Skip to main content
Download PDF

Integrated bioinformatics analysis reveals that OPRK1 inhibits ferroptosis and induces enzalutamide resistance in prostate cancer

European Journal of Medical Research volume 30, Article number: 279 (2025) Cite this article

Abstract

Enzalutamide (Enz) is employed in the management of castration-resistant prostate cancer (CRPC). However, a substantial subset of patients may develop resistance to Enz, thereby reducing its therapeutic effectiveness. The underlying mechanisms contributing to the development of Enz resistance in PCa, whether arising from androgen deprivation or the burden of Enz treatment, remain inadequately understood. OPRK1 plays a key role in Enz resistance through ferroptosis inhibition, which is detected by the analysis of Gene Expression Omnibus (GEO) databases. Silencing OPRK1 via small interfering RNA (siRNA) resulted in the activation of ferroptosis signaling in LNCaP cells. These findings indicate that OPRK1 significantly contributes to Enz resistance in PCa and may serve as a promising therapeutic target for resistant patients.

Graphical Abstract

Introduction

Prostate cancer (PCa) is one of the most common malignancies among older males. In 2024, approximately 35,250 deaths will be caused by PCa in the United States [1]. The conventional therapeutic approach for localized patients younger than 75 years often includes radical prostatectomy combined with neoadjuvant endocrine therapy [2]. Despite these interventions, biochemical recurrence occurs in up to 50% of patients following prostatectomy [3]. Considering the involvement of the androgen receptor (AR) in the progression of prostate cancer (PCa), androgen deprivation therapy (ADT) is the established treatment for advanced PCa [4]. However, the inability of ADT to completely eradicate all PCa cell populations results in the eventual development of castration-resistant prostate cancer (CRPC) [5]. CRPC is the final stage of the disease and is usually fatal within months [6]. Despite advancements in treatment modalities, CRPC remains incurable, underscoring the necessity of elucidating its molecular mechanisms to facilitate the development of novel therapeutic strategies.

Enzalutamide (Enz), a next-generation AR pathway inhibitor, functions by binding to the AR's ligand-binding domain, inhibiting AR nuclear translocation, and suppressing AR-mediated transcription [7,8,9]. Clinical evidence has demonstrated that Enz extends the survival of patients with CRPC [10]. Nevertheless, the development of resistance to Enz in these tumors presents an unavoidable challenge. Several mechanisms underlying resistance to Enz have been identified, including hypersensitivity due to AR gene amplification, promiscuity in AR ligand specificity, and ligand-independent activation [11,12,13,14,15,16,17,18]. It is plausible that these models are not mutually exclusive. Further investigation into the molecular mechanisms of Enz treatment is essential for developing therapies to manage resistant prostate tumors.

The kappa opioid receptor (KOR), or opioid receptor kappa1 (OPRK1), is a G protein-coupled receptor that serves as an endogenous receptor for synthetic opioids [19, 20]. This receptor plays a crucial role in pain perception and is responsible for mediating the hypokinetic, analgesic, and aversive effects linked to synthetic opioids. Recent investigations have identified the KOR as a potential therapeutic target in the treatment of human malignant tumors [21,22,23]. While OPRK1 expression is known to increase in human PCa tissues after androgen deprivation or progression to CRPC, its precise role in Enz resistance is yet to be determined [22, 24, 25].

Recent bioinformatics advancements have improved our grasp of tumor molecular mechanisms, heavily influenced by the chosen analysis algorithm. Weighted Gene Co-expression Network Analysis (WGCNA) was used in this study to identify disease-related gene modules and hub genes, bypassing the need to analyze differential expression genes (DEGs) [26]. The study identifies OPRK1 as a key gene in Enz resistance in PCa. High OPRK1 levels are linked to poor prognosis. Overexpressing OPRK1 inhibits ferroptosis, a recently identified iron-dependent cell death [27, 28], which has a tremendous therapeutic potential in cancer treatment, could contribute to targeting drug resistance. Combining OPRK1 targeting with Enz treatment may enhance drug sensitivity and inhibit PCa cell growth. As a result, OPRK1 is likely to be a promising therapeutic intervention choice in patients who are resistant to Enz.

Materials and methods

Preprocessing of GEO data and DEG identification

This study utilized data from the GEO database, with detailed dataset information provided in Supplemental Table 1. Genes exhibiting significant differences between patients with sensitivity and resistance to Enz were identified through differential gene expression analysis. Genes were deemed differentially expressed if they exhibited a log2FC > 1 and a p-value < 0.05 in the GSE51872, GSE56829, and GSE69249 datasets [29,30,31]. Supplement methods provide additional information.

WGCNA

Clustering of common modules was carried out using R's WGCNA package with a 0.25 cut-off threshold. Materials and methods provides details about the modules that exhibit co-expression patterns with phenotypes.

Single-cell RNA sequencing analysis (scRNA-seq)

In GSE168668 [32], hub gene expression was validated, excluding genes with fewer than 200 detections. Supplement methods provide additional information.

Cell culture and reagents

Cell culture

LNCaP cells were sourced from the Cell Bank of Culture at the Chinese Academy of Sciences. The transfection of cells was carried out according to established protocols using siRNA with sequences provided in Supplemental Table 2.

Reagents

Enzalutamide (S1250) and erastin (S7242) were sourced from Selleck (Shanghai, China). Supplement methods provide additional information.

Western blotting

Cells were exposed to 10 μM Enz for 72 h following normal cell growth prior to protein extraction for Western Blotting analysis. RIPA buffer was used for cell lysis. Protein separation was performed via electrophoresis, followed by transfer onto PVDF membranes. Detailed information regarding the primary antibodies employed in this study is provided in Supplemental Table 3. Supplement methods provide additional information.

Colony formation assay

Cells were seeded at 104 per well in cell culture dishes and incubated for 14 days in 1640 medium, with or without 10 µM Enz or 5 μM erastin. After incubation, cells were fixed with 4% paraformaldehyde, stained with 1% crystal violet for 20 min, and quantified colony number. Supplement methods provide additional information.

Statistics

Independent samples t-tests were conducted to determine statistical significance (p < 0.05), with non-significance indicated as n.s.

Results

Identification and functional enrichment of key Enz resistance-related DEGs from the GEO database

A search in the Gene Expression Omnibus (GEO) database was conducted to identify potential target genes associated with Enz resistance in PCa for subsequent comprehensive analysis. Gene expression levels were compared between three Enz-treated VCaP cell samples and three DMSO-treated VCaP cell samples from the GSE51872 dataset. This comparison identified 143 upregulated and 124 downregulated differentially expressed genes (DEGs) with log2FC > 1 and p < 0.05 (Fig. 1A). A comparative analysis of gene expression levels was performed on 8 CRPC and 8 control samples using the GSE56829 dataset. This analysis identified 185 upregulated and 184 downregulated DEGs with log2FC > 1 and p < 0.05 (Fig. 1B). Furthermore, a similar analysis was performed on 3 Enz-treated LNCaP cell samples and 3 DMSO-treated LNCaP cell samples from the GSE69249 dataset, which revealed 216 upregulated and 234 downregulated DEGs (Fig. 1C). The heatmaps depicted the expression pattern of these DEGs (Fig. 1D–F).

Fig. 1
figure 1

Identification and functional enrichment of key Enz resistance-related DEGs from the GEO database A Volcano plots illustrating the DEGs in dataset GSE51872, with red dots representing upregulated genes and blue dots representing downregulated genes. B Volcano plots illustrating the DEGs in dataset GSE56829, with red dots representing upregulated genes and blue dots representing downregulated genes. C Volcano plots illustrating the DEGs in dataset GSE69249, with red dots representing upregulated genes and blue dots representing downregulated genes. D Heatmap depicting the DEGs across various samples in dataset GSE51872. E Heatmap depicting the DEGs across various samples in dataset GSE56829. F Heatmap illustrating DEGs across various samples in dataset GSE69249. G GO pathway enrichment analysis of DEGs in dataset GSE51872. (H) GO pathway enrichment analysis of DEGs in dataset GSE56829. I GO pathway enrichment analysis of DEGs in dataset GSE69249. J KEGG pathway enrichment analysis of DEGs in dataset GSE51872. K KEGG pathway enrichment analysis of DEGs in dataset GSE56829. L KEGG pathway enrichment analysis of DEGs in dataset GSE69249

Full size image

To identify biological traits linked to Enz resistance, upregulated DEGs in Enz-treated groups were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway methods. Figure 1G–I illustrates the principal GO terms associated with the response to Enz. DEGs in VCAP and LNCaP cells treated with Enz were significantly enriched in pathways related to cell population proliferation regulation, and hormone response, indicating a potential regulatory effect of Enz on PCa. Figure 1J–L illustrates the main KEGG terms linked to the Enz response. The DEGs identified in cell samples were mainly linked to steroid hormone biosynthesis, cell adhesion molecules, and cytochrome P450 drug metabolism. This underscores the importance of conducting a comprehensive investigation into the molecular pathogenic mechanisms to advance our understanding of Enz resistance.

