دورية أكاديمية
Hepatocellular carcinoma stage: an almost loss of fatty acid metabolism and gain of glucose metabolic pathways dysregulation.
| العنوان: | Hepatocellular carcinoma stage: an almost loss of fatty acid metabolism and gain of glucose metabolic pathways dysregulation. |
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| المؤلفون: | Balakrishnan K; Department of Biotechnology, Saroj Institute of Technology and Management (SITM), 12th KM Stone, Lucknow-Sultanpur Road, Lucknow, Uttar Pradesh, 226002, India. karkbioscience@gmail.com. |
| المصدر: | Medical oncology (Northwood, London, England) [Med Oncol] 2022 Oct 08; Vol. 39 (12), pp. 247. Date of Electronic Publication: 2022 Oct 08. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Springer Country of Publication: United States NLM ID: 9435512 Publication Model: Electronic Cited Medium: Internet ISSN: 1559-131X (Electronic) Linking ISSN: 13570560 NLM ISO Abbreviation: Med Oncol Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: 2011- : New York : Springer Original Publication: Northwood, Middlesex, England : Science and Technology Letters, c1994- |
| مواضيع طبية MeSH: | Acyl-CoA Dehydrogenases*/metabolism , Carcinoma, Hepatocellular*/pathology , Liver Neoplasms*/pathology, Acetyl-CoA Carboxylase/genetics ; Acetyl-CoA Carboxylase/metabolism ; Carcinogenesis ; Fatty Acids/metabolism ; Glucose/metabolism ; Glutamine/metabolism ; Humans ; Metabolic Networks and Pathways ; Oxidoreductases ; Phosphoglycerate Kinase/metabolism ; Pyruvate Dehydrogenase Acetyl-Transferring Kinase ; Pyruvates |
| مستخلص: | Cancer cells rewire the metabolic processes beneficial for cancer cell proliferation, survival, and their progression. In this study, metabolic processes related to glucose, glutamine, and fatty acid metabolism signatures were collected from the molecular signatures database and investigated in the context of energy metabolic pathways through available genome-wide expression profiles of liver cancer cohorts by gene sets-based pathway activation scoring analysis. The outcomes of this study portray that the fatty acid metabolism, transport, and its storage related signatures are highly expressed across early stages of liver tumors and on the contrary, the gene sets related to glucose transport and glucose metabolism are prominently activated in the hepatocellular carcinoma (HCC) stage. Based on the results, these metabolic pathways are clearly dysregulated across specific stages of carcinogenesis. The identified dimorphic metabolic pathway dysregulation patterns are further reconfirmed by examining corresponding metabolic pathway genes expression patterns across various stages encompassing profiles. Recurrence is the primary concern in the carcinogenesis of liver tumors due to liver tissues regeneration. Hence, to further explore these dysregulation effects on recurrent cirrhosis and recurrent HCC sample containing profile GSE20140 was examined and interestingly, this result also reiterated these differential metabolic pathways dysregulation. In addition, a recently established metabolome profile for the massive panel of cancer cell-lines, including liver cancer cell-lines, was used for further exploration. These findings also reassured those differential metabolites abundance of the fatty acid and glucose metabolic pathways enlighten those dimorphic metabolic pathways dysregulation. Moreover, ROC curves of fatty acid metabolic pathway genes such as acetyl-CoA carboxylase (ACACB), acyl-CoA dehydrogenase long chain (ACADL), and acyl-CoA dehydrogenase medium chain (ACADM) as well as glucose metabolic pathway genes such as phosphoglycerate kinase (PGK1), pyruvate dehydrogenase (PDHA1), pyruvate dehydrogenase kinase (PDK1) demonstrated greater sensitivity and specificity in the corresponding stage-specific tumors with significant p-values (p < 0.05). Furthermore, overall survival (OS) and recurrence-free survival (RFS) studies also reconfirmed that the rate-limiting genes expression of fatty acid and glucose metabolic pathways reveal better and poor survival in HCC patient cohorts, respectively. In conclusion, all these results clearly show that metabolic rewiring and the existence of two diverse metabolic pathways dysregulation involving fatty acid and glucose metabolism across the stages of liver tumors have been identified. These findings might be useful for developing therapeutic target treatments in stage-specific tumors. (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.) |
| References: | Russo FP, Zanetto A, Pinto E, Battistella S, Penzo B, Burra P, Farinati F. Hepatocellular carcinoma in chronic viral hepatitis: where do we stand? Int J Mol Sci. 2022;23(1):500. (PMID: 35008926874514110.3390/ijms23010500) Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A cancer J clin. 2021;71(3):209–49. (PMID: 10.3322/caac.21660) Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin. 2018;68(6):394–424. (PMID: 10.3322/caac.21492) Petta S, Craxì A. Hepatocellular carcinoma and non-alcoholic fatty liver disease: from a clinical to a molecular association. Curr Pharm Des. 2010;16:741–52. (PMID: 2038808410.2174/138161210790883787) Cholankeril G, Patel R, Khurana S, Satapathy SK. Hepatocellular carcinoma in non-alcoholic steatohepatitis: current knowledge and implications for management. World J Hepatol. 2017;9:533–43. (PMID: 28469809539580210.4254/wjh.v9.i11.533) Hanahan D. Hallmarks of cancer: new dimensions. Cancer Discov. 2022;12(1):31–46. (PMID: 3502220410.1158/2159-8290.CD-21-1059) Zhang X, Guo J, Kaboli PJ, Zhao Q, Xiang S, Shen J, Zhao Y, Du F, Wu X, Li M, Ji H, Yang X, Xiao Z, Wen Q. Analysis of key genes regulating the warburg effect in patients with gastrointestinal cancers and selective inhibition of this metabolic pathway in liver cancer cells. OTT. 2020;13:7295–304. (PMID: 10.2147/OTT.S257944) Berndt N, Eckstein J, Heucke N, Gajowski R, Stockmann M, Meierhofer D, Holzhütter H-G. Characterization of lipid and lipid droplet metabolism in human HCC. Cells. 2019;8(5):512. (PMID: 656248410.3390/cells8050512) Wang W, He Q, Yan W, Sun J, Chen Z, Liu Z, Lu Z, Hou J, Shao Y, Zhou X, Wang A. High glucose enhances the metastatic potential of tongue squamous cell carcinoma via the PKM2 pathway. Oncotarget. 2017;8:111770–9. (PMID: 29340090576235810.18632/oncotarget.22907) Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, Yefanov A, Lee H, Zhang N, Robertson CL, Serova N, Davis S, Soboleva A. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013;41:D991–5. (PMID: 2319325810.1093/nar/gks1193) Wurmbach E, Chen Y, Khitrov G, Zhang W, Roayaie S, Schwartz M, Fiel I, Thung S, Mazzaferro V, Bruix J, Bottinger E, Friedman S, Waxman S, Llovet JM. Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology. 2007;45:938–47. (PMID: 1739352010.1002/hep.21622) Villanueva A, Hoshida Y, Battiston C, Tovar V, Sia D, Alsinet C, Cornella H, Liberzon A, Kobayashi M, Kumada H, Thung SN, Bruix J, Newell P, April C, Fan J-B, Roayaie S, Mazzaferro V, Schwartz ME, Llovet JM. Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology. 2011;140:1501-12.e2. (PMID: 2132049910.1053/j.gastro.2011.02.006) Eun J., Nam S., (2017). Identifying novel drivers of human hepatocellular carcinoma and revealing clinical relevance as early diagnostic and prognostic biomarker, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89377. Darpolor MM, Basu SS, Worth A, Nelson DS, Clarke-Katzenberg RH, Glickson JD, Kaplan DE, Blair IA. The aspartate metabolism pathway is differentiable in human hepatocellular carcinoma: transcriptomics and (13) C-isotope based metabolomics. NMR Biomed. 2014;27:381–9. (PMID: 24497316396277910.1002/nbm.3072) Grinchuk OV, Yenamandra SP, Iyer R, Singh M, Lee HK, Lim KH, Chow PK-H, Kuznetsov VA. Tumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma. Mol Oncol. 2018;12:89–113. (PMID: 2911747110.1002/1878-0261.12153) Lamb JR, Zhang C, Xie T, Wang K, Zhang B, Hao K, Chudin E, Fraser HB, Millstein J, Ferguson M, Suver C, Ivanovska I, Scott M, Philippar U, Bansal D, Zhang Z, Burchard J, Smith R, Greenawalt D, Cleary M, Derry J, Loboda A, Watters J, Poon RTP, Fan ST, Yeung C, Lee NPY, Guinney J, Molony C, Emilsson V, Buser-Doepner C, Zhu J, Friend S, Mao M, Shaw PM, Dai H, Luk JM, Schadt EE. Predictive genes in adjacent normal tissue are preferentially altered by sCNV during tumorigenesis in liver cancer and may rate limiting. PLoS ONE. 2011;6: e20090. (PMID: 21750698313002910.1371/journal.pone.0020090) Kishikawa T, Otsuka M, Tan PS, Ohno M, et al. Decreased miR122 in hepatocellular carcinoma leads to chemoresistance with increased arginine. Oncotarget. 2015;6(10):8339–52. (PMID: 25826076448075610.18632/oncotarget.3234) Finn RS, Aleshin A, Dering J, Yang P, Ginther C, Desai A, Zhao D, Euw E, von, Busuttil R.W. & Slamon D.J. Molecular subtype and response to dasatinib, an Src/Abl small molecule kinase inhibitor, in hepatocellular carcinoma cell lines in vitro. Hepatology. 2013;57:1838–46. (PMID: 2329986010.1002/hep.26223) Wilson CL, Miller CJ. Simpleaffy: a bioconductor package for affymetrix quality control and data analysis. Bioinformatics. 2005;21:3683–5. (PMID: 1607688810.1093/bioinformatics/bti605) Freedman JA, Tyler DS, Nevins JR, Augustine CK. Use of gene expression and pathway signatures to characterize the complexity of human melanoma. Am J Pathol. 2011;178:2513–22. (PMID: 21641377312435810.1016/j.ajpath.2011.02.037) Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102:15545–50. (PMID: 16199517123989610.1073/pnas.0506580102) Cheadle C, Vawter MP, Freed WJ, Becker KG. Analysis of microarray data using Z score transformation. J Mol Diagn. 2003;5:73–81. (PMID: 12707371190732210.1016/S1525-1578(10)60455-2) Tarca AL, Romero R, Draghici S. Analysis of microarray experiments of gene expression profiling. Am J Obstet Gynecol. 2006;195:373–88. (PMID: 16890548243525210.1016/j.ajog.2006.07.001) Li C, Wong WH. DNA-Chip Analyzer (dChip). In: Parmigiani G, Garrett ES, Irizarry RA, Zeger SL, editors. The Analysis of Gene Expression Data Statistics for Biology and Health. New York, NY: Springer; 2003. Murrell P. R Graphics. 3rd ed. Boca Raton: Chapman and Hall/CRC Book Publication; 2018. p. 441. Nagy Á, Lánczky A, Menyhárt O, Győrffy B. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep. 2018;8:9227. (PMID: 29907753600393610.1038/s41598-018-27521-y) Menyhárt O, Nagy Á, Győrffy B. Determining consistent prognostic biomarkers of overall survival and vascular invasion in hepatocellular carcinoma. Royal Soc Open Sci. 2018;5:181006. (PMID: 10.1098/rsos.181006) Bland JM, Altman DG. The logrank test. BMJ. 2004;328:1073. (PMID: 1511779740385810.1136/bmj.328.7447.1073) DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. (PMID: 320313210.2307/2531595) Li H, Ning S, Ghandi M, Kryukov GV, Gopal S, Deik A, Souza A, Pierce K, Keskula P, Hernandez D, Ann J, Shkoza D, Apfel V, Zou Y, Vazquez F, Barretina J, Pagliarini RA, Galli GG, Root DE, Hahn WC, Tsherniak A, Giannakis M, Schreiber SL, Clish CB, Garraway LA, Sellers WR. The landscape of cancer cell line metabolism. Nat Med. 2019;25:850–60. (PMID: 31068703662904110.1038/s41591-019-0404-8) Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74. (PMID: 2137623010.1016/j.cell.2011.02.013) Fouad YA, Aanei C. Revisiting the hallmarks of cancer. Am J Cancer Res. 2017;7:1016–36. (PMID: 285600555446472) Benjamin DI, Cravatt BF, Nomura DK. Global profiling strategies for mapping dysregulated metabolic pathways in cancer. Cell Metab. 2012;16:565–77. (PMID: 23063552353974010.1016/j.cmet.2012.09.013) Deberardinis RJ, Sayed N, Ditsworth D, Thompson CB. Brick by brick: metabolism and tumor cell growth. Curr Opin Genet Dev. 2008;18:54–61. (PMID: 18387799247621510.1016/j.gde.2008.02.003) Zhuang X, Chen Y, Wu Z, Xu Q, Chen M, Shao M, Cao X, Zhou Y, Xie M, Shi Y, Zeng Y, Bu H. Mitochondrial miR-181a-5p promotes glucose metabolism reprogramming in liver cancer by regulating the electron transport chain. Carcinogenesis. 2020;41:972–83. (PMID: 3162846210.1093/carcin/bgz174) DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB. The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab. 2008;7:11–20. (PMID: 1817772110.1016/j.cmet.2007.10.002) Desbats MA, Giacomini I, Prayer-Galetti T, Montopoli M. Metabolic plasticity in chemotherapy resistance. Front Oncol. 2020;10:281. (PMID: 32211323706890710.3389/fonc.2020.00281) Martin JD, Fukumura D, Duda DG, Boucher Y, Jain RK. Reengineering the tumor microenvironment to alleviate hypoxia and overcome cancer heterogeneity. Cold Spring Harb Perspect Med. 2016;6: a027094. (PMID: 27663981513175110.1101/cshperspect.a027094) Sinkala M, Mulder N, Patrick Martin D. Metabolic gene alterations impact the clinical aggressiveness and drug responses of 32 human cancers. Commun biol. 2019;2(1):1–14. (PMID: 10.1038/s42003-019-0666-1) Pavlova NN, Thompson CB. The emerging hallmarks of cancer metabolism. Cell Metab. 2016;23(1):27–47. (PMID: 26771115471526810.1016/j.cmet.2015.12.006) Biswas S, Lunec J, Bartlett K. Non-glucose metabolism in cancer cells—is it all in the fat? Cancer Metastasis Rev. 2012;31:689–98. (PMID: 2270684610.1007/s10555-012-9384-6) Pike LS, Smift AL, Croteau NJ, Ferrick DA, Wu M. Inhibition of fatty acid oxidation by etomoxir impairs NADPH production and increases reactive oxygen species resulting in ATP depletion and cell death in human glioblastoma cells. Biochim Biophys Acta. 2011;1807:726–34. (PMID: 2169224110.1016/j.bbabio.2010.10.022) Romero-Garcia S, Lopez-Gonzalez JS, B´ ez-Viveros JL, Aguilar-Cazares D, Prado-Garcia H. Tumor cell metabolism. Cancer Biol Ther. 2011;12:939–48. (PMID: 22057267328091210.4161/cbt.12.11.18140) Yoo HC, Yu YC, Sung Y, Han JM. Glutamine reliance in cell metabolism. Exp Mol Med. 2020;52(9):1496–516. (PMID: 32943735808061410.1038/s12276-020-00504-8) Menendez JA. Fine-tuning the lipogenic/lipolytic balance to optimize the metabolic requirements of cancer cell growth: molecular mechanisms and therapeutic perspectives. Biochim Biophys Acta. 2010;1801:381–91. (PMID: 1978215210.1016/j.bbalip.2009.09.005) Rencelj A, Gvozdenovic N, Cemazar M. MitomiRs: their roles in mitochondria and importance in cancer cell metabolism. Radiol Oncol. 2021;55(4):379–92. (PMID: 34821131864779210.2478/raon-2021-0042) Hay N. Reprogramming glucose metabolism in cancer: can it be exploited for cancer therapy? Nat Rev Cancer. 2016;16:635–49. (PMID: 27634447551680010.1038/nrc.2016.77) Iansante V, Choy PM, Fung SW, Liu Y, Chai J-G, Dyson J, Del Rio A, D’Santos C, Williams R, Chokshi S, Anders RA, Bubici C, Papa S. PARP14 promotes the Warburg effect in hepatocellular carcinoma by inhibiting JNK1-dependent PKM2 phosphorylation and activation. Nat Commun. 2015;6:7882. (PMID: 26258887491831910.1038/ncomms8882) Sun L, Zhang H, Gao P. Metabolic reprogramming and epigenetic modifications on the path to cancer. Protein Cell. 2021. https://doi.org/10.1007/s13238-021-00846-7 . (PMID: 10.1007/s13238-021-00846-7348130549243210) Kowalik MA, Columbano A, Perra A. Emerging role of the pentose phosphate pathway in hepatocellular carcinoma. Front Oncol. 2017;7(87):2017. https://doi.org/10.3389/fonc.2017.00087.eCollection . (PMID: 10.3389/fonc.2017.00087.eCollection) Altman BJ, Stine ZE, Dang CV. From Krebs to clinic: glutamine metabolism to cancer therapy. Nat Rev Cancer. 2016;16:619–34. (PMID: 27492215548441510.1038/nrc.2016.71) Jones RG, Thompson CB. Tumor suppressors and cell metabolism: a recipe for cancer growth. Genes Dev. 2009;23:537–48. (PMID: 19270154276349510.1101/gad.1756509) Grande S, Palma A, Ricci-Vitiani L, Luciani AM, Buccarelli M, Biffoni M, Molinari A, Calcabrini A, D’Amore E, Guidoni L, Pallini R, Viti V, Rosi A. Metabolic heterogeneity evidenced by MRS among patient-derived glioblastoma multiforme stem-like cells accounts for cell clustering and different responses to drugs. Stem Cells International. 2018;2018: e3292704. (PMID: 10.1155/2018/3292704) Bayin NS, Frenster JD, Sen R, Si S, Modrek AS, Galifianakis N, Dolgalev I, Ortenzi V, Illa-Bochaca I, Khahera A, Serrano J, Chiriboga L, Zagzag D, Golfinos JG, Doyle W, Tsirigos A, Heguy A, Chesler M, Barcellos-Hoff MH, Snuderl M, Placantonakis DG. Notch signaling regulates metabolic heterogeneity in glioblastoma stem cells. Oncotarget. 2017;8:64932–53. (PMID: 29029402563030210.18632/oncotarget.18117) |
| فهرسة مساهمة: | Keywords: Fatty acid metabolism; Glucose metabolism; Hepatocellular carcinoma; Liver cancer |
| المشرفين على المادة: | 0 (Fatty Acids) 0 (Pyruvate Dehydrogenase Acetyl-Transferring Kinase) 0 (Pyruvates) 0RH81L854J (Glutamine) EC 1.- (Oxidoreductases) EC 1.3.- (Acyl-CoA Dehydrogenases) EC 2.7.2.3 (Phosphoglycerate Kinase) EC 6.4.1.2 (Acetyl-CoA Carboxylase) IY9XDZ35W2 (Glucose) |
| تواريخ الأحداث: | Date Created: 20221008 Date Completed: 20221011 Latest Revision: 20221101 |
| رمز التحديث: | 20221213 |
| DOI: | 10.1007/s12032-022-01839-0 |
| PMID: | 36209296 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1559-131X |
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| DOI: | 10.1007/s12032-022-01839-0 |