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Lipidomic Approach To Identify Patterns In Phospholipid Profiles And Define Class Differences In Mammary Epithelial And Breast Cancer Cells
M. Luísa Dória, Zita Cotrim, Barbara Gazolla de Macedo, Cláudia Simões, P. Domíngues, L. Helguero, M. Rosário Domingues
Published 2011 · Biology, Medicine
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Breast cancer is the leading cause of cancer-related deaths in women. Altered cellular functions of cancer cells lead to uncontrolled cellular growth and morphological changes. Cellular biomembranes are intimately involved in the regulation of cell signaling; however, they remain largely understudied. Phospholipids (PLs) are the main constituents of biological membranes and play important functional, structural and metabolic roles. The aim of this study was to establish if patterns in the PL profiles of mammary epithelial cells and breast cancer cells differ in relation to degree of differentiation and metastatic potential. For this purpose, PLs were analyzed using a lipidomic approach. In brief, PLs were extracted using Bligh and Dyer method, followed by a separation of PL classes by thin layer chromatography, and subsequent analysis by mass spectrometry (MS). Differences and similarities were found in the relative levels of PL content between mammary epithelial and breast cancer cells and between breast cancer cells with different levels of aggressiveness. When compared to the total PL content, phosphatidylcholine levels were reduced and lysophosphatydilcholines increased in the more aggressive cancer cells; while phosphatidylserine levels remained unchanged. MS analysis showed alterations in the classes of phosphatidylcholine, lysophosphatidylcholine, sphingomyelin, and phosphatidylinositides. In particular, the phosphatidylinositides, which are signaling molecules that affect proliferation, survival, and migration, showed dramatic alterations in their profile, where an increase of phosphatdylinositides saturated fatty acids chains and a decrease in C20 fatty acids in cancer cells compared with mammary epithelial cells was observed. At present, information about PL changes in cancer progression is lacking. Therefore, these data will be useful as a starting point to define possible PLs with prospective as biomarkers and disclose metabolic pathways with potential for therapy.
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