# Identification of disulfidptosis in esophageal squamous cell carcinoma based on single-cell and bulk RNA-seq data to predict prognosis and treatment response

## TL;DR

This study identifies disulfidptosis-related molecular subtypes in esophageal cancer, creates a prognostic model, and suggests potential treatments based on RNA-seq data.

## Contribution

A novel prognostic signature for ESCC based on disulfidptosis genes and identification of potential therapeutic drugs.

## Key findings

- Two disulfidptosis subtypes with survival differences were identified in ESCC.
- A prognostic model was developed, with high scores correlating with worse patient outcomes.
- AZD8186 and JQ1 were suggested as potential therapies for high-risk patients.

## Abstract

Our study aims to identify the molecular subtypes of genes associated with disulfidptosis in esophageal squamous cell carcinoma(ESCC), develop a prognostic model, and identify potential therapeutic targets.

Based on the GSE53625 expression profile data, we identified molecular subtypes with significant survival differences through consensus cluster analysis. Subsequently, univariate Cox, multivariate Cox, and LASSO-Cox regression analysis were used to establish risk stratification models. The transcriptome data of the TCGA-ESCC cohort and the GSE160269 single-cell sequencing dataset were integrated to verify the biological significance of the model, and further analyze the heterogeneity of the tumor immune microenvironment, explore the differences in the intercellular communication network, and screen potential targeted drugs, providing a theoretical basis for subsequent translational research.

We identified two distinct patterns of disulfidptosis expression with significant differences in overall survival. Then, we constructed the prognostic signature of disulfidptosis, and results showed patients with high score had worse prognosis. Univariate and multivariate Cox analysis demonstrated that the constructed prognostic signature was an independent prognostic factor and was validated in an independent validation set. The two subgroups differed in the proportion of immune cell infiltration and related signaling pathways in ESCC. The exploration of immunotherapy data confirmed our prognostic signature also had certain predictive power for immunotherapy. Drug screening suggested AZD8186 and JQ1 as potential therapies for high-score patients.

This study provides a new prognostic signature for ESCC, explores new therapeutic targets, and provides new theoretical support for personalized treatment.