Prostate Cancer Gene Research Articles

Plasma cell-free DNA chromatin immunoprecipitation profiling depicts phenotypic and clinical heterogeneity in advanced prostate cancer.

Cell phenotype underlies prostate cancer presentation and treatment resistance and can be regulated by epigenomic features. However, the osteotropic tendency of prostate cancer limits access to metastatic tissue, meaning most prior insights into prostate cancer chromatin biology are from preclinical models that do not fully represent disease complexity. Noninvasive chromatin immunoprecipitation of histones in plasma cell-free in humans may enable capture of disparate prostate cancer phenotypes. Here, we analyzed activating promoter- and enhancer-associated H3K4me2 from cfDNA in metastatic prostate cancer enriched for divergent patterns of metastasis and diverse clinical presentation. H3K4me2 density across prostate cancer genes, accessible chromatin, and lineage-defining transcription factor binding sites correlated strongly with circulating tumor DNA (ctDNA) fraction-demonstrating capture of prostate cancer-specific biology and informing the development of a statistical framework to adjust for ctDNA fraction. Chromatin hallmarks mirrored synchronously measured clinico-genomic features: bone versus liver-predominant disease, serum PSA, biopsy-confirmed histopathological subtype, and RB1 deletions convergently indicated phenotype segregation along an axis of differential androgen receptor activity and neuroendocrine identity. Detection of lineage switching after sequential progression on systemic therapy in select patients indicates potential utility for individualized resistance monitoring. Epigenomic footprints of metastasis-induced normal tissue destruction were evident in bulk cfDNA from two patients. Finally, a public epigenomic resource was generated using a distinct chromatin marker that has not been widely investigated in prostate cancer. These results provide insight into the adaptive molecular landscape of aggressive prostate cancer and endorse plasma cfDNA chromatin profiling as a biomarker source and biological discovery tool.

Prevalence of SPOP and IDH Gene Mutations in Prostate Cancer in a Jordanian Population.

Speckle-type POZ (SPOP) is described as an essential tumor suppressor factor in gastric cancer, colorectal cancer, and prostate cancer (PCa). SPOP gene mutations were reported in primary human PCa. Isocitrate dehydrogenase-1 (IDH1) oncogene mutations were detected in gliomas, acute myeloid leukemia, some benign and malignant cartilaginous tumors, and only 1% of PCa. This study aimed to investigate the prevalence of mutations of SPOP and IDH1 genes in PCa in the Jordanian population. One hundred formalin-fixed paraffin-embedded tissue samples were collected from patients diagnosed with prostate adenocarcinoma. The obtained specimens were subjected to genomic DNA extraction, PCR amplification, and direct sequencing of exons 4, 5, 6, and 7 of the SPOP gene and exon 6 of the IDH1 gene. SPOP gene mutations were found in 17% of PCa cases, while no mutation was detected in the screened exon 6 of the IDH1 gene. Clinicopathological data demonstrated a strong correlation between prostate-specific antigen (PSA) levels and both Gleason score (GS) and the International Society of Urological Pathology (ISUP) grade group (GG). There was no significant correlation between PSA levels and age (p = 0.816) nor there were significant associations for SPOP mutational status with age (p = 0.659), PSA levels (p = 0.395), GS (p = 0.259), and ISUP GG (p = 0.424) in the tested population. The study found a strong correlation between PSA levels and both GS and ISUP GG. It also identified a high frequency (17%) of SPOP gene mutations in Jordanian Arab PCa patients, mainly in exon 7. No IDH1 mutations were detected in exon 6.

Identification and validation of cigarette smoking-related genes in predicting prostate cancer development through bioinformatic analysis and experiments

The morbidity and mortality rates of prostate cancer (PCa) are high among elderly men worldwide. Several factors, such as heredity, obesity, and environment are associated with the occurrence of PCa. Cigarette smoking, which is also an important factor in the development of PCa, can lead to genetic alterations and consequently promote PCa development. However, the smoking-induced genetic alterations in PCa are unclear. This study aimed to identify the potential smoking-related genes associated with PCa development. The smoking-related differentially expressed genes (DEGs) were identified using the Gene Expression Omnibus (GEO) which included lots of PCa datasets. DEGs were subjected to protein–protein interaction (PPI) network analysis to identify the hub genes. The pathways in which these hub genes were enriched were identified. The Cancer Genome Atlas (TCGA) dataset was used to examine the expression of smoking-related genes in PCa samples and estimate their value in predicting tumor progression and prognosis. In total, 110 smoking-related DEGs were got from GSE68135 dataset which included microarray data of PCa patients with smoking or not and 14 smoking-related key genes associated with PCa were identified from PPI network. The expression of the following seven key genes was altered in TCGA PCa patients: EWSR1, SRSF6, COL6A3, FBLN1, DCN, CYP2J2, and PLA2G2A. EWSR1, SRSF6, FBLN1, and CYP2J2 also influenced PCa progression. Additionally, EWSR1 influenced disease-free survival. In the logistic regression model, CYP2J2, which exhibited the highest risk scores, was identified as the risk gene for PCa. We also found one of the smoking-related genes: EWSR1 was truly upregulated in clinical PCa patients and influenced PCa cells invasion and proliferation. This study identified the function of smoking-related genes involved in the progression of PCa.

Mechanisms of Immune Evasion in PTEN Loss Prostate Cancer

PTEN (phosphatase and tensin homolog) is a frequently lost tumor suppressor gene in prostate cancer, leading to aggressive tumor behavior and poor clinical outcomes. PTEN loss results in aberrant activation of the PI3K/AKT/mTOR pathway, promoting oncogenesis. These alterations also lead to an immunosuppressive tumor microenvironment with altered immune cell infiltration, cytokine profiles, and immune checkpoint regulation. This review aims to provide a comprehensive overview of the mechanisms underlying PTEN loss in prostate cancer and the consequent immune alterations observed in this subtype, thus underscoring the importance of understanding PTEN-mediated immune modulation for the development of effective therapeutic interventions in prostate cancer.

5hmC-profiles in Puerto Rican Hispanic/Latino men with aggressive prostate cancer.

Puerto Rican (PR) Hispanic/Latino (H/L) men are an understudied population that has the highest prostate cancer (PCa) specific mortality among other Hispanic populations. Little information is known about the higher mortality in PR H/L men. It is thought that epigenetic changes in key genes may play a critical role in aggressive tumors. We aimed to identify key 5-hydroxymethylcytosine (5hmC) changes in PR H/L men with aggressive PCa. We performed sequencing analysis using the 5hmC-enriched DNA from 22 prostate tumors and 24 adjacent normal FFPE samples. We identified 808 differentially methylated genes (DMGs) in tumors compared to adjacent normal tissues (FDR<0.05, log2FC>|0.4|). Pathway analysis of DMGs demonstrated that DNA repair pathway was most upregulated in tumors. Since 5hmC abundance positively correlates with gene expression levels, we further investigated 808 DMGs in TCGA PCa gene expression data. Further, we identified 59 DMGs (80.1%, FDR<0.05, ΔGE (gene expression) >|1|) with significant gene expression changes in the same direction. Additionally, we identified 111 aggressiveness-related DMGs, of which, two hypomethylated genes ( CCDC122 , NUDT15 ) and four hypermethylated genes ( PVT1 , RPL30 , TRMT12 , UBR5 ) were found to be altered at transcriptomic level in a concordant manner in PR H/L PCa patients (N=86). The aberrant 5hmC (N=55) and GE (N=497) changes in these six genes were also associated with progression-free survival in the mixed PCa population. In conclusion, our study identified 59 DMGs showing concordant epigenetic and transcriptomic changes in tumor tissues and 111 DMGs showing association with aggressive PCa among PR H/L men.

Characterizing adipocytokine-related signatures for prognosis prediction in prostate cancer.

Prostate cancer (PCa) is a prevalent malignant tumor in males, with a significant incidence of biochemical recurrence (BCR) despite advancements in treatment. Adipose tissue surrounding the prostate, known as periprostatic adipose tissue (PPAT), contributes to PCa invasion through adipocytokine production. However, the relationship between adipocytokine-related genes and PCa prognosis remains understudied. This study was conducted to provide a theoretical basis and serve as a reference for the use of adipocytokine-related genes as prognostic markers in PCa. Transcriptome and survival data of PCa patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential gene expression analysis was conducted using the DESeq2 and limma packages. Prognostic genes were identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. A prognostic model was developed and validated utilizing receiver operating characteristic (ROC) and Kaplan-Meier (K-M) curves. Assessments of immune cell infiltration and drug sensitivity were also carried out. Subsequently, the function of BNIP3L gene in PCa was verified. A total of 47 adipocytokine-related differentially expressed genes (DEGs) were identified. Five genes (PPARGC1A, APOE, BNIP3L, STEAP4, and C1QTNF3) were selected as prognostic markers. The prognostic model demonstrated significant predictive accuracy in both training and validation cohorts. Patients with higher risk scores exhibited poorer survival outcomes. Immune cell infiltration analysis revealed that the high-risk group had increased immune and ESTIMATE scores, while the low-risk group had higher tumor purity. In vitro experiments confirmed the suppressive effects of BNIP3L on PCa cell proliferation, migration, and invasion. The prognostic model independently predicts the survival of patients with PCa, aiding in prognostic prediction and therapeutic efficacy. It expands the study of adipocytokine-related genes in PCa, presenting novel targets for treatment.

Clinical relevance of protein-truncating variants of germline DNA repair genes in prostate cancer

BackgroundInterpreting genetic variants remains a challenge in prostate cancer (PCa). Although many annotation tools are available for prioritizing causal variants, the clinical relevance of these variants is rarely studied.MethodsWe collected a cohort study that included 274 PCa patients from June 2017 to December 2020 and sequenced 19 DNA damage repair (DDR) genes in these patients and explored the clinical consequence of these different approaches. We also examined all-cause and PCa-specific survival in DDR gene mutation carriers compared to non-carriers after androgen receptor (AR)-directed therapy.ResultsWe identified 13 variants from 19 DDR genes in a total of 14 (5.1%) patients who had at least one presumed pathogenic mutation using different annotation methods. Four variants were annotated as pathogenic, 11 variants were predicted as protein-truncating variants (PTVs), four variants received proxy-deleterious (Combined Annotation-Dependent Depletion scores of > 30), and only one variant was identified as a pathogenic variant or as having a functional effect by all three methods. PCa patients with PTVs were significantly associated with early onset, high cancer stage, and a worse response to AR-directed treatment. However, patients carrying a proxy-deleterious variant were only associated with a higher T (tumor) stage and N (node) stage than those without such a variant, but not associated with other clinical characteristics. In patients treated with AR-directed therapy, patients with a PTV showed an increased risk of all-cause death (adjusted hazard ratio (aHR) = 3.51, 95% confidence interval (CI): 1.06 ~ 11.56) and PCa-specific death (aHR = 4.49, 95% CI: 1.87 ~ 10.77) compared to non-PTV carriers after adjustment. We were unable to examine gene-specific risks due to the small number of patients.ConclusionsPTVs may assist in guiding treatment and early screening in PCa, while population-specific data for pathogenic variants are still being amassed.

Disulfidptosis-related subtype and prognostic signature in prostate cancer

BackgroundDisulfidptosis refers to cell death caused by the accumulation and bonding of disulfide in the cytoskeleton protein of SLC7A11-high level cells under glucose deprivation. However, the role of disulfidptosis-related genes (DRGs) in prostate cancer (PCa) classification and regulation of the tumor microenvironment remains unclear.MethodsFirstly, we analyzed the expression and mutation landscape of DRGs in PCa. We observed the expression levels of SLC7A11 in PCa cells through in vitro experiments and assessed the inhibitory effect of the glucose transporter inhibitor BAY-876 on SLC7A11-high cells using CCK-8 assay. Subsequently, we performed unsupervised clustering of the PCa population and analyzed the differentially expressed genes (DEGs) between clusters. Using machine learning techniques to select a minimal gene set and developed disulfidoptosis-related risk signatures for PCa. We analyzed the tumor immune microenvironment and the sensitivity to immunotherapy in different risk groups. Finally, we validated the accuracy of the prognostic signatures genes using single-cell sequencing, qPCR, and western blot.ResultsAlthough SLC7A11 can increase the migration ability of tumor cells, BAY-876 effectively suppressed the viability of prostate cancer cells, particularly those with high SLC7A11 expression. Based on the DRGs, PCa patients were categorized into two clusters (A and B). The risk label, consisting of a minimal gene set derived from DEGs, included four genes. The expression levels of these genes in PCa were initially validated through in vitro experiments, and the accuracy of the risk label was confirmed in an external dataset. Cluster-B exhibited higher expression levels of DRG, representing lower risk, better prognosis, higher immune cell infiltration, and greater sensitivity to immune checkpoint blockade, whereas Cluster A showed the opposite results. These findings suggest that DRGs may serve as targets for PCa classification and treatment. Additionally, we constructed a nomogram that incorporates DRGs and clinical pathological features, providing clinicians with a quantitative method to assess the prognosis of PCa patients.ConclusionThis study analyzed the potential connection between disulfidptosis and PCa, and established a prognostic model related to disulfidptosis, which holds promise as a valuable tool for the management and treatment of PCa patients.

Expression and role of CNIH2 in prostate cancer.

Prostate cancer is one of the most common cancers in men and poses a significant threat to global male health. Traditional prostate cancer assessment methods have certain limitations, necessitating the identification of new prognostic factors and treatment targets. Our study revealed that low expression of the cornichon family AMPA receptor auxiliary protein 2 (CNIH2) gene was associated with a better progression-free survival rate in prostate cancer patients. The area under the receiver operating characteristic (ROC) curve (AUC) showed that the prognostic ability of the CNIH2 gene was high at 1, 3, and 5 years. The gene was an independent prognostic factor according to multivariate analysis. Functional verification experiments showed that knocking down the CNIH2 gene could inhibit the proliferation, migration and invasion of prostate cancer cells and could also inhibit tumor growth in nude mice. Our study is the first to reveal the important role of the CNIH2 gene in prostate cancer. This discovery provides a new research direction for individualized treatment and prognostic evaluation of prostate cancer.

Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer

Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients’ disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.

Abstract C092: Transcriptome analysis of human endogenous retroviral elements in tumor microenvironment of prostate cancer patients of different ancestry

Abstract Background: Prostate cancer (PCa) poses unique challenges for Non-Hispanic Black Americans (BA), who often face poorer outcomes than Non-Hispanic White Americans (WA). Even after adjusting for socioeconomic factors, BAs are diagnosed younger and experience more severe clinical effects, potentially due to the tumor microenvironment. Within PCa, Endogenous Retroviruses (ERVs) have been shown to regulate immune response genes and have prognostic value in PCa. Despite constituting 8% of the human, ERVs' role in the tumor microenvironment remains incompletely understood. These findings highlight the need to target microenvironment dysregulation in solid tumor therapies, especially for ERV expression in prostate cancer. Methods: Using RNA-sequencing, we investigated the expression of ERVs in BA (n=18) and WA (n=14) tumor adjacent stroma (TAS) samples. We analyzed reads by mapping them to the human genome (hg38). We then used bedtools and data from the Human Endogenous Retrovirus database (HERVd) to calculate the number of reads at each ERV locus (n=519,060). Differentially expressed ERV elements were then identified in an Audic Claverie Test (fold- change cutoff of 2 and padj &amp;lt; 0.05). The expression of protein-coding genes in the same BA (n=18) and WA (n=14) TAS samples was investigated using RNA-sequencing. Regulation of significant ERV elements were compared to the regulation of nearby immune response genes between the two races. We developed primary stroma (carcinoma associated fibroblast, CAFs) culture of different races with PCa using fresh prostatectomy tissues. To investigate the biological functions of ERVs in CAFs, we developed FANA oligonucleotides (synthetic single- stranded nucleic acid analogs that can modulate gene expression by enzymatic degradation of a target RNA) and corresponding primers to knock down specific ERV sequences near key immune response genes in prostate cancer. Results: We examined 32 prostatectomy specimens of BA (n=18) and WA (n=14) PCa. Of the 5,786 statistically significant differentially expressed ERV elements between BA and WA TAS that were identified, 3,274 elements had an increase in ERV expression for BA TAS, and 2,512 elements had a decrease in ERV expression for BA TAS. 61 MER41 elements were found to be differentially expressed between the two races. The MER family sequences contain binding sites for transcription factors that are crucial in cancer, such as STAT1. This transcription factor can regulate genes involved in cell proliferation, apoptosis, and DNA repair. MER41 is in close proximity (within 5000 base pairs) to IFNA6, an immune response gene. We found both MER41 and IFNA6 to be downregulated in BA TAS, as compared to their WA counterparts. We developed FANA oligonucleotides to knockdown MER41E, MER61-Int, HERVL-Int, MLT1A, and LTR61E1 to investigate their effect on nearby gene expression. Conclusion: The CAF models from PCa patients of different races can be used to explore the biological function of ERV inactivation on anti-tumor immune response genes within the tumor microenvironment. Citation Format: Tara S. K. Jennings, Vinay Kumar, Anton N. Nguyen, Michael M. Ittmann, Patricia Castro, Farah Rahmatpanah. Transcriptome analysis of human endogenous retroviral elements in tumor microenvironment of prostate cancer patients of different 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 C092.

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.

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.

Telomerase related molecular subtype and risk model reveal immune activity and evaluate prognosis and immunotherapy response in prostate cancer

BackgroundProstate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear.MethodsSomatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored.ResultsTEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients’ sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT’s potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated.ConclusionsTEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Using gene and gene-set association tests to identify lethal prostate cancer genes.

Recent advances in the detection and treatment of prostate cancer (PCa) have reduced morbidity and mortality from this common cancer. Despite these improvements, PCa remains the second leading cause of cancer death in men in the United States. Further understanding of the genetic underpinnings of lethal PCa is required to drive risk detection and prevention and ultimately reduce mortality. We therefore set out to identify germline variants associated with cases of lethal prostate cancer (LPCa). Using a two-stage study design, we compared whole-exome sequencing data of 550 LPCa patients to 488 healthy male controls. Men were classified as having LPCa based on medical record review. Candidate genes were identified using gene- and gene-set-based rare truncating variant association tests. Case-control burden testing through Firth's penalized logistic regression and case-gnomAD allelic burden testing through a one-sided mid-p Fisher's exact test were conducted. Each gene's p-values from these tests were combined into an omnibus p-value for candidate gene selection. In the subsequent validation stage, genes were assessed using the UK Biobank and Firth's penalized logistic regression for each ancestry, combined through meta-analysis. Gene-based rare variant association tests identified 12 genes nominally associated with LPCa. Rare-variant association tests identified a gene set with a significantly higher burden of truncating germline mutations in LPCa patients than controls. Combining gene- and gene-set test results, four nominally significant genes (PPP1R3A, TG, PPFIBP2, and BTN3A3) were selected as candidates. Subsequent validation using the UK Biobank found that PPP1R3A was significantly associated with LPCa risk (odds ratio 2.34, CI 1.20-4.59). Specifically, pGln662ArgfsTer7 was identified as the predominant variant in PPP1R3A among LPCa patients in our dataset. Both individual gene and gene-set analyses identified candidates associated with LPCa. The novel association of PPP1R3A and LPCa risk merits further investigation.

