for ESCC. Each platform has its own advantages and limitations, and the reliability of the results obtained from different platforms might have inconsistent result. NMR is non-invasive, rapid, and can detect metabolites in vivo, but has lower sensitivity and limited dynamic range. LC-MS and GC-MS offer improved sensitivity and resolution, while CE-MS is often used for metabolite profiling due to its high sensitivity(Liu and Zhong, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Combinations of multiple analytical techniques, including GC-MS, NMR, and LC-MS, have been widely used to improve the sensitivity, specificity, and selectivity of metabolite detection in recent years(Gao and Xu, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The 9 studies reviewed in this study covered NMR, LC-MS, GC-MS and CE-MS, the combination of multiple analytical techniques provided a comprehensive overview of the differentially expressed metabolites in ESCC. This study revealed that there was a total of 495 unique differential metabolites, 58 high-frequency metabolites with consistent trends.\u003c/p\u003e \u003cp\u003eBased on the results of collected differential metabolites, especially high-frequency metabolites, and the pathway enrichment analysis, dysregulated amino acid metabolism was the most significant metabolic feature in ESCC. The high-frequency metabolite table showed 19 amino acids was reported to altered in ESCC. Most of amino acids, such as L-arginine, glutamate, L-proline, L-aspartic acid, were significantly accumulated in ESCC tissue compared to normal tissue, which indicating an increased uptake of amino acids in ESCC. Glutamine was the only down-regulated amino acid reported in ESCC, and it might be caused by the factor that consumption of arginine was much higher than its absorption from extracellular environment. While glycine and asparagine had inconsistent change trends in different studies. Additionally, the abnormal amino acid metabolism observed in ESCC may serve as a potential biomarker for diagnosis and monitoring of the disease. Targeting amino acid metabolism pathways could be a promising therapeutic strategy, as inhibitors of enzymes involved in amino acid metabolism have shown promising results in inhibiting tumor growth and improving survival. Our previous study(Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)[22] revealed a significant alteration in amino acid metabolism, such as tryptophan metabolism, was significantly up-regulated in ESCC, and its corresponding amino acid transporters, such as SLC7A5, SLC1A5 and SLC16A10, were evidently over-expressed in ESCC. Some studies have shown that, amino acid transporters, such as SLC7A5 and SLC1A5, are over-expressed in several tumors and essential for cancer cell growth(Wang and Zou, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). And the pharmacologic inhibition and knockdown/knockout of these transporters can significantly suppress the proliferation of cancer cells(Kanai, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, nutritional interventions that target specific amino acids, such as arginine or glutamine, may also be beneficial for patients with ESCC.\u003c/p\u003e \u003cp\u003eBased on the results from enrichment analysis of high-frequency metabolites and joint-pathway analysis, arginine and proline metabolism was illustrated to be a significantly dysregulated pathway in ESCC. Among the top 5 significantly enriched pathways of the high-frequency metabolites, the urea cycle, which was found deregulation in several cancers to maximize the body nitrogen incorporation into tumor growth(Keshet et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), is included in arginine and proline metabolism. The ammonia recycling exists down stream of arginine and proline metabolism to recover ammonia and keep the balance of nitrogen metabolism in the body, which performs a similar function to the urea cycle. Both of the aspartate metabolism and glycine and serine metabolism have more or less intersection with arginine and proline metabolism through transamination. All of these results hint at the importance of altered arginine and proline metabolism in ESCC.