Identification of a Fibroblast-Related Prognostic Model in Glioma Based on Bioinformatics Methods.

ABSTRACT:

Background

Glioma is the most common primary tumor of the central nervous system with a high lethality rate. This study aims to mine fibroblast-related genes with prognostic value and construct a corresponding prognostic model.

Methods

A glioma-related TCGA (The Cancer Genome Atlas) cohort and a CGGA (Chinese Glioma Genome Atlas) cohort were incorporated into this study. Variance expression profiling was executed via the "limma" R package. The "clusterProfiler" R package was applied to perform a GO (Gene Ontology) analysis. The Kaplan-Meier (K-M) curve, LASSO regression analysis, and Cox analyses were implemented to determine the prognostic genes. A fibroblast-related risk model was created and affirmed by independent cohorts. We derived enriched pathways between the fibrobl

SUBMITTER: Zhang H 

PROVIDER: S-EPMC9687522 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature