Skip to main content
Top
Published in: Journal of Translational Medicine 1/2022

Open Access 01-12-2022 | Review

Untargeted metabolomics analysis of esophageal squamous cell cancer progression

Authors: Tao Yang, Ruting Hui, Jessica Nouws, Maor Sauler, Tianyang Zeng, Qingchen Wu

Published in: Journal of Translational Medicine | Issue 1/2022

Login to get access

Abstract

90% of esophageal cancer are esophageal squamous cell carcinoma (ESCC) and ESCC has a very poor prognosis and high mortality. Nevertheless, the key metabolic pathways associated with ESCC progression haven’t been revealed yet. Metabolomics has become a new platform for biomarker discovery over recent years. We aim to elucidate dominantly metabolic pathway in all ESCC tumor/node/metastasis (TNM) stages and adjacent cancerous tissues. We collected 60 postoperative esophageal tissues and 15 normal tissues adjacent to the tumor, then performed Liquid Chromatography with tandem mass spectrometry (LC–MS/MS) analyses. The metabolites data was analyzed with metabolites differential and correlational expression heatmap according to stage I vs. con., stage I vs. stage II, stage II vs. stage III, and stage III vs. stage IV respectively. Metabolic pathways were acquired by Kyoto Encyclopedia of Genes and Genomes. (KEGG) pathway database. The metabolic pathway related genes were obtained via Gene Set Enrichment Analysis (GSEA). mRNA expression of ESCC metabolic pathway genes was detected by two public datasets: gene expression data series (GSE)23400 and The Cancer Genome Atlas (TCGA). Receiver operating characteristic curve (ROC) analysis is applied to metabolic pathway genes. 712 metabolites were identified in total. Glycerophospholipid metabolism was significantly distinct in ESCC progression. 16 genes of 77 genes of glycerophospholipid metabolism mRNA expression has differential significance between ESCC and normal controls. Phosphatidylserine synthase 1 (PTDSS1) and Lysophosphatidylcholine Acyltransferase1 (LPCAT1) had a good diagnostic value with Area under the ROC Curve (AUC) > 0.9 using ROC analysis. In this study, we identified glycerophospholipid metabolism was associated with the ESCC tumorigenesis and progression. Glycerophospholipid metabolism could be a potential therapeutic target of ESCC progression.
Appendix
Available only for authorised users
Literature
1.
go back to reference Huang X, Zhou X, Hu Q, Sun B, Deng M, Qi X, Lü M. Advances in esophageal cancer: a new perspective on pathogenesis associated with long non-coding RNAs. Cancer Lett. 2018;413:94–101.PubMed Huang X, Zhou X, Hu Q, Sun B, Deng M, Qi X, Lü M. Advances in esophageal cancer: a new perspective on pathogenesis associated with long non-coding RNAs. Cancer Lett. 2018;413:94–101.PubMed
2.
go back to reference Li D, Zhang L, Liu Y, Sun H, Onwuka JU, Zhao Z, Tian W, Xu J, Zhao Y, Xu H. Specific DNA methylation markers in the diagnosis and prognosis of esophageal cancer. Aging. 2019;11(23):11640–58.PubMedPubMedCentral Li D, Zhang L, Liu Y, Sun H, Onwuka JU, Zhao Z, Tian W, Xu J, Zhao Y, Xu H. Specific DNA methylation markers in the diagnosis and prognosis of esophageal cancer. Aging. 2019;11(23):11640–58.PubMedPubMedCentral
3.
go back to reference Li B, Hong P, Zheng CC, Dai W, Chen WY, Yang QS, Han L, Tsao SW, Chan KT, Lee NPY, et al. Identification of miR-29c and its target FBXO31 as a key regulatory mechanism in esophageal cancer chemoresistance: functional validation and clinical significance. Theranostics. 2019;9(6):1599–613.PubMedPubMedCentral Li B, Hong P, Zheng CC, Dai W, Chen WY, Yang QS, Han L, Tsao SW, Chan KT, Lee NPY, et al. Identification of miR-29c and its target FBXO31 as a key regulatory mechanism in esophageal cancer chemoresistance: functional validation and clinical significance. Theranostics. 2019;9(6):1599–613.PubMedPubMedCentral
4.
go back to reference Tramontano AC, Chen Y, Watson TR, Eckel A, Hur C, Kong CY. Esophageal cancer treatment costs by phase of care and treatment modality, 2000–2013. Cancer Med. 2019;8(11):5158–72.PubMedPubMedCentral Tramontano AC, Chen Y, Watson TR, Eckel A, Hur C, Kong CY. Esophageal cancer treatment costs by phase of care and treatment modality, 2000–2013. Cancer Med. 2019;8(11):5158–72.PubMedPubMedCentral
5.
go back to reference Song Y, Li L, Ou Y, Gao Z, Li E, Li X, Zhang W, Wang J, Xu L, Zhou Y, et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature. 2014;509(7498):91–5.PubMed Song Y, Li L, Ou Y, Gao Z, Li E, Li X, Zhang W, Wang J, Xu L, Zhou Y, et al. Identification of genomic alterations in oesophageal squamous cell cancer. Nature. 2014;509(7498):91–5.PubMed
6.
go back to reference Baba Y, Yoshida N, Kinoshita K, Iwatsuki M, Yamashita YI, Chikamoto A, Watanabe M, Baba H. Clinical and prognostic features of patients with esophageal cancer and multiple primary cancers: a retrospective single-institution study. Ann Surg. 2018;267(3):478–83.PubMed Baba Y, Yoshida N, Kinoshita K, Iwatsuki M, Yamashita YI, Chikamoto A, Watanabe M, Baba H. Clinical and prognostic features of patients with esophageal cancer and multiple primary cancers: a retrospective single-institution study. Ann Surg. 2018;267(3):478–83.PubMed
8.
go back to reference Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019;20(6):353–67.PubMedPubMedCentral Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nat Rev Mol Cell Biol. 2019;20(6):353–67.PubMedPubMedCentral
9.
go back to reference Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17(7):451–9.PubMedPubMedCentral Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17(7):451–9.PubMedPubMedCentral
10.
go back to reference Amberg A, Riefke B, Schlotterbeck G, Ross A, Senn H, Dieterle F, Keck M. NMR and MS methods for metabolomics. Methods Mol Biol. 2017;1641:229–58.PubMed Amberg A, Riefke B, Schlotterbeck G, Ross A, Senn H, Dieterle F, Keck M. NMR and MS methods for metabolomics. Methods Mol Biol. 2017;1641:229–58.PubMed
11.
go back to reference Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted metabolomics. Curr Protoc Mol Biol. 2012;Chapter 30:Unit 30.32.31–24. Roberts LD, Souza AL, Gerszten RE, Clish CB. Targeted metabolomics. Curr Protoc Mol Biol. 2012;Chapter 30:Unit 30.32.31–24.
12.
go back to reference Zhu ZJ, Qi Z, Zhang J, Xue WH, Li LF, Shen ZB, Li ZY, Yuan YL, Wang WB, Zhao J. Untargeted metabolomics analysis of esophageal squamous cell carcinoma discovers dysregulated metabolic pathways and potential diagnostic biomarkers. J Cancer. 2020;11(13):3944–54.PubMedPubMedCentral Zhu ZJ, Qi Z, Zhang J, Xue WH, Li LF, Shen ZB, Li ZY, Yuan YL, Wang WB, Zhao J. Untargeted metabolomics analysis of esophageal squamous cell carcinoma discovers dysregulated metabolic pathways and potential diagnostic biomarkers. J Cancer. 2020;11(13):3944–54.PubMedPubMedCentral
13.
go back to reference Tokunaga M, Kami K, Ozawa S, Oguma J, Kazuno A, Miyachi H, Ohashi Y, Kusuhara M, Terashima M. Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. Int J Oncol. 2018;52(6):1947–58.PubMed Tokunaga M, Kami K, Ozawa S, Oguma J, Kazuno A, Miyachi H, Ohashi Y, Kusuhara M, Terashima M. Metabolome analysis of esophageal cancer tissues using capillary electrophoresis-time-of-flight mass spectrometry. Int J Oncol. 2018;52(6):1947–58.PubMed
14.
go back to reference Chen Z, Dai Y, Huang X, Chen K, Gao Y, Li N, Wang D, Chen A, Yang Q, Hong Y, et al. Combined metabolomic analysis of plasma and tissue reveals a prognostic risk score system and metabolic dysregulation in esophageal squamous cell carcinoma. Front Oncol. 2020;10:1545.PubMedPubMedCentral Chen Z, Dai Y, Huang X, Chen K, Gao Y, Li N, Wang D, Chen A, Yang Q, Hong Y, et al. Combined metabolomic analysis of plasma and tissue reveals a prognostic risk score system and metabolic dysregulation in esophageal squamous cell carcinoma. Front Oncol. 2020;10:1545.PubMedPubMedCentral
15.
go back to reference Wu Y, Hu L, Liang Y, Li J, Wang K, Chen X, Meng H, Guan X, Yang K, Bai Y. Up-regulation of lncRNA CASC9 promotes esophageal squamous cell carcinoma growth by negatively regulating PDCD4 expression through EZH2. Mol Cancer. 2017;16(1):150.PubMedPubMedCentral Wu Y, Hu L, Liang Y, Li J, Wang K, Chen X, Meng H, Guan X, Yang K, Bai Y. Up-regulation of lncRNA CASC9 promotes esophageal squamous cell carcinoma growth by negatively regulating PDCD4 expression through EZH2. Mol Cancer. 2017;16(1):150.PubMedPubMedCentral
16.
go back to reference Chan AW, Gill RS, Schiller D, Sawyer MB. Potential role of metabolomics in diagnosis and surveillance of gastric cancer. World J Gastroenterol. 2014;20(36):12874–82.PubMedPubMedCentral Chan AW, Gill RS, Schiller D, Sawyer MB. Potential role of metabolomics in diagnosis and surveillance of gastric cancer. World J Gastroenterol. 2014;20(36):12874–82.PubMedPubMedCentral
18.
go back to reference Sahu D, Lotan Y, Wittmann B, Neri B, Hansel DE. Metabolomics analysis reveals distinct profiles of nonmuscle-invasive and muscle-invasive bladder cancer. Cancer Med. 2017;6(9):2106–20.PubMedPubMedCentral Sahu D, Lotan Y, Wittmann B, Neri B, Hansel DE. Metabolomics analysis reveals distinct profiles of nonmuscle-invasive and muscle-invasive bladder cancer. Cancer Med. 2017;6(9):2106–20.PubMedPubMedCentral
19.
go back to reference Noreldeen HAA, Liu X, Xu G. Metabolomics of lung cancer: analytical platforms and their applications. J Sep Sci. 2020;43(1):120–33.PubMed Noreldeen HAA, Liu X, Xu G. Metabolomics of lung cancer: analytical platforms and their applications. J Sep Sci. 2020;43(1):120–33.PubMed
20.
go back to reference Abooshahab R, Gholami M, Sanoie M, Azizi F, Hedayati M. Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation. Endocrine. 2019;65(1):1–14.PubMed Abooshahab R, Gholami M, Sanoie M, Azizi F, Hedayati M. Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation. Endocrine. 2019;65(1):1–14.PubMed
21.
go back to reference Dou Y, Kawaler EA, Cui Zhou D, Gritsenko MA, Huang C, Blumenberg L, Karpova A, Petyuk VA, Savage SR, Satpathy S, et al. Proteogenomic characterization of endometrial carcinoma. Cell. 2020;180(4):729-748.e726.PubMedPubMedCentral Dou Y, Kawaler EA, Cui Zhou D, Gritsenko MA, Huang C, Blumenberg L, Karpova A, Petyuk VA, Savage SR, Satpathy S, et al. Proteogenomic characterization of endometrial carcinoma. Cell. 2020;180(4):729-748.e726.PubMedPubMedCentral
22.
