Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jchromb.2009.06.015
DC FieldValue
dc.titleLipid-based biomarkers for cancer
dc.contributor.authorFernandis, A.Z.
dc.contributor.authorWenk, M.R.
dc.date.accessioned2011-11-29T05:58:59Z
dc.date.available2011-11-29T05:58:59Z
dc.date.issued2009
dc.identifier.citationFernandis, A.Z., Wenk, M.R. (2009). Lipid-based biomarkers for cancer. Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences 877 (26) : 2830-2835. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jchromb.2009.06.015
dc.identifier.issn15700232
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/28748
dc.description.abstractLipids play important and diverse roles in cells. Most obvious functions are storage of chemical energy, provision of structural support of biological membranes and signaling. All these cellular processes are of critical relevance to cells which undergo transformation, cancer progression and metastasis. Thus, it is likely that certain classes of lipids are reflective for the cellular physiology in cancer cells and tissue. Here we discuss key roles of lipids involved in cancer as well as challenges for development of novel lipid-based biomarkers. Special emphasis will be given to mass spectrometry based analysis of lipids. Such technology has been successfully used for qualitative and quantitative analysis of lipids with very different chemistries. Comparative analysis, often in case-control regimes, and either in non-targeted (e.g. by liquid chromatography-single stage mass spectrometry) or targeted (i.e. by tandem mass spectrometry) fashion yields vast arrays of information. Uni-variate (such as Student's t-test or Mann-Whitney U-test) and multivariate statistics (principal components analysis, machine learning and regression analysis) are next used to identify variations in individual lipid species and/or to lower dimensions for visualization and grouping of cases and controls. As a result surrogate (single or multi-parameter) markers are identified which form the basis for functional validation as well as potential translation to alternative analytical readouts. © 2009 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jchromb.2009.06.015
dc.sourceScopus
dc.subjectBiomarker
dc.subjectCancer
dc.subjectLipidomic
dc.subjectMass spectrometry
dc.subjectMultiple reaction monitoring
dc.typeReview
dc.contributor.departmentBIOCHEMISTRY
dc.description.doi10.1016/j.jchromb.2009.06.015
dc.description.sourcetitleJournal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences
dc.description.volume877
dc.description.issue26
dc.description.page2830-2835
dc.description.codenJCBAA
dc.identifier.isiut000269224200016
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