GG-NER's role in androgen receptor signaling inhibitor response for advanced prostate cancer

Abstract

Background: Advanced prostate cancer (PCa) often initially responds to androgen receptor signaling inhibitors (ARSI) but frequently develops resistance, driven by tumor heterogeneity and therapeutic pressure. Addressing the clinical challenge of identifying non-responsive patients and discovering new therapeutic targets is urgently needed.

Methods: We utilized single-sample gene set enrichment analysis (ssGSEA) to elucidate the influence of the GG-NER pathway on ARSI response in PCa. We then constructed and validated a prognostic model based on this pathway using LASSO regression, Kaplan-Meier analysis, Cox regression, and ROC analysis. Additionally, we mapped tumor mutations to delineate the mutational landscapes across different risk groups and explored functional pathways through GO, KEGG, and GSEA analyses. The impact of the GG-NER pathway on enzalutamide sensitivity and DNA repair in PCa was further validated through CCK-8 assays, colony formation assays, in vivo experiments, and immunofluorescence.

Results: ssGSEA indicated a trend of GG-NER pathway upregulation in patients with poor ARSI response. The GG-NER characteristic gene score (NECGS) identified a high-risk group with diminished ARSI response, serving as an independent prognostic indicator with strong predictive power. This high-risk group exhibited elevated TP53 mutation frequencies and significant enrichment in key pathways such as ribosome and mitochondrial functions, as well as MYC and E2F signaling. Experimental validation confirmed that targeting the GG-NER pathway or its key gene, ACTL6A, significantly reduces enzalutamide resistance in resistant cell lines and increases γH2AX expression.

Conclusion: NECGS effectively predicts ARSI response in PCa, and our comprehensive analysis underscores the critical role of the GG-NER pathway in enzalutamide resistance, positioning ACTL6A as a potential therapeutic target for PCa.

Keywords: ACTL6A; ARSI; Enzalutamide; GG-NER; Prostate cancer.

Fig. 1

Flowchart of the study design

Fig. 2

Expression of the GG-NER pathway in mCRPC cohorts. A&B. ssGSEA scoring analysis of upregulated and downregulated signaling pathways in Cohorts 1 and 2. C&D. Kaplan-Meier curves showing the association between GG-NER pathway gene set expression and overall survival and ARSI response in prostate cancer in Cohorts 1 and 2. E. the hologram of GG-NER pathway from Reactome database

Fig. 3

Impact of the GG-NER pathway on ARSI response in prostate cancer. A&B. CCK-8 assay evaluating the effect of HAMNO on the sensitivity of LNCaP_ENZR and C4-2B_ENZR cells to enzalutamide (0µM, 2.5µM, 5µM, 12.5µM, 25µM, 50µM and each treatment gradient was set with five replicates). C&D. Colony formation assay assessing HAMNO’s influence on the sensitivity of LNCaP_ENZR and C4-2B_ENZR cells to enzalutamide. E. Overall image of subcutaneous xenografts in different treatment groups. F. Tumor volume changes across different groups. G. Tumor weight across different groups. H&I. Immunofluorescence detection of DNA double-strand break damage marker (γH2AX) in LNCaP_ENZR and C4-2B_ENZR cells treated with HAMNO and (or) enzalutamide

Fig. 4

Construction and validation of the NECGS model. A&D. KM plots and risk curves showing the correlation between NECGS and ARSI response, including ACTL6A, PARP2, and RUVBL1 expression in training and validation sets. B&E. Univariate and multivariate Cox regression analyses of NECGS’s impact on ARSI response in training and validation sets. C&F. ROC curve analyses of NECGS’s predictive efficacy for ARSI response in training and validation sets

Fig. 5

Prognostic performance of NECGS in TCGA pan-cancer. (A) Boxplot of NECGS levels across various cancer types. (B) Univariate Cox regression analysis of NECGS and overall survival (OS) in different tumor types. (C) Relationship between NECGS and progression-free interval (PFI) in various tumors

Fig. 6

Mutation panorama of training set, validation set and TCGA-PRAD set. (A) Mutation overview of the top 10 driver genes in the training set. (B) Mutation overview of the top 10 driver genes in the validation set. (C) Mutation overview of the top 10 driver genes in the TCGA-PRAD set

Fig. 7

Functional enrichment analysis of risk score. A&B. GO analysis of upregulated and downregulated pathways in the high-risk group, including biological process (BP), cellular component (CC), and molecular function (MF). C&D. KEGG analysis of upregulated and downregulated pathways in the high-risk group. E&F. GSEA analysis of enriched signaling pathways in the high-risk groups in the training set and TCGA-PRAD cohort

Fig. 8

Experimental validation of ACTL6A’s role in enzalutamide sensitivity. A&B. Western blot validation of ACTL6A knockdown efficiency by siRNA. C&D. CCK-8 assay detecting changes in sensitivity to enzalutamide (0µM, 2.5µM, 5µM, 12.5µM, 25µM, 50µM; and each treatment gradient was set with five replicates) after ACTL6A knockdown (duration of siRNA transfection for 48 h). E&F. Colony formation assay assessing changes in sensitivity to enzalutamide post-ACTL6A knockdown (siRNA transfection was performed every 96 h). G. Western blot validation of ACTL6A knockdown efficiency by shRNA. H. Overall image of subcutaneous xenografts in different treatment groups. I. Tumor volume changes across different groups. J. Tumor weight across different groups