Predicting Outcomes in Esophageal Squamous Cell Carcinoma Using scRNA-Seq and Bulk RNA-Seq: A Model Development and...

Area of Science:

• Oncology
• Molecular Biology
• Genetics

Background:

• Altered glucose metabolism is an early hallmark of esophageal squamous cell carcinoma (ESCC).
• This metabolic shift is observable as a pink-color sign under endoscopy after iodine staining.
• Metabolic scoring may offer novel insights for ESCC prognosis and treatment selection.

Purpose of the Study:

• To develop a predictive model for esophageal squamous cell carcinoma (ESCC) prognosis based on glucose metabolism.
• To identify key glucose metabolism-related genes for risk stratification in ESCC patients.

Main Methods:

• Utilized single-cell RNA sequencing (scRNA-seq) data from multiple databases (GEO, TCGA, MSKCC).
• Identified 558 differential genes and selected four hub genes (SERP1, CTSC, RAP2B, SSR4) related to glucose metabolism.
• Developed and validated a risk prognostic model using these hub genes in an external cohort.

Main Results:

• A risk score (RS) model categorized patients into low- and high-risk groups (LRG and HRG).
• High-risk group (HRG) patients exhibited poorer survival and reduced drug sensitivity compared to LRG.
• Significant differences in immune microenvironment and pathway enrichment were observed between risk groups.
• Hub gene expression varied across ESCC tissues, neoplasia grades, and normal adjacent tissues.

Conclusions:

• Four glucose metabolism-related hub genes (SERP1, CTSC, RAP2B, SSR4) form a validated predictive model for ESCC.
• The developed risk score (RS) is an independent prognostic factor for ESCC.
• This model offers a novel tool for patient stratification and treatment planning in clinical practice.