go back to reference Jin X, Liu L, Wu J, Jin X, Yu G, Jia L, Wang F, Shi M, Lu H, Liu J, et al. A multi-omics study delineates new molecular features and therapeutic targets for esophageal squamous cell carcinoma. Clin Transl Med. 2021;11(9):e538.PubMedPubMedCentral Jin X, Liu L, Wu J, Jin X, Yu G, Jia L, Wang F, Shi M, Lu H, Liu J, et al. A multi-omics study delineates new molecular features and therapeutic targets for esophageal squamous cell carcinoma. Clin Transl Med. 2021;11(9):e538.PubMedPubMedCentral
23.
go back to reference Yuan M, Kremer DM, Huang H, Breitkopf SB, Ben-Sahra I, Manning BD, Lyssiotis CA, Asara JM. Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC-MS/MS. Nat Protoc. 2019;14(2):313–30.PubMedPubMedCentral Yuan M, Kremer DM, Huang H, Breitkopf SB, Ben-Sahra I, Manning BD, Lyssiotis CA, Asara JM. Ex vivo and in vivo stable isotope labelling of central carbon metabolism and related pathways with analysis by LC-MS/MS. Nat Protoc. 2019;14(2):313–30.PubMedPubMedCentral
24.
go back to reference Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L, Fischer B, Pratt B, Egertson J, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30(10):918–20.PubMedPubMedCentral Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L, Fischer B, Pratt B, Egertson J, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30(10):918–20.PubMedPubMedCentral
25.
go back to reference Davis VW, Schiller DE, Eurich D, Bathe OF, Sawyer MB. Pancreatic ductal adenocarcinoma is associated with a distinct urinary metabolomic signature. Ann Surg Oncol. 2013;20(Suppl 3):S415-423.PubMed Davis VW, Schiller DE, Eurich D, Bathe OF, Sawyer MB. Pancreatic ductal adenocarcinoma is associated with a distinct urinary metabolomic signature. Ann Surg Oncol. 2013;20(Suppl 3):S415-423.PubMed
26.
go back to reference Alonezi S, Tusiimire J, Wallace J, Dufton MJ, Parkinson JA, Young LC, Clements CJ, Park JK, Jeon JW, Ferro VA, et al. Metabolomic profiling of the synergistic effects of melittin in combination with cisplatin on ovarian cancer cells. Metabolites. 2017;7(2):14.PubMedCentral Alonezi S, Tusiimire J, Wallace J, Dufton MJ, Parkinson JA, Young LC, Clements CJ, Park JK, Jeon JW, Ferro VA, et al. Metabolomic profiling of the synergistic effects of melittin in combination with cisplatin on ovarian cancer cells. Metabolites. 2017;7(2):14.PubMedCentral
27.
go back to reference Wu X, Zhu JC, Zhang Y, Li WM, Rong XL, Feng YF. Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient. Chem Biol Interact. 2016;256:71–84.PubMed Wu X, Zhu JC, Zhang Y, Li WM, Rong XL, Feng YF. Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient. Chem Biol Interact. 2016;256:71–84.PubMed
28.
go back to reference Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, Rutledge DN, Savarin P. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol Biosyst. 2015;11(1):13–9.PubMed Triba MN, Le Moyec L, Amathieu R, Goossens C, Bouchemal N, Nahon P, Rutledge DN, Savarin P. PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters. Mol Biosyst. 2015;11(1):13–9.PubMed
29.
go back to reference Pasikanti KK, Esuvaranathan K, Hong Y, Ho PC, Mahendran R, Raman Nee Mani L, Chiong E, Chan EC. Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry. J Proteome Res. 2013;12(9):3865–73.PubMed Pasikanti KK, Esuvaranathan K, Hong Y, Ho PC, Mahendran R, Raman Nee Mani L, Chiong E, Chan EC. Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry. J Proteome Res. 2013;12(9):3865–73.PubMed
30.
