Therapeutic Target For Prostate Cancer Research Articles

Chimeric SFT2D2-TBX19 Promotes Prostate Cancer Progression by Encoding TBX19-202 Protein and Stabilizing Mitochondrial ATP Synthase through ATP5F1A Phosphorylation.

Specific chimeric RNAs and their products are consistently regarded as ideal tumor diagnostic markers and therapeutic targets. Chimeric RNAs can mediate tumor cell plasticity, neuroendocrine processes, polarization of tumor-associated macrophages, and resistance to chemotherapy and immunotherapy. However, the discovery of chimeric RNAs in prostate cancer is still in its early stages. This study identifies the chimeric SFT2D2-TBX19 as a novel transcript encoding the TBX19-202 protein. Both TBX19-202 and its parental TBX19, which share homologous amino acid sequences, enhance prostate cancer cell proliferation, migration, and invasion. Additionally, SFT2D2-TBX19 also functions as a lncRNA, interacting with the ATP synthase F1 subunit ATP5F1A, thereby increasing ATP5F1A phosphorylation mediated by TNK2/ACK1, which stabilizes the interaction between ATP5F1A and ATP5F1B. The region spanning 1801-2400bp of SFT2D2-TBX19 and the intermediate structural domain of ATP5F1A are crucial functional areas. This stabilization of ATP5F1A and ATP5F1B enhances mitochondrial ATP synthase activity and ATP production. Even under conditions of mitochondrial vulnerability, SFT2D2-TBX19 protects mitochondrial structural stability to maintain prostate cancer cell proliferation. This research provides comprehensive evidence that chimeric SFT2D2-TBX19 promotes prostate cancer progression by encoding the TBX19-202 protein and stabilizing mitochondrial ATP synthase via ATP5F1A phosphorylation. These findings highlight SFT2D2-TBX19 as a potential therapeutic target for prostate cancer.

Single-cell sequencing analysis revealed that NEAT1 was a potential biomarker and therapeutic target of prostate cancer.

Prostate cancer (PCa) usually manifests atypical symptoms in the early stage, and once symptoms appear, most PCa patients have developed to the advanced stage, failing to undergo radical surgery. In this study, PCa occurrence-related biomarkers were explored based on single-cell RNA sequencing (scRNA-seq) data. scRNA-seq data of prostate normal (Normal), benign prostatic hyperplasia (BPH), and PCa (Tumor) samples were acquired from the Gene Expression Omnibus (GEO). Cellular subsets associated with PCa occurrence were obtained using cell annotation. Additionally, the mRNA expression of nuclear enriched abundant transcript 1 (NEAT1) was detected by quantitative real-time PCR (qRT-PCR). The effects of NEAT1 on cell proliferation and apoptosis were analyzed by 5-ethynyl-2-deoxyuridine (EdU) and flow cytometry. Subsequently, cell-derived xenograft (CDX) models were constructed and divided into the LV-NC and LV-shNEAT1 groups. After the tumor tissues of CDX model mice in each group were extracted, the cell growth and Ki67 expression were observed separately using hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). Ten cellular subsets were obtained via cell annotation, and significantly differential changes were observed between Basal intermediate and Luminal during the course of BPH to PCa. NEAT1-Luminal was highly recruited in the Tumor group with low stemness and high malignancy scores. Matrix metallopeptidase 7 (MMP7)- keratin 17 (KRT17)-Basal intermediate had high ratios in the Tumor group with low stemness and high malignancy scores. The results of pseudotime analysis revealed that NEAT1-Luminal in the Tumor group were consistently distributed with tumor stage cells. In vitro assays showed that NEAT1 expression was elevated in PCa cells, and NEAT1 knockdown could inhibit cell proliferation and induce apoptosis. CDX assays indicated that silencing NEAT1 could reduce the growth rate of PCa tumor volume in CDX model mice. H&E staining results showed that nuclei of tumor cells were reduced and exhibited lighter color in the LV-shNEAT1 group compared with the LV-NC group. IHC results showed that Ki67 positivity was significantly lower in the LV-shNEAT1 group than in the LV-NC group. NEAT1 expression is increased in PCa, and NEAT1 can be a potential biomarker and therapeutic target for PCa.

Dihydroartemisinin Modulates Prostate Cancer Progression by Regulating Multiple Genes via the Transcription Factor NR2F2.

This study aimed to investigate the effect of dihydroartemisinin (DHA) on DU145 cells and the role of NR2F2 (COUP-TFII) and its potential target genes in this process. GSE122625 was used to identify differentially expressed genes (DEGs) between the DHA-treated and control groups. Protein-protein interaction (PPI) network analysis was performed to identify hub genes, and the ChEA3 database was used to identify potential transcription factors. qRT-PCR and Western blot were used to validate the expression of genes of interest and functional assays were performed to evaluate the effect of DHA on DU145 and PC-3 cells. To solidify the regulatory relationship of NR2F2 with EFNB2, EBF1, ETS1, and VEGFA, a Chromatin Immunoprecipitation (ChIP) experiment was performed. We identified 85 DEGs in DU145 cells treated with DHA, and PPI network analysis identified NR2F2 as a hub gene and potential transcription factor. The regulatory network of NR2F2 and its potential target genes (EFNB2, EBF1, ETS1, and VEGFA) was constructed, and the expression of these genes was upregulated in DHA-treated cells compared to control cells. Functional assays showed that DHA treatment inhibited epithelial-mesenchymal transition, reduced inflammation, and promoted apoptosis in DU145 and PC-3 cells. Furthermore, NR2F2 knockdown receded the DHA-induced upregulation of target genes and functional changes of DU145 and PC-3 cells. The outcomes of ChIP unequivocally pointed to a positive regulatory role of NR2F2 in these gene expressions. Our study suggests that DHA treatment affects the functions of DU145 and PC-3 cells by regulating the expression of NR2F2 and its potential target genes, and NR2F2 may serve as a potential therapeutic target for prostate cancer.

Discovery of Thiadiazoleamide Derivatives as Potent, Selective, and Orally Available Antagonists Disrupting Androgen Receptor Homodimer.

Androgen receptor (AR) is an important therapeutic target for prostate cancer (PCa) treatment, but prolonged use of AR antagonists has led to variant drug-resistant mutations. Since all marketed AR antagonists target the ligand binding pocket (LBP) of AR, to mitigate cross-resistance, a new drug pocket named Dimer Interface Pocket was discovered and a novel AR antagonist M17-B15 was identified. M17-B15 showed strong in vitro efficacy against PCa but had poor pharmacokinetic properties in vivo. In this study, through rational design and structure-activity relationship exploration, a series of thiadiazoleamide derivatives represented by N29 (IC50 = 0.018 μM) were identified with dominant AR antagonistic activity and remarkable anti-PCa activity in vitro. Furthermore, N29 effectively inhibited a series of typical drug-resistant AR mutants. The improved oral bioavailability of N29 facilitated its efficacy via oral administration, significantly inhibiting LNCaP xenograft tumor in vivo, presenting a promising therapeutic application for PCa.

Abstract C097: PVT1 exon 9-dependent overexpression of RSAD2 is a characteristic of prostate cancer in males of African ancestry

Abstract Aggressive prostate cancer (PCa), including castrate-resistant and neuroendocrine subtypes, is projected to increase in incidence 5% each year. Aggressive PCa disproportionally impacts African American men, has limited effective treatment options and high mortality rates. Although racial disparities are established in PCa development, attributed to multiple contributory factors including systemic racism, differences in socioeconomic status and distrust in the medical system, work is still needed to understand the contribution of biological factors. We found the exon 9 region of plasmacytoma variant translocation 1 (PVT1) overexpressed in aggressive PCa cell lines derived from African American (AA) men. We found PVT1 exon 9 overexpression induced phenotypic changes to normal prostate epithelial cells in vitro and led to tumor formation in vivo. RNA-sequencing analysis revealed 156 significantly differentially expressed genes influenced by PVT1 exon 9 overexpression and radical S-adenosyl methionine domain containing 2 (RSAD2) was the top significantly overexpressed gene (log2Fold change 1.71, p-value < 0.05). We utilized the GSE223405 publicly available dataset to identify whether our identified PVT1 exon 9 downstream genes were enriched in AA prostate cancer cells. RSAD2 was significantly overexpressed in AA prostate cancer cells compared to prostate cancer cells from men of European ancestry (moEA) (log2Fold change 6.18, p-value < 0.05). Comparing normal cells to prostate cancer cells between these groups, we found that normal AA prostate cells had significantly higher expression of RSAD2 (log2fold change 4.25, p-value < 0.001) whereas moEA samples did not (log2fold change 0.837, p-value=0.150). From analysis of data obtained from cBioPortal, we found RSAD2 alteration significantly correlates (p-value < 0.001) with self-identified race and copy number overexpression of RSAD2 was found to be enriched in AA patients (18.16% moEA versus 24% AA). Finally, we found 88.6% of the significantly differentially expressed genes within our PVT1 exon 9 RNA-sequencing dataset were significantly differentially expressed in GSE223405 AA samples and of those, 51.7% of genes exhibited a similar differential expression pattern suggesting that the PVT1 exon 9/RSAD2 dependent pathway may be more prominent in AA men. Mechanistically, we found two differing pathways of RSAD2 overexpression in aggressive prostate cancer cell models, one dependent and one independent of PVT1 exon 9 overexpression, with similar but differing biological functions related to epigenetic processes and metabolism. Ongoing work is investigating the RSAD2 and PVT1 exon 9 expression axis in a diverse set of human prostate cancer tissues and investigating their utilization as a therapeutic target in prostate cancer. Citation Format: Rachel E. Sexton-Bonacci1, Murtaza Barkarar1, Chindeum Udekwu1, Olorunseun O. Ogunwobi1. PVT1 exon 9-dependent overexpression of RSAD2 is a characteristic of prostate cancer in males of African ancestry [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C097.

Ferroptosis-related gene signature predicts prognosis and immune microenvironment in prostate cancer.

Ferroptosis, an iron-dependent form of programmed cell death, significantly impacts cancer, yet its link to prostate cancer (PCa) prognosis remains underexplored. This study aims to develop and validate a ferroptosis-related gene signature to predict PCa prognosis and immune microenvironment differences, potentially identifying therapeutic targets. RNA-sequencing data of 478 PCa patients and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. We investigated the disease-free survival (DFS) rates of the high- and low-risk groups using the Kaplan-Meier method. Functional differences between the high- and low-risk groups were investigated by a gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The link between ferroptosis risk score and immune status was examined using CIBERSORT. The expression levels of core prognostic genes in benign prostatic hyperplasia (BPH) and PCa were verified using quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC). A novel ferroptosis-related prognostic gene signature was established and tested in the Gene Expression Omnibus (GEO) database based on univariate and multivariate Cox regression analyses. Patients with PCa were classified into high- and low-risk groups based on this ferroptosis signature. Patients in the high-risk group had worse outcomes than those in the low-risk group. The predictive accuracy of the model was demonstrated by a receiver operating characteristic (ROC) analysis. An additional enrichment analysis of TCGA cohort revealed the immune-related pathways were significantly upregulated in the high-risk group, with areas under the curve (AUCs) of 0.85 at 1 year, 0.82 at 3 years, and 0.76 at 5 years. In the GEO cohort, the AUCs reached 0.69 at 1 year, 0.74 at 3 years, and 0.75 at 5 years. An additional enrichment analysis indicated a significant upregulation of cytokine-related pathways, immune receptor activity, and other immune-related pathways in the high-risk group. Furthermore, the analysis revealed that the proportions of mast cells and plasma cells were significantly lower in the high-risk group compared to the low-risk group of PCa patients. Conversely, the proportion of regulatory T cells (Tregs) was significantly higher in the high-risk group than in the low-risk group. According to the qRT-PCR, Western blot, and IHC results, DRD4, SRC, AKR1C2, and AIFM2 expression was significantly higher in PCa than BPH. We also showed that the ferrostatin 1-treated LNCaP cells had higher expression levels of DRD4, SRC, and AKR1C2. A prognostic signature of eight ferroptosis-related genes (FRGs) that may accurately predict PCa patient outcomes was constructed and validated. FRGs may contribute to anti-tumor immunity and serve as therapeutic targets in PCa.

Machine learning based androgen receptor regulatory gene-related random forest survival model for precise treatment decision in prostate cancer

BackgroundIt has been demonstrated that aberrant androgen receptor (AR) signaling contributes to the pathogenesis of prostate cancer (PCa). To date, the most efficacious strategy for the treatment of PCa remains to target the AR signaling axis. However, numerous PCa patients still face the issue of overtreatment or undertreatment. The establishment of a precise risk prediction model is urgently needed to distinguish patients with high-risk and select appropriate treatment modalities. MethodsIn this study, a consensus AR regulatory gene-related signature (ARS) was developed by integrating a total of 101 algorithm combinations of 10 machine learning algorithms. We evaluated the value of ARS in predicting patient prognosis and the therapeutic effects of the various treatments. Additionally, we conducted a screening of therapeutic targets and agents for high-risk patients, followed by the verification in vitro and in vivo. ResultsARS was an independent risk factor for biochemical recurrence and distant metastasis in PCa patients. The enhanced and consistent prognostic predictive capability of ARS across various platforms was confirmed when compared with 44 previously published signatures. More importantly, PCa patients in the ARShigh group benefit more from PARP inhibitors and immunotherapy, while chemotherapy, radiotherapy, and AR-targeted therapy are more effective for ARSlow patients. The results of in silico screening suggest that AURKB could potentially serve as a promising therapeutic target for ARShigh patients. ConclusionsCollectively, this prediction model based on AR regulatory genes holds great clinical translational potential to solve the dilemma of treatment choice and identify potential novel therapeutic targets in PCa.

Exploration of PVT1 as a biomarker in prostate cancer.

Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients' quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a protein-protein interaction network to predict core genes, analyzed survival data, plotted heatmaps and radar charts, and predicted microRNAs for core genes through miRTarBase. Two prostate cancer datasets (GSE46602 and GSE55909) were analyzed, identifying 710 DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that DEGs were primarily involved in organic acid metabolism and the P53 signaling pathway. Gene set enrichment analysis and Metascape analyses further confirmed the significance of these pathways. After constructing the weighted gene co-expression network analysis network, 3 core genes (DDX21, NOP56, plasmacytoma variant translocation 1 [PVT1]) were identified. Survival analysis indicated that core genes are closely related to patient prognosis. Through comparative toxicogenomics database and miRNA prediction analysis, PVT1 was considered to play a crucial role in the development of prostate cancer. The PVT1 gene is highly expressed in prostate cancer and has the potential to become a diagnostic biomarker and therapeutic target for prostate cancer.

NL13, a novel curcumin analogue and polo like kinase 4 inhibitor, induces cell cycle arrest and apoptosis in prostate cancer models.

Prostate cancer remains a major public health burden worldwide. Polo like kinase 4 (PLK4) has emerged as a promising therapeutic target in prostate cancer due to its key roles in cell cycle regulation and tumour progression. This study aims to develop and characterize the novel curcumin analogue NL13 as a potential therapeutic agent and PLK4 inhibitor against prostate cancer. NL13 was synthesized and its effects were evaluated in prostate cancer cells and mouse xenograft models. Kinome screening and molecular modelling identified PLK4 as the primary target. Antiproliferative and proapoptotic mechanisms were explored via cell cycle, apoptosis, gene and protein analyses. Compared with curcumin, NL13 exhibited much greater potency in inhibiting PC3 (IC50, 3.51 μM vs. 35.45 μM) and DU145 (IC50, 2.53 μM vs. 29.35 μM) prostate cancer cells viability and PLK4 kinase activity (2.32 μM vs. 246.88 μM). NL13 induced G2/M cell cycle arrest through CCNB1/CDK1 down-regulation and triggered apoptosis via caspase-9/caspase-3 cleavage. These effects were mediated by PLK4 inhibition, which led to the inactivation of the AKT signalling pathway. In mice, NL13 significantly inhibited tumour growth and modulated molecular markers consistent with in vitro findings, including decreased p-AKT and increased cleaved caspase-9/3. NL13, a novel PLK4-targeted curcumin analogue, exerts promising anticancer properties against prostate cancer by disrupting the PLK4-AKT-CCNB1/CDK1 and apoptosis pathways. NL13 represents a promising new agent for prostate cancer therapy.

NAT10 Promotes Prostate Cancer Growth and Metastasis by Acetylating mRNAs of HMGA1 and KRT8.

N4-acetylcytidine (ac4C) is essential for the development and migration of tumor cells. According to earlier research, N-acetyltransferase 10 (NAT10) can increase messenger RNAs (mRNAs) stability by catalyzing the synthesis of ac4C. However, little is known about NAT10 expression and its role in the acetylation modifications in prostate cancer (PCa). Thus, the biological function of NAT10 in PCa is investigated in this study. Compared to paraneoplastic tissues, the expression of NAT10 is significantly higher in PCa. The NAT10 expression is strongly correlated with the pathological grade, clinical stage, Gleason score, T-stage, and N-stage of PCa. NAT10 has the ability to advance the cell cycle and the epithelial-mesenchymal transition (EMT), both of which raise the malignancy of tumor cells. Mechanistically, NAT10 enhance the stability of high mobility group AT-hook 1 (HMGA1) by acetylating its mRNA, thereby promoting cell cycle progression to improve cell proliferation. In addition, NAT10 improve the stability of Keratin 8 (KRT8) by acetylating its mRNA, which promotes the progression of EMT to improve cell migration. This findings provide a potential prognostic or therapeutic target for PCa.

GPC2 promotes prostate cancer progression via MDK-mediated activation of PI3K/AKT signaling pathway

Prostate cancer is a major medical problem for men worldwide. Advanced prostate cancer is currently incurable. Recently, much attention was paid to the role of GPC2 in the field of oncology. Nevertheless, there have been no investigations of GPC2 and its regulatory mechanism in prostate cancer. Here, we revealed a novel action of GPC2 and a tumor promoting mechanism in prostate cancer. GPC2 was upregulated in prostate cancer tissues and cell lines. Higher expression of GPC2 was correlated with higher Gleason score, lymphatic metastasis, and worse overall survival in prostate cancer patients. Decreased expression of GPC2 inhibited cell proliferation, migration, and invasion in prostate cancer, whereas GPC2 overexpression promoted these properties. Mechanistically, GPC2 promoted the activation of PI3K/AKT signaling pathway through MDK. The rescue assay results in prostate cancer cells demonstrated that overexpression of MDK could attenuate GPC2 knockdown induced inactivation of PI3K/AKT signaling and partly reverse GPC2 knockdown induced inhibition of cell proliferation, migration, and invasion. In all, our study identified GPC2 as an oncogene in prostate cancer. GPC2 promoted prostate cancer cell proliferation, migration, and invasion via MDK-mediated activation of PI3K/AKT signaling pathway. GPC2 might be a promising prognosis predictor and potential therapeutic target in prostate cancer.

Targeting prostate cancer via therapeutic targeting of PIM-1 kinase by Naringenin and Quercetin

PIM-1 kinase belongs to the Ser/Thr kinases family, an attractive therapeutic target for prostate cancer. Here, we screened about 100 natural substances to find potential PIM-1 inhibitors. Two natural compounds, Naringenin and Quercetin, were finally selected based on their PIM-1 inhibitory potential and binding affinities. The docking score of Naringenin and Quercetin with PIM-1 is −8.4 and − 8.1 kcal/mol, respectively. Fluorescence binding studies revealed a strong affinity (Ka values, 3.1 × 104 M−1 and 4.6 × 107 M−1 for Naringenin and Quercetin, respectively) with excellent IC50 values for Naringenin and Quercetin (28.6 μM and 34.9 μM, respectively). Both compounds inhibited the growth of prostate cancer cells (LNCaP) in a dose-dependent manner, with the IC50 value of Naringenin at 17.5 μM and Quercetin at 8.88 μM. To obtain deeper insights into the PIM-1 inhibitory effect of Naringenin and Quercetin, we performed extensive molecular dynamics simulation studies, which provided insights into the binding mechanisms of PIM-1 inhibitors. Finally, Naringenin and Quercetin were suggested to serve as potent PIM-1 inhibitors, offering targeted treatments of prostate cancer. In addition, our findings may help to design novel Naringenin and Quercetin derivatives that could be effective in therapeutic targeting of prostate cancer.

LncRNA PITPNA-AS1 mediates the diagnostic potential of miR-129-5p in prostate cancer

BackgroundLncRNA has an effective value in many diseases, which has long been applied in the diagnosis, treatment and prognosis of prostate cancer. This study focused on lncRNA PITPNA-AS1, and its diagnostic potential in prostate cancer has been explored.MethodsThe expression of PITPNA-AS1 and miR-129-5p in prostate cancer serum and sample cells was determined by real-time quantitative polymerase chain reaction (RT-qPCR). The relationship between the expression of PITPNA-AS1 and clinicopathological parameters was considered. ROC curve prompted the diagnostic value of PITPNA-AS1. The effect of PITPNA-AS1 on prostate cancer cells was verified using vitro cells assay. Luciferase activity assay and RIP assay demonstrated the sponge relationship of PITPNA-AS1 to miR-129-5p.ResultsPITPNA-AS1 level was increased, while miR-129-5p was obviously decreased in prostate cancer. PITPNA-AS1 expression was associated with Gleason grade, lymph node metastasis and TNM stage in patients. The area under the curve (AUC) was 0.910, with high sensitivity and specificity. PITPNA-AS1 was elucidated to directly target miR-129-5p, whereas silencing PITPNA-AS1 negatively affected prostate cancer cell proliferation, migration and invasion. Intervention of miR-129-5p inhibitor reversed the effect of silencing PITPNA-AS1 on cells.ConclusionsPITPNA-AS1 was relatively highly expressed in prostate cancer and mediated the pathophysiological process of patients, which may serve as a diagnostic indicator. Silencing of the PITPNA-AS1 sponge miR-129-5p inhibited the biological function of the cells, indicating that PITPNA-AS1 may represent a novel therapeutic target for prostate cancer.

Biological determinants of PSMA expression, regulation and heterogeneity in prostate cancer.

Prostate-specific membrane antigen (PSMA) is an important cell-surface imaging biomarker and therapeutic target in prostate cancer. The PSMA-targeted theranostic 177Lu-PSMA-617 was approved in 2022 for men with PSMA-PET-positive metastatic castration-resistant prostate cancer. However, not all patients respond to PSMA-radioligand therapy, in part owing to the heterogeneity of PSMA expression in the tumour. The PSMA regulatory network is composed of a PSMA transcription complex, an upstream enhancer that loops to the FOLH1 (PSMA) gene promoter, intergenic enhancers and differentially methylated regions. Our understanding of the PSMA regulatory network and the mechanisms underlying PSMA suppression is evolving. Clinically, molecular imaging provides a unique window into PSMA dynamics that occur on therapy and with disease progression, although challenges arise owing to the limited resolution of PET. PSMA regulation and heterogeneity - including intertumoural and inter-patient heterogeneity, temporal changes, lineage dynamics and the tumour microenvironment - affect PSMA theranostics. PSMA response and resistance to radioligand therapy are mediated by a number of potential mechanisms, and complementary biomarkers beyond PSMA are under development. Understanding the biological determinants of cell surface target regulation and heterogeneity can inform precision medicine approaches to PSMA theranostics as well as other emerging therapies.

Casein Kinase 1α-A Target for Prostate Cancer Therapy?

The androgen receptor (AR) is a key driver of prostate cancer (PCa) and, as such, current mainstay treatments target this molecule. However, resistance commonly arises to these therapies and, therefore, additional targets must be evaluated to improve patient outcomes. Consequently, alternative approaches for indirectly targeting the AR are sought. AR crosstalk with other signalling pathways, including several protein kinase signalling cascades, has been identified as a potential route to combat therapy resistance. The casein kinase 1 (CK1) family of protein kinases phosphorylate a multitude of substrates, allowing them to regulate a diverse range of pathways from the cell cycle to DNA damage repair. As well as its role in several signalling pathways that are de-regulated in PCa, mutational data suggest its potential to promote prostate carcinogenesis. CK1α is one isoform predicted to regulate AR activity via phosphorylation and has been implicated in the progression of several other cancer types. In this review, we explore how the normal biological function of CK1 is de-regulated in cancer, the impact on signalling pathways and how this contributes towards prostate tumourigenesis, with a particular focus on the CK1α isoform as a novel therapeutic target for PCa.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

USP47 deficiency in mice modulates tumor infiltrating immune cells and enhances antitumor immune responses in prostate cancer

This study investigates the role of USP47, a deubiquitinating enzyme, in the tumor microenvironment and its impact on antitumor immune responses. Analysis of TCGA database revealed distinct expression patterns of USP47 in various tumor tissues and normal tissues. Prostate adenocarcinoma showed significant downregulation of USP47 compared to normal tissue. Correlation analysis demonstrated a positive association between USP47 expression levels and infiltrating CD8+ T cells, neutrophils, and macrophages, while showing a negative correlation with NKT cells. Furthermore, using Usp47 knockout mice, we observed a slower tumor growth rate and reduced tumor burden. The absence of USP47 led to increased infiltration of immune cells, including neutrophils, macrophages, NK cells, NKT cells, and T cells. Additionally, USP47 deficiency resulted in enhanced activation of cytotoxic T lymphocytes (CTLs) and altered T cell subsets within the tumor microenvironment. These findings suggest that USP47 plays a critical role in modulating the tumor microenvironment and promoting antitumor immune responses, highlighting its potential as a therapeutic target in prostate cancer.

Integrating single-cell RNA-sequencing and bulk RNA-sequencing data to explore the role of mitophagy-related genes in prostate cancer

Prostate cancer (PCa) is the most common malignancy of the male urinary system. Mitophagy, as a type of autophagy, can remove damaged mitochondria in cells. Mitophagy-related genes (MRGs) have been shown to play critical roles in the development of PCa. To this end, based on the comprehensive analysis of RNA-seq and scRNA-seq data of PCa samples and their controls, this paper identified PCa subtypes and constructed a prognostic model. In this paper, we downloaded scRNA-seq and RNA-seq data from Gene Expression Omnibus (GEO) and TCGA database. Based on the R package “Seurat” to process the scRNA-seq data, a total of five cell types were identified. Each cell population was scored based on the R package “AUCell” and using the intersection genes between MRGs and each cell population. The B cell population was then identified as a high-scoring cell population. Differentially expressed genes in RNA-seq data were identified based on the R package “limma” and intersected with previously intersected genes. Then, based on univariate Cox regression analysis and Lasso-Cox regression analysis, the prognostic genes were screened, and the risk model was constructed (composed of ADH5, CAT, BCAT2, DCXR, OGT, and FUS). The model is validated on internal and external test sets. Independent prognostic analysis identified age, N stage, and risk score as independent prognostic factors. This paper's risk models and prognostic genes can provide a reference for developing novel therapeutic targets for PCa.

Blocking lipid synthesis induces DNA damage in prostate cancer and increases cell death caused by PARP inhibition.

Androgen deprivation therapy (ADT) is the primary treatment for prostate cancer; however, resistance to ADT invariably develops, leading to castration-resistant prostate cancer (CRPC). Prostate cancer progression is marked by increased de novo synthesis of fatty acids due to overexpression of fatty acid synthase (FASN), making this enzyme a therapeutic target for prostate cancer. Inhibition of FASN results in increased intracellular amounts of ceramides and sphingomyelin, leading to DNA damage through the formation of DNA double-strand breaks and cell death. We found that combining a FASNi with the poly-ADP ribose polymerase (PARP) inhibitor olaparib, which induces cell death by blocking DNA damage repair, resulted in a more pronounced reduction in cell growth than that caused by either drug alone. Human CRPC organoids treated with a combination of PARP and FASNi were smaller, had decreased cell proliferation, and showed increased apoptosis and necrosis. Together, these data indicate that targeting FASN increases the therapeutic efficacy of PARP inhibitors by impairing DNA damage repair, suggesting that combination therapies should be explored for CRPC.

Exploration of vitamin D metabolic activity-related biological effects and corresponding therapeutic targets in prostate cancer

BackgroundPrevious studies have unequivocally demonstrated that the vitamin D (VD) metabolism pathway significantly influences prognosis and sensitivity to hormone therapy in prostate cancer (PCa). However, the precise underlying mechanism remains unclear.MethodsWe performed molecular profiling of 1045 PCa patients, leveraging genes linked to VD synthesis and VD receptors. We then identified highly variable gene modules with substantial associations with patient stratification. Subsequently, we intersected these modules with differentially expressed genes between PCa and adjacent paracancerous tissues. Following a meticulous process involving single-factor regression and LASSO regression to eliminate extraneous variables and construct a prognostic model. Within the high-risk subgroup defined by the calculated risk score, we analyzed their differences in cell infiltration, immune status, mutation landscape, and drug sensitivity. Finally, we selected Apolipoprotein E (APOE), which featured prominently in this model for further experimental exploration to evaluate its potential as a therapeutic target.ResultsThe prognostic model established in this study had commendable predictive efficacy. We observed diminished infiltration of various T-cell subtypes and reduced expression of co-stimulatory signals from antigen-presenting cells. Mutation analysis revealed that the high-risk cohort harbored a higher frequency of mutations in the TP53 and FOXA genes. Notably, drug sensitivity analysis suggested the heightened responsiveness of high-risk patients to molecular inhibitors targeting the Bcl-2 and MAPK pathways. Finally, our investigation also confirmed that APOE upregulates the proliferative and invasive capacity of PCa cells and concurrently enhances resistance to androgen receptor antagonist therapy.ConclusionThis comprehensive study elucidated the potential mechanisms through which this metabolic pathway orchestrates the biological behavior of PCa and findings hold promise in advancing the development of combination therapies in PCa.