Acetyltransferase NAT10 promotes an immunosuppressive microenvironment by modulating CD8+ T cell activity in prostate cancer

Abstract

N-acetyltransferase 10 (NAT10), an enzyme responsible for ac4C acetylation, is implicated in cancer progression, though its specific biological function in prostate cancer remains insufficiently understood. This study clarifies NAT10's role in prostate cancer and its effects on the tumor immune microenvironment. NAT10 expression and clinical relevance were assessed through bioinformatics, RT-qPCR, and IHC analyses, comparing prostate cancer tissues with normal controls. The impact of NAT10 on tumor cell proliferation, migration, and invasion was investigated via in vitro assays-including CCK-8, EdU, wound healing, and 3D-Transwell-as well as in vivo mouse xenograft models and organoid studies. Further, NAT10's influence on immune cell infiltration was examined using flow cytometry, IHC, cell co-culture assays, and ELISA to elucidate downstream chemokine effects, specifically targeting CD8⁺ T cells. Findings indicated significant upregulation of NAT10 in prostate cancer cells, enhancing their proliferative and invasive capacities. Notably, NAT10 suppresses CD8⁺ T cell recruitment and cytotoxicity through the CCL25/CCR9 axis, fostering an immunosuppressive microenvironment that exacerbates tumor progression. An ac4C modification score was also devised based on NAT10's downstream targets, providing a novel predictive tool for evaluating immune infiltration and forecasting immunotherapy responses in patients with prostate cancer. This study underscores NAT10's pivotal role in modulating the prostate cancer immune microenvironment, offering insights into the immune desert phenomenon and identifying NAT10 as a promising therapeutic target for improving immunotherapy efficacy.

Keywords: Ac4C acetylation; Immune microenvironment; Immunotherapy; Prostate cancer.

Fig. 1

Significantly high expression of NAT10 in mCRPC is associated with prostate malignancy progression. a Expression levels of NAT10 in nmCRPC versus mCRPC samples (p < 0.001). b Protein three-dimensional structure of NAT10. c Expression levels of NAT10 in prostate single-cell sequencing. d Protein profile of NAT10. e Kaplan–Meier curves showed a significant negative correlation between NAT10 and the patient’s PFI. f-j Differential Analysis of NAT10 in Prostate Cancer Clinical Characteristics Subgroups

Fig. 2

NAT10 levels in vitro and in vivo affect malignant progression of prostate cancer. a Immunofluorescence results showing NAT10 expression in different prostate cancer patient tissues. b IHC results showing NAT10 expression in tissues of different prostate cancer patients. c Organoid assay demonstrating prostate cancer organoid survival 48 h after addition of si-NAT10. d A mouse subcutaneous tumor formation assay demonstrated a significant decrease in the proliferative capacity of prostate cancer cells after knockdown of NAT10 at the in vivo level. e Bar graph showing transcript levels of NAT10 in prostate normal cell lines (RWPE-1) versus prostate cancer cell lines (VCaP, C4-2, PC3, DU145, LNCaP, and 22RV1). f-g Plate cloning and EdU assay demonstrates changes in cell proliferation capacity after knockdown of NAT10. h–k 3D-Transwell, Wounding-healing and Transwell assay demonstrated the changes in cell invasion and migration ability after knockdown of NAT10. (l) WB experiments demonstrated changes in the expression of proliferation- and invasion-related proteins after knockdown of NAT10. p value calculated using the Wilcoxon-test. (*p < 0.05; **p < 0.01; ***p < 0.001)

Fig. 3

NAT10 exhibits a regulatory association with immune cells within the immune microenvironment. a-c GSEA demonstrates the possible functions of NAT10, and the pathways involved. d-e Evaluation of immune cell infiltration in TGCA-PRAD, ICGC-PRAD, and TCGA-MCRPC datasets by CIBERSORT, MCPcounter, and ssGSEA algorithms. f IHC demonstrates the expression of CD8 in CRPC and HSPC samples. g Correlation of NAT10 with CD8⁺T cells explored by flow cytometry. (*p < 0.05; **p < 0.01; ***p < 0.001)

Fig. 4

NAT10 fosters a prostate cancer-suppressive immune microenvironment by modulating the CCL25/CCR9 axis. a The tumor-killing capacity of CD8⁺ T cells was assessed by flow cytometry after co-culturing CD8⁺ T cells with tumor cells. b The recruitment of tumor cells to CD8⁺ T cells was assessed using a Transwell assay, with tumor cells placed in the lower chamber and immune cells in the upper chamber. c-e Chemokines regulated downstream of NAT10 were screened using the Best database and subsequently validated through RT-qPCR experiments. f–h The changes in CCL25 and its receptor CCR9 in tumor cells after NAT10 knockdown were elucidated using RT-qPCR, ELISA, and flow cytometry assays. i Hypothesized mechanism of NAT10 Regulation of CD8⁺ T Cell infiltration via the CCL25/CCR9 Axis. (*p < 0.05; **p < 0.01; ***p < 0.001)

Fig. 5

Establishment and analysis of tumor immune microenvironment scores in PCa. a Samples from TCGA-PARD and ICGC-PRAD were integrated by a debatch function and demonstrated using Principal Component Analysis (PCA). b Bar graph showing the distribution of TCGA-PRAD versus ICGC-PRAD immune cells. c Tumor immune cell infiltration between different subgroups based on tumor microenvironment score. d Expression of HLA-related genes in high versus low immune score groups. e Expression levels of immune, stromal, tumor purity and microenvironmental scores in the high and low immune score groups. f Expression of PD-L1 and CTLA-4 in high versus low immune score groups. (*p < 0.05; **p < 0.01; ***p < 0.001)

Fig. 6

Identification of genes associated with ac4C, tumor immune microenvironment and malignant progression in CRPC. a Volcano diagram showing differential genes between normal and tumor tissues in the prostate. b Wayne diagram showing intersecting genes of DEGs with ac4C-related genes. c GO analysis demonstrates possible functions of intersecting genes. d-f The correlation of different modules with NAT10, the tumor immune microenvironment score, and the clinical factors was analyzed by clustering the genes and after dimensionality reduction. g Correlation of genes in the analysis module (**p < 0.01; ***p < 0.001)

Fig. 7

Developing the ac4C-related subtype. a-b Genes associated with prognosis in the module were screened by Unicox analysis and de-fitted by Lasso regression analysis. c-d Clustering dendrogram of PCa samples and genes clustering results. e Heat map showing the correlation between Clinicopathological features and expression of principal component genes. f Survival curves show prognostic differences between subtypes. g Box line plot showing differences in tumor-infiltrating immune cell between subtypes. (*p < 0.05; **p < 0.01; ***p < 0.001; #p > 0.05)

Fig. 8

Validation of principal component genes within subtypes from the cellular level. a Correlation analysis of principal component genes with NAT10. b Validation of principal component gene expression levels in RWPE-1, PC3 and DU145 cell lines. c Validation of sublocalization of principal component genes in cells in osteosarcoma cell lines and protein expression in high and low grade samples of prostate cancer (Green represents the target proteins, while red indicates microtubule proteins in the cellular sub-localization images). d The proportion of patients responding to immunotherapy in groups with low and high NAT10. e The survival curves depict the prognostic outcomes ofl patients stratiflied by high and low expression levels ofl NAT10 and PD-L1