Construction of a novel gene-based model for prognosis prediction of clear cell renal cell carcinoma.

ABSTRACT:

Background

Clear cell renal cell carcinoma (ccRCC) comprises the majority of kidney cancer death worldwide, whose incidence and mortality are not promising. Identifying ideal biomarkers to construct a more accurate prognostic model than conventional clinical parameters is crucial.

Methods

Raw count of RNA-sequencing data and clinicopathological data were acquired from The Cancer Genome Atlas (TCGA). Tumor samples were divided into two sets. Differentially expressed genes (DEGs) were screened in the whole set and prognosis-related genes were identified from the training set. Their common genes were used in LASSO and best subset regression which were performed to identify the best prognostic 5 genes. The gene-based risk score was developed based on the Cox coefficient of the i

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC6986036 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature