Machine learning-based prognosis signature for survival prediction of patients with clear cell renal cell carcinoma.

ABSTRACT: The sole clinicopathological characteristic is not enough for the prediction of survival of patients with clear cell renal cell carcinoma (ccRCC). However, the survival prediction model constructed by machine learning technology for patients with ccRCC using clinicopathological features is rarely reported yet. In this study, a total of 5878 patients diagnosed as ccRCC from four independent patient cohorts were recruited. The least absolute shrinkage and selection operator analysis was implemented to identify optimal clinicopathological characteristics and calculate each coefficient to construct the prognosis model. In addition, weighted gene co-expression network and gene enrichment analysis associated with risk score were also carried out. Three clinicopathologic features were selected fo

SUBMITTER: Chen S 

PROVIDER: S-EPMC9489730 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature