Comprehensive analysis of prognostic immune-related genes and drug sensitivity in cervical cancer.

ABSTRACT:

Background

Cervical cancer (CC) is the leading cause of cancer-related death in women. A limited number of studies have investigated whether immune-prognostic features can be used to predict the prognosis of CC. This study aimed to develop an improved prognostic risk scoring model (PRSM) for CC based on immune-related genes (IRGs) to predict survival and determine the key prognostic IRGs.

Methods

We downloaded the gene expression profiles and clinical data of CC patients from the TCGA and GEO databases. The ESTIMATE algorithm was used to calculate the score for both immune and stromal cells. Differentially expressed genes (DEGs) in different subpopulations were analyzed by "Limma". A weighted gene co-expression network analysis (WGCNA) was used to establish a DEG co-expressi

SUBMITTER: Pi YN 

PROVIDER: S-EPMC8638517 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature