Metabolic features of prostate malignancy
Altered metabolism is an inherent property of cancer and provides a rich opportunity for exploring its underlying biology and developing diagnostic biomarkers. We used desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to investigate the spatial distribution of small metabolites and lipids within prostate biopsy core tissue. Thirty-five cores across 18 cases were analyzed and 965 regions of interest (ROIs) were selected corresponding to pathologically-validated benign or cancer tissue. A range of multivariate statistics were conducted to identify differentially abundant metabolites as well as for construction of a metabolic classifier of cancer tissue. Of the 25 differentially abundant metabolites, we identified increased citrate in benign tissue and increased glutamate in cancer tissue. We also identified increased levels of lyso-phosphatidylethanolamine (PE) in benign tissue and increased fatty acids (FAs) and phospholipids in cancer tissue, including; PEs, phosphatidylinositols (PIs) and phosphatidylcholines (PChs). Our data is suggestive of differential lipid metabolism in prostate cancer (PCa) needed to support development and progression of the disease. We also found that the metabolic profile of PCa was reflective of the grade of the case rather than the grade of the individual biopsy core. Differential metabolites observed between grades of PCa displayed evidence of further dysregulated lipid metabolism with increasing aggressiveness. Additionally, we utilized a training cohort to construct a principal component analysis (PCA)/linear discriminant analysis (LDA) model which achieved an overall accuracy of 97%. Independent validation of this model on the remaining cases displayed an overall accuracy of 85%. Further studies will validate this PCa specific metabolic pathway as well as explore additional grades of PCa to identify metabolic profiles associated with each stage of aggressiveness. Ultimately, the validated accuracy of this classifier and the correlation of differentially abundant metabolites with established cancer metabolism, indicates that DESI-MSI is an effective tool for elucidating cancer metabolism with the potential for clinical translation.
URI for this recordhttp://hdl.handle.net/1974/24417
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