Development and Clinical Validation of a Seven-Gene Prognostic Signature Based on Multiple Machine Learning Algorithms in Kidney Cancer.

ABSTRACT: About a third of patients with kidney cancer experience recurrence or cancer-related progression. Clinically, kidney cancer prognoses may be quite different, even in patients with kidney cancer at the same clinical stage. Therefore, there is an urgent need to screen for kidney cancer prognosis biomarkers. Differentially expressed genes (DEGs) were identified using kidney cancer RNA sequencing data from the Gene Expression Omnibus (GEO) database. Biomarkers were screened using random forest (RF) and support vector machine (SVM) models, and a multigene signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. Univariate and multivariate Cox regression analyses were performed to explore the relationships between clinical features and prog

SUBMITTER: Tian M 

PROVIDER: S-EPMC7917425 | biostudies-literature | 2021 Jan-Dec

REPOSITORIES: biostudies-literature