A Unique Approach: Biomimetic Graphdiyne-Based Nanoplatform to Treat Prostate Cancer by Combining Cuproptosis and Enhanced Chemodynamic Therapy.

Purpose: Current treatment approaches for Prostate cancer (PCa) often come with debilitating side effects and limited therapeutic outcomes. There is urgent need for an alternative effective and safe treatment for PCa. Methods: We developed a nanoplatform to target prostate cancer cells based on graphdiyne (GDY) and a copper-based metal-organic framework (GDY-CuMOF), that carries the chemotherapy drug doxorubicin (DOX) for cancer treatment. Moreover, to provide GDY-CuMOF@DOX with homotypic targeting capability, we coated the PCa cell membrane (DU145 cell membrane, DCM) onto the surface of GDY-CuMOF@DOX, thus obtaining a biomimetic nanoplatform (DCM@GDY-CuMOF@DOX). The nanoplatform was characterized by using transmission electron microscope, atomic force microscope, X-ray diffraction, etc. Drug release behavior, antitumor effects in vivo and in vitro, and biosafety of the nanoplatform were evaluated. Results: We found that GDY-CuMOF exhibited a remarkable capability to load DOX mainly through π-conjugation and pore adsorption, and it responsively released DOX and generated Cu+ in the presence of glutathione (GSH). In vivo experiments demonstrated that this nanoplatform exhibits remarkable cell-killing efficiency by generating lethal reactive oxygen species (ROS) and mediating cuproptosis. In addition, DCM@GDY-CuMOF@DOX effectively suppresses tumor growth in vivo without causing any apparent side effects. Conclusion: The constructed DCM@GDY-CuMOF@DOX nanoplatform integrates tumor targeting, drug-responsive release and combination with cuproptosis and chemodynamic therapy, offering insights for further biomedical research on efficient PCa treatment.

ACOX2 Serves as a Favorable Indicator Related to Lipid Metabolism and Oxidative Stress for Biochemical Recurrence in Prostate Cancer.

Given the heterogeneity of tumors, there is an urgent need for accurate prognostic parameters in prostate cancer (PCa) patients. Lipid metabolism (LM) reprogramming and oxidative stress (OS) play a vital role in the progression of PCa. In this work, we identified five LM-OS-related genes (including ACOX2, PPRAGC1A, PTGS1, PTGS2, and HAO1) associated with the biochemical recurrence (BCR) of PCa. Subsequently, a prognostic signature was established based on these five genes. Kaplan-Meier survival estimates, receiver operating characteristic curves, and relationship analysis between risk score and clinical characters were applied to measure the robustness of the signature in an external cohort. A nomogram of risk score combined with clinical characteristics was constructed for clinical application. Functional enrichment analysis suggested that the underlying mechanism related to the signature included the calcium signaling, lipid transport, and cell cycle signaling pathways. Furthermore, WEE1 inhibitor was identified as a potential agent related to the cell cycle for high-risk patients. The mRNA expression and the prognostic value of the five genes were determined, and ACOX2 was identified as the key gene related to the prognostic signature. The protein expression of ACOX2 was measured in a prostate tissue microarray through an immunohistochemistry assay, confirming the bioinformatics results. By constructing the ACOX2-overexpressing PCa cell lines PC-3 and 22Rv1, the biological function of PCa cells was investigated. The cell viability, colony formation, migration, and invasion ability of PCa cell lines overexpressing ACOX2 were hindered. Decreased cellular lipid content and elevated cellular ROS content were observed in ACOX2-overexpressing PCa cell lines with reduced G2/M phases. In conclusion, this work presents the first prognostic signature specifically focused on LM-OS for PCa. ACOX2 could serve as a favorable indicator for the BCR in PCa. Further experiments are required to identify the potential underlying mechanism.

Arginase 2 is a Diagnostic and Prognostic Marker for Prostate Cancer and Is Associated with Metabolism.

Objective: Metabolism, a basic need and biochemical process for cell survival and proliferation, is closely connected with the pathogenesis and progression of prostate cancer. Methods: A four-gene signature construct that includes CKM (CKM), CD38, Enoyl Coenzyme A(EHHADH), and Arginase 2(ARG2) was created by bioinformatics. Finally, hub genes were validated by IHC and in vitro experiments. Results: The results showed the AUCs of the logistic regression and neural networks diagnostic model for the diagnosis of two subtypes were 0.920 and 0.936, respectively. The risk score demonstrated by univariable and multivariable Cox analysis is an independent predictive component of the prognostic signature for DFS. According to immunohistochemical analyses, ARG2 and CD38 expression levels were considerably under-expressed, but CKM and EHHADH expression levels were significantly overexpressed. Furthermore, The expression of ARG2 was significantly down-regulated in the late Gleason score. Finally, we found that ARG2 is lowly expressed in prostate cancer cells. Furthermore, based on the effect of ARG2 on the malignant phenotype of PCa in vitro, we also found that ARG2 may be a tumor suppressor that plays an important role in inhibiting proliferation, migration, and invasion. Conclusions: These findings suggest that ARG2 has been tentatively identified as a new target for research into how PCa develops in metabolism and for the development of innovative targeted treatments.

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-cell subtype, which are related to poor disease stage and immunotherapy response in various cancers. METHODS: 10 machine-learning algorithms and their combinations were applied in this work. A stable Tstr-related score (TCs) was constructed to predict the outcomes and PD-1 blockade treatment response in ccRCC patients. A nomogram based on TCs for personalized prediction of patient prognosis was constructed. Functional enrichment analysis and TimiGP algorithm were used to explore the underlying role of Tstr in ccRCC. The key TCs-related gene was identified by comprehensive analysis, and the bioinformatics results were verified by immunohistochemistry using a tissue microarray. RESULTS: A robust TCs was constructed and validated in four independent cohorts. TCs accurately predicted the prognosis and PD-1 blockade treatment response in ccRCC patients. The novel nomogram was able to precisely predict the outcomes of ccRCC patients. The underlying biological process of Tstr was related to acute inflammatory response and acute-phase response. Mast cells were identified to be involved in the role of Tstr as a protective factor in ccRCC. TNFS13B was shown to be the key TCs-related gene, which was an independent predictor of unfavorable prognosis. The protein expression analysis of TNFSF13B was consistent with the mRNA analysis results. High expression of TNFSF13B was associated with poor response to PD-1 blockade treatment. CONCLUSIONS: This study provides a Tstr cell-related score for predicting outcomes and PD-1 blockade therapy response in ccRCC. Tstr cells may exert their pro-tumoral role in ccRCC, acting against mast cells, in the acute inflammatory tumor microenvironment. TNFSF13B could serve as a key biomarker related to TCs.

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts.

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

Lamin B1: a novel biomarker in adult and pediatric adrenocortical carcinoma.

Adrenocortical carcinoma (ACC) is a malignancy with a poor prognosis and high mortality rate. A high tumor mutational burden (TMB) has been found to be associated with poor prognosis in ACC. Thus, exploring ACC biomarkers based on TMB holds significant importance for patient risk stratification. In our research, we utilized weighted gene coexpression network analysis and an assay for transposase-accessible chromatin with high-throughput sequencing to identify genes associated with TMB. Through the comprehensive analysis of various public datasets, Lamin B1 (LMNB1) was identified as a biomarker associated with a high TMB and low chromatin accessibility. Immunohistochemical staining demonstrated high expression of LMNB1 in ACC compared to noncancerous tissues. Functional enrichment analyses revealed that the function of LMNB1 is associated with cell proliferation and division. Furthermore, cell assays suggested that LMNB1 promotes tumor proliferation and invasion. In addition, mutation analysis suggested that the high expression of LMNB1 is associated with TP53 mutations. Additionally, LMNB1 was highly expressed in the vast majority of solid tumors across cancers. In our immune analysis, we discovered that the high expression of LMNB1 might suppress the infiltration of CD8+ T cells in the ACC microenvironment. In summary, LMNB1 is a predictive factor for the poor prognosis of adult and pediatric ACC. Its high expression in ACC is positively associated with high TMB and lower chromatin accessibility, and it promotes ACC cell proliferation and invasion. Therefore, LMNB1 holds promise as a novel biomarker and potential therapeutic target for ACC.

FKBP10 promotes clear cell renal cell carcinoma progression and regulates sensitivity to the HIF2α blockade by facilitating LDHA phosphorylation.

Renal cell carcinoma (RCC) is one of the three major malignant tumors of the urinary system and originates from proximal tubular epithelial cells. Clear cell renal cell carcinoma (ccRCC) accounts for approximately 80% of RCC cases and is recognized as a metabolic disease driven by genetic mutations and epigenetic alterations. Through bioinformatic analysis, we found that FK506 binding protein 10 (FKBP10) may play an essential role in hypoxia and glycolysis pathways in ccRCC progression. Functionally, FKBP10 promotes the proliferation and metastasis of ccRCC in vivo and in vitro depending on its peptidyl-prolyl cis-trans isomerase (PPIase) domains. Mechanistically, FKBP10 binds directly to lactate dehydrogenase A (LDHA) through its C-terminal region, the key regulator of glycolysis, and enhances the LDHA-Y10 phosphorylation, which results in a hyperactive Warburg effect and the accumulation of histone lactylation. Moreover, HIFα negatively regulates the expression of FKBP10, and inhibition of FKBP10 enhances the antitumor effect of the HIF2α inhibitor PT2385. Therefore, our study demonstrates that FKBP10 promotes clear cell renal cell carcinoma progression and regulates sensitivity to HIF2α blockade by facilitating LDHA phosphorylation, which may be exploited for anticancer therapy.

Silencing HOXC13 exerts anti-prostate cancer effects by inducing DNA damage and activating cGAS/STING/IRF3 pathway.

BACKGROUND: Advanced prostate cancer (PCa) will develop into castration-resistant prostate cancer (CRPC) and lead to poor prognosis. As the primary subtype of CRPC, CRPC-AR accounts for the major induction of PCa heterogeneity. CRPC-AR is mainly driven by 25 transcription factors (TFs), which we speculate may be the key factors driving PCa toward CRPC. Therefore, it is necessary to clarify the key regulator and its molecular mechanism mediating PCa progression. METHODS: Firstly, we downloaded transcriptomic data and clinical information from TCGA-PRAD. The characteristic gene cluster was identified by PPI clustering, GO enrichment, co-expression correlation and clinical feature analyses for 25 TFs. Then, the effects of 25 TFs expression on prognosis of PCa patients was analyzed using univariate Cox regression, and the target gene was identified. The expression properties of the target gene in PCa tissues were verified using tissue microarray. Meanwhile, the related mechanistic pathway of the target gene was mined based on its function. Next, the target gene was silenced by small interfering RNAs (siRNAs) for cellular function and mechanistic pathway validation. Finally, CIBERSORT algorithm was used to analyze the infiltration levels of 22 immune cells in PCa patients with low and high expression of target gene, and validated by assaying the expression of related immunomodulatory factor. RESULTS: We found that HOX family existed independently in 25 TFs, among which HOXC10, HOXC12 and HOXC13 had unique clinical features and the PCa patients with high HOXC13 expression had the worst prognosis. In addition, HOXC13 was highly expressed in tumor tissues and correlated with Gleason score and pathological grade. In vitro experiments demonstrated that silencing HOXC13 inhibited 22RV1 and DU145 cell function by inducing cellular DNA damage and activating cGAS/STING/IRF3 pathway. Immune infiltration analysis revealed that high HOXC13 expression suppressed infiltration of γδ T cells and plasma cells and recruited M2 macrophages. Consistent with these results, silencing HOXC13 up-regulated the transcriptional expression of IFN-ß, CCL2, CCL5 and CXCL10. CONCLUSION: HOXC13 regulates PCa progression by mediating the DNA damage-induced cGAS/STING/IRF3 pathway and remodels TIME through regulation of the transcription of the immune factors IFN-ß, CCL2, CCL5 and CXCL10.

