Multiomics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy

Abstract

Abstract Background α-Enolase (ENO1) is a crucial molecular target for tumor therapy and has emerged as a research hotspot in recent decades. Here, we aimed to explore the role of ENO1 in bladder cancer (BLCA) and then construct a signature to predict the prognosis and treatment response of BLCA.Methods Differential expression, prognosis analysis and in vitro cell experiments were used to reveal the value of ENO1 in BLCA. The R package "Seurat" was used for single-cell RNA sequence (scRNA-seq) data processing. The R package “singleR” and cellMarker website were used to annotate cells. The FindAllMarkers function and “limma” were used to screen hub genes. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were used to construct the signature. Differences in prognosis and treatment between high- and low-risk groups were investigated.Results ENO1 was highly expressed in BLCA tissues, as verified by IHC, and was associated with poor prognosis. The analysis of the tumor immune microenvironment by bulk sequencing and scRNA-seq showed that ENO1 was associated with CD8 + T-cell exhaustion. Additionally, the in vitro results showed that ENO1 could promote the proliferation and invasion of BLCA cells. Then, the analysis of epithelial cells (ECs) revealed that ENO1 might promote BLCA progression by metabolism, the cell cycle and some carcinogenic pathways. A total of 249 hub genes were obtained from differentially expressed genes between ENO1-related ECs, and we used LASSO analysis to construct a novel signature that not only accurately predicted the prognosis of BLCA patients but also predicted the response to treatment for BLCA. Finally, we constructed a nomogram to better guide clinical application.Conclusion Through multiomics analysis, we found that ENO1 was overexpressed in bladder cancer and associated with poor prognosis, CD8 + T-cell exhaustion and epithelial heterogeneity. Finally, the prognosis and treatment of patients can be well predicted by constructing an epithelial cell prognostic signature.