# Development and validation of a novel signature to predict the survival and affect the immune microenvironment of esophageal squamous cell carcinoma: epigenetic-related genes

## TL;DR

This study creates a new model using epigenetic genes to predict survival and understand immune changes in esophageal cancer patients.

## Contribution

A novel 13-gene epigenetic signature is developed to predict ESCC survival and modulate the immune microenvironment.

## Key findings

- A 13-gene epigenetic-related signature was identified and validated for ESCC prognosis.
- High-risk patients showed increased immune cell infiltration and immune checkpoint expression.
- Drug sensitivity analysis identified four potential therapeutic agents for ESCC treatment.

## Abstract

Esophageal squamous cell carcinoma (ESCC) is a malignancy characterized by extensive epigenetic dysregulation. This study aims to develop a robust prognostic model utilizing epigenetic-related genes (ERGs) to improve survival prediction in ESCC patients, while simultaneously elucidating potential mechanisms underlying immune microenvironment modulation.

This study employed transcriptomic data from The Cancer Genome Atlas (TCGA) as the training cohort and data from GSE53625 in the Gene Expression Omnibus (GEO) as an independent validation cohort. A total of 796 epigenetic regulator genes (ERGs) were curated from the EpiFactors database and intersected with TCGA-ESCC gene expression profiles to identify ESCC-associated ERGs. Differential expression analysis was then conducted to identify differentially expressed ERGs (DE-ERGs). Using univariate Cox and LASSO regression analyses, a prognostic risk model was constructed and thoroughly evaluated through risk stratification curves, survival status distribution maps, risk score heatmaps, survival analysis, ROC curves, and multivariate Cox regression. Further analyses included assessing the prognostic model’s association with clinical features and risk stratification. To investigate the immune microenvironment, immune cell infiltration correlation, single-sample gene set enrichment analysis (ssGSEA), and immune checkpoint profiling were performed. Drug sensitivity analysis was also carried out to identify potential therapeutic agents showing differential efficacy between risk subgroups. Finally, the expression patterns of key prognostic ERGs were validated using RT-qPCR.

Through comprehensive differential expression analysis, we identified 345 DE-ERGs in ESCC. A robust prognostic signature comprising 13 critical ERGs—PIWIL4, SATB1, GSE1, NCOR1, BUB1, SAP30L, CHEK1, MASTL, ATM, BMI1, DNAJC2, UBE2D1, and SSRP1—was established using univariate Cox regression followed by LASSO penalized regression analysis. The prognostic efficacy of this signature was confirmed through multidimensional assessments using independent GEO datasets. Immunological characterization revealed significant enrichment of CD8+ T cells, DCs, and pDCs in high-risk patients, along with elevated cytolytic activity, HLA expression, and MHC class I activity. Additionally, three immune checkpoint molecules—TMIGD2, IDO1, and CD44—were found to be differentially expressed between risk groups. Drug sensitivity analysis identified four promising therapeutic compounds—PD-0325901, Bryostatin-1, ATRA, and Roscovitine—with potential clinical utility for ESCC treatment. Experimental validation via RT-qPCR confirmed consistent overexpression of GSE1, NCOR1, BUB1, CHEK1, UBE2D1, and SSRP1 in ESCC cell lines, whereas PIWIL4 and ATM showed significant downregulation.

The findings of this study offer clinically relevant insights for prognostic stratification and characterization of the immune microenvironment in ESCC patients. Moreover, these results provide novel perspectives that may contribute to the development of more effective prognostic tools and targeted therapeutic strategies for ESCC management.