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Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients

Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients

Authors :
Hanping Shi
Zhenpeng Yang
Li Deng
Shuai Lu
Benqiang Rao
Wanni Zhao
Lina Wen
Bing Wang
Huazhen Tang
Xin Wang
Xibo Sun
Zhanzhi Zhang
Pingping Jia
Nana Gao
Bingdong Zhang
Publication Year :
2021
Publisher :
Research Square Platform LLC, 2021.

Abstract

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.

Details

Database :
OpenAIRE
Accession number :
edsair.doi...........d696026daa3d010ef9d67c3779f02bf6
Full Text :
https://doi.org/10.21203/rs.3.rs-719491/v1