Cancer markers: Integratively annotated classification

• Reuse of publicly available gene expression data leads to novel integrative annotations.
• Cancer markers with omics and clinical characterizations are not easy to retrieve.
• Classified markers are proposed based on compilations of re-annotated variables.
• Multiple pathway annotations are utilized to improve data consistency.
• Class connectivity is considered in the provided study on colon cancer.

Translational cancer genomics research aims to ensure that experimental knowledge is subject to computational analysis, and integrated with a variety of records from omics and clinical sources. The data retrieval from such sources is not trivial, due to their redundancy and heterogeneity, and the presence of false evidence. In silico marker identification, therefore, remains a complex task that is mainly motivated by the impact that target identification from the elucidation of gene co-expression dynamics and regulation mechanisms, combined with the discovery of genotype–phenotype associations, may have for clinical validation. Based on the reuse of publicly available gene expression data, our aim is to propose cancer marker classification by integrating the prediction power of multiple annotation sources. In particular, with reference to the functional annotation for colorectal markers, we indicate a classification of markers into diagnostic and prognostic classes combined with susceptibility and risk factors.