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.
Keywords: AE; Adenocarcinoma versus Normal; Adenoma versus Carcinoma; Adenoma versus Normal; Amino acid derivative process; ArrayExpress; CF; Clinical factors; Clinical re-annotation; Colon carcinoma versus Normal; D-ca; D-cb; E-c; Exposed versus Non-exposed; GEO; Gene Expression Omnibus; Marker classification; NOI; Non-empty intersections; P-ca; P-cb; P-cc; S-c; Stage II recurrence versus Stage II recurrence free; TCGA; The Cancer Genome Atlas; Translational cancer genomics; Treated versus Non-treated; acdp.
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