A method for visualization of "omic" datasets for sphingolipid metabolism to predict potentially interesting differences.

Sphingolipids are structurally diverse and their metabolic pathways highly complex, which makes it difficult to follow all of the subspecies in a biological system, even using "lipidomic" approaches. This report describes a method to use transcriptomic data to visualize and predict potential differences in sphingolipid composition, and it illustrates its use with published data for cancer cell lines and tumors. In addition, several novel sphingolipids that were predicted to differ between MDA-MB-231 and MCF7 cells based on published microarray data for these breast cancer cell lines were confirmed by mass spectrometry. For the data that we were able to find for these comparisons, there was a significant match between the gene expression data and sphingolipid composition (P < 0.001 by Fisher's exact test). Upon considering the large number of gene expression datasets produced in recent years, this simple integration of two types of "omic" technologies ("transcriptomics" to direct "sphingolipidomics") might facilitate the discovery of useful relationships between sphingolipid metabolism and disease, such as the identification of new biomarkers.