Background: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors.Methods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated.Results: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately.Conclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic.