Construction and validation of acetylation-related gene signatures for immune landscape analysis and prognostication risk prediction in luminal breast cancer.

Abstract

Background: Epigenetic acetylation plays an essential role in the development and drug resistance of luminal breast cancer. However, the acetylation regulatory network in luminal breast cancer remains underexplored.

Methods: We used the TCGA-BRCA database to explore the acetylation regulatory network in luminal breast cancer. Spearman correlation coefficients, Cox proportional hazards, and the STRING database were used to identify genes that were correlated with acetylation regulatory molecules in luminal breast cancer and could predict patient outcomes. An acetylation regulatory risk model was constructed via Consensus Cluster Plus and the LASSO risk model. GSEA, K‒M survival analysis, and receiver operating characteristic (ROC) curve analysis were used to analyze survival and possible regulatory pathways of the risk model. TIDE, Microenvironment Cell Populations-counter, and CIBERSORT algorithms were used to analyze the immune landscape of the risk model population. Patients' tumor specimens were used to detect the expression of KAT2B and TAF1L. The luminal breast cancer cell lines MCF-7 and T47D were used in cell viability, Transwell, western blotting, and RT‒qPCR experiments to confirm the risk model. Mouse model was constructed for in vivo validation of KAT2B and TAF1L function.

Results: In our study, we utilized the TCGA-BRCA database to conduct a comprehensive analysis of the acetylation regulatory pattern in luminal breast cancer. Using Consensus Cluster Plus and the LASSO risk model, we screened 6 acetylation-related genes (KAT2B, TAF1L, CDC37, CCDC107, C17orf106, and ASPSCR1) and constructed a 6-gene risk model of luminal breast cancer. Based on this model, luminal breast cancer patients were classified into high- and low-risk subgroups. The high-risk subgroup had a poor prognosis. Further analysis revealed that the high-risk subgroup was associated with lower CD8 + T-cell infiltration and greater responsiveness to immune checkpoint inhibitor therapy. In vitro and in vivo experiments revealed that knockdown of KAT2B and TAF1L dramatically inhibited tumor cell proliferation. In vitro experiments also showed knockdown of KAT2B and TAF1L dramatically inhibited tumor cell migration, increased lymphocyte infiltration, and significantly upregulated the expression of CD8 + T-cell-associated chemokines in luminal breast cancer cells.

Conclusions: In this study, we successfully constructed a 6-gene acetylation-associated risk model for luminal breast cancer, providing a new direction and evidence for personalized treatment. Our results also suggested that KAT2B and TAF1L might serve as potential therapeutic targets in luminal breast cancer.