A Breg-associated lncRNA signature predicts prognosis and immune landscape in esophageal carcinoma.

BACKGROUND

Esophageal carcinoma (ESCA) is a highly aggressive malignancy with poor prognosis, and growing evidence indicates that the tumor immune microenvironment, particularly regulatory B cells (Bregs), has been increasingly recognized as an essential contributor to tumor progression. However, the mechanisms linking Breg-associated long non-coding RNAs (lncRNAs) to ESCA prognosis have not been elucidated. This study aimed to develop a Breg-associated lncRNA prognostic signature for ESCA and to characterize its associations with the tumor immune microenvironment, mutational landscape, and therapeutic drug sensitivity.

METHODS

Transcriptome and clinical data for ESCA patients were retrieved from The Cancer Genome Atlas (TCGA). Consensus clustering based on Breg marker gene expression was conducted to identify immune-related molecular subtypes. Differentially expressed lncRNAs were identified, and prognostic candidates were screened based on the results of univariate Cox regression. A prognostic signature was subsequently established by integrating least absolute shrinkage and selection operator (LASSO) selection with multivariate Cox analysis, and was further assessed in both training and validation cohorts. Kaplan-Meier survival curves, time-dependent receiver operating characteristic (ROC) analysis, and Cox regression were applied to comprehensively assess the predictive capacity of the signature. To facilitate clinical application, a nomogram was generated by combining the risk score with key clinicopathological variables. Multiple bioinformatics approaches were employed to characterize tumor mutational burden (TMB), immune infiltration, and functional pathways. Drug response profiles were predicted via the OncoPredict algorithm based on pharmacogenomic data from the GDSC2 database.

RESULTS

A four-lncRNA prognostic signature consisting of LINC00298, AC003077.1, GPC6-AS2, and COPDA1 was developed and effectively stratified ESCA patients into high- and low-risk groups. The model showed strong predictive performance, achieving area under the curve (AUC) values of 0.728, 0.755, and 0.751 for predicting 1-, 2-, and 3-year overall survival, respectively. The risk score remained prognostically relevant across clinical subgroups, and integration into a nomogram improved predictive accuracy. The signature was constructed from lncRNAs differentially expressed across Breg-defined molecular subtypes, and the resulting risk score reflects the Breg-associated transcriptional state of the tumor. Consistent with a Breg-mediated immunosuppressive phenotype, high-risk patients exhibited elevated TMB, increased mutational frequency in several driver genes, and significantly reduced infiltration of multiple immune cell subsets-including activated T cells, B cells, macrophages, natural killer (NK) cells, and regulatory T cells-within the tumor microenvironment, indicating that the Breg-associated lncRNA program contributes to immune evasion in ESCA. Drug response prediction also indicated that the high-risk group showed differential sensitivity to several candidate compounds, implying potential therapeutic relevance.

CONCLUSIONS

In this study, we developed a Breg-associated lncRNA signature that enables prognostic prediction and provides insights into the immune microenvironment of ESCA. Furthermore, the nomogram integrating the risk score and clinical factors could provide a quantitative reference for individualized survival assessment.