Machine learning explores the prognostic and immuno-oncological impact of mitochondrial unfolded protein response in CESC.

ABSTRACT:

Background

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) pose significant global health challenges. While the mitochondrial unfolded protein response (UPR^mt) is known to influence cancer biology, its specific role in CESC remains unclear.

Methods

We employed machine learning to analyze UPR^mt genes in CESC using TCGA multi-omics data. Our comprehensive analysis included genetic alterations, prognostic significance, tumor-immune interactions, single-cell transcriptomics, pathway enrichment, and drug sensitivity assessments.

Results

ATF5 emerged as the most significant prognostic factor among UPR^mt genes, with high expression correlating with better overall survival. High ATF5 expression was associated with an im

SUBMITTER: Liu KS 

PROVIDER: S-EPMC12102417 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature