Colitis-associated colorectal cancer is the most serious complication of ulcerative colitis. Long-term chronic inflammation increases the incidence of CAC in UC patients. Compared with sporadic colorectal cancer, CAC means multiple lesions, worse pathological type and worse prognosis. Macrophage is a kind of innate immune cell, which play an important role both in inflammatory response and tumor immunity. Macrophages are polarized into two phenotypes under different conditions: M1 and M2. In UC, enhanced macrophage infiltration produces a large number of inflammatory cytokines, which promote tumorigenesis of UC. M1 polarization has an anti-tumor effect after CAC formation, whereas M2 polarization promotes tumor growth. M2 polarization plays a tumor-promoting role. Some drugs have been shown to that prevent and treat CAC effectively by targeting macrophages.

Given the poor prognosis of metastatic colorectal cancer (mCRC), this research aimed to investigate the correlation between tumor size and prognosis, and develop a novel prediction model to guide individualized treatment. Patients pathologically diagnosed with mCRC were recruited from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, and were randomly divided (7:3 ratio) into a training cohort (n = 5597) and a validation cohort (n = 2398). Kaplan–Meier curves were used to analyze the relationship between tumor size and overall survival (OS). Univariate Cox analysis was applied to assess the factors associated with the prognosis of mCRC patients in the training cohort, and then multivariate Cox analysis was used to construct a nomogram model. The area under the receiver-operating characteristics curve (AUC) and calibration curve were used to evaluate the predictive ability of the model. Patients with larger tumors had a worse prognosis. While brain metastases were associated with larger tumors compared to liver or lung metastases, bone metastases tended to be associated with smaller tumors. Multivariate Cox analysis revealed that tumor size was an independent prognostic risk factor (HR 1.28, 95% CI 1.19–1.38), in addition to the other ten variables (age, race, primary site, grade, histology, T stage, N stage, chemotherapy, CEA level and metastases site). The 1-, 3-, and 5-year OS nomogram model yielded AUC values of more than 0.70 in both the training and validation cohorts, and its predictive performance was superior to that of the traditional TNM stage. Calibration plots demonstrated a good agreement between the predicted and observed 1-, 3-, and 5-year OS outcomes in both cohorts. The size of primary tumor was found to be significantly associated with prognosis of mCRC, and was also correlated with specific metastatic organ. In this study, we presented the first effort to create and validate a novel nomogram for predicting 1-, 3- and 5-year OS probabilities of mCRC. The prognostic nomogram was demonstrated to have an excellent predictive ability in estimating individualized OS of patients with mCRC.