Cervical cancer molecular subtype identification and prognosis classification by a metabolism-related gene expression.

ABSTRACT:

Background

The high molecular phenotype heterogeneity of cervical cancer (CC) is the main focus of individualized therapy. Molecular classification may lead to personal treatment and new drug discovery. We summarized the molecular features by establishing a new classification of metabolism-related gene expression profiles.

Methods

Clinical information and messenger ribonucleic acid (mRNA) expression were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Twenty-two immune cells were detected by CIBERSORT method. K-means clustering algorithm based on 258 metabolism-related genes was used for CC classification. Univariate and multivariate Cox regression analyses were carried out to find out the optimal metabolism-related genes. A predictive model

SUBMITTER: Chen X 

PROVIDER: S-EPMC12605780 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature