Results
To identify the novel AR-driven signature in PRAD, we integrated scRNA-seq, TCGA-PRAD and other PRAD datasets to identify the essentially oncogenic AR-drivers. The supplementary assays were also conducted to demonstrate the in vitro and in vivo roles of identified targets. The screening procedures were illustrated on Figure 1 . To deal with the scRNA-seq data, we downloaded the raw data from the GEO dataset via the accession number of GSE99795 . In this study, we found that 48 LNCaP single cells and one representative bulk cell RNA sample (1 ng) were collected for SMART-seq2 amplification and later single-cell RNA-seq (total 144 single cells and three bulk cell samples) for each of three treatment groups. All of the treatment groups were harvested from after synchronizing the cells at the G1/S phase with a double thymidine block and androgen depriving the cells for ~24 h. Treatment groups 2 and 3 were cultured in the absence and presence of androgen (1 nM R1881) for 12 h, respectively. Treatment group 1 was a baseline comparison treatment group and was collected right after cell synchronization and androgen deprivation (considered 0 h). Then, the scRNA-seq data of 171 files was downloaded and combined into one matrix and we transformed the gene symbols by referencing the gene transfer format (GTF) file. We exhibited the quality control chart in Figure 1A , in which the range of detected gene numbers and the sequencing numbers of each cell lines were shown. In this process, the cells with a percentage of mitochondrial sequencing count >5% were therefore excluded. Meanwhile, the significantly positive associations between the detected gene accounts and the sequencing depth were observed with Pearson’s r = 0.25 ( Figure 2B ). The cluster analysis exhibited two distinct groups between data derived from the 12 androgen-treated cells and those derived from the 12 h untreated cells ( Figure 2B ). In addition, we performed the variance analysis, where the top 10 significantly differentially expressed genes (DEGs) were exhibited and noted in red, like EAF2, CDC20, PLK1, as well as CCNB2 ( Figure 2C ). We extracted the top 800 variant genes based on the scRNA-seq and conducted the cluster analysis in three group samples (Control; 12 h−, R1881; 12 h+, R18821), which was illustrated in the heatmap diagram ( Figure 2D ). The principal component analysis based on the scRNA-seq could successfully distinguish the groups in either 12 h androgen-treated cells and 12 h untreated cells, indicating the differential transcriptome features within them ( Figure 2E ). Gene ontology (GO) analysis suggested that these DEGs were mainly enriched in cell cycle, lipid biosynthetic process, dendrite development, as well as fatty-acyl-CoA metabolic process ( Figure 2F ).

To identify the essential ARGs with prognostic significance in PRAD, we selected the top 53 DEGs and performed the LASSO analysis, where 10 hub genes were found ( Figs. 3A and 3B ). Accordingly, the significant differential expression of 10 ARGs in two clusters were shown in dot plot ( Figure 3C ). The ARGs signature was thus constructed via multi-variate Cox regression analysis, where 3-, 5-, and 7-year of the areas under the curve (AUCs) of the ROC curves were 0.789, 0.799 and 0.837, respectively ( Figure 3D ). Lastly, we calculated the ARGs scores for each patient in TCGA-PRAD cohort and divided the samples into high-risk and low-risk groups, and Kaplan-Meier analysis revealed that patients in high-risk groups suffered from worse survival outcomes relative to those from the low-risk groups with log-rank test P < 0.001 ( Figure 3E ).

Since we have already established the ARGs model, we intended to figure out the underlying relationships between ARGs and other clinical variables. Expectedly, high ARGs scores correlated positively with clinical T stages ( P = 3.414e−15), positive rates of lymph nodes ( P = 3.815e−08), as well as advanced Gleason scores ( P = 3.352e−23) ( Figs. 4A – 4C ). Besides, we integrated the common risk variables in PRAD and performed the uni-variate Cox regression analysis and found four risk factors in PRAD, like Gleason scores, ARGs, T and N stages ( Figure 4D ). Subsequently, multi-variate Cox regression analysis further demonstrated that ARGs is an independent factor in PRAD, along with the traditional factor of Gleason score ( Figure 4E ). Given that PSA is a classcal predictor for PRAD risks, we found that PSA levels could effectively predict the progression risks of patients with 3-year AUC = 0.719, 5-year AUC = 0.716 and 7-year AUC = 0.765 ( Figure 4E ). Patients with high PSA levels had the shorter progression-free survival compared with those with low Gleason scores, as indicated by the Kaplan-Meier analysis with P < 0.001 ( Figure 4F ). Lastly, the decision curve analysis (DCA) was further utilized to assess the performance of ARGs model and PSA in predicting clinical outcomes of PRAD patients. DCA demonstrated that the combined model (ARGs and PSA levels) showed the best net benefit for 5-year progression-free survival (PFS) relative to either unique factor alone ( Figure 4G ).

The ARGs model was further validated by other three independent PRAD cohorts, including the GSE116918 dataset ( N = 248), GSE70769 dataset ( N = 92) and MSKCC cohort ( N = 140). In GSE116918 , the AUCs for 3-, 5-, and 7-year progression-free survival predictions for the risk scores were 0.705, 0.766, and 0.76, respectively. Patients in high-risk groups had worse PFS outcomes relative to those in low-risk groups with log-rank test P = 6.387e−05 ( Figs. 5A and 5B) . In addition, in the GSE70769 cohort, the AUCs for 3-, 5-, and 7-year progression-free survival predictions for the risk scores were 0.775, 0.793, and 0.773, individually ( Figs. 5C and 5D) . Patients in the high-risk groups suffered from the worse PFS outcomes compared with those in low-risk groups with log-rank test P = 1.407e−04 ( Figs. 5E and 5F ). Consistent with the above findings, we observed the similar results in the MSKCC cohort. Collectively, these data suggested that the ARGs model was a robust predictive factor in the PRAD patients.

Utilizing the unsupervised clustering algorithm, we conducted consensus clustering analysis to distinguish PRAD patients in the training cohort into subgroups based on the expression of ARGs. The K = 2 was identified with the optimal clustering stability ( Figs. 5G – 5I ). We further conducted the PCA analysis and could classify the patients into two distinct groups with individual features (cluster 1 & cluster 2) in Figure 5J . Kaplan-Meier analysis implicated that patients in the cluster two group had worse PFS outcomes compared with those in the cluster 1 group, in which the log-rank test P value was 0.013 ( Figure 5K ). In addition, we also performed the GSEA between the cluster 2 and cluster 1 groups and we found that several oncogenic crosstalk was significantly enriched, including cell cycle, DNA replication, notch signaling pathway and p53 signaling ( Figure 5L ).

We further calculated the TMB variable in the TCGA-PRAD cohort, matched with corresponding ARGs scores. The mutational landscape showed that mutation events occurred more frequently in the high-risk ARGs group versus low-risk group, as exhibited by the waterfall plot. Particularly, we compared the differential mutation rates of mutated genes distributed in more than 5% of all samples, and TP53, KMT2D or PTEN harbored more mutation sites in high-risk group relative those in the low-risk group, as indicated by the Chi-square test ( Fig. S1A ). Besides, TMB levels correlated positively with ARGs scores with Pearson’s r = 0.278 ( Fig. S1B ). Intriguingly, TMB is also a prognostic factor in PRAD with log-rank test P = 0.021 ( Fig. S1B ).

Given the above success in finding ARGs in PCa, we thus intended to obtain the AR-related regulators that are specifically required for PCa cells. Next, we conducted the validation screens using the individual shRNAs to investigate inhibitory effects in 22RV-1 and C4-2B cells with specific knockdown of identified targets. As shown by the 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay, we found that silencing of ORC1 showed the most pronounced suppressive effect on cell viability among the candidates including ORC1, USP4S, HANK3, PPP1R1B, MYLB2, INPP5B, EXO1, DYNLL1, CENPF and B4GALT1 ( Figure 6A ). ORC1 ablation clearly led to the most decrease of cell growth in both 22RV-1 and C4-2B cells. Therefore, we focused on ORC1 for following functional studies. Utilizing the shRNAs targeting ORC1, the ORC1 was knocked down in PCa cells, which was confirmed by western blot ( Figure 6B ). Expectedly, ORC1 inhibition could notably suppress the cell proliferation rates compared with the cells from control group, as indicated by CCK-8 assays ( Figure 6B ). We then detected the expression levels of ORC1 in TCGA-PRAD cohort and observed that ORC1 expressed highly in tumor samples versus normal tissues ( Figure 6C ). The same results were found in GSE94767 ( Figure 6D ). Intriguingly, elevated ORC1 levels correlated with high Gleason score levels ( Figure 6E ). Lastly, Kaplan-Meier analysis in TCGA-PRAD and GSE116918 datasets showed that patients with high ORC1 levels had the worse outcomes with shorter PFS months compared with patients with low ORC1 ( Figs. 6F and 6G ). Taken together, we revealed that ORC1 is an essential AR-related hit in PRAD and has the prognostic significance.

To elucidate the function of ORC1 in PRAD, we established stable ORC1-overexpressing PRAD cells (22RV-1 and C4-2B). The up-regulation of ORC1 significantly enhanced PRAD cell soft agar colony formation efficiency ( Figure 7A ). Besides, the self-renewal potentiality of PRAD cells was also increased when cells were transfected with ORC1, indicating that ORC1 could also maintain stemness features of PRAD ( Figure 7B ). Next, we intended to figure out the underlying regulations between AR and ORC1. Firstly, we found AR overexpression could notably elevate ORC1 expression levels, whereas AR knockdown decreased its levels, along with another known AR downstream target of PSA ( Figure 7C ). Positive correlations between AR and ORC1 were confirmed in the TCGA-PRAD samples ( Figure 7D ). Additionally, the chromatin immunoprecipitation (ChIP) assay was performed to show the promoter region of ORC1 was enriched in AR binding and H3K27ac signals ( Figure 7E ). Knockdown of AR could markedly decrease the enrichment of H3K27ac signals in the promoter of ORC1 ( Figure 7E ). These data implicated that AR directly binds at the promoter of ORC1 that accounts for the transcription and upregulation of ORC1. Lastly, in order to determine whether ORC1 regulates Enza-R, we observed that ORC1 overexpression attenuated the sensitivity of C4-2B-Parental cells to enzalutamide, whereas ORC1 ablation compromised the resistance of C4-2B-Enza-R cells to enzalutamide ( Figure 7F ). Lastly, we further established the C4-2B-Enza-R tumor xenograft models and found that ORC1 knockdown could render C4-2B-Enza-R tumors sensitive to enzalutamide treatment, as quantified by tumor volumes and weight ( Figs. 7G – 7I ). Collectively, AR could directly activate ORC1 transcription that mediates the Enza-R in PRAD cells.