# Development and validation of a succinylation-related prognostic model for esophageal squamous cell carcinoma based on multi-omics bioinformatics analysis

## TL;DR

This study develops a seven-gene model based on succinylation to predict survival in esophageal squamous cell carcinoma patients and identifies immune and drug resistance patterns.

## Contribution

A novel seven-gene succinylation-related prognostic signature for ESCC was developed and validated as an independent biomarker.

## Key findings

- A seven-gene succinylation-related signature was associated with significantly different overall survival in ESCC patients.
- High-risk tumors showed an immunosuppressive tumor microenvironment with reduced NK cell infiltration and increased monocyte abundance.
- Signature genes were linked to resistance to multiple anticancer agents and were predominantly expressed in malignant and immune cells.

## Abstract

Esophageal squamous cell carcinoma (ESCC), an aggressive malignancy with poor prognosis, requires reliable prognostic biomarkers. Protein succinylation, a critical post-translational modification implicated in cancer biology, has not yet been systematically investigated for its prognostic significance in ESCC.

Transcriptomic data from GSE53624 (n = 119) and The Cancer Genome Atlas (TCGA)-ESCC (n = 95) cohorts were analyzed in this study. Succinylation-related genes (SRGs) were identified via weighted gene co-expression network analysis (WGCNA), a curated SRG set, and differential expression analysis. A prognostic signature was constructed using LASSO Cox regression and validated internally and externally. Immune infiltration was assessed by CIBERSORT/ssGSEA. miRNA-mRNA and protein-protein interactions (PPIs) and drug sensitivity were evaluated. Gene localization was confirmed with single-cell RNA sequencing (scRNA-seq).

41 high-confidence SRGs associated with ESCC were identified. A robust seven-gene prognostic signature (IGFBP3, CMA1, FN1, CTSG, TIMP1, MBL2, and SP5) was established. Patients stratified into high- and low-risk groups exhibited significantly different overall survival (OS) in both the training cohort (GSE53624, P < 0.001) and the validation cohort (TCGA, P = 0.026). The risk score remained an independent prognostic factor and was incorporated into a predictive nomogram. High-risk tumors were characterized by an immunosuppressive tumor microenvironment (TME), with reduced eosinophil and natural killer (NK) cell infiltration and increased monocyte abundance. In addition, signature genes were associated with resistance to multiple anticancer agents. scRNA-seq analysis revealed predominant expression of these genes in malignant cells, fibroblasts, and myeloid cells.

A novel seven-gene succinylation-related signature was established and validated as an independent prognostic biomarker for ESCC. This signature captures immunosuppressive features of high-risk tumors and may provide a foundation for individualized therapeutic strategies.

The online version contains supplementary material available at 10.1186/s41065-026-00653-2.