go back to reference Sui W, Gan Q, Liu F, Ou M, Wang B, Liao S, Lai L, Chen H, Yang M, Dai Y. Dynamic metabolomics study of the bile acid pathway during perioperative primary hepatic carcinoma following liver transplantation. Ann Transplant. 2020;25: e921844.PubMedPubMedCentral Sui W, Gan Q, Liu F, Ou M, Wang B, Liao S, Lai L, Chen H, Yang M, Dai Y. Dynamic metabolomics study of the bile acid pathway during perioperative primary hepatic carcinoma following liver transplantation. Ann Transplant. 2020;25: e921844.PubMedPubMedCentral
31.
go back to reference Cheng M, An S, Li J. CDKN2B-AS may indirectly regulate coronary artery disease-associated genes via targeting miR-92a. Gene. 2017;629:101–7.PubMed Cheng M, An S, Li J. CDKN2B-AS may indirectly regulate coronary artery disease-associated genes via targeting miR-92a. Gene. 2017;629:101–7.PubMed
32.
go back to reference Cao H, Zhang Y, Chu Z, Zhao B, Wang H, An L. MAP-1B, PACS-2 and AHCYL1 are regulated by miR-34A/B/C and miR-449 in neuroplasticity following traumatic spinal cord injury in rats: preliminary explorative results from microarray data. Mol Med Rep. 2019;20(4):3011–8.PubMedPubMedCentral Cao H, Zhang Y, Chu Z, Zhao B, Wang H, An L. MAP-1B, PACS-2 and AHCYL1 are regulated by miR-34A/B/C and miR-449 in neuroplasticity following traumatic spinal cord injury in rats: preliminary explorative results from microarray data. Mol Med Rep. 2019;20(4):3011–8.PubMedPubMedCentral
33.
go back to reference Zhang X, Xu L, Shen J, Cao B, Cheng T, Zhao T, Liu X, Zhang H. Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum. Biochim Biophys Acta. 2013;1832(8):1207–16.PubMed Zhang X, Xu L, Shen J, Cao B, Cheng T, Zhao T, Liu X, Zhang H. Metabolic signatures of esophageal cancer: NMR-based metabolomics and UHPLC-based focused metabolomics of blood serum. Biochim Biophys Acta. 2013;1832(8):1207–16.PubMed
34.
go back to reference Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites. 2019;9(3):57.PubMedCentral Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites. 2019;9(3):57.PubMedCentral
35.
go back to reference Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41(Database issue):D991–5.PubMed Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41(Database issue):D991–5.PubMed
36.
go back to reference Toro-Domínguez D, Martorell-Marugán J, López-Domínguez R, García-Moreno A, González-Rumayor V, Alarcón-Riquelme ME, Carmona-Sáez P. ImaGEO: integrative gene expression meta-analysis from GEO database. Bioinformatics. 2019;35(5):880–2.PubMed Toro-Domínguez D, Martorell-Marugán J, López-Domínguez R, García-Moreno A, González-Rumayor V, Alarcón-Riquelme ME, Carmona-Sáez P. ImaGEO: integrative gene expression meta-analysis from GEO database. Bioinformatics. 2019;35(5):880–2.PubMed
37.
go back to reference Chang YT, Huang CS, Yao CT, Su SL, Terng HJ, Chou HL, Chou YC, Chen KH, Shih YW, Lu CY, et al. Gene expression profile of peripheral blood in colorectal cancer. World J Gastroenterol. 2014;20(39):14463–71.PubMedPubMedCentral Chang YT, Huang CS, Yao CT, Su SL, Terng HJ, Chou HL, Chou YC, Chen KH, Shih YW, Lu CY, et al. Gene expression profile of peripheral blood in colorectal cancer. World J Gastroenterol. 2014;20(39):14463–71.PubMedPubMedCentral
38.
