The data presented in this study encourage us to start a prospective multicenter studyand experimental studies on the role of the CB2-63 QQ variant in the different stages of HCV infection. Colorectal cancer is the third most common cancer and the fourth-leading cause of cancer death worldwide, with a lifetime risk in Western European and North American populations around 5%. Many gene expression profiling studies on CRC have been performed in the last decade using microarray technology. According to their potential clinical applications, they can be classified into three groups : studies on carcinogenesis process, UCPH-102 studies on prognosis prediction, and studies on treatment response prediction. They show little overlap in the identified genes, and no reliable signature useful in clinical practice has been found. Currently, the International Union Against Cancer TNM classification of malignant tumours based on clinicopathological staging remains the standard for CRC prognostication. We focused on the studies on prognosis prediction, which comprise a heterogeneous group of GEP studies. They aim to identify a gene expression profile to discriminate more aggressive from less aggressive CRC, based on different features related to disease progression, such as the existence of recurrence, the presence of metastasis, or survival data. To date,CeMMEC13 only one metaanalysis of ten GEP studies has reported a list of 13 genes differentially expressed in CRC with good versus bad prognosis, reported by at least two independent studies. Multiple reasons have been proposed to explain this lack of reproducibility in the GEP studies on CRC, such as underpowered studies, lack of validation of results, differences in experimental protocol and statistical pitfalls in analysing microarray expression data for cancer outcome. Changes in biological characteristics require coordinated variation in expression of gene sets which regulate biological activity, and this information can hardly be extracted from changes in expression of individual genes when overlapping among studies is so low. Enrichment analysis tools, which estimate overrepresentation of particular gene categories or pathways in a gene list, are a promising strategy to identify biological categories implicated in the investigated process.