Metabolic subtypes and immune landscapes in esophageal squamous cell carcinoma: prognostic implications and potential for personalized therapies.

ABSTRACT:

Background

This study aimed to identify metabolic subtypes in ESCA, explore their relationship with immune landscapes, and establish a metabolic index for accurate prognosis assessment.

Methods

Clinical, SNP, and RNA-seq data were collected from 80 ESCA patients from the TCGA database and RNA-seq data from the GSE19417 dataset. Metabolic genes associated with overall survival (OS) and progression-free survival (PFS) were selected, and k-means clustering was performed. Immune-related pathways, immune infiltration, and response to immunotherapy were predicted using bioinformatic algorithms. Weighted gene co-expression network analysis (WGCNA) was conducted to identify metabolic genes associated with co-expression modules. Lastly, cell culture and functional analysis were perfo

SUBMITTER: Yu XW 

PROVIDER: S-EPMC10875771 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature