Stemness signature RBBP7 reprograms the immune microenvironment to inform a prognostic model in esophageal carcinoma.

Esophageal carcinoma has high mortality and poor prognosis. Current multimodal therapies remain limited by scarce actionable targets and suboptimal systemic efficacy. Tumor stemness programs sustain invasive, therapy-resistant cells and may offer new opportunities for precision stratification and treatment. The present study has demonstrated the multifaceted roles of stemness genes in esophageal cancer through integrated multi-omics research. Firstly, the analysis of bulk RNA-seq revealed dysregulation of stemness genes in cancer. Utilizing the high-dimensional WGCNA approach in the context of single cell RNA-seq, we have successfully identified modules that are associated with tumor stemness. Subsequently, Cox regression and LASSO analysis were employed to identify prognostic genes and construct a predictive model. CellChat and functional enrichment studies explored crosstalk between model genes and the microenvironment, while multiple experiments validated the efficacy of these model genes. Through multimodal analysis, stemness exhibits significant differences between esophageal cancer and adjacent normal tissue. By integrating multiple algorithms, we constructed a stemness gene prognostic model. This model accurately predicts prognosis and drug response, with its 1-, 2-, and 3-year survival predictions outperforming TNM staging. The model gene RBBP7 emerges as the most influential prognostic factor. Its overexpression mediates heightened tumor cell stemness and correlated with T follicular helper cells infiltration, thereby reshaping the tumor microenvironment. The stemness gene model has been demonstrated to possess the capacity to accurately predict the prognosis of patients diagnosed with esophageal cancer. It is noteworthy that the model gene RBBP7 has been identified as a promising therapeutic target for addressing the issue of stemness in esophageal cancer.