Enz resistance gene co-expression networks revealed by WGCNA

We explored gene co-expression modules in this study, emphasizing key modules related to Enz resistance, using WGCNA instead of DEGs. Cluster dendrograms produced by hierarchical clustering are illustrated in Fig. 2A (GSE51872), Fig. 2B (GSE56829), and Fig. 2C (GSE69249), respectively. The phenotypic groups (DMSO or Enza, Control or CRPC) were incorporated into the co-expression network analysis. Genes exhibiting high hub modularity were designated as hub genes within the modular-trait correlation analysis. A heatmap was subsequently employed to visualize the associations between gene modules and clinical traits. Subsequently, modules demonstrating significant co-expression patterns and associations with phenotypes were identified. Notably, a positive correlation between module traits and Enz response or CRPC was observed in the datasets, specifically identified as the turquoise module (GSE51872, correlation coefficient [CC] = 0.95, p = 0.003), the brown module (GSE56829, CC = 0.86, p = 2e−05), and the blue module (GSE69249, CC = 0.97, p = 0.002), as illustrated in Fig. 2D–F.

Fig. 2
figure 2

Enz resistance gene co-expression networks revealed by WGCNA A WGCNA was performed on Enz response samples from dataset GSE51872. The resulting dendrogram illustrated the clustering of DEGs according to various metrics. Each branch corresponded to an individual gene, while the colors beneath the branches denoted distinct co-expression modules. B Similarly, WGCNA was conducted on Enz resistance samples from dataset GSE56829. The dendrogram depicted the clustering of DEGs based on different metrics, with each branch representing a single gene and each color beneath the branches indicating a specific co-expression module. C WGCNA was conducted on Enz resistance samples in dataset GSE69249. The resulting dendrogram illustrated the clustering of DEGs according to various metrics. Each branch of the dendrogram corresponded to an individual gene, while the colors beneath the branches indicated distinct co-expression modules. D The heatmap depicted the correlation between gene modules and the response to Enz treatment. The correlation coefficients within each cell of the heatmap represented the strength of the association between gene modules and specific traits, with the intensity of the correlation decreasing from red to blue. E The heatmap illustrated the correlation between gene modules and the progression of CRPC. The correlation coefficient within each cell denoted the strength of the association between gene modules and traits, with values decreasing from red to blue. F Similarly, the heatmap depicted the correlation between gene modules and the response to Enz. The correlation coefficient within each cell represented the relationship between gene modules and traits, with values decreasing from red to blue. G The Venn diagram visualized the common hub genes shared between DEGs and those derived from WGCNA based on the GSE51872 dataset. H The common hub genes identified between DEGs and WGCNA from the GSE56829 dataset were visualized using a Venn diagram. I Similarly, the common hub genes identified between DEGs and WGCNA from the GSE69249 dataset were visualized using a Venn diagram. J The top enriched GO pathways among the common hub genes from the GSE51872 dataset were analyzed, with the horizontal axis representing the p-value of GO terms on Metascape. K The top enriched GO pathways among the common hub genes from the GSE56829 dataset were analyzed, with the horizontal axis representing the p-value of GO terms on Metascape. L The top enriched GO pathways among common hub genes from the GSE69249. The horizontal axis represented the p-value of GO terms on Metascape

Full size image

A comprehensive analysis of DEGs and the genes responsible for Enz resistance was carried out using shared hub genes between DEGs and WGCNA modules. The results led to the identification of 103 genes in GSE51872, 153 genes in GSE56829, and 33 genes in GSE69249 (Fig. 2G–I). Enrichment analysis of the common candidates among three GEO datasets was conducted using the Metascape platform (http://www.metascape.org/). The predominant Metascape terms identified in dataset GSE51872 encompassed the negative regulation of hormone secretion. In addition, the principal GO enrichment results for dataset GSE56829 involved the response to steroid hormones. GO analysis of dataset GSE69249 highlighted the regulation of GTPase activity and the glycerophospholipid biosynthetic process as top terms (Fig. 2J–L).

Hub genes in PCa and their expression and prognosis

To improve clinical decisions and stratify Enz-resistant conditions, consistent data from Enz-resistant samples are essential. Based on the comparative analysis of three GEO datasets, it was determined that BTG2, OPRK1, PTGER2, and TSPAN3 have consistently been identified across all datasets (Fig. 3A). Figure 3B shows the mRNA levels of candidates in the TCGA. OPRK1 is highly expressed in PCa samples and linked to patient prognosis by Kaplan–Meier analysis (Fig. 3C). OPRK1 also shows significant differences between control and Enz-treated groups (Fig. 3D–F). These findings indicate that OPRK1 could be a potential biomarker for Enz resistance in PCa.

Fig. 3
figure 3

Hub genes in PCa and their expression and prognosis A The common hub genes shared among the three GEO datasets were visualized using a Venn diagram. B The expression levels of four selected genes were analyzed in the TCGA database, comparing PCa samples (red) and normal samples (gray). C Kaplan–Meier curves were generated to assess disease-free survival (DFS) in PCa patients, stratified by high versus low expression of the four selected genes in the TCGA dataset. D The expression levels of hub genes were estimated in the GSE51872 dataset. E The expression levels of hub genes were estimated in the GSE56829 dataset. F The expression levels of hub genes were estimated in the GSE69249 dataset. *p < 0.05

Full size image

Signaling pathways downstream of OPRK1 identified by scRNA-seq

Following the identification of OPRK1's role in enzalutamide resistance in prostate cancer (PCa), we conducted an analysis of single-cell RNA sequencing (scRNA-seq) data from various PCa samples to elucidate the underlying molecular mechanisms. We examined the gene expression profiles from two enzalutamide-resistant samples (RESA and RESB) and one DMSO-treated sample within the GSE168668 dataset. After adjusting for sequencing depth, gene counts, and normalization, we selected 2,000 highly variable genes for further analysis. Post-quality control, we analyzed 11,485 cells, each with a median of 19,647 genes. Dimensionality reduction was performed using “RunPCA,” resulting in the identification of 11 clusters at a resolution of 0.5 (Fig. 4A). A heatmap showed the top 5 variable genes per cluster (Fig. 4B). CellChat visualization outputs were extensively analyzed for various purposes. The cell groups were represented by different color schemes in the hierarchy plot, whereas the likelihood of communicating was indicated by color intensity in the hierarchy plot (Fig. 4C). The findings indicate that clusters 6 and 9 predominantly acted as senders, while clusters 3 and 5 primarily functioned as recipients. The remaining clusters exhibited dual roles, serving both as senders and recipients.

Fig. 4
figure 4

Signaling pathways downstream of OPRK1 identified by scRNA-seq A The UMAP technique was employed to categorize cells into 11 distinct clusters, each represented by a unique color corresponding to its designated phenotype. B The heatmap illustrates the expression levels of the top five DEGs across each cell cluster. C The cell–cell communication signaling network among the 11 clusters was analyzed using CellChat. The right panel depicts the spatial distribution of cell clusters based on the number of their significant incoming (Y-axis) and outgoing (X-axis) signaling interactions. D Heatmap illustrating the CellChat signaling within each cluster. The left panel depicts the outgoing signaling patterns, represented by the expression weight values of signaling molecules, while the right panel illustrates the incoming signaling patterns, indicated by the expression weight values of signaling receptors. The gradient from white to dark green signifies a range from low to high expression weight values in the heatmap. E The inferred network of the MIF signaling pathway. F The heatmap illustrates the enrichment of various pathways across 11 distinct cell clusters as determined by GSVA. Each column corresponds to a specific group or subpopulation of cells, while each row represents an individual pathway. The intensity of red coloration indicates higher scores, whereas blue coloration signifies lower scores. G Feature plots depict the spatial distribution of BTG2, OPRK1, PTGER2, and TSPAN3 across the 11 cell clusters

Full size image

Additionally, the analysis examined interactions among the 11 clusters, considering each as both target and source. These bidirectional interactions involved pathways like MIF, LAMININ, COLLAGEN, NOTCH, and WNT (Fig. 4D). Our study explored the MIF-ACKR3 signaling pathway in cluster communications to understand Enz resistance in PCa (Fig. 4E).

Additionally, Gene Set Variation Analysis (GSVA) was conducted across 11 clusters to identify the main activated signaling pathways. The analysis identified activation levels of the HALLMARK_WNT_BETA_CATENIN_SIGNALING pathway in cluster 0, the HALLMARK_IL6_JAK_STAT3_SIGNALING pathway in cluster 1, and the HALLMARK_TNFA_SIGNALING_VIA_NFKB pathway in cluster 2, among others (Fig. 4F). A further diagram in Fig. 4G depicts the expression patterns and distribution of hub genes BTG2, OPRK1, PTGER2, and TSPAN3.

Subsequently, we observed an elevated expression level of OPRK1 in the Enz-resistant group RESA (Fig. 5A). To elucidate the downstream mechanisms mediated by OPRK1, cells were stratified into OPRK1-positive (OPRK1+) and OPRK1-negative (OPRK1−) groups to analyze gene expression differences. GO enrichment analysis indicated significant enrichment in pathways related to cell survival, including the apoptotic process, endoplasmic reticulum stress, and programmed cell death. KEGG enrichment analysis identified the involvement of oxidative phosphorylation, glycolysis/gluconeogenesis, and ferroptosis pathways (Fig. 5B–C). Furthermore, the Gene Set Enrichment Analysis (GSEA) results demonstrate significant upregulation of ferroptosis in OPRK1− cells, suggesting an active state of this pathway (Fig. 5D).

Fig. 5
figure 5

OPRK1 plays a role in PCa Enz response in vitro A Violin plots illustrating the expression levels of the OPRK1 gene across 11 distinct cell clusters in four scRNA-seq samples. B The most significantly enriched GO pathways among DEGs in OPRK1-positive (OPRK1+) and OPRK1-negative (OPRK1−) cells were identified and depicted graphically. C The most significantly enriched KEGG pathways among DEGs in OPRK1+ and OPRK1− cells were identified and depicted graphically. D The ferroptosis pathway was identified and graphically represented using GSEA. E Western blot analysis of OPRK1 and ferroptosis-related protein levels in LNCaP cell lines. F Quantitative analysis of the Western blotting results was conducted using ImageJ software. G Colony formation assay of LNCaP cells was performed following a 14 day treatment with 10 μM Enz or 5 μM erastin. H Quantitative analysis of the colony formation assay was also performed using ImageJ software. Error bars represent the standard deviation (± SD). Statistical significance is indicated as follows: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001, ns indicates non-significant differences

Full size image

OPRK1 plays a role in PCa Enz response in vitro

Researchers have previously shown that inhibition of phosphoglycerate dehydrogenase induces ferroptosis in castration-resistant prostate cancer cells and overcomes their resistance to enzalutamide [33,34,35]. To further address this, an in vitro PCa model using LNCaP cells was developed to assess Enz response mediated by OPRK1 and promotes resistance by inhibiting the ferroptosis pathway. The levels of OPRK1, ACSL4, and GPX4 were tested in LNCaP cells treated with 10 μM Enz for a specified period. Western blot analysis confirmed that Enz stimulation in LNCaP cells upregulated the ferroptosis pathway. Our results indicate that the development of resistance to Enz is associated with the activation of the ferroptosis pathway in OPRK1+ cells. Furthermore, we utilized siRNA to downregulate OPRK1 gene expression in LNCaP cells treated with Enz to investigate the relationship between OPRK1 and the activation of the ferroptosis pathway.

Our findings revealed that the reduction of OPRK1 levels led to a modest activation of ferroptosis in LNCaP cells (Fig. 5E–F). Inhibition of OPRK1 has been demonstrated to effectively enhance ferroptosis activation in LNCaP cells, underscoring the pivotal role of OPRK1 in modulating the ferroptosis pathway and affecting the response to Enz in PCa. Furthermore, colony formation assays reveal that OPRK1 inhibition results in increased ferroptosis activation and a concomitant reduction in PCa cell proliferation (Fig. 5G–H). The findings from our in vitro experiments collectively suggest that the inhibition of OPRK1 may induce ferroptosis, consequently affecting the viability of PCa cells. These results highlight the therapeutic potential of targeting OPRK1 as a strategy to overcome resistance to Enz.

Discussion

A major clinical challenge is identifying patients at risk for lethal resistance and metastatic prostate cancer [36, 37]. The scarcity of reliable tissue samples has impeded research on CRPC and Enz resistance. Extensive sequencing of human prostate cancer tissue is difficult due to the challenge of obtaining enough replicate samples from the same patient [38,39,40]. Moreover, acquiring matched tissue samples from both castration-sensitive and resistant tumors is often highly challenging. To address these issues, we have employed RNA-seq data from GEO datasets, incorporating cell samples subjected to Enz treatment, to conduct a robust comparative analysis. This study shows that OPRK1 is upregulated in Enz-resistant PCa and linked to poor outcomes. Depleting OPRK1 activates the ferroptosis pathway, which, combined with Enz, induces synthetic lethality and inhibits PCa progression.

This study explored OPRK1’s role in Enz resistance in prostate cancer by analyzing gene expression in Enz-resistant samples. Recent advancements in bioinformatics have enhanced our understanding of the molecular mechanisms underlying tumors, with significant influence exerted by the selected analytical algorithms. In this study, WGCNA was employed to identify gene modules and hub genes associated with disease, thereby circumventing the necessity to analyze DEGs. WGCNA is considered more effective than DEG analysis for pinpointing key gene modules. Analysis of three GEO datasets revealed crucial gene modules linked to the Enz response. The essential gene OPRK1 was identified through DEGs and WGCNA, and confirmed by scRNA-seq and in vitro experiments. The knockdown of OPRK1 has been demonstrated to activate the ferroptosis signaling pathway in PCa cells, thereby elucidating the underlying mechanisms contributing to Enz resistance in this malignancy.

Previous research indicates that OPRK1 plays a crucial role in post-castration survival and the development of castration-resistant prostate cancer, speeding up the clinical use of OPRK1-targeting therapies for this deadly condition [24, 25, 41]. Furthermore, another study has indicated that OPRK1 is predominantly expressed on tumor cells in triple-negative breast cancer patients, whereas OGFR and TLR4 are more highly expressed on immune cells, suggesting a potential relationship between OPRK1 and various malignancies [42]. Our research indicates that OPRK1 may be crucial in developing Enz resistance. Using siRNA, we downregulated OPRK1 in LNCaP cells and evaluated its effect on ferroptosis pathway activation under Enz burden. Inhibiting OPRK1 may activate ferroptosis, impacting PCa cell viability. Targeting OPRK1 shows promise as a therapeutic strategy to overcome Enz resistance.

Despite comprehensive analyses and experiments, limitations exist. Cellular-level studies may not fully represent whole organisms due to tissue complexity. Furthermore, the absence of data from patients resistant to Enz represents a notable limitation of this study. Additionally, although the research demonstrated that OPRK1 inhibits ferroptosis and contributes to Enz resistance, the precise molecular mechanisms involved remain inadequately understood. Further studies are needed to validate the potential of OPRK1 inhibitors in improving patient outcomes. This study concentrated on OPRK1 as a pivotal gene in Enz resistance; however, other central genes identified through bioinformatics analysis were not comprehensively validated in terms of their functional roles. Consequently, further investigations employing animal models and human clinical trials are necessary to substantiate these findings.

Conclusion

This study used bioinformatics to identify OPRK1 as a key gene in Enz resistance. In vitro experiments with Enz-treated LNCaP cells confirmed OPRK1's role, offering new insights into Enz resistance mechanisms.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

PCa:

Prostate cancer

AR:

Androgen receptor

Enz:

Enzalutamide

CRPC:

Castration-resistant prostate cancer

GEO:

Gene Expression Omnibus

SiRNA:

Small interfering RNA

ADT:

Androgen deprivation therapy

OPRK1:

Opioid receptor kappa1 (OPRK1)

WGCNA:

Weighted gene co-expression network analysis

DEG:

Differentially expressed gene

scRNA-seq:

Single-cell RNA sequencing

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

TCGA:

The Cancer Genome Atlas

GSVA:

Gene set variation analysis

References

  1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74:12–49.

    Article  PubMed  Google Scholar 

  2. Qiu H, Cao S, Xu R. Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Commun (Lond). 2021;41:1037–48.

    Article  PubMed  Google Scholar 

  3. Wang M, Dai B, Liu Q, Zhang X. Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer. Cancer Cell Int. 2024;24:297.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Huang Q, Mitchell C, Theodoulou E, Lee ACK, Brown J. Implementation of fracture risk assessment in men with prostate cancer requiring long-term androgen deprivation therapy: a systematic scoping review using the i-PARIHS implementation framework. J Cancer Surviv. 2024;71(3):209.

    Google Scholar 

  5. Akkus E, Arslan Ç, Ürün Y. Advancements in platinum chemotherapy for metastatic castration-resistant prostate cancer: insights and perspectives. Cancer Treat Rev. 2024;130: 102818.

    Article  PubMed  CAS  Google Scholar 

  6. Watson PA, Arora VK, Sawyers CL. Emerging mechanisms of resistance to androgen receptor inhibitors in prostate cancer. Nat Rev Cancer. 2015;15:701–11.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Lennep BW, et al. Enzalutamide: understanding and managing drug interactions to improve patient safety and drug efficacy. Drug Saf. 2024;47:617–41.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Scott LJ. Enzalutamide: a review in castration-resistant prostate cancer. Drugs. 2018;78:1913–24.

    Article  PubMed  Google Scholar 

  9. Rodriguez-Vida A, Galazi M, Rudman S, Chowdhury S, Sternberg CN. Enzalutamide for the treatment of metastatic castration-resistant prostate cancer. Drug Des Devel Ther. 2015;9:3325–39.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Zhen L, et al. A net-work meta-analysis of the cardiac safety for next-generation hormonal agents in treating castration-resistant prostate cancer: how to choose drugs appropriately? Crit Rev Oncol Hematol. 2024;196: 104273.

    Article  PubMed  Google Scholar 

  11. Harris AE, et al. Exploring anti-androgen therapies in hormone dependent prostate cancer and new therapeutic routes for castration resistant prostate cancer. Front Endocrinol. 2022;13:1006101.

    Article  Google Scholar 

  12. Chen HJ, et al. SLC4A4 is a novel driver of enzalutamide resistance in prostate cancer. Cancer Lett. 2024;597: 217070.

    Article  PubMed  CAS  Google Scholar 

  13. Lokeshwar SD, Klaassen Z, Saad F. Treatment and trials in non-metastatic castration-resistant prostate cancer. Nat Rev Urol. 2021;18:433–42.

    Article  PubMed  Google Scholar 

  14. Chen H, et al. CRISPR genome-wide screening identifies PAK1 as a critical driver of ARSI cross-resistance in prostate cancer progression. Cancer Lett. 2024;587: 216725.

    Article  PubMed  CAS  Google Scholar 

  15. Zheng Z, et al. The crucial role of AR-V7 in enzalutamide-resistance of castration-resistant prostate cancer. Cancers. 2022;14(2022):10.

    Google Scholar 

  16. Chen H, et al. Non-drug efflux function of ABCC5 promotes enzalutamide resistance in castration-resistant prostate cancer via upregulation of P65/AR-V7. Cell Death Discov. 2022;8:241.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Labrecque MP, et al. Molecular profiling stratifies diverse phenotypes of treatment-refractory metastatic castration-resistant prostate cancer. J Clin Invest. 2019;129:4492–505.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Beltran H, et al. Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat Med. 2016;22:298–305.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Shao J, Fei Y, Xiao J, Wang L, Zou S, Yang J. The role of miRNA-144-3p/Oprk1/KOR in nicotine dependence and nicotine withdrawal in male rats. Nicotine Tob Res. 2023;25:1856–64.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Ji H, et al. OPRK1 promoter hypermethylation increases the risk of Alzheimer’s disease. Neurosci Lett. 2015;606:24–9.

    Article  PubMed  CAS  Google Scholar 

  21. Li H, Ma Z, Lei Y. The expression of kappa-opioid receptor promotes the migration of breast cancer cells in vitro. BMC Anesthesiol. 2021;21:210.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wang L, Yao Y, Xu C, Wang X, Wu D, Hong Z. Exploration of the tumor mutational burden as a prognostic biomarker and related hub gene identification in prostate cancer. Technol Cancer Res Treat. 2021;20:15330338211052154.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Wu S, Chen J, Huang E, Zhang Y. Pan-cancer analysis and validation of opioid-related receptors reveals the immunotherapeutic value of toll-like receptor 4. Int J Gen Med. 2023;16:5527–48.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Makino Y, et al. Comprehensive genomics in androgen receptor-dependent castration-resistant prostate cancer identifies an adaptation pathway mediated by opioid receptor kappa 1. Commun Biol. 2022;5:299.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Yamashita H, Shuman L, Warrick JI, Raman JD, Degraff DJ. Androgen represses opioid growth factor receptor (OGFR) in human prostate cancer LNCaP cells and OGFR expression in human prostate cancer tissue. Am J Clin Exp Urol. 2018;6:164–71.

    PubMed  PubMed Central  Google Scholar 

  26. Rezaei Z, et al. Identification of early diagnostic biomarkers via WGCNA in gastric cancer. Biomed Pharmacother. 2022;145: 112477.

    Article  PubMed  CAS  Google Scholar 

  27. Wengler MR, Talbot NJ. Mechanisms of regulated cell death during plant infection by the rice blast fungus Magnaporthe oryzae. Cell Death Differ. 2025. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41418-024-01442-y.

    Article  PubMed  Google Scholar 

  28. Puente-Cobacho B, et al. De novo lipogenesis protects dormant breast cancer cells from ferroptosis and promotes metastasis. Redox Biol. 2024;80: 103480.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Arora VK, et al. Glucocorticoid receptor confers resistance to antiandrogens by bypassing androgen receptor blockade. Cell. 2013;155:1309–22.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Knuuttila M, et al. Castration induces up-regulation of intratumoral androgen biosynthesis and androgen receptor expression in an orthotopic VCaP human prostate cancer xenograft model. Am J Pathol. 2014;184:2163–73.

    Article  PubMed  CAS  Google Scholar 

  31. Li L, et al. Androgen receptor inhibitor-induced ‘“BRCAness”’ and PARP inhibition are synthetically lethal for castration-resistant prostate cancer. Sci Signal. 2017. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/scisignal.aam7479.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Taavitsainen S, et al. Single-cell ATAC and RNA sequencing reveal pre-existing and persistent cells associated with prostate cancer relapse. Nat Commun. 2021;12:5307.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Wang J, et al. Inhibition of phosphoglycerate dehydrogenase induces ferroptosis and overcomes enzalutamide resistance in castration-resistant prostate cancer cells. Drug Resist Updat. 2023;70:100985.

    Article  PubMed  CAS  Google Scholar 

  34. Sun R, et al. Androgen receptor variants confer castration resistance in prostate cancer by counteracting antiandrogen-induced ferroptosis. Cancer Res. 2023;83:3192–204.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Nie J, Zhang P, Liang C, Yu Y, Wang X. ASCL1-mediated ferroptosis resistance enhances the progress of castration-resistant prostate cancer to neurosecretory prostate cancer. Free Radic Biol Med. 2023;205:318–31.

    Article  PubMed  CAS  Google Scholar 

  36. Chen H, et al. The modified prostate health index (PHI) outperforms PHI density in the detection of clinical prostate cancer within the PSA grey zone. Int Urol Nephrol. 2022;54:749–56.

    Article  PubMed  CAS  Google Scholar 

  37. Rajan P, Elliott DJ, Robson CN, Leung HY. Alternative splicing and biological heterogeneity in prostate cancer. Nat Rev Urol. 2009;6:454–60.

    Article  PubMed  CAS  Google Scholar 

  38. Gao L, et al. KIF15-mediated stabilization of AR and AR-V7 contributes to enzalutamide resistance in prostate cancer. Cancer Res. 2021;81:1026–39.

    Article  PubMed  CAS  Google Scholar 

  39. Rajan P, et al. Next-generation sequencing of advanced prostate cancer treated with androgen-deprivation therapy. Eur Urol. 2014;66:32–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Rajan P, et al. Effect of comorbidity on prostate cancer-specific mortality: a prospective observational study. J Clin Oncol. 2017;35:3566–74.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Romanuik TL, Wang G, Morozova O, Delaney A, Marra MA, Sadar MD. LNCaP Atlas: gene expression associated with in vivo progression to castration-recurrent prostate cancer. BMC Med Genom. 2010;3:43.

    Article  Google Scholar 

  42. Montagna G, et al. Intraoperative opioids are associated with improved recurrence-free survival in triple-negative breast cancer. Br J Anaesth. 2021;126:367–76.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all the individuals who took part in the present work.

Funding

The present research was funded by Basic Science Foundation of Shanxi Province (202303021222340).

Author information

Authors and Affiliations

  1. Department of Urology, The First Hospital of Shanxi Medical University, 85 Jiefang South Street, Yingze District, Taiyuan, 030001, Shanxi, People’s Republic of China

    Liangrong Zhang & Xiaoming Cao

  2. Department of Urology, Shanxi Provincial People’s Hospital, Taiyuan, 030012, Shanxi, People’s Republic of China

    Liangrong Zhang, Xiaodong Wen & Xiangkai Zhang

  3. Department of Nephrology, Shanxi Provincial People’s Hospital, Taiyuan, 030012, Shanxi, People’s Republic of China

    Yanqin Liu

  4. Department of Radiology, Shanxi Provincial People’s Hospital, Taiyuan, 030012, Shanxi, People’s Republic of China

    Peng Fan

Authors
  1. Liangrong Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yanqin Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiaodong Wen
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Xiangkai Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Peng Fan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Xiaoming Cao
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

L Zhang and X Cao contributed to the conception and design of the manuscript. Y Liu and X Wen contributed to the acquisition of data. X zhang and P Fan contributed to the manuscript writing. L Zhang contributed to the data management and analysis.

Corresponding author

Correspondence to Xiaoming Cao.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in this study were in accordance with the ethical standards of the Ethics Committee of The First Hospital of Shanxi Medical University (NO. DWYJ-2023-094) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Liu, Y., Wen, X. et al. Integrated bioinformatics analysis reveals that OPRK1 inhibits ferroptosis and induces enzalutamide resistance in prostate cancer. Eur J Med Res 30, 279 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02484-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40001-025-02484-9

Keywords

European Journal of Medical Research

ISSN: 2047-783X

Contact us