\u003c/p\u003e \u003cp\u003eArginine, as an important amino acid that plays a critical role in cellular metabolism and immune function(Szefel et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), was found up-regulated in ESCC in this study. Existing literature reports, altered arginine and proline metabolism has been observed in various tumors, particularly those with chemo resistance and poor prognosis. Arginine is obtained by cells through two pathways under normal physiological conditions: production via the ornithine cycle by ASS1(Szlosarek, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and transport into cells via the CAT1(Satriano, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In this study, IHC staining was performed and the result verified the up-regulating expression of CAT1 and the down-regulating expression of ASS1 in ESCC, which implied that CAT1 might be the main cause of increased arginine uptake in ESCC, and targeting this transporter might be a potential therapeutic strategy for this type of cancer. Other reference reported that circulating arginine promotes tumor growth(Poillet-Perez et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). And this study further supported the concept with CCK8 and clone-formation assays demonstrating that arginine enhances the capacity of proliferation in ESCC cell lines. Therefore, blocking arginine uptake through CAT1 or other means could be a viable therapeutic strategy for ESCC. However, more research is needed to fully understand the role of arginine in ESCC growth and to determine the best approach for targeting arginine and proline metabolism in cancer therapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe ornithine is first synthesized from glutamine via glutaminase (GLS), pyrroline-5-carboxylate synthase (P5CS) and ornithine aminotransferase (OAT), or be generated from proline via proline oxidase (PO). Ornithine then enters the urea cycle (shown in the blue area). The enzyme ornithine carbamoyltransferase (OCT) converts the ornithine to citrulline and the argininosuccinate synthetase1 (ASS1) combines citrulline with aspartate to generate argininosuccinate. After that, the enzyme argininosuccinate lyase (ASL) will remove fumaric acid from argininosuccinate to generate arginine. Extracellular arginine can be transported into cells by the cationic amino acid transporters (CAT1) as well. Arginine then converted into ornithine and urea by arginase I/II (ARGI/II). Subsequently, ornithine can be recycled back into arginine through the urea cycle or further converted to polyamines in spermidine and spermine metabolism through ornithine decarboxylase (ODC1) (showed yellow area). Among the above metabolites, up-regulated differentially expressed metabolites show red, down-regulated differentially expressed metabolites show blue and non-differentially expressed metabolites show grey.\u003c/p\u003e \u003cp\u003eNotably, spermidine and spermine biosynthesis, as a downstream metabolic reaction to arginine and proline metabolism, also shown significant alterations in the results of differential metabolite enrichment analysis, 8 out of 18 metabolites in this pathway were differential metabolites, 7 of which showed up-regulation, including ornithine, pyrophosphate, S-adenosylmethionine, spermine, spermidine, putrescine, and 5 were high-frequency metabolites, including adenosine triphosphate, ornithine, S-adenosylmethionine, spermidine, putrescine. In this study, polyamines, including spermidine, spermine, and putrescine, are up-regulated metabolites and IHC staining results indicated the up-regulation of ODC1, a key enzyme involved in polyamine synthesis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e Studies reported that, polyamines are essential for normal cell growth and their depletion results in cytostasis. Dysregulation of polyamine metabolism is common in many cancers, such as prostate cancer, colorectal cancer and ovarian cancer(Du and Han, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Holbert et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Elevated polyamine levels are necessary for transformation and tumor progression and targeting polyamine metabolism with inhibitors such as difluoromethylornithine (DFMO), inhibitor of ODC1(Casero et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), has shown promising results in phase I trials for various cancers. These findings highlight the metabolic specificity of polyamine synthesis in ESCC and suggest the potential feasibility of polyamine metabolic inhibitors in ESCC treatment, which should be further explored.\u003c/p\u003e \u003cp\u003eIn conclusion, the joint-pathway analysis of differential genes and differential metabolites explored a relatively wide metabolic landscape for ESCC, and revealed the amino acid metabolism pathways, such as arginine and proline metabolism pathway and polyamine metabolism, as the potential targets for ESCC. However, further functional studies are needed for investigating the potential clinical significance of these metabolic targets in ESCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research was supported by grants from the National Natural Science Foundation of China (No. 81672315,81302840), from the Medical and the Health Science Project of Zhejiang Province (2022KY622), from the Zhejiang Provincial Natural Science Foundation of China (LY23H010002), Key R\u0026amp;D Program Projects in Zhejiang Province (2018C04009), from the Medical and the Health Science Project of Zhejiang Province (2020KY487).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e5.Data availability statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated for this study can be found in the GEO/GSE53625/ https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53625.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e6.Ethics statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Research Ethics Committee of Zhejiang Cancer Hospital, China. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e7.Author contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYang Chen, Zhongjian Chen, and Weimin Mao conceived, designed the study, interpreted the data, wrote the first draft of the manuscript, and contributed to the final version of the manuscript. Yang Chen, Huan Yang, Xiancong Huang and Ruting Wang performed the experiment, conducted the bioinformatics analysis. All authors approved the submitted version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e8.Funding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by grants from the National Natural Science Foundation of China (No. 81672315,81302840), from the Medical and the Health Science Project of Zhejiang Province (2022KY622), from the Zhejiang Provincial Natural Science Foundation of China (LY23H010002), Key R\u0026amp;D Program Projects in Zhejiang Province (2018C04009), from the Medical and the Health Science Project of Zhejiang Province (2020KY487).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e9.Acknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Biobank in Zhejiang Cancer Hospital for providing all the samples in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e10.\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003eConflict of interest statement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interests, we do not have any possible conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaba, Y., Yoshida, N., Kinoshita, K., Iwatsuki, M., Yamashita, Y.I., Chikamoto, A., Watanabe, M. and Baba, H. (2018) Clinical and Prognostic Features of Patients With Esophageal Cancer and Multiple Primary Cancers: A Retrospective Single-institution Study. \u003cem\u003eAnn Surg\u003c/em\u003e \u003cstrong\u003e267,\u003c/strong\u003e 478-483.\u003c/li\u003e\n\u003cli\u003eCasero, R.A., Jr., Murray Stewart, T. and Pegg, A.E. (2018) Polyamine metabolism and cancer: treatments, challenges and opportunities. \u003cem\u003eNat Rev Cancer\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e 681-695.\u003c/li\u003e\n\u003cli\u003eChen, Z., Dai, Y., Huang, X., Chen, K., Gao, Y., Li, N., Wang, D., Chen, A., Yang, Q., Hong, Y., Zeng, S. and Mao, W. (2020) Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma. \u003cem\u003eFront Oncol\u003c/em\u003e \u003cstrong\u003e10,\u003c/strong\u003e 1545.\u003c/li\u003e\n\u003cli\u003eChen, Z., Gao, Y., Huang, X., Yao, Y., Chen, K., Zeng, S. and Mao, W. (2021) Tissue-based metabolomics reveals metabolic biomarkers and potential therapeutic targets for esophageal squamous cell carcinoma. \u003cem\u003eJ Pharm Biomed Anal\u003c/em\u003e \u003cstrong\u003e197,\u003c/strong\u003e 113937.