One of the most desirable applications of Omics in cancer has been the discovery of molecular markers (Biomarkers) that are capable of diagnosing tumours at early stages.
Even prominent cases such as sarcosine, a glycine metabolic de- rivative, claimed to be a marker for prostate cancer progression (Sreekumar et al., 2009), has later been questioned in its clinical usefulness (Issaq and Veenstra, 2011). One of the major reasons for this is the fact that most of these molecules (irrespective of these being proteins, nuclide acids or small molecule metabolites) are part of normal physiological processes and often fluctuate due to either normal homeostasis or other ‘confounding’ diseases, predominantly inflammation, infection, injury, food poisoning, and ageing. In add- ition, preanalytical variability due to inconsistent clinical sample handling remains a problem. For instance, collection of blood sam- ples and handling at 4 ° C leads to platelet activation and promotes blood clotting (Kaisar et al., 2016a; Kaisar et al., 2016b), and many blood markers may simply relate to these to common events irre- spective of the patient’s disease status. Consequently, a very small number of candidate molecules have made the cut and were taken forward towards the clinic (Swami, 2010; Henry and Hayes, 2012).
Cancer multiomics Deep insights into cancer biology, in general, and biomarker dis- covery in particular, have more recently benefited from efforts to integrate -omics data sets, such as transcriptomics, genomics, proteomics, and metabolomics data (Casado-Vela et al., 2011).