go back to reference Haug K, Cochrane K, Nainala VC, Williams M, Chang J, Jayaseelan KV, O’Donovan C. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 2020;48(D1):D440-d444.PubMed Haug K, Cochrane K, Nainala VC, Williams M, Chang J, Jayaseelan KV, O’Donovan C. MetaboLights: a resource evolving in response to the needs of its scientific community. Nucleic Acids Res. 2020;48(D1):D440-d444.PubMed
39.
go back to reference Zhao S, Liu H, Su Z, Khoo C, Gu L. Identifying cranberry juice consumers with predictive OPLS-DA models of plasma metabolome and validation of cranberry juice intake biomarkers in a double-blinded, randomized, Placebo-controlled, cross-over study. Mol Nutr Food Res. 2020;64(11): e1901242.PubMed Zhao S, Liu H, Su Z, Khoo C, Gu L. Identifying cranberry juice consumers with predictive OPLS-DA models of plasma metabolome and validation of cranberry juice intake biomarkers in a double-blinded, randomized, Placebo-controlled, cross-over study. Mol Nutr Food Res. 2020;64(11): e1901242.PubMed
40.
go back to reference Pandey R, Caflisch L, Lodi A, Brenner AJ, Tiziani S. Metabolomic signature of brain cancer. Mol Carcinog. 2017;56(11):2355–71.PubMedPubMedCentral Pandey R, Caflisch L, Lodi A, Brenner AJ, Tiziani S. Metabolomic signature of brain cancer. Mol Carcinog. 2017;56(11):2355–71.PubMedPubMedCentral
41.
go back to reference Jing F, Hu X, Cao Y, Xu M, Wang Y, Jing Y, Hu X, Gao Y, Zhu Z. Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile. IUBMB Life. 2018;70(6):553–62.PubMed Jing F, Hu X, Cao Y, Xu M, Wang Y, Jing Y, Hu X, Gao Y, Zhu Z. Discriminating gastric cancer and gastric ulcer using human plasma amino acid metabolic profile. IUBMB Life. 2018;70(6):553–62.PubMed
42.
go back to reference Barberini L, Noto A, Fattuoni C, Satta G, Zucca M, Cabras MG, Mura E, Cocco P. The metabolomic profile of lymphoma subtypes: a pilot study. Molecules. 2019;24(13):2367.PubMedCentral Barberini L, Noto A, Fattuoni C, Satta G, Zucca M, Cabras MG, Mura E, Cocco P. The metabolomic profile of lymphoma subtypes: a pilot study. Molecules. 2019;24(13):2367.PubMedCentral
43.
go back to reference Phan TK, Bindra GK, Williams SA, Poon IKH, Hulett MD. Combating human pathogens and cancer by targeting phosphoinositides and their metabolism. Trends Pharmacol Sci. 2019;40(11):866–82.PubMed Phan TK, Bindra GK, Williams SA, Poon IKH, Hulett MD. Combating human pathogens and cancer by targeting phosphoinositides and their metabolism. Trends Pharmacol Sci. 2019;40(11):866–82.PubMed
44.
go back to reference Kouznetsova VL, Kim E, Romm EL, Zhu A, Tsigelny IF. Recognition of early and late stages of bladder cancer using metabolites and machine learning. Metabolomics. 2019;15(7):94.PubMed Kouznetsova VL, Kim E, Romm EL, Zhu A, Tsigelny IF. Recognition of early and late stages of bladder cancer using metabolites and machine learning. Metabolomics. 2019;15(7):94.PubMed
45.
go back to reference Ridgway ND. The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Crit Rev Biochem Mol Biol. 2013;48(1):20–38.PubMed Ridgway ND. The role of phosphatidylcholine and choline metabolites to cell proliferation and survival. Crit Rev Biochem Mol Biol. 2013;48(1):20–38.PubMed
46.
