Metabolomics is the study of low molecular weight molecules or metabolites produced within cells and biological systems. It involves technologies such as mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR) that can measure hundreds of thousands of unique chemical entities (UCEs). The metabolome provides one of the most accurate reflections of cellular activity at the functional level and can be leveraged to discern mechanistic information during normal and disease states. The advantages of metabolomics over other “omics” include its high sensitivity and ability to enable the analysis of relatively few metabolites compared with the number of genes and messenger RNAs (mRNAs). In clinical samples, metabolites are more stable than proteins or RNA. In fact, metabolomic profiling in basic, epidemiologic, clinical, and translational studies has revealed potential new biomarkers of disease and therapeutic outcome and has led to a novel mechanistic understanding of pathogenesis. These potential biomarkers include novel metabolites associated with cancer initiation, regression, and recurrence. Unlike genomics or even proteomics, however, the degree of metabolite complexity and heterogeneity within biological systems presents unique challenges that require specialized skills and resources to overcome. This article discusses epidemiologic studies of altered metabolite profiles in several cancers as well as challenges in the field and potential approaches to overcoming them.

Intraoperative brain cancer detection with Raman spectroscopy in humans

This review discusses about identification and characterization of biomarkers in brain cancer utilizing proteomic analysis which is a powerful tool for the discovery of cancer molecular markers. Proteomic analysis allows the characterization of proteins at pictogram level with mass spectrometry (MS) and changes in the levels inherent to the pathophysiology of any cell type, tissue, or whole organism. Protein markers identified by this approach could discriminate cancerous from normal cells. As demonstrated here, proteomic analysis may be efficiently used to identify new indicators for the diagnosis and prognosis of cancer progression. The 2D-DIGE method has been one of the mainstream technologies used for proteomic investigations. In this method, proteins are separated in the first dimension according to charge by isoelectric focusing, followed by separation in the second dimension according to molecular weight, using polyacrylamide gel electrophoresis. Using this approach, up to 1,000 protein spots could be separated and visualized in a single experiment. Gels of different samples are compared and analyzed using computer software, and differentially expressed protein spots are then excised and identified using MS. In this manuscript, we reviewed the relevance of biomarkers in brain cancers and cancer proteomics and the possibility of application of 2D-DIGE-MS method in discovery of potential biomarkers in brain cancers especially glioblastoma multiforme (GBM) which has high rate of recurrence and resistance to chemotherapy.