Single-cell transcriptomics unveils pyroptosis-related immune microenvironment dynamics and prognostic modeling in...

Esophageal cancer remains a leading cause of global cancer mortality with limited therapeutic efficacy. This study aims to characterize pyroptosis-related immune mechanisms in esophageal cancer and develop a clinical prediction model to improve prognostic evaluation.

Single-cell RNA sequencing data from seven esophageal cancer tissues and one normal control were integrated to construct a cellular atlas. Immune subpopulations were isolated, and pyroptosis-associated differentially expressed genes were identified. We intersected these genes with pyroptosis-related genes(PRG), identifying 13 key pyroptosis genes. Computational analyses included pyroptosis scoring, subcluster re-analysis, pathway enrichment, and intercellular communication mapping. Pseudotime trajectory analysis was applied to high-pyroptosis immune subsets. A clinical prognostic model incorporating PRG was validated using bulk sequencing data, followed by immune infiltration quantification and chemotherapy response assessment.

By integrating single-cell and bulk transcriptomic datasets, we found that pyroptosis-related genes were enriched in immune-inflammatory pathways in ESCC immune cells. B cells showed extensive interactions within the ESCC immune microenvironment. The identified pyroptosis-related genes appeared to be involved in immune-cell state transitions and intercellular communication. A two-gene pyroptosis-related prognostic model based on GSDMB and CYCS showed moderate predictive performance in the TCGA training cohort. However, its performance in the external GSE53625 validation cohort was limited, indicating that the model should be interpreted as an exploratory prognostic signature rather than a clinically established prediction tool. A nomogram integrating the risk score with pathological stage was further constructed to explore its potential clinical applicability.