\u003c/li\u003e\n\u003cli\u003eDomper Arnal, M.J., Ferrandez Arenas, A. and Lanas Arbeloa, A. (2015) Esophageal cancer: Risk factors, screening and endoscopic treatment in Western and Eastern countries. \u003cem\u003eWorld J Gastroenterol\u003c/em\u003e \u003cstrong\u003e21,\u003c/strong\u003e 7933-43.\u003c/li\u003e\n\u003cli\u003eDu, T. and Han, J. (2021) Arginine Metabolism and Its Potential in Treatment of Colorectal Cancer. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e \u003cstrong\u003e9,\u003c/strong\u003e 658861.\u003c/li\u003e\n\u003cli\u003eGao, P. and Xu, G. (2015) Mass-spectrometry-based microbial metabolomics: recent developments and applications. \u003cem\u003eAnal Bioanal Chem\u003c/em\u003e \u003cstrong\u003e407,\u003c/strong\u003e 669-80.\u003c/li\u003e\n\u003cli\u003eGriffin, J.L. and Shockcor, J.P. (2004) Metabolic profiles of cancer cells. \u003cem\u003eNat Rev Cancer\u003c/em\u003e \u003cstrong\u003e4,\u003c/strong\u003e 551-61.\u003c/li\u003e\n\u003cli\u003eHasin, Y., Seldin, M. and Lusis, A. (2017) Multi-omics approaches to disease. \u003cem\u003eGenome Biol\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e 83.\u003c/li\u003e\n\u003cli\u003eHolbert, C.E., Cullen, M.T., Casero, R.A., Jr. and Stewart, T.M. (2022) Polyamines in cancer: integrating organismal metabolism and antitumour immunity. \u003cem\u003eNat Rev Cancer\u003c/em\u003e \u003cstrong\u003e22,\u003c/strong\u003e 467-480.\u003c/li\u003e\n\u003cli\u003eKanai, Y. (2022) Amino acid transporter LAT1 (SLC7A5) as a molecular target for cancer diagnosis and therapeutics. \u003cem\u003ePharmacol Ther\u003c/em\u003e \u003cstrong\u003e230,\u003c/strong\u003e 107964.\u003c/li\u003e\n\u003cli\u003eKeshet, R., Szlosarek, P., Carracedo, A. and Erez, A. (2018) Rewiring urea cycle metabolism in cancer to support anabolism. \u003cem\u003eNat Rev Cancer\u003c/em\u003e \u003cstrong\u003e18,\u003c/strong\u003e 634-645.\u003c/li\u003e\n\u003cli\u003eLi, C., Wang, Q., Ma, J., Shi, S., Chen, X., Yang, H. and Han, J. (2017) Integrative Pathway Analysis of Genes and Metabolites Reveals Metabolism Abnormal Subpathway Regions and Modules in Esophageal Squamous Cell Carcinoma. \u003cem\u003eMolecules\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLi, Y., Xu, F., Chen, F., Chen, Y., Ge, D., Zhang, S. and Lu, C. (2021) Transcriptomics based multi-dimensional characterization and drug screen in esophageal squamous cell carcinoma. \u003cem\u003eEBioMedicine\u003c/em\u003e \u003cstrong\u003e70,\u003c/strong\u003e 103510.\u003c/li\u003e\n\u003cli\u003eLiu, Q.T. and Zhong, X.Y. (2019) [Application of metabolomics in neonatal clinical practice]. \u003cem\u003eZhongguo Dang Dai Er Ke Za Zhi\u003c/em\u003e \u003cstrong\u003e21,\u003c/strong\u003e 942-948.\u003c/li\u003e\n\u003cli\u003eMartinez-Outschoorn, U.E., Peiris-Pages, M., Pestell, R.G., Sotgia, F. and Lisanti, M.P. (2017) Cancer metabolism: a therapeutic perspective. \u003cem\u003eNat Rev Clin Oncol\u003c/em\u003e \u003cstrong\u003e14,\u003c/strong\u003e 113.\u003c/li\u003e\n\u003cli\u003eNicholson, J.K., Connelly, J., Lindon, J.C. and Holmes, E. (2002) Metabonomics: a platform for studying drug toxicity and gene function. \u003cem\u003eNat Rev Drug Discov\u003c/em\u003e \u003cstrong\u003e1,\u003c/strong\u003e 153-61.\u003c/li\u003e\n\u003cli\u003ePoillet-Perez, L., Xie, X., Zhan, L., Yang, Y., Sharp, D.W., Hu, Z.S., Su, X., Maganti, A., Jiang, C., Lu, W., Zheng, H., Bosenberg, M.W., Mehnert, J.M., Guo, J.Y., Lattime, E., Rabinowitz, J.D. and White, E. (2018) Autophagy maintains tumour growth through circulating arginine. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e563,\u003c/strong\u003e 569-573.\u003c/li\u003e\n\u003cli\u003eQu, Y., Feng, J., Wu, X., Bai, L., Xu, W., Zhu, L., Liu, Y., Xu, F., Zhang, X., Yang, G., Lv, J., Chen, X., Shi, G.H., Wang, H.K., Cao, D.L., Xiang, H., Li, L., Tan, S., Gan, H.L., Sun, M.H., Qiu, J., Zhang, H., Zhao, J.Y., Ye, D. and Ding, C. (2022) A proteogenomic analysis of clear cell renal cell carcinoma in a Chinese population. \u003cem\u003eNat Commun\u003c/em\u003e \u003cstrong\u003e13,\u003c/strong\u003e 2052.\u003c/li\u003e\n\u003cli\u003eSatriano, J. (2004) Arginine pathways and the inflammatory response: interregulation of nitric oxide and polyamines: review article. \u003cem\u003eAmino Acids\u003c/em\u003e \u003cstrong\u003e26,\u003c/strong\u003e 321-9.\u003c/li\u003e\n\u003cli\u003eSchmidt, D.R., Patel, R., Kirsch, D.G., Lewis, C.A., Vander Heiden, M.G. and Locasale, J.W. (2021) Metabolomics in cancer research and emerging applications in clinical oncology. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e \u003cstrong\u003e71,\u003c/strong\u003e 333-358.\u003c/li\u003e\n\u003cli\u003eSun, C., Li, T., Song, X., Huang, L., Zang, Q., Xu, J., Bi, N., Jiao, G., Hao, Y., Chen, Y., Zhang, R., Luo, Z., Li, X., Wang, L., Wang, Z., Song, Y., He, J. and Abliz, Z. (2019) Spatially resolved metabolomics to discover tumor-associated metabolic alterations. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e116,\u003c/strong\u003e 52-57.\u003c/li\u003e\n\u003cli\u003eSzefel, J., Danielak, A. and Kruszewski, W.J. (2019) Metabolic pathways of L-arginine and therapeutic consequences in tumors. \u003cem\u003eAdv Med Sci\u003c/em\u003e \u003cstrong\u003e64,\u003c/strong\u003e 104-110.\u003c/li\u003e\n\u003cli\u003eSzlosarek, P.W. (2014) Arginine deprivation and autophagic cell death in cancer. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e111,\u003c/strong\u003e 14015-6.\u003c/li\u003e\n\u003cli\u003eTokunaga, M., Kami, K., Ozawa, S., Oguma, J., Kazuno, A., Miyachi, H., Ohashi, Y., Kusuhara, M. and Terashima, M. (2018) Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. \u003cem\u003eInt J Oncol\u003c/em\u003e \u003cstrong\u003e52,\u003c/strong\u003e 1947-1958.\u003c/li\u003e\n\u003cli\u003eWang, L., Chen, J., Chen, L., Deng, P., Bu, Q., Xiang, P., Li, M., Lu, W., Xu, Y., Lin, H., Wu, T., Wang, H., Hu, J., Shao, X., Cen, X. and Zhao, Y.L. (2013) 1H-NMR based metabonomic profiling of human esophageal cancer tissue. \u003cem\u003eMol Cancer\u003c/em\u003e \u003cstrong\u003e12,\u003c/strong\u003e 25.\u003c/li\u003e\n\u003cli\u003eWang, W. and Zou, W. (2020) Amino Acids and Their Transporters in T Cell Immunity and Cancer Therapy. \u003cem\u003eMolecular Cell\u003c/em\u003e \u003cstrong\u003e80,\u003c/strong\u003e 384-395.\u003c/li\u003e\n\u003cli\u003eWard, P.S. and Thompson, C.B. (2012) Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. \u003cem\u003eCancer Cell\u003c/em\u003e \u003cstrong\u003e21,\u003c/strong\u003e 297-308.\u003c/li\u003e\n\u003cli\u003eWu, H., Xue, R., Lu, C., Deng, C., Liu, T., Zeng, H., Wang, Q. and Shen, X. (2009) Metabolomic study for diagnostic model of oesophageal cancer using gas chromatography/mass spectrometry. \u003cem\u003eJ Chromatogr B Analyt Technol Biomed Life Sci\u003c/em\u003e \u003cstrong\u003e877,\u003c/strong\u003e 3111-7.\u003c/li\u003e\n\u003cli\u003eXi, Y., Lin, Y., Guo, W., Wang, X., Zhao, H., Miao, C., Liu, W., Liu, Y., Liu, T., Luo, Y., Fan, W., Lin, A., Chen, Y., Sun, Y., Ma, Y., Niu, X., Zhong, C., Tan, W., Zhou, M., Su, J., Wu, C. and Lin, D. (2022) Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e \u003cstrong\u003e7,\u003c/strong\u003e 53.\u003c/li\u003e\n\u003cli\u003eXu, J., Cao, W., Shao, A., Yang, M., Andoh, V., Ge, Q., Pan, H.W. and Chen, K.P. (2022) Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy. \u003cem\u003eBiomed Res Int\u003c/em\u003e \u003cstrong\u003e2022,\u003c/strong\u003e 7819235.\u003c/li\u003e\n\u003cli\u003eXu, J., Chen, Y., Zhang, R., Song, Y., Cao, J., Bi, N., Wang, J., He, J., Bai, J., Dong, L., Wang, L., Zhan, Q. and Abliz, Z. (2013) Global and targeted metabolomics of esophageal squamous cell carcinoma discovers potential diagnostic and therapeutic biomarkers. \u003cem\u003eMol Cell Proteomics\u003c/em\u003e \u003cstrong\u003e12,\u003c/strong\u003e 1306-18.\u003c/li\u003e\n\u003cli\u003eYan, J., Risacher, S.L., Shen, L. and Saykin, A.J. (2018) Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. \u003cem\u003eBrief Bioinform\u003c/em\u003e \u003cstrong\u003e19,\u003c/strong\u003e 1370-1381.\u003c/li\u003e\n\u003cli\u003eYang, T., Hui, R., Nouws, J., Sauler, M., Zeng, T. and Wu, Q. (2022) Untargeted metabolomics analysis of esophageal squamous cell cancer progression. \u003cem\u003eJ Transl Med\u003c/em\u003e \u003cstrong\u003e20,\u003c/strong\u003e 127.\u003c/li\u003e\n\u003cli\u003eYang, Y.M., Hong, P., Xu, W.W., He, Q.Y. and Li, B. (2020) Advances in targeted therapy for esophageal cancer. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e \u003cstrong\u003e5,\u003c/strong\u003e 229.\u003c/li\u003e\n\u003cli\u003eZang, B., Wang, W., Wang, Y., Li, P., Xia, T., Liu, X., Chen, D., Piao, H.L., Qi, H. and Ma, Y. (2021) Metabolomic Characterization Reveals ILF2 and ILF3 Affected Metabolic Adaptions in Esophageal Squamous Cell Carcinoma. \u003cem\u003eFront Mol Biosci\u003c/em\u003e \u003cstrong\u003e8,\u003c/strong\u003e 721990.\u003c/li\u003e\n\u003cli\u003eZhang, J., Bowers, J., Liu, L., Wei, S., Gowda, G.A., Hammoud, Z. and Raftery, D. (2012) Esophageal cancer metabolite biomarkers detected by LC-MS and NMR methods. \u003cem\u003ePLoS One\u003c/em\u003e \u003cstrong\u003e7,\u003c/strong\u003e e30181.\u003c/li\u003e\n\u003cli\u003eZhu, G., Li, X., Li, J., Zhou, W., Chen, Z., Fan, Y., Jiang, Y., Zhao, Y., Sun, G. and Mao, W. (2020) Arsenic trioxide (ATO) induced degradation of Cyclin D1 sensitized PD-1/PD-L1 checkpoint inhibitor in oral and esophageal squamous cell carcinoma. \u003cem\u003eJ Cancer\u003c/em\u003e \u003cstrong\u003e11,\u003c/strong\u003e 6516-6529.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"esophageal squamous cell carcinoma, metabolic pathway, joint-pathway analysis, amino acid metabolism, arginine and proline metabolism.","lastPublishedDoi":"10.21203/rs.3.rs-3117927/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3117927/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eMetabolic reprogramming plays a crucial role in tumor development by modifying tumor cell metabolism, which was also found in esophageal squamous cell carcinoma (ESCC).\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aims to explore the altered metabolic pathways for ESCC through joint-pathway analysis of differentially expressed metabolites and genes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDifferentially expressed metabolites in ESCC were collected from published tissue-based metabolomics studies. Differentially expressed genes in ESCC were obtained using bioinformatic analysis of online ESCC transcriptome data. Then, joint-pathway analysis was performed to explore the altered metabolic pathways in ESCC. Immunohistochemistry (IHC) staining and arginine-deprivation experiments were conducted to verified the key enzymes in metabolic pathway and their potential function in ESCC.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 9 tissue-based metabolomics studies revealed 495 differentially expressed metabolites in ESCC. Enrichment analysis of the 69 high-frequency metabolites, defined as reported by over 2 studies, showed that the top enriched pathways were urea cycle, arginine and proline metabolism and ammonia recycling. Besides, bioinformatic analysis of a dataset (GSE53625) showed 2679 differentially expressed genes in ESCC. Joint-pathway analysis illustrated that the top 5 significantly altered metabolic pathways were glycerolipid metabolism, ascorbate and aldarate metabolism, histidine metabolism, arginine and proline metabolism, and linoleic acid metabolism. IHC staining and arginine-deprivation experiments revealed the up-regulating of arginine transporter (CAT1) and characteristic of arginine-dependent proliferation in ESCC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study revealed the altered amino acid metabolism, especially arginine and proline metabolism, as the most significant metabolic characteristic in ESCC. However, further functional study is needed.\u003c/p\u003e","manuscriptTitle":"Metabolomics and transcriptomics joint analysis reveals altered amino acid metabolism in esophageal squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-30 17:32:40","doi":"10.21203/rs.3.rs-3117927/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"52f53f06-0160-4c2c-b310-106a3756b49a","owner":[],"postedDate":"June 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-09T14:44:44+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-30 17:32:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3117927","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3117927","identity":"rs-3117927","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}