A novel lactylation-related signature for predicting esophageal cancer prognosis and immune infiltration.

BACKGROUND

Esophageal cancer (ESCA) is a prevalent malignancy with high morbidity and mortality. It has been demonstrated that lactylation, a novel post-translational modification, is linked to tumor progression and immunity. The objective of this study was to look into the potential role of lactylation-related genes in the prognosis and immune microenvironment of ESCA.

METHODS

Lactylation-related prognostic genes (LRGs) in ESCA were screened using The Cancer Genome Atlas (TCGA) data. A consensus clustering analysis was conducted on the LRGs, and functional enrichment and immunoassays were performed for different clusters. We utilized least absolute shrinkage and selection operator (LASSO) and multivariate stepwise regression to develop a novel LRG prognostic signature. Subsequently, we assessed the performance of the signature through validation sets. Furthermore, we investigated the immune-related phenotypes of the signature and its response to treatment, as well as its sensitivity to drugs. Finally, experimental verification was performed through immunohistochemical (IHC) staining.

RESULTS

We identified 29 LRGs associated with ESCA, and we classified ESCA patients into two lactylation clusters with different prognostic and immune infiltration patterns based on gene expression differences. A new prognostic signature consisting of three prognostic genes (, , ) was constructed, demonstrating exceptional prediction accuracy in both internal and external datasets. Riskscores derived from this signature were independent prognostic factors for ESCA. Significant differences regarding clinical features, underlying functions, immune infiltration, reactions to immune therapy, and susceptibility to drugs were observed in the two riskscore groups. Finally, IHC staining confirmed the roles of these three genes in ESCA.

CONCLUSIONS

This study established a new ESCA prognostic signature composed of three lactylation-related genes, which can contribute to predicting prognosis and guiding clinical treatment.