go back to reference Uchiyama Y, Hayasaka T, Masaki N, Watanabe Y, Masumoto K, Nagata T, Katou F, Setou M. Imaging mass spectrometry distinguished the cancer and stromal regions of oral squamous cell carcinoma by visualizing phosphatidylcholine (16:0/16:1) and phosphatidylcholine (18:1/20:4). Anal Bioanal Chem. 2014;406(5):1307–16.PubMed Uchiyama Y, Hayasaka T, Masaki N, Watanabe Y, Masumoto K, Nagata T, Katou F, Setou M. Imaging mass spectrometry distinguished the cancer and stromal regions of oral squamous cell carcinoma by visualizing phosphatidylcholine (16:0/16:1) and phosphatidylcholine (18:1/20:4). Anal Bioanal Chem. 2014;406(5):1307–16.PubMed
47.
go back to reference Calzada E, Onguka O, Claypool SM. Phosphatidylethanolamine metabolism in health and disease. Int Rev Cell Mol Biol. 2016;321:29–88.PubMed Calzada E, Onguka O, Claypool SM. Phosphatidylethanolamine metabolism in health and disease. Int Rev Cell Mol Biol. 2016;321:29–88.PubMed
48.
go back to reference Wang YT, Lin MR, Chen WC, Wu WH, Wang FS. Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers. FEBS Open Bio. 2021;11(8):2078–94.PubMedCentral Wang YT, Lin MR, Chen WC, Wu WH, Wang FS. Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers. FEBS Open Bio. 2021;11(8):2078–94.PubMedCentral
49.
go back to reference Wei C, Dong X, Lu H, Tong F, Chen L, Zhang R, Dong J, Hu Y, Wu G, Dong X. LPCAT1 promotes brain metastasis of lung adenocarcinoma by up-regulating PI3K/AKT/MYC pathway. J Exp Clin Cancer Res. 2019;38(1):95.PubMedPubMedCentral Wei C, Dong X, Lu H, Tong F, Chen L, Zhang R, Dong J, Hu Y, Wu G, Dong X. LPCAT1 promotes brain metastasis of lung adenocarcinoma by up-regulating PI3K/AKT/MYC pathway. J Exp Clin Cancer Res. 2019;38(1):95.PubMedPubMedCentral
50.
go back to reference Bi J, Ichu TA, Zanca C, Yang H, Zhang W, Gu Y, Chowdhry S, Reed A, Ikegami S, Turner KM, et al. Oncogene amplification in growth factor signaling pathways renders cancers dependent on membrane lipid remodeling. Cell Metab. 2019;30(3):525-538.e528.PubMedPubMedCentral Bi J, Ichu TA, Zanca C, Yang H, Zhang W, Gu Y, Chowdhry S, Reed A, Ikegami S, Turner KM, et al. Oncogene amplification in growth factor signaling pathways renders cancers dependent on membrane lipid remodeling. Cell Metab. 2019;30(3):525-538.e528.PubMedPubMedCentral
51.
go back to reference Du Y, Wang Q, Zhang X, Wang X, Qin C, Sheng Z, Yin H, Jiang C, Li J, Xu T. Lysophosphatidylcholine acyltransferase 1 upregulation and concomitant phospholipid alterations in clear cell renal cell carcinoma. J Exp Clin Cancer Res. 2017;36(1):66.PubMedPubMedCentral Du Y, Wang Q, Zhang X, Wang X, Qin C, Sheng Z, Yin H, Jiang C, Li J, Xu T. Lysophosphatidylcholine acyltransferase 1 upregulation and concomitant phospholipid alterations in clear cell renal cell carcinoma. J Exp Clin Cancer Res. 2017;36(1):66.PubMedPubMedCentral
Metadata
Title
Untargeted metabolomics analysis of esophageal squamous cell cancer progression
Authors
Tao Yang
Ruting Hui
Jessica Nouws
Maor Sauler
Tianyang Zeng
Qingchen Wu
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2022
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-022-03311-z

Other articles of this Issue 1/2022

Journal of Translational Medicine 1/2022 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine