LEADER 04133nam 22010813a 450 001 9910367563603321 005 20250203235429.0 010 $a9783039213467 010 $a3039213466 024 8 $a10.3390/books978-3-03921-346-7 035 $a(CKB)4100000010106110 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/42643 035 $a(ScCtBLL)91546fb1-130b-40d9-8a02-24111f479c02 035 $a(OCoLC)1163855517 035 $a(oapen)doab42643 035 $a(EXLCZ)994100000010106110 100 $a20250203i20192019 uu 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aCancer Metabolomics 2018$fPaula Guedes De Pinho, Márcia Carvalho, Joana Pinto 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 electronic resource (184 p.) 311 08$a9783039213450 311 08$a3039213458 330 $aThe metabolomics approach, defined as the study of all endogenously-produced low-molecular-weight compounds, appeared as a promising strategy to define new cancer biomarkers. Information obtained from metabolomic data can help to highlight disrupted cellular pathways and, consequently, contribute to the development of new-targeted therapies and the optimization of therapeutics. Therefore, metabolomic research may be more clinically translatable than other omics approaches, since metabolites are closely related to the phenotype and the metabolome is sensitive to many factors. Metabolomics seems promising to identify key metabolic pathways characterizing features of pathological and physiological states. Thus, knowing that tumor metabolism markedly differs from the metabolism of normal cells, the use of metabolomics is ideally suited for biomarker research. Some works have already focused on the application of metabolomic approaches to different cancers, namely lung, breast and liver, using urine, exhaled breath and blood. In this Special Issue we contribute to a more complete understanding of cancer disease using metabolomics approaches. 606 $aBiology, life sciences$2bicssc 610 $acell transporters 610 $apharmacodynamics 610 $acell growth 610 $ain vitro study 610 $ametabolomic signatures 610 $aendometabolome 610 $alung cancer 610 $ametabolomics 610 $achemometric methods 610 $abladder cancer 610 $amTOR 610 $ametabolite profiling 610 $ametabolic pathways 610 $ahepatocellular carcinoma 610 $aglutamate 610 $asenescence MCF7 610 $abreath analysis 610 $abio actives 610 $abiomarker 610 $agas chromatography?mass spectrometry (GC?MS) 610 $aGC-MS 610 $alung 610 $aomics 610 $anutraceuticals 610 $aglutaminase 610 $ametabolism 610 $aacylcarnitines 610 $aErwinaze 610 $aKidrolase 610 $aglutathione 610 $atargeted metabolomics 610 $aapoptosis 610 $aSLC1A5 610 $aessential amino acids 610 $acancer progression 610 $aASCT2 610 $aHR MAS 610 $aalanine 610 $aanalytical platforms 610 $avolatile organic compound 610 $aglutaminolysis 610 $aisotope tracing analysis 610 $aasparaginase 610 $avitamin E 610 $abreast cancer 610 $aprognosis 610 $aearly diagnosis 610 $atocotrienols 610 $aNMR 610 $aprostate cancer 610 $ain vitro 610 $acancer 610 $aMDA-MB-231 615 7$aBiology, life sciences 700 $aDe Pinho$b Paula Guedes$01787258 702 $aCarvalho$b Márcia 702 $aPinto$b Joana 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910367563603321 996 $aCancer Metabolomics 2018$94320110 997 $aUNINA