Colorectal cancer (CRC) is one of the most common cancer and cause of cancer death worldwide. Population-based screening programs for average-risk populations have proven effective in reducing both incidence and mortality of CRC through early detection of cancer. The fecal immunochemical testing (FIT), has still a suboptimal diagnostic yield, with both missed adenomas and, mainly, unnecessary colonoscopies.The identification of novel, non-invasive biomarkers is currently one of the research areas driving most expenditure forces in the field of CRC.A large body of evidence shows that alterations of the gut microbiome and the enrichment of specific taxa(e.g. Fusobacterium nucleatum, Parvimonas micra, and others) are involved in the pathogenesis of CRC. Moreover, recent studies, have discovered common microbial signatures able to reproducibly discriminate between patients with CRC and healthy controls. The goal of this observational study to develop a gut microbiome-based diagnostic tool for the identification of CRC and advanced colorectal adenomas in patients enrolled in the national colorectal cancer (CRC) screening program (50-74 years old) and among who refer to all centers involved in this study for screening colonoscopy with positivity of FIT, of both sex. The primary endpoint of the study is to develop a gut microbiome-based diagnostic tool for the identification of CRC and advanced colorectal adenomas in patients involved in the national CRC screening program at 24 months, using both statistical and machine learning approaches The secondary endpoints are: * The association of clinical and colonoscopy outcomes with FIT results at 24 months * The characterization of gut microbiome from an ecological, taxonomic, phylogenetic and functional point of view at 24 months * The association between microbiome signatures with clinical and colonoscopy outcomes at 24 months, through statistical and machine-learning algorithms At baseline, enrolled patients will provide a fecal sample within 2 weeks from enrollment and demographic, clinical characteristics and laboratory data will be recorded. Enrolled patients will be scheduled for colonoscopy, as for clinical practice, within 4 weeks from the positive FIT and histology of resected lesions will be assessed by experienced pathologists according to the WHO classification and the Vienna criteria. Clinical, endoscopic and microbial data will be combined through statistical and machine learning algorithms to identify specific microbial biomarkers associated with CRC and develop a new diagnostic tool, based on a scoring system. This tool will be validated, and its diagnostic performances will be compared with traditional screening methods.