Establishment of a prognostic risk model for prostate cancer based on Gleason grading and cuprotosis related genes

IntroductionProstate cancer (PCa) is common in aging males, diagnosed via the Gleason grading system. The study explores the unexamined prognostic value of cuprotosis, a distinct cell death type, alongside Gleason grades in PCa.MethodsWe explored Cuprotosis-related genes (CRGs) in prostate cancer (PCa), using NMF on TCGA-PRAD data for patient classification and WGCNA to link genes with Gleason scores and prognosis. A risk model was crafted via LASSO Cox regression. STX3 knockdown in PC-3 cells, analyzed for effects on cell behaviors and tumor growth in mice, highlighted its potential therapeutic impact.ResultsWe identified five genes crucial for a prognostic risk model, with higher risk scores indicating worse prognosis. Survival analysis and ROC curves confirmed the model’s predictive accuracy in TCGA-PRAD and GSE70769 datasets. STX3 was a key adverse prognostic factor, with its knockdown significantly reducing mRNA and protein levels, impairing PC-3 cell functions. In vivo, STX3 knockdown in PC-3 cells led to significantly smaller tumors in nude mice, underscoring its potential therapeutic value.ConclusionOur prognostic model, using five genes linked to Gleason scores, effectively predicts prostate cancer outcomes, offering a novel treatment strategy angle.

Towards a 'clicked' PSMA targeting gene delivery bioconjugate-polyplex for prostate cancer.

Prostate cancer is the most common cancer in men in the UK with over 50 000 new cases diagnosed each year and although therapeutic advances in surgery, anti-androgens, radio- and chemotherapy have increased survival rates, there still remains a need for new treatments to combat the most aggressive forms of the disease. Gene therapy offers promise as an alternative approach but is reliant on selective targeting to the cancer cell surface. Herein we describe the novel construction of a prostate specific membrane antigen (PSMA) binding bioconjugate-polyplex, based on a glutamate-urea peptide scaffold using 'click' chemistry, which we demonstrate is capable of targeted delivery of a GFP gene to PSMA overexpressing prostate cancer cells, and therefore may have potential future application as part of a prostate cancer gene delivery therapy.

Dissecting prostate Cancer: Single-Cell insight into Macrophage Diversity, molecular Prognosticators, and the role of Peptidylprolyl Isomerase F

BackgroundProstate cancer remains a prominent challenge in oncology, with advanced stages showing poor prognosis. The tumor microenvironment (TME), and particularly tumor-associated macrophages (TAMs), plays a crucial role in disease progression. This study explores the single-cell transcriptomics of prostate cancer, determines macrophage heterogeneity, identifies prognostic gene markers, and assesses the role of PPIF in TAMs. MethodsSingle-cell RNA sequencing data from the GEO database (GSE176031) and transcriptome data from the TCGA were processed to characterize cell populations and identify prognostic genes in prostate cancer. Macrophage subpopulations were examined through clustering, followed by gene set scoring based on migration, activation, and proliferation. PPIF expression in macrophages was investigated using multiplex immunofluorescence staining on matched prostate cancer and adjacent non-tumoral tissues. ResultsThe single-cell analysis identified 9,178 cells, categorized into 10 principal cell types, with macrophages constituting a significant part of the immune microenvironment. Four macrophage subgroups demonstrated distinct functional pathways: phagocytic, immune-regulatory, and proliferative. A total of 39 genes correlated with prostate cancer prognosis were identified, of which 10 carried the most significant prognostic information. Peptidylprolyl Isomerase F (PPIF) expression was significantly higher in TAMs from tumor tissue than normal tissue, indicating its potential regulatory role in the immune microenvironment. ConclusionThe intricate cellular architecture of the prostate cancer TME has been elucidated, with a focus on macrophage heterogeneity and functional specialization. Prognostic genes, including PPIF, were associated with survival outcomes, providing potential therapeutic targets. PPIF’s prominent expression in TAMs may serve as a lever in cancer progression, warranting further investigation as a biomarker and a molecule of interest for therapeutic targeting within the prostate cancer milieu.

Construction and Validation of a Prognostic Model Based on Mitochondrial Genes in Prostate Cancer.

This study attempted to build a prostate cancer (PC) prognostic risk model with mitochondrial feature genes. PC-related MTGs were screened for Cox regression analyses, followed by establishing a prognostic model. Model validity was analyzed via survival analysis and receiver operating characteristic (ROC) curves, and model accuracy was validated in the GEO dataset. Combining risk score with clinical factors, the independence of the risk score was verified by using Cox analysis, followed by generating a nomogram. The Gleason score, microsatellite instability (MSI), immune microenvironment, and tumor mutation burden were analyzed in two risk groups. Finally, the prognostic feature genes were verified through a q-PCR test. Ten PC-associated MTGs were screened, and a prognostic model was built. Survival analysis and ROC curves illustrated that the model was a good predictor for the risk of PC. Cox regression analysis revealed that risk score acted as an independent prognostic factor. The Gleason score and MSI in the high-risk group were substantially higher than in the low-risk group. Levels of ESTIMATE Score, Immune Score, Stromal Score, immune cells, immune function, immune checkpoint, and immunopheno score of partial immune checkpoints in the high-risk group were significantly lower than in the low-risk group. Genes with the highest mutation frequencies in the two groups were SPOP, TTN, and TP53. The q-PCR results of the feature genes were consistent with the gene expression results in the database. The 10-gene model based on MTGs could accurately predict the prognosis of PC patients and their responses to immunotherapy.

Efficacy of first line (1L) poly ADP-ribose polymerase inhibitors (PARPi) in combination with androgen pathway inhibitors (API) by homologous recombination repair (HRR) genes in metastatic castration-resistant prostate cancer (mCRPC): A living meta-analysis.

e17059 Background: It is unclear if patients with different HRR gene-mutated mCRPC derive consistent benefit from treatment with PARPi and API combination. Approval labels for PARPi+API are inconsistent across regulatory bodies in the US (Food and Drug Administration, FDA) and Europe (European Medical Agency, EMA), adding to confusion among practicing clinicians. Therefore, we aimed to assess the differential efficacy of PARP+API therapy by different genes using up-to-date trial evidence. Methods: We maintain a living meta-analysis on mCRPC treatments that is updated when new data becomes available. Here, we report data from phase III randomized control trials (RCTs) assessing PARPi+API in 1L mCRPC. The outcomes of interest included radiographic progression-free survival (rPFS) and overall survival (OS). Hazard ratios (HR) for rPFS and OS by different genes were summarized using random effects meta-analysis via inverse variance approach. Results: We have screened 27376 references since the inception of this living review. Here, we report data from three trials (15 references) with a total of 1618 patients. Frequently reported HRR gene mutations in the included trials were BRCA2 (35%), ATM (21%), CDK12 (12.2%), CHEK2 (12.2%), BRCA1 (4.6%), and PALB2 (3.4%). In terms of rPFS, statistical benefit was observed in BRCA (0.28; 95% CI: 0.13-0.59) and CDK12 (0.58; 95% CI: 0.35-0.95) subgroups, whereas no statistically significant benefit was seen in PALB2 (0.53; 95% CI: 0.21-1.32), ATM (0.93; 95% CI, 0.57-1.53), and CHEK2 (0.92; 95% CI: 0.53-1.61) subgroups. For OS, benefit was observed in BRCA (0.47; 95%CI: 0.31-0.71) subgroup. While not statistically significant, a signal of meaningful OS benefit was observed in PALB2 (0.33; 95%CI: 0.10-1.16) but no OS benefit was observed in ATM (0.97; 95% CI: 0.57-1.67), CDK12 (0.80; 95% CI: 0.36-1.78), and CHEK2 (0.81; 95% CI: 0.37-1.75) subgroups. Conclusions: Current results show that mCRPC patients with BRCA and CDK12 alterations had delayed disease progression with PARPi+API as 1L therapy. BRCA mutations are also associated with improved OS, while patients with CHEK2 and ATM derived no significant benefit with PARPi+API combination. [Table: see text]