In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer.

ABSTRACT:

Background

Aberrant lipid metabolism is an alteration common to many types of cancer. Dysregulation of lipid metabolism is considered a major risk factor for bladder cancer. Accordingly, we focused on genes related to lipid metabolism and screened novel markers for predicting the prognosis of bladder cancer.

Methods

RNA-seq data for bladder cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The nonnegative matrix factorization (NMF) algorithm was used to classify the molecular subtypes. Weighted correlation network analysis (WGCNA) was applied to identify coexpressed genes, and least absolute shrinkage and selection operator (LASSO) multivariate Cox analysis was used to construct a prognostic risk model. External validation

SUBMITTER: Sun X 

PROVIDER: S-EPMC9038413 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature