Integration of copy number and transcriptomics provides risk stratification in prostate cancer: A discovery and validation cohort study

Identification and Validation of Alkaliptosis Resistance-Associated Genes in Prostate Cancer Via Transcriptome Sequencing and Prediction of Biochemical Recurrence

Androgen deprivation therapy (ADT) is the primary treatment strategy for prostate cancer. However, despite an initially favorable response, tumors inevitably progress to castration-resistant prostate cancer (CRPC). Therefore, the exploration of new therapeutic approaches targeting CRPC has become imperative. Increasing evidence suggests that hypoxia plays a crucial role in the development of CRPC. In this study, we found that the emergence of alkaliptosis resistance and the expression of its marker, CA9, significantly contribute to the progression of castration resistance induced by hypoxia. This study utilized bioinformatics approaches to identify genetic determinants associated with alkaliptosis resistance and explored the clinical significance of these marker genes. Transcriptomic sequencing was performed on the DU145 prostate cancer cell line, which had been induced to acquire alkaliptosis resistance. Using least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic risk model consisting of 12 genes, including ADORA2A, KCNG4, SEC14L5, B3GAT2, SLFNL1, FAM72D, CBWD3, PPM1K, STARD4, DEPDC1B, MATN3, and DDIAS was developed. The risk model score demonstrated a strong correlation with key patient clinical characteristics, including Gleason score, PSA levels, T stage, and N stage, and was also associated with immune therapy response and biochemical recurrence-free survival (BCRFS). Furthermore, ADORA2A expression in cellular models was found to be a critical factor in promoting alkaliptosis resistance.

Accelerated growth and local progression of radiorecurrent prostate cancer in an orthotopic bioluminescent mouse model

Globally, prostate cancer is the second most common malignancy in males, with over 400 thousand men dying from the disease each year. A common treatment modality for localized prostate cancer is radiotherapy. However, up to half of high-risk patients can relapse with radiorecurrent prostate cancer, the aggressive clinical progression of which remains severely understudied. To address this, we have established an orthotopic mouse model for study that recapitulates the aggressive clinical progression of radiorecurrent prostate cancer. Radiorecurrent DU145 cells which survived conventional fraction (CF) irradiation were orthotopically injected into the prostates of athymic nude mice and monitored with bioluminescent imaging. CF tumours exhibited higher take rates and grew more rapidly than treatment-naïve parental tumours (PAR). Pathohistological analysis revealed extensive seminal vesicle invasion and necrosis in CF tumours, recapitulating the aggressive progression towards locally advanced disease exhibited by radiorecurrent tumours clinically. RNA sequencing of CF and PAR tumours identified ROBO1, CAV1, and CDH1 as candidate targets of radiorecurrent progression associated with biochemical relapse clinically. Together, this study presents a clinically relevant orthotopic model of radiorecurrent prostate cancer progression that will enable discovery of targets for therapeutic intervention to improve outcomes in prostate cancer patients.

NUF2 is associated with cancer stem cell characteristics and a potential drug target for prostate cancer

Background

Cancer stem cells are characterized by self-renewal, clonal tumor initiation capacity, and treatment resistance, which play essential roles in the tumor progression of prostate cancer (PCa). In this study, we aim to explore the features of cancer stemness and characterize the expression of stem cell-related genes for PCa.

Methods

We downloaded RNA-seq data and related clinical information from The Cancer Genome Atlas (TCGA) database. The mRNA stemness index (mRNAsi) was analyzed for various clinical features, overall survival (OS), and disease-free survival (DFS), and a weighted gene co-expression network analysis (WGCNA) was performed to identify crucial gene modules and key genes, which may play a role in CSCs. The key gene functions were verified using multiple databases, including the TCGA and Gene Expression Omnibus database (GEO). Next, we explored the potential function of the modules and genes obtained using WGCNA using an enrichment analysis. Finally, we performed in vitro experiments for further verification.

Results

We found that mRNAsi were higher in PCa tissues than in normal tissues, and the mRNAsi were closely related to the clinical characteristics of PCa. A total of 16 key genes associated with the mRNAsi scores were identified by WGCNA analysis, including NCAPG, NEK2, DLGAP5, CENPA, CENPF, TPX2, GTSE1, KIF4A, NEIL3, CDC25C, UBE2C, CDCA5, MELK, SKA3, NUF2, and BIRC5. These genes were explicitly highly expressed in PCa across TCGA cancers and were validated in 3 independent GEO PCa datasets. The functional annotations of the key genes were linked with the cell proliferation processes. NUF2 may be a potential biomarker for PCa. In vitro experiments showed that knockdown NUF2 reduced the proliferation and migration of PCa cells.

Conclusion

The 16 key genes identified in this study significantly correlate with PCa stem cell characteristics and showed prognosis-oriented effects in PCa patients. Further, the NUF2 gene may be used as a drug target for treating PCa.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Increased translation driven by non-canonical EZH2 creates a synthetic vulnerability in enzalutamide-resistant prostate cancer

Overcoming resistance to therapy is a major challenge in castration-resistant prostate cancer (CRPC). Lineage plasticity towards a neuroendocrine phenotype enables CRPC to adapt and survive targeted therapies. However, the molecular mechanisms of epigenetic reprogramming during this process are still poorly understood. Here we show that the protein kinase PKCλ/ι-mediated phosphorylation of enhancer of zeste homolog 2 (EZH2) regulates its proteasomal degradation and maintains EZH2 as part of the canonical polycomb repressive complex (PRC2). Loss of PKCλ/ι promotes a switch during enzalutamide treatment to a non-canonical EZH2 cistrome that triggers the transcriptional activation of the translational machinery to induce a transforming growth factor β (TGFβ) resistance program. The increased reliance on protein synthesis creates a synthetic vulnerability in PKCλ/ι-deficient CRPC.

PLXNA1 confers enzalutamide resistance in prostate cancer via AKT signaling pathway

Although targeting the androgen signaling pathway by androgen receptor (AR) inhibitors, including enzalutamide, has shown therapeutic effectiveness, inevitable emergence of acquired resistance remains a critical challenge in the treatment of advanced prostate cancer (PCa). Recognizing targetable genomic aberrations that trigger endocrine treatment failure holds great promise for advancing therapeutic interventions. Here, we characterized PLXNA1, amplified in a subset of PCa patients, as a contributor to enzalutamide resistance (ENZR). Elevated PLXNA1 expression facilitated PCa proliferation under enzalutamide treatment due to AKT signaling activation. Mechanistically, PLXNA1 recruited NRP1 forming a PLXNA1-NRP1 complex, which in turn potentiated the phosphorylation of the AKT. Either inhibiting PLXNA1-NRP1 complex with an NRP1 inhibitor, EG01377, or targeting PLXNA1-mediated ENZR with AKT inhibitors, abolished the pro-resistance phenotype of PLXNA1. Taken together, combination of AKT inhibitor and AR inhibitors presents a promising therapeutic strategy for PCa, especially in advanced PCa patients exhibiting PLXNA1 overexpression.

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, and DNMT3B) was validated across independent cohorts and patient-derived xenograft (PDX) models. This signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the seven-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

Understanding the Biological Basis of Polygenic Risk Scores and Disparities in Prostate Cancer: A Comprehensive Genomic Analysis

Objectives

For prostate cancer (PCa), hundreds of risk variants have been identified. It remains unknown whether the polygenic risk score (PRS) that combines the effects of these variants is also a sufficiently informative metric with relevance to the molecular mechanisms of carcinogenesis in prostate. We aimed to understand the biological basis of PRS and racial disparities in the cancer.

Methods

We performed a comprehensive analysis of the data generated (deposited in) by several genomic and/or transcriptomic projects (databases), including the GTEx, TCGA, 1000 Genomes, GEO and dbGap. PRS was constructed from 260 PCa risk variants that were identified by a recent trans-ancestry meta-analysis and contained in the GTEx dataset. The dosages of risk variants and the multi-ancestry effects on PCa incidence estimated by the meta-analysis were used in calculating individual PRS values.

Results

The following novel results were obtained from our analyses. (1) In normal prostate samples from healthy European Americans (EAs), the expression levels of 540 genes (termed PRS genes) were associated with the PRS (P < .01). (2) Ubiquitin-proteasome system in high-PRS individuals' prostates was more active than that in low-PRS individuals' prostates. (3) Nine PRS genes play roles in the cancer progression-relevant parts, which are frequently hit by somatic mutations in PCa, of PI3K-Akt/RAS-MAPK/mTOR signaling pathways. (4) The expression profiles of the top significant PRS genes in tumor samples were capable of predicting malignant PCa relapse after prostatectomy. (5) The transcriptomic differences between African American and EA samples were incompatible with the patterns of the aforementioned associations between PRS and gene expression levels.

Conclusions

This study provided unique insights into the relationship between PRS and the molecular mechanisms of carcinogenesis in prostate. The new findings, alongside the moderate but significant heritability of PCa susceptibility contributed by the risk variants, suggest the aptness and inaptness of PRS for explaining PCa and disparities.

Hypoxia-Associated Gene Signatures Are Not Prognostic in High-Risk Localized Prostate Cancers Undergoing Androgen Deprivation Therapy With Radiation Therapy

PURPOSE

Men with high-risk prostate cancer (PCa) are treated with androgen deprivation therapy (ADT) and radiation therapy, but the disease reoccurs in 30% of patients. Biochemical recurrence of PCa after treatment is influenced by tumor hypoxia. Tumors with high levels of hypoxia are aggressive, resistant to treatment, and have increased metastatic capacity. Gene expression signatures derived from diagnostic biopsies can predict tumor hypoxia and radiosensitivity, but none are in routine clinical use, due to concerns about the applicability of these biomarkers to new patient cohorts. There has been no or limited testing in cohorts of high-risk PCa.

METHODS AND MATERIALS

We generated transcriptomic data for cohorts of patients with high-risk PCa. Patients were treated with ADT followed by external beam radiation therapy with or without a brachytherapy boost. Biomarkers curated from the literature were calculated from pretreatment biopsy gene expression data. The primary endpoint for survival analyses was biochemical recurrence-free survival and the secondary endpoints were distant metastasis-free survival and overall survival.

RESULTS

The performance of the selected biomarkers was poor, with none achieving prognostic significance for biochemical recurrence-free survival or distant metastasis-free survival in any cohort. The brachytherapy boost cohort received shorter durations of ADT than the conventionally fractionated or hypofractionated cohorts (Wilcoxon rank sum test, P = 2.1 × 10-18 and 2.3 × 10-10, respectively) and had increased risk of distant metastasis (log-rank test, P = 8 × 10-4). There were no consistent relationships between biomarker score and outcome for any of the endpoints.

CONCLUSIONS

Hypoxia and radiosensitivity biomarkers were not prognostic in patients with high-risk PCa treated with ADT plus radiation therapy. We speculate that the lack of prognostic capability could be caused by the variable hypoxia-modifying effects of the ADT that these high-risk patients received before and during definitive treatment with radiation therapy. A deeper understanding of biomarker construction, performance, and inter-cohort transferability in relation to patient characteristics, sample handling, and treatment modalities is required before hypoxia biomarkers can be recommended for routine clinical use in the pretreatment setting.

Exploring the interaction between immune cells in the prostate cancer microenvironment combining weighted correlation gene network analysis and single-cell sequencing: An integrated bioinformatics analysis

BACKGROUND

The rise of treatment resistance and variability across malignant profiles has made precision oncology an imperative in today's medical landscape. Prostate cancer is a prevalent form of cancer in males, characterized by significant diversity in both genomic and clinical characteristics. The tumor microenvironment consists of stroma, tumor cells, and various immune cells. The stromal components and tumor cells engage in mutual communication and facilitate the development of a low-oxygen and pro-cancer milieu by producing cytokines and activating pro-inflammatory signaling pathways.

METHODS

In order to discover new genes associated with tumor cells that interact and facilitate a hypoxic environment in prostate cancer, we conducted a cutting-edge bioinformatics investigation. This included analyzing high-throughput genomic datasets obtained from the cancer genome atlas (TCGA).

RESULTS

A combination of weighted gene co-expression network analysis and single-cell sequencing has identified nine dysregulated immune hub genes (AMACR, KCNN3, MME, EGFR, FLT1, GDF15, KDR, IGF1, and KRT7) that are believed to have significant involvement in the biological pathways involved with the advancement of prostate cancer enviriment. In the prostate cancer environment, we observed the overexpression of GDF15 and KRT7 genes, as well as the downregulation of other genes. Additionally, the cBioPortal platform was used to investigate the frequency of alterations in the genes and their effects on the survival of the patients. The Kaplan-Meier survival analysis indicated that the changes in the candidate genes were associated with a reduction in the overall survival of the patients.

CONCLUSIONS

In summary, the findings indicate that studying the genes and their genomic changes may be used to develop precise treatments for prostate cancer. This approach involves early detection and targeted therapy.

Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis

BACKGROUND

Prostate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.

METHODS

This study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.

RESULTS

An ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.

CONCLUSIONS

This study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven 7-Gene Stemness Signature that Predicts Progression

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting 7-gene signature (KMT5C, MEN1, TYMS, IRF5, DNMT3B, CDC25B and DPP4) was validated across independent cohorts and patient-derived xenograft (PDX) models. The signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the 7-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

The Proteogenomics of Prostate Cancer Radioresistance

Prostate cancer is frequently treated with radiotherapy. Unfortunately, aggressive radioresistant relapses can arise, and the molecular underpinnings of radioresistance are unknown. Modern clinical radiotherapy is evolving to deliver higher doses of radiation in fewer fractions (hypofractionation). We therefore analyzed genomic, transcriptomic, and proteomic data to characterize prostate cancer radioresistance in cells treated with both conventionally fractionated and hypofractionated radiotherapy. Independent of fractionation schedule, resistance to radiotherapy involved massive genomic instability and abrogation of DNA mismatch repair. Specific prostate cancer driver genes were modulated at the RNA and protein levels, with distinct protein subcellular responses to radiotherapy. Conventional fractionation led to a far more aggressive biomolecular response than hypofractionation. Testing preclinical candidates identified in cell lines, we revealed POLQ (DNA Polymerase Theta) as a radiosensitizer. POLQ-modulated radioresistance in model systems and was predictive of it in large patient cohorts. The molecular response to radiation is highly multimodal and sheds light on prostate cancer lethality.

SIGNIFICANCE

Radiation is standard of care in prostate cancer. Yet, we have little understanding of its failure. We demonstrate a new paradigm that radioresistance is fractionation specific and identified POLQ as a radioresistance modulator.

Expression of MxA in esophageal cancer cell lines can influence sensitivity to chemotherapeutic agents but this does not require apoptosis

Esophageal cancer is a poor prognosis cancer characterized by intrinsic or acquired resistance to chemotherapeutic agents. The primary determinants of treatment failure are unknown. Expression of an anti-viral protein, myxovirus resistance protein A (MxA) is de-regulated in many cancers, including esophageal cancer, and its activity has been linked to apoptosis. This study has assessed whether MxA expression can influence the response of esophageal cancer cells to the chemotherapeutic agents 5-fluorouracil (5-FU) or oxaliplatin. MxA protein was differentially expressed in a panel of five esophageal cancer cell lines. KYSE450 and KYSE140 cells did not express MxA and were apoptosis incompetent. FLO-1, KYSE270, and OE21 cells expressed MxA, were more drug-sensitive and were apoptosis competent. MxA was artificially overexpressed in cell lines with no endogenous expression (KYSE450 and KYSE140). This increased the resistance of KYSE450 but not KYSE140 cells. Both cell lines remained apoptosis incompetent. We then evaluated siRNA knockdown of MxA in FLO-1 cells and CRISPR knockout in OE21 cells. Knockdown of MxA significantly increased drug sensitivity and caspase-3 activation in FLO-1 cells. OE21-MX1KO cells were also more drug-sensitive, but in contrast to FLO-1 cells, caspase-3 activation was reduced. Collectively these data indicate that MxA can promote resistance to chemotherapy, but this does not always correspond with effects on apoptosis. Effects on apoptosis are cell line specific, suggesting that other co-operating pathways determine the overall impact of MxA. Importantly, in cancer cells that overexpress the protein, drug sensitivity can be improved by interfering with MxA.

An 18-gene signature of recurrence-associated endothelial cells predicts tumor progression and castration resistance in prostate cancer

BACKGROUND

The prognostic and therapeutic implications of endothelial cells (ECs) heterogeneity in prostate cancer (PCa) are poorly understood.

METHODS

We investigated associations of EC heterogeneity with PCa recurrence and castration resistance in 8 bulk transcriptomic and 4 single-cell RNA-seq cohorts. A recurrence-associated EC (RAEC) signature was constructed by comparing 11 machine learning algorithms through nested cross-validation. Functional relevances of RAEC-specific genes were also tested.

RESULTS

A subset of ECs was significantly associated with recurrence in primary PCa and named RAECs. RAECs were characteristic of tip and immature cells and were enriched in migration, angiogenesis, and collagen-related pathways. We then developed an 18-gene RAEC signature (RAECsig) representative of RAECs. Higher RAECsig scores independently predicted tumor recurrence and performed better or comparably compared to clinicopathological factors and commercial gene signatures in multiple PCa cohorts. Of the 18 RAECsig genes, FSCN1 was upregulated in ECs from PCa with higher Gleason scores; and the silencing of FSCN1, TMEME255B, or GABRD in ECs either attenuated tube formation or inhibited PCa cell proliferation. Finally, higher RAECsig scores predicted castration resistance in both primary and castration-resistant PCa.

CONCLUSION

This study establishes an endothelial signature that links a subset of ECs to prostate cancer recurrence and castration resistance.

SLC4A4 is a novel driver of enzalutamide resistance in prostate cancer

The androgen receptor signaling inhibitor (ARSI) enzalutamide (Enz) has shown critical efficacy in the treatment of advanced prostate cancer (PCa). However, the development of drug resistance is a significant factor contributing to mortality in PCa patients. We aimed to explore the key mechanisms of Enz-resistance. Through analysis of GEO databases, we identified SLC4A4 as a novel driver in Enz resistance. Long-term Enz treatment leads to the up-regulation of SLC4A4, which in turn mediates P53 lactylation via the NF-κB/STAT3/SLC4A4 axis, ultimately leading to the development of Enz resistance and progression of PCa. SLC4A4 knockdown overcomes Enz resistance both in vitro and in vivo. Hence, our results suggest that targeting SLC4A4 could be a promising therapeutic strategy for Enz resistance. STATEMENT OF SIGNIFICANCE: SLC4A4 is a novel driver of enzalutamide resistance.

DHODH inhibition represents a therapeutic strategy and improves abiraterone treatment in castration-resistant prostate cancer

Castration-resistant prostate cancer (CRPC) is an aggressive disease with poor prognosis, and there is an urgent need for more effective therapeutic targets to address this challenge. Here, we showed that dihydroorotate dehydrogenase (DHODH), an enzyme crucial in the pyrimidine biosynthesis pathway, is a promising therapeutic target for CRPC. The transcript levels of DHODH were significantly elevated in prostate tumors and were negatively correlated with the prognosis of patients with prostate cancer. DHODH inhibition effectively suppressed CRPC progression by blocking cell cycle progression and inducing apoptosis. Notably, treatment with DHODH inhibitor BAY2402234 activated androgen biosynthesis signaling in CRPC cells. However, the combination treatment with BAY2402234 and abiraterone decreased intratumoral testosterone levels and induced apoptosis, which inhibited the growth of CWR22Rv1 xenograft tumors and patient-derived xenograft organoids. Taken together, these results establish DHODH as a key player in CRPC and as a potential therapeutic target for advanced prostate cancer.

Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server

Background

Prostate cancer (PCa) is one of the leading causes of cancer death in men. About 30% of PCa will develop a biochemical recurrence (BCR) following initial treatment, which significantly contributes to prostate cancer-related deaths. In clinical practice, accurate prediction of PCa recurrence is crucial for making informed treatment decisions. However, the development of reliable models and biomarkers for predicting PCa recurrence remains a challenge. In this study, the aim is to establish an effective and reliable tool for predicting the recurrence of PCa.

Methods

We systematically screened and analyzed potential datasets to predict PCa recurrence. Through quality control analysis, low-quality datasets were removed. Using meta-analysis, differential expression analysis, and feature selection, we identified key genes associated with recurrence. We also evaluated 22 previously published signatures for PCa recurrence prediction. To assess prediction performance, we employed nine machine learning algorithms. We compared the predictive capabilities of models constructed using clinical variables, expression data, and their combinations. Subsequently, we implemented these machine learning models into a user-friendly web server freely accessible to all researchers.

Results

Based on transcriptomic data derived from eight multicenter studies consisting of 733 PCa patients, we screened 23 highly influential genes for predicting prostate cancer recurrence. These genes were used to construct the Prostate Cancer Recurrence Prediction Signature (PCRPS). By comparing with 22 published signatures and four important clinicopathological features, the PCRPS exhibited a robust and significantly improved predictive capability. Among the tested algorithms, Random Forest demonstrated the highest AUC value of 0.72 in predicting PCa recurrence in the testing dataset. To facilitate access and usage of these machine learning models by all researchers and clinicians, we also developed an online web server (https://urology1926.shinyapps.io/PCRPS/) where the PCRPS model can be freely utilized. The tool can also be used to (1) predict the PCa recurrence by clinical information or expression data with high accuracy. (2) provide the possibility of PCa recurrence by nine machine learning algorithms. Furthermore, using the PCRPS scores, we predicted the sensitivity of 22 drugs from GDSC2 and 95 drugs from CTRP2 to the samples. These predictions provide valuable insights into potential drug sensitivities related to the PCRPS score groups.

Conclusion

Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment for PCa.

Combined SNPs sequencing and allele specific proteomics capture reveal functional causality underpinning the 2p25 prostate cancer susceptibility locus

Genome wide association studies (GWASs) have identified numerous risk loci associated with prostate cancer, yet unraveling their functional significance remains elusive. Leveraging our high-throughput SNPs-seq method, we pinpointed rs4519489 within the multi-ancestry GWAS-discovered 2p25 locus as a potential functional SNP due to its significant allelic differences in protein binding. Here, we conduct a comprehensive analysis of rs4519489 and its associated gene, NOL10, employing diverse cohort data and experimental models. Clinical findings reveal a synergistic effect between rs4519489 genotype and NOL10 expression on prostate cancer prognosis and severity. Through unbiased proteomics screening, we reveal that the risk allele A of rs4519489 exhibits enhanced binding to USF1, a novel oncogenic transcription factor (TF) implicated in prostate cancer progression and prognosis, resulting in elevated NOL10 expression. Furthermore, we elucidate that NOL10 regulates cell cycle pathways, fostering prostate cancer progression. The concurrent expression of NOL10 and USF1 correlates with aggressive prostate cancer characteristics and poorer prognosis. Collectively, our study offers a robust strategy for functional SNP screening and TF identification through high-throughput SNPs-seq and unbiased proteomics, highlighting the rs4519489-USF1-NOL10 regulatory axis as a promising biomarker or therapeutic target for clinical diagnosis and treatment of prostate cancer.

Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer

Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.

Loss of feedback regulation between FAM3B and androgen receptor driving prostate cancer progression

BACKGROUND

Although the fusion of the transmembrane serine protease 2 gene (TMPRSS2) with the erythroblast transformation-specific-related gene (ERG), or TMPRSS2-ERG, occurs frequently in prostate cancer, its impact on clinical outcomes remains controversial. Roughly half of TMPRSS2-ERG fusions occur through intrachromosomal deletion of interstitial genes and the remainder via insertional chromosomal rearrangements. Because prostate cancers with deletion-derived TMPRSS2-ERG fusions are more aggressive than those with insertional fusions, we investigated the impact of interstitial gene loss on prostate cancer progression.

METHODS

We conducted an unbiased analysis of transcriptome data from large collections of prostate cancer samples and employed diverse in vitro and in vivo models combined with genetic approaches to characterize the interstitial gene loss that imposes the most important impact on clinical outcome.

RESULTS

This analysis identified FAM3B as the top-ranked interstitial gene whose loss is associated with a poor prognosis. The association between FAM3B loss and poor clinical outcome extended to fusion-negative prostate cancers where FAM3B downregulation occurred through epigenetic imprinting. Importantly, FAM3B loss drives disease progression in prostate cancer. FAM3B acts as an intermediator of a self-governing androgen receptor feedback loop. Specifically, androgen receptor upregulates FAM3B expression by binding to an intronic enhancer to induce an enhancer RNA and facilitate enhancer-promoter looping. FAM3B, in turn, attenuates androgen receptor signaling.

CONCLUSION

Loss of FAM3B in prostate cancer, whether through the TMPRSS2-ERG translocation or epigenetic imprinting, causes an exit from this autoregulatory loop to unleash androgen receptor activity and prostate cancer progression. These findings establish FAM3B loss as a new driver of prostate cancer progression and support the utility of FAM3B loss as a biomarker to better define aggressive prostate cancer.

Lysine methyltransferase SMYD2 enhances androgen receptor signaling to modulate CRPC cell resistance to enzalutamide

Androgen receptors (ARs) play key roles in prostate cancer (PCa) progression and castration-resistant prostate cancer (CRPC) resistance to drug therapy. SET and MYND domain containing protein 2 (SMYD2), a lysine methyltransferase, has been reported to promote tumors by transcriptionally methylating important oncogenes or tumor repressor genes. However, the role of SMYD2 in CRPC drug resistance remains unclear. In this study, we found that SMYD2 expression was significantly upregulated in PCa tissues and cell lines. High SMYD2 expression indicated poor CRPC-free survival and overall survival in patients. SMYD2 knockdown dramatically inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) potential of 22Rv1 and C4-2 cells. Conversely, ectopic overexpression of SMYD2 promoted these effects in 22Rv1 and C4-2 cells. Mechanistically, SMYD2 methylated and phosphorylated ARs to affect AR ubiquitination and proteasome degradation, which further alters the AR transcriptome in CRPC cells. Importantly, the SMYD2 inhibitor AZ505 had a synergistic therapeutic effect with enzalutamide in CRPC cells and mouse models; however, it could also re-sensitize resistant CRPC cells to enzalutamide. Our findings demonstrated that SMYD2 enhances the methylation and phosphorylation of ARs and affects AR ubiquitination and proteasome degradation to modulate CRPC cell resistance to enzalutamide, indicating that SMYD2 serves as a crucial oncogene in PCa and is an ideal therapeutic target for CRPC.

Androgen deprivation triggers a cytokine signaling switch to induce immune suppression and prostate cancer recurrence

Androgen deprivation therapy (ADT) is an effective but not curative treatment for advanced and recurrent prostate cancer (PC). We investigated the mechanisms controlling the response to androgen-deprivation by surgical castration in genetically-engineered mouse models (GEMM) of PC, using high frequency ultrasound imaging to rigorously measure tumor volume. Castration initially causes almost all tumors to shrink in volume, but many tumors subsequently recur within 5-10 weeks. Blockade of tumor necrosis factor (TNF) signaling a few days in advance of castration surgery, using a TNFR2 ligand trap, prevents regression in a PTEN-deficient GEMM. Following tumor regression, a basal stem cell-like population within the tumor increases along with TNF protein levels. Tumor cell lines in culture recapitulate these in vivo observations, suggesting that basal stem cells are the source of TNF. When TNF signaling blockade is administered immediately prior to castration, tumors regress but recurrence is prevented, implying that a late wave of TNF secretion within the tumor, which coincides with the expression of NFkB regulated genes, drives recurrence. The inhibition of signaling downstream of one NFkB-regulated protein, chemokine C-C motif ligand 2 (CCL2), prevents post-castration tumor recurrence, phenocopying post-castration (late) TNF signaling blockade. CCL2 was originally identified as a macrophage chemoattractant and indeed at late times after castration gene sets related to chemotaxis and migration are up-regulated. Importantly, enhanced CCL2 signaling during the tumor recurrence phase coincides with an increase in pro-tumorigenic macrophages and a decrease in CD8 T cells, suggesting that recurrence is driven at least in part by tumor immunosuppression. In summary, we demonstrate that a therapy-induced switch in TNF signaling, a consequence of the increased stem cell-like character of the residual tumor cells surviving ADT, induces an immunosuppressive tumor microenvironment and concomitant tumor recurrence.

Acquired copy number variation in prostate tumours: a review of common somatic copy number alterations, how they are formed and their clinical utility

Prostate cancer is one of the most commonly diagnosed cancers in men and unfortunately, disease will progress in up to a third of patients despite primary treatment. Currently, there is a significant lack of prognostic tests that accurately predict disease course; however, the acquisition of somatic chromosomal variation in the form of DNA copy number variants may help understand disease progression. Notably, studies have found that a higher burden of somatic copy number alterations (SCNA) correlates with more aggressive disease, recurrence after surgery and metastasis. Here we will review the literature surrounding SCNA formation, including the roles of key tumour suppressors and oncogenes (PTEN, BRCA2, NKX3.1, ERG and AR), and their potential to inform diagnostic and prognostic clinical testing to improve predictive value. Ultimately, SCNAs, or inherited germline alterations that predispose to SCNAs, could have significant clinical utility in diagnostic and prognostic tests, in addition to guiding therapeutic selection.

A reliable transcriptomic risk-score applicable to formalin-fixed paraffin-embedded biopsies improves outcome prediction in localized prostate cancer

BACKGROUND

Clinical manifestation of prostate cancer (PCa) is highly variable. Aggressive tumors require radical treatment while clinically non-significant ones may be suitable for active surveillance. We previously developed the prognostic ProstaTrend RNA signature based on transcriptome-wide microarray and RNA-sequencing (RNA-Seq) analyses, primarily of prostatectomy specimens. An RNA-Seq study of formalin-fixed paraffin-embedded (FFPE) tumor biopsies has now allowed us to use this test as a basis for the development of a novel test that is applicable to FFPE biopsies as a tool for early routine PCa diagnostics.

METHODS

All patients of the FFPE biopsy cohort were treated by radical prostatectomy and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of 176 FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves and Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. In addition, we developed a prostate single-cell atlas of 41 PCa patients from 5 publicly available studies to analyze gene expression of ProstaTrend genes in different cell compartments.

RESULTS

Validation of the TRS using the original ProstaTrend signature in the cohort of FFPE biopsies revealed a relevant impact of FFPE-associated degradation on gene expression and consequently no significant association with prognosis (Cox-regression, p-value > 0.05) in FFPE tissue. However, the TRS based on the new version of the ProstaTrend-ffpe signature, which included 204 genes (of originally 1396 genes), was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value < 0.001) and retained prognostic relevance when adjusted for Gleason Grade Groups. We confirmed a significant association with BCR in 9 independent cohorts including 1109 patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that ProstaTrend-ffpe was among the best-ranked panels. We generated a PCa cell atlas to associate ProstaTrend genes with cell lineages or cell types. Tumor-specific luminal cells have a significantly higher TRS than normal luminal cells in all analyzed datasets. In addition, TRS of epithelial and luminal cells was correlated with increased Gleason score in 3 studies.

CONCLUSIONS

We developed a prognostic gene-expression signature for PCa that can be applied to FFPE biopsies and may be suitable to support clinical decision-making.

Identification of Molecular Markers Associated with Prostate Cancer Subtypes: An Integrative Bioinformatics Approach

Prostate cancer (PCa) is characterised by androgen dependency. Unfortunately, under anti-androgen treatment pressure, castration-resistant prostate cancer (CRPC) emerges, characterised by heterogeneous cell populations that, over time, lead to the development of different androgen-dependent or -independent phenotypes. Despite important advances in therapeutic strategies, CRPC remains incurable. Context-specific essential genes represent valuable candidates for targeted anti-cancer therapies. Through the investigation of gene and protein annotations and the integration of published transcriptomic data, we identified two consensus lists to stratify PCa patients' risk and discriminate CRPC phenotypes based on androgen receptor activity. ROC and Kaplan-Meier survival analyses were used for gene set validation in independent datasets. We further evaluated these genes for their association with cancer dependency. The deregulated expression of the PCa-related genes was associated with overall and disease-specific survival, metastasis and/or high recurrence risk, while the CRPC-related genes clearly discriminated between adeno and neuroendocrine phenotypes. Some of the genes showed context-specific essentiality. We further identified candidate drugs through a computational repositioning approach for targeting these genes and treating lethal variants of PCa. This work provides a proof-of-concept for the use of an integrative approach to identify candidate biomarkers involved in PCa progression and CRPC pathogenesis within the goal of precision medicine.

An endoplasmic reticulum stress-related signature featuring ASNS for predicting prognosis and immune landscape in prostate cancer

Prostate cancer (PRAD) is one of the common malignant tumors of the urinary system. In order to predict the treatment results for PRAD patients, this study proposes to develop a risk profile based on endoplasmic reticulum stress (ERS). Based on the Memorial Sloan-Kettering Cancer Center (MSKCC) cohort and the Gene Expression Omnibus database (GSE70769), we verified the predictive signature. Using a random survival forest analysis, prognostically significant ERS-related genes were found. An ERS-related risk score (ERscore) was created using multivariable Cox analysis. In addition, the biological functions, genetic mutations and immune landscape related to ERscore are also studied to reveal the underlying mechanisms related to ERS in PRAD. We further explored the ERscore-related mechanisms by profiling a single-cell RNA sequencing (scRNA-seq) dataset (GSE137829) and explored the oncogenic role of ASNS in PRAD through in vitro experiments. The risk signature composed of eight ERS-related genes constructed in this study is an independent prognostic factor and validated in the MSKCC and GSE70769 data sets. The scRNA-seq data additionally revealed that several carcinogenic pathways were noticeably overactivated in the group with high ERS scores. As one of the prognostic genes, ASNS will significantly inhibit the proliferation, migration and invasion abilities of PRAD cells after its expression is interfered with. In conclusion, this study developed a novel risk-specific ERS-based clinical treatment strategy for patients with PRAD.

Distinct mesenchymal cell states mediate prostate cancer progression

In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin's role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

NCAPG2 promotes prostate cancer malignancy and stemness via STAT3/c-MYC signaling

BACKGROUND

Prostate cancer (PCa) is the second leading cause of cancer-related mortality among men worldwide, and its incidence has risen substantially in recent years. Therefore, there is an urgent need to identify novel biomarkers and precise therapeutic targets for managing PCa progression and recurrence.

METHODS

We investigated the clinical significance of NCAPG2 in PCa by exploring public datasets and our tissue microarray. Receiver operating characteristic (ROC) curve and survival analyses were performed to evaluate the correlation between NCAPG2 and PCa progression. Cell proliferation, wound healing, transwell, flow cytometry, cell cycle, tumor sphere formation, immunofluorescence (IF), co-immunoprecipitation (co-IP), and chromatin immunoprecipitation (ChIP) assays were conducted to further elucidate the molecular mechanism of NCAPG2 in PCa. Subcutaneous and orthotopic xenograft models were applied to investigate the effects of NCAPG2 on PCa proliferation in vivo. Tandem mass tag (TMT) quantitative proteomics was utilized to detect proteomic changes under NCAPG2 overexpression.

RESULTS

NCAPG2 was significantly upregulated in PCa, and its overexpression was associated with PCa progression and unfavorable prognosis. Knockdown of NCAPG2 inhibited the malignant behavior of PCa cells, whereas its overexpression promoted PCa aggressiveness. NCAPG2 depletion attenuated the development and growth of PCa in vivo. TMT quantitative proteomics analyses indicated that c-MYC activity was strongly correlated with NCAPG2 expression. The malignancy-promoting effect of NCAPG2 in PCa was mediated via c-MYC. NCAPG2 could directly bind to STAT3 and induce STAT3 occupancy on the MYC promoter, thus to transcriptionally activate c-MYC expression. Finally, we identified that NCAPG2 was positively correlated with cancer stem cell (CSC) markers and enhanced self-renewal capacity of PCa cells.

CONCLUSIONS

NCAPG2 is highly expressed in PCa, and its level is significantly associated with PCa prognosis. NCAPG2 promotes PCa malignancy and drives cancer stemness via the STAT3/c-MYC signaling axis, highlighting its potential as a therapeutic target for PCa.

Integrated analysis of single-cell and bulk transcriptomics develops a robust neuroendocrine cell-intrinsic signature to predict prostate cancer progression

Neuroendocrine prostate cancer (NEPC) typically implies severe lethality and limited treatment options. The precise identification of NEPC cells holds paramount significance for both research and clinical applications, yet valid NEPC biomarker remains to be defined. Methods: Leveraging 11 published NE-related gene sets, 11 single-cell RNA-sequencing (scRNA-seq) cohorts, 15 bulk transcriptomic cohorts, and 13 experimental models of prostate cancer (PCa), we employed multiple advanced algorithms to construct and validate a robust NEPC risk prediction model. Results: Through the compilation of a comprehensive scRNA-seq reference atlas (comprising a total of 210,879 single cells, including 66 tumor samples) from 9 multicenter datasets of PCa, we observed inconsistent and inefficient performance among the 11 published NE gene sets. Therefore, we developed an integrative analysis pipeline, identifying 762 high-quality NE markers. Subsequently, we derived the NE cell-intrinsic gene signature, and developed an R package named NEPAL, to predict NEPC risk scores. By applying to multiple independent validation datasets, NEPAL consistently and accurately assigned NE feature and delineated PCa progression. Intriguingly, NEPAL demonstrated predictive capabilities for prognosis and therapy responsiveness, as well as the identification of potential epigenetic drivers of NEPC. Conclusion: The present study furnishes a valuable tool for the identification of NEPC and the monitoring of PCa progression through transcriptomic profiles obtained from both bulk and single-cell sources.

The SMYD3-MAP3K2 signaling axis promotes tumor aggressiveness and metastasis in prostate cancer

Aberrant activation of Ras/Raf/mitogen-activated protein kinase (MAPK) signaling is frequently linked to metastatic prostate cancer (PCa); therefore, the characterization of modulators of this pathway is critical for defining therapeutic vulnerabilities for metastatic PCa. The lysine methyltransferase SET and MYND domain 3 (SMYD3) methylates MAPK kinase kinase 2 (MAP3K2) in some cancers, causing enhanced activation of MAPK signaling. In PCa, SMYD3 is frequently overexpressed and associated with disease severity; however, its molecular function in promoting tumorigenesis has not been defined. We demonstrate that SMYD3 critically regulates tumor-associated phenotypes via its methyltransferase activity in PCa cells and mouse xenograft models. SMYD3-dependent methylation of MAP3K2 promotes epithelial-mesenchymal transition associated behaviors by altering the abundance of the intermediate filament vimentin. Furthermore, activation of the SMYD3-MAP3K2 signaling axis supports a positive feedback loop continually promoting high levels of SMYD3. Our data provide insight into signaling pathways involved in metastatic PCa and enhance understanding of mechanistic functions for SMYD3 to reveal potential therapeutic opportunities for PCa.

Integrative multi-omics analysis unveils stemness-associated molecular subtypes in prostate cancer and pan-cancer: prognostic and therapeutic significance

BACKGROUND

Prostate cancer (PCA) is the fifth leading cause of cancer-related deaths worldwide, with limited treatment options in the advanced stages. The immunosuppressive tumor microenvironment (TME) of PCA results in lower sensitivity to immunotherapy. Although molecular subtyping is expected to offer important clues for precision treatment of PCA, there is currently a shortage of dependable and effective molecular typing methods available for clinical practice. Therefore, we aim to propose a novel stemness-based classification approach to guide personalized clinical treatments, including immunotherapy.

METHODS

An integrative multi-omics analysis of PCA was performed to evaluate stemness-level heterogeneities. Unsupervised hierarchical clustering was used to classify PCAs based on stemness signature genes. To make stemness-based patient classification more clinically applicable, a stemness subtype predictor was jointly developed by using four PCA datasets and 76 machine learning algorithms.

RESULTS

We identified stemness signatures of PCA comprising 18 signaling pathways, by which we classified PCA samples into three stemness subtypes via unsupervised hierarchical clustering: low stemness (LS), medium stemness (MS), and high stemness (HS) subtypes. HS patients are sensitive to androgen deprivation therapy, taxanes, and immunotherapy and have the highest stemness, malignancy, tumor mutation load (TMB) levels, worst prognosis, and immunosuppression. LS patients are sensitive to platinum-based chemotherapy but resistant to immunotherapy and have the lowest stemness, malignancy, and TMB levels, best prognosis, and the highest immune infiltration. MS patients represent an intermediate status of stemness, malignancy, and TMB levels with a moderate prognosis. We further demonstrated that these three stemness subtypes are conserved across pan-tumor. Additionally, the 9-gene stemness subtype predictor we developed has a comparable capability to 18 signaling pathways to make tumor diagnosis and to predict tumor recurrence, metastasis, progression, prognosis, and efficacy of different treatments.

CONCLUSIONS

The three stemness subtypes we identified have the potential to be a powerful tool for clinical tumor molecular classification in PCA and pan-cancer, and to guide the selection of immunotherapy or other sensitive treatments for tumor patients.

Prostate cancer radiogenomics reveals proliferative gene expression programs associated with distinct MRI-based hypoxia levels

BACKGROUND AND PURPOSE

The biology behind individual hypoxia levels in patient tumors is poorly understood. Here, we used radiogenomics to identify associations between magnetic resonance imaging (MRI)-based hypoxia levels and biological processes derived from gene expression data in prostate cancer.

MATERIALS AND METHODS

For 85 prostate cancer patients, MRI-based hypoxia images were constructed by combining diffusion-weighted images reflecting oxygen consumption and supply. The ability to differentiate hypoxia levels in these images was verified by comparison with matched biopsy sections stained for the hypoxia marker pimonidazole. For MRI-defined hypoxia levels, corresponding hypoxic fractions were calculated and correlated with biopsy gene expression profiles. Biological processes were predicted by gene set enrichment analysis (GSEA) and validated by immunohistochemistry (Ki67 proliferation marker, reactive stroma grade) and RT-PCR (MYC).

RESULTS

Genes with correlation between expression level and hypoxic fraction were identified for 56 MRI-based hypoxia levels. At all levels, GSEA identified proliferation as the predominant biological process enriched among the correlating genes. Two independent proliferative gene signatures were developed. The Peak1 signature, upregulated at moderate/severe hypoxia, reflected MYC upregulation and high Ki67-proliferation index of cancer cells in pimonidazole-positive regions. The Peak2 signature, upregulated at mild to non-hypoxic levels, was associated with fibroblast gene signature and reactive stroma grade. High scores of both Peak1 and Peak2 indicated elevated risk of biochemical recurrence in multiple cohorts.

CONCLUSION

Radiogenomics identified two gene expression programs activated at different hypoxia levels, reflecting proliferation of cancer cells and stroma cells. Genes involved in these programs could be candidate targets for intervention.

Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing

Background

Prostate cancer (PCa) ranks as the second most prevalent malignancy among males on a global scale. Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear.

Methods

Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data.

Results

We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection.

Conclusion

IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.

Identification and validation of cancer-associated fibroblast-related subtypes and the prognosis model of biochemical recurrence in prostate cancer based on single-cell and bulk RNA sequencing

BACKGROUND

Cancer-associated fibroblasts (CAFs) are an essential component of the tumor immune microenvironment that are involved in extracellular matrix (ECM) remodeling. We aim to investigate the characteristics of CAFs in prostate cancer and develop a biochemical recurrence (BCR)-related CAF signature for predicting the prognosis of PCa patients.

METHODS

The bulk RNA-seq and relevant clinical information were obtained from the TCGA and GEO databases, respectively. The infiltration scores of CAFs in prostate cancer patients were calculated using the MCP counter and EPIC algorithms. The single-cell RNA sequencing (scRNA-seq) was downloaded from the GEO database. Subsequently, univariate Cox regression analysis was employed to identify prognostic genes associated with CAFs. We identified two subtypes (C1 and C2) of prostate cancer that were associated with CAFs via non-negative matrix factorization (NMF) clustering. In addition, the BCR-related CAF signatures were constructed using Lasso regression analysis. Finally, a nomogram model was established based on the risk score and clinical characteristics of the patients.

RESULTS

Initially, we found that patients with high CAF infiltration scores had shorter biochemical recurrence-free survival (BCRFS) times. Subsequently, CAFs in four pairs of tumors and paracancerous tissues were identified. We discovered 253 significantly differentially expressed genes, of which 13 had prognostic significance. Using NMF clustering, we divided PCa patients into C1 and C2 subgroups, with the C1 subgroup having a worse prognosis and substantially enriched cell cycle, homologous recombination, and mismatch repair pathways. Furthermore, a BCR-related CAFs signature was established. Multivariate COX regression analysis confirmed that the BCR-related CAFs signature was an independent prognostic factor for BCR in PCa. In addition, the nomogram was based on the clinical characteristics and risk scores of the patient and demonstrated high accuracy and reliability for predicting BCR. Lastly, our findings indicate that the risk score may be a useful tool for predicting PCa patients' sensitivity to immunotherapy and drug treatment.

CONCLUSION

NMF clustering based on CAF-related genes revealed distinct TME immune characteristics between groups. The BCR-related CAF signature accurately predicted prognosis and immunotherapy response in prostate cancer patients, offering a promising new approach to cancer treatment.

Identification of senescence-related lncRNA prognostic index correlating with prognosis and radiosensitivity in prostate cancer patients

BACKGROUND

An increasing number of studies are shown how crucial a role cellular senescence plays in tumor development. In this study, we developed a senescence-related lncRNA prognostic index (SRLPI) to forecast radiosensitivity and the probability of biochemical recurrence (BCR) in patients with prostate cancer (PCa).

METHODS

PCa cohorts in TCGA and GEO databases were downloaded. Senescence-and prognosis-related lncRNA with differential expression in tumor and normal samples were identified and used to establish the SRLPI score. Mutation landscape, function pathway, tumor stemness and heterogeneity and tumor immune microenvironment were also analyzed. We performed the analysis using R 3.6.3 and the appropriate tools.

RESULTS

A SRLPI score was constructed based on SNHG1 and MIAT in the TCGA cohort. Our classification of PCa patients into high- and low-risk groups was based on the median SRLPI score. When compared to the low-SRLPI group, the high-SRLPI group was more vulnerable to BCR (HR: 3.33). In terms of BCR-free survival and metastasis-free survival, the GSE116918 showed similar findings. Surprisingly, the SRLPI score demonstrated a high level of radiosensitivity for diagnosis (AUC: 0.98). Age, Gleason score, T stage, N stage, positive lymph nodes, and residual tumor were all significantly greater in patients with high SRLPI scores. Furthermore, this score was significantly related to markers of senescence. Protein secretion and androgen response were found to be substantially enriched in the low-SRLPI group, whereas E2F targets were found to be strongly enriched in the high-SRLPI group for pathway analysis. For the tumor microenvironment assessment, B cells, CD8+ T cells, immune score and TIDE score were positively related to SRLPI score while endothelial level was negatively associated with SRLPI score with statistical significance.

CONCLUSIONS

We developed a SRLPI score that was related to prognosis and radiosensitivity and might be helpful in clinical practice.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted "ComBat" to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package "Seurat" in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan-Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Inhibition of the serine/threonine kinase BUB1 reverses taxane resistance in prostate cancer

Men with incurable castration resistant prostate cancer (CRPC) are typically treated with taxanes; however, drug resistance rapidly develops. We previously identified a clinically relevant seven gene network in aggressive CRPC, which includes the spindle assembly checkpoint (SAC) kinase BUB1. Since SAC is deregulated in taxane resistant PC, we evaluated BUB1 and found that it was over-expressed in advanced PC patient datasets and taxane resistant PC cells. Treatment with a specific BUB1 kinase inhibitor re-sensitized resistant CRPC cells, including cells expressing constitutively active androgen receptor (AR) variants, to clinically used taxanes. Consistent with a role of AR variants in taxane resistance, ectopically expressed AR-V7 increased BUB1 levels and reduced sensitivity to taxanes. This work shows that disruption of BUB1 kinase activity reverted resistance to taxanes, which is essential to advancing BUB1 as a potential therapeutic target for intractable chemotherapy resistant CRPC including AR variant driven CRPC, which lacks durable treatment options.

The SGLT2 inhibitor canagliflozin suppresses growth and enhances prostate cancer response to radiotherapy

Radiotherapy is a non-invasive standard treatment for prostate cancer (PC). However, PC develops radio-resistance, highlighting a need for agents to improve radiotherapy response. Canagliflozin, an inhibitor of sodium-glucose co-transporter-2, is approved for use in diabetes and heart failure, but is also shown to inhibit PC growth. However, whether canagliflozin can improve radiotherapy response in PC remains unknown. Here, we show that well-tolerated doses of canagliflozin suppress proliferation and survival of androgen-sensitive and insensitive human PC cells and tumors and sensitize them to radiotherapy. Canagliflozin blocks mitochondrial respiration, promotes AMPK activity, inhibits the MAPK and mTOR-p70S6k/4EBP1 pathways, activates cell cycle checkpoints, and inhibits proliferation in part through HIF-1α suppression. Canagliflozin mediates transcriptional reprogramming of several metabolic and survival pathways known to be regulated by ETS and E2F family transcription factors. Genes downregulated by canagliflozin are associated with poor PC prognosis. This study lays the groundwork for clinical investigation of canagliflozin in PC prevention and treatment in combination with radiotherapy.

Combined CRISPRi and proteomics screening reveal a cohesin-CTCF-bound allele contributing to increased expression of RUVBL1 and prostate cancer progression

Genome-wide association studies along with expression quantitative trait locus (eQTL) mapping have identified hundreds of single-nucleotide polymorphisms (SNPs) and their target genes in prostate cancer (PCa), yet functional characterization of these risk loci remains challenging. To screen for potential regulatory SNPs, we designed a CRISPRi library containing 9,133 guide RNAs (gRNAs) to cover 2,166 candidate SNP loci implicated in PCa and identified 117 SNPs that could regulate 90 genes for PCa cell growth advantage. Among these, rs60464856 was covered by multiple gRNAs significantly depleted in screening (FDR < 0.05). Pooled SNP association analysis in the PRACTICAL and FinnGen cohorts showed significantly higher PCa risk for the rs60464856 G allele (p value = 1.2 × 10-16 and 3.2 × 10-7, respectively). Subsequent eQTL analysis revealed that the G allele is associated with increased RUVBL1 expression in multiple datasets. Further CRISPRi and xCas9 base editing confirmed that the rs60464856 G allele leads to elevated RUVBL1 expression. Furthermore, SILAC-based proteomic analysis demonstrated allelic binding of cohesin subunits at the rs60464856 region, where the HiC dataset showed consistent chromatin interactions in prostate cell lines. RUVBL1 depletion inhibited PCa cell proliferation and tumor growth in a xenograft mouse model. Gene-set enrichment analysis suggested an association of RUVBL1 expression with cell-cycle-related pathways. Increased expression of RUVBL1 and activation of cell-cycle pathways were correlated with poor PCa survival in TCGA datasets. Our CRISPRi screening prioritized about one hundred regulatory SNPs essential for prostate cell proliferation. In combination with proteomics and functional studies, we characterized the mechanistic role of rs60464856 and RUVBL1 in PCa progression.

METTL1 promotes tumorigenesis through tRNA-derived fragment biogenesis in prostate cancer

Newly growing evidence highlights the essential role that epitranscriptomic marks play in the development of many cancers; however, little is known about the role and implications of altered epitranscriptome deposition in prostate cancer. Here, we show that the transfer RNA N7-methylguanosine (m7G) transferase METTL1 is highly expressed in primary and advanced prostate tumours. Mechanistically, we find that METTL1 depletion causes the loss of m7G tRNA methylation and promotes the biogenesis of a novel class of small non-coding RNAs derived from 5'tRNA fragments. 5'tRNA-derived small RNAs steer translation control to favour the synthesis of key regulators of tumour growth suppression, interferon pathway, and immune effectors. Knockdown of Mettl1 in prostate cancer preclinical models increases intratumoural infiltration of pro-inflammatory immune cells and enhances responses to immunotherapy. Collectively, our findings reveal a therapeutically actionable role of METTL1-directed m7G tRNA methylation in cancer cell translation control and tumour biology.

Neurokinin-1 receptor drives PKCɑ-AURKA/N-Myc signaling to facilitate the neuroendocrine progression of prostate cancer

The widespread application of antiandrogen therapies has aroused a significant increase in the incidence of NEPC, a lethal form of the disease lacking efficient clinical treatments. Here we identified a cell surface receptor neurokinin-1 (NK1R) as a clinically relevant driver of treatment-related NEPC (tNEPC). NK1R expression increased in prostate cancer patients, particularly higher in metastatic prostate cancer and treatment-related NEPC, implying a relation with the progression from primary luminal adenocarcinoma toward NEPC. High NK1R level was clinically correlated with accelerated tumor recurrence and poor survival. Mechanical studies identified a regulatory element in the NK1R gene transcription ending region that was recognized by AR. AR inhibition enhanced the expression of NK1R, which mediated the PKCα-AURKA/N-Myc pathway in prostate cancer cells. Functional assays demonstrated that activation of NK1R promoted the NE transdifferentiation, cell proliferation, invasion, and enzalutamide resistance in prostate cancer cells. Targeting NK1R abrogated the NE transdifferentiation process and tumorigenicity in vitro and in vivo. These findings collectively characterized the role of NK1R in tNEPC progression and suggested NK1R as a potential therapeutic target.

Aspartoacylase suppresses prostate cancer progression by blocking LYN activation

BACKGROUND

Globally, despite prostate cancer (PCa) representing second most prevalent malignancy in male, the precise molecular mechanisms implicated in its pathogenesis remain unclear. Consequently, elucidating the key molecular regulators that govern disease progression could substantially contribute to the establishment of novel therapeutic strategies, ultimately advancing the management of PCa.

METHODS

A total of 49 PCa tissues and 43 adjacent normal tissues were collected from January 2017 to December 2021 at Zhongnan Hospital of Wuhan University. The advanced transcriptomic methodologies were employed to identify differentially expressed mRNAs in PCa. The expression of aspartoacylase (ASPA) in PCa was thoroughly evaluated using quantitative real-time PCR and Western blotting techniques. To elucidate the inhibitory role of ASPA in PCa cell proliferation and metastasis, a comprehensive set of in vitro and in vivo assays were conducted, including orthotopic and tumor-bearing mouse models (n = 8 for each group). A combination of experimental approaches, such as Western blotting, luciferase assays, immunoprecipitation assays, mass spectrometry, glutathione S-transferase pull-down experiments, and rescue studies, were employed to investigate the underlying molecular mechanisms of ASPA's action in PCa. The Student's t-test was employed to assess the statistical significance between two distinct groups, while one-way analysis of variance was utilized for comparisons involving more than two groups. A two-sided P value of less than 0.05 was deemed to indicate statistical significance.

RESULTS

ASPA was identified as a novel inhibitor of PCa progression. The expression of ASPA was found to be significantly down-regulated in PCa tissue samples, and its decreased expression was independently associated with patients' prognosis (HR = 0.60, 95% CI 0.40-0.92, P = 0.018). Our experiments demonstrated that modulation of ASPA activity, either through gain- or loss-of-function, led to the suppression or enhancement of PCa cell proliferation, migration, and invasion, respectively. The inhibitory role of ASPA in PCa was further confirmed using orthotopic and tumor-bearing mouse models. Mechanistically, ASPA was shown to directly interact with the LYN and inhibit the phosphorylation of LYN as well as its downstream targets, JNK1/2 and C-Jun, in both PCa cells and mouse models, in an enzyme-independent manner. Importantly, the inhibition of LYN activation by bafetinib abrogated the promoting effect of ASPA knockdown on PCa progression in both in vitro and in vivo models. Moreover, we observed an inverse relationship between ASPA expression and LYN activity in clinical PCa samples, suggesting a potential regulatory role of ASPA in modulating LYN signaling.

CONCLUSION

Our findings provide novel insights into the tumor-suppressive function of ASPA in PCa and highlight its potential as a prognostic biomarker and therapeutic target for the management of this malignancy.

Precision medicine for prostate cancer-improved outcome prediction for low-intermediate risk disease using a six-gene copy number alteration classifier

A multiplex 6-gene copy number classifier was used to distinguish between low- or intermediate-risk prostate cancer patients. The study analysed a cohort of 448 patients and previously published datasets from radical prostatectomies. The classifier performs better than conventional stratification methods, is low cost, and can be performed easily in clinical laboratories.

A DNA copy number alteration classifier as a prognostic tool for prostate cancer patients

BACKGROUND

Distinguishing between true indolent and potentially life-threatening prostate cancer is challenging in tumours displaying clinicopathologic features associated with low or intermediate risk of relapse. Several somatic DNA copy number alterations (CNAs) have been identified as potential prognostic biomarkers, but the standard cytogenetic method to assess them has a limited multiplexing capability.

METHODS

Multiplex ligation-dependent probe amplification (MLPA) targeting 14 genes was optimised to survey 448 tumours of patients with low or intermediate risk (Grade Group 1-3, Gleason score ≤7) who underwent radical prostatectomy. A 6-gene CNA classifier was developed using random survival forest and Cox proportional hazard modelling to predict biochemical recurrence.

RESULTS

The classifier score was significantly associated with biochemical recurrence after adjusting for standard clinicopathologic variables and the known prognostic index CAPRA-S score with a hazard ratio of 2.17 and 1.80, respectively (n = 406, P < 0.01). The prognostic value of this classifier was externally validated in published CNA data from three radical prostatectomy cohorts and one radiation therapy pre-treatment biopsy cohort.

CONCLUSION

The 6-gene CNA classifier generated by a single MLPA assay compatible with the small quantities of DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue specimens has the potential to improve the clinical management of patients with low or intermediate risk disease.

Identification of novel molecular subtypes and a signature to predict prognosis and therapeutic response based on cuproptosis-related genes in prostate cancer

Background

Prostate cancer (PCa) is the most common malignant tumor of the male urinary system. Cuproptosis, as a novel regulated cell death, remains unclear in PCa. This study aimed to investigate the role of cuproptosis-related genes (CRGs) in molecular stratification, prognostic prediction, and clinical decision-making in PCa.

Methods

Cuproptosis-related molecular subtypes were identified by consensus clustering analysis. A prognostic signature was constructed with LASSO cox regression analyses with 10-fold cross-validation. It was further validated in the internal validation cohort and eight external validation cohorts. The tumor microenvironment between the two risk groups was compared using the ssGSEA and ESTIMATE algorithms. Finally, qRT-PCR was used to explore the expression and regulation of these model genes at the cellular level. Furthermore, 4D Label-Free LC-MS/MS and RNAseq were used to investigate the changes in CRGs at protein and RNA levels after the knockdown of the key model gene B4GALNT4.

Results

Two cuproptosis-related molecular subtypes with significant differences in prognoses, clinical features, and the immune microenvironment were identified. Immunosuppressive microenvironments were associated with poor prognosis. A prognostic signature comprised of five genes (B4GALNT4, FAM83D, COL1A, CHRM3, and MYBPC1) was constructed. The performance and generalizability of the signature were validated in eight completely independent datasets from multiple centers. Patients in the high-risk group had a poorer prognosis, more immune cell infiltration, more active immune-related functions, higher expression of human leukocyte antigen and immune checkpoint molecules, and higher immune scores. In addition, anti-PDL-1 immunotherapy prediction, somatic mutation, chemotherapy response prediction, and potential drug prediction were also analyzed based on the risk signature. The validation of five model genes' expression and regulation in qPCR was consistent with the results of bioinformatics analysis. Transcriptomics and proteomics analyses revealed that the key model gene B4GALNT4 might regulate CRGs through protein modification after transcription.

Conclusion

The cuproptosis-related molecular subtypes and the prognostic signature identified in this study could be used to predict the prognosis and contribute to the clinical decision-making of PCa. Furthermore, we identified a potential cuproptosis-related oncogene B4GALNT4 in PCa, which could be used as a target to treat PCa in combination with cuproptosis.

Prediction of recurrence from metabolites and expression of TOP2A and EZH2 in prostate cancer patients treated with radiotherapy

BACKGROUND

The dual upregulation of TOP2A and EZH2 gene expression has been proposed as a biomarker for recurrence in prostate cancer patients to be treated with radical prostatectomy. A low tissue level of the metabolite citrate has additionally been connected to aggressive disease and recurrence in this patient group. However, for radiotherapy prostate cancer patients, few prognostic biomarkers have been suggested. The main aim of this study was to use an integrated tissue analysis to evaluate metabolites and expression of TOP2A and EZH2 as predictors for recurrence among radiotherapy patients.

METHODS

From 90 prostate cancer patients (56 received neoadjuvant hormonal treatment), 172 transrectal ultrasound-guided (TRUS) biopsies were collected prior to radiotherapy. Metabolic profiles were acquired from fresh frozen TRUS biopsies using high resolution-magic angle spinning MRS. Histopathology and immunohistochemistry staining for TOP2A and EZH2 were performed on TRUS biopsies containing cancer cells (n = 65) from 46 patients, where 24 of these patients (n = 31 samples) received hormonal treatment. Eleven radical prostatectomy cohorts of a total of 2059 patients were used for validation in a meta-analysis.

RESULTS

Among radiotherapy patients with up to 11 years of follow-up, a low level of citrate was found to predict recurrence, p = 0.001 (C-index = 0.74). Citrate had a higher predictive ability compared with individual clinical variables, highlighting its strength as a potential biomarker for recurrence. The dual upregulation of TOP2A and EZH2 was suggested as a biomarker for recurrence, particularly for patients not receiving neoadjuvant hormonal treatment, p = 0.001 (C-index = 0.84). While citrate was a statistically significant biomarker independent of hormonal treatment status, the current study indicated a potential of glutamine, glutamate and choline as biomarkers for recurrence among patients receiving neoadjuvant hormonal treatment, and glucose among patients not receiving neoadjuvant hormonal treatment.

CONCLUSION

Using an integrated approach, our study shows the potential of citrate and the dual upregulation of TOP2A and EZH2 as biomarkers for recurrence among radiotherapy patients.

ATP8B1: A prognostic prostate cancer biomarker identified via genetic analysis

BACKGROUND

Controlling the asymmetric distribution of phospholipids across biological membranes plays a pivotal role in the life cycle of cells; one of the most important contributors that maintain this lipid asymmetry are phospholipid-transporting adenosine triphosphatases (ATPases). Although sufficient information regarding their association with cancer exists, there is limited evidence linking the genetic variants of phospholipid-transporting ATPase family genes to prostate cancer in humans.

METHODS

In this study, we investigated the association of 222 haplotype-tagging single-nucleotide polymorphisms (SNPs) in eight phospholipid-transporting ATPase genes with cancer-specific survival (CSS) and overall survival (OS) of 630 patients treated with androgen-deprivation therapy (ADT) for prostate cancer.

RESULTS

After multivariate Cox regression analysis and multiple testing correction, we found that ATP8B1 rs7239484 was remarkably associated with CSS and OS after ADT. A pooled analysis of multiple independent gene-expression datasets demonstrated that ATP8B1 was under-expressed in tumor tissues and that a higher ATP8B1 expression was associated with a better patient prognosis. Moreover, we established highly invasive sublines using two human prostate cancer cell lines to mimic cancer progression traits in vitro. The expression of ATP8B1 was consistently downregulated in both highly invasive sublines.

CONCLUSION

Our study indicates that rs7239484 is a prognostic factor for patients treated with ADT and that ATP8B1 can potentially attenuate prostate cancer progression.

Landscape of prostate-specific membrane antigen heterogeneity and regulation in AR-positive and AR-negative metastatic prostate cancer

Tumor expression of prostate-specific membrane antigen (PSMA) is lost in 15-20% of men with castration-resistant prostate cancer (CRPC), yet the underlying mechanisms remain poorly defined. In androgen receptor (AR)-positive CRPC, we observed lower PSMA expression in liver lesions versus other sites, suggesting a role of the microenvironment in modulating PSMA. PSMA suppression was associated with promoter histone 3 lysine 27 methylation and higher levels of neutral amino acid transporters, correlating with 18F-fluciclovine uptake on positron emission tomography imaging. While PSMA is regulated by AR, we identified a subset of AR-negative CRPC with high PSMA. HOXB13 and AR co-occupancy at the PSMA enhancer and knockout models point to HOXB13 as an upstream regulator of PSMA in AR-positive and AR-negative prostate cancer. These data demonstrate how PSMA expression is differentially regulated across metastatic lesions and in the context of the AR, which may inform selection for PSMA-targeted therapies and development of complementary biomarkers.

Prognostic Values of Gene Copy Number Alterations in Prostate Cancer

Whilst risk prediction for individual prostate cancer (PCa) cases is of a high priority, the current risk stratification indices for PCa management have severe limitations. This study aimed to identify gene copy number alterations (CNAs) with prognostic values and to determine if any combination of gene CNAs could have risk stratification potentials. Clinical and genomic data of 500 PCa cases from the Cancer Genome Atlas stable were retrieved from the Genomic Data Commons and cBioPortal databases. The CNA statuses of a total of 52 genetic markers, including 21 novel markers and 31 previously identified potential prognostic markers, were tested for prognostic significance. The CNA statuses of a total of 51/52 genetic markers were significantly associated with advanced disease at an odds ratio threshold of ≥1.5 or ≤0.667. Moreover, a Kaplan-Meier test identified 27/52 marker CNAs which correlated with disease progression. A Cox Regression analysis showed that the amplification of MIR602 and deletions of MIR602, ZNF267, MROH1, PARP8, and HCN1 correlated with a progression-free survival independent of the disease stage and Gleason prognostic group grade. Furthermore, a binary logistic regression analysis identified twenty-two panels of markers with risk stratification potentials. The best model of 7/52 genetic CNAs, which included the SPOP alteration, SPP1 alteration, CCND1 amplification, PTEN deletion, CDKN1B deletion, PARP8 deletion, and NKX3.1 deletion, stratified the PCa cases into a localised and advanced disease with an accuracy of 70.0%, sensitivity of 85.4%, specificity of 44.9%, positive predictive value of 71.67%, and negative predictive value of 65.35%. This study validated prognostic gene level CNAs identified in previous studies, as well as identified new genetic markers with CNAs that could potentially impact risk stratification in PCa.