N6-methyladenosine modified lncRNAs signature for stratification of biochemical recurrence in prostate cancer.

Nonmutational epigenetic reprogramming is a crucial mechanism contributing to the pronounced heterogeneity of prostate cancer (PCa). Among these mechanisms, N6-methyladenosine (m6A)-modified long non-coding RNAs (lncRNAs) have emerged as key players. However, the precise roles of m6A-modified lncRNAs in PCa remain to be elucidated. In this study, methylated RNA immunoprecipitation sequencing (MeRIP-seq) was conducted on primary and metastatic PCa samples, leading to the identification of 21 lncRNAs exhibiting differential methylation and expression patterns. We further established a PCa prognostic signature, named m6A-modified lncRNA score (mLs), based on 9 differential methylated lncRNAs in 4 multicenter cohorts. The high mLs score cohort exhibited a tendency for earlier biochemical recurrence (BCR) compared to the low mLs score cohort. Remarkably, the predictive performance of the mLs score surpassed that of five previously reported lncRNA-based signatures. Functional enrichment analysis underscored a negative correlation between the mLs score and lipid metabolism. Additionally, through MeRIP-qPCR, we pinpointed a hub gene, MIR210HG, which was validated through in vitro and in vivo experiments. These findings collectively illuminate the landscape of m6A-methylated lncRNAs in PCa tissue via MeRIP-seq and harness this information to prognosticate PCa outcomes using the mLs score. Furthermore, our study validates, both experimentally and mechanistically, the facilitative role of MIR210HG in driving PCa progression.

Metformin escape in prostate cancer by activating the PTGR1 transcriptional program through a novel super-enhancer.

The therapeutic efficacy of metformin in prostate cancer (PCa) appears uncertain based on various clinical trials. Metformin treatment failure may be attributed to the high frequency of transcriptional dysregulation, which leads to drug resistance. However, the underlying mechanism is still unclear. In this study, we found evidences that metformin resistance in PCa cells may be linked to cell cycle reactivation. Super-enhancers (SEs), crucial regulatory elements, have been shown to be associated with drug resistance in various cancers. Our analysis of SEs in metformin-resistant (MetR) PCa cells revealed a correlation with Prostaglandin Reductase 1 (PTGR1) expression, which was identified as significantly increased in a cluster of cells with metformin resistance through single-cell transcriptome sequencing. Our functional experiments showed that PTGR1 overexpression accelerated cell cycle progression by promoting progression from the G0/G1 to the S and G2/M phases, resulting in reduced sensitivity to metformin. Additionally, we identified key transcription factors that significantly increase PTGR1 expression, such as SRF and RUNX3, providing potential new targets to address metformin resistance in PCa. In conclusion, our study sheds new light on the cellular mechanism underlying metformin resistance and the regulation of the SE-TFs-PTGR1 axis, offering potential avenues to enhance metformin's therapeutic efficacy in PCa.

A reactive oxygen species-related signature to predict prognosis and aid immunotherapy in clear cell renal cell carcinoma.

Background: Clear cell renal cell carcinoma (ccRCC) is a malignant disease containing tumor-infiltrating lymphocytes. Reactive oxygen species (ROS) are present in the tumor microenvironment and are strongly associated with cancer development. Nevertheless, the role of ROS-related genes in ccRCC remains unclear. Methods: We describe the expression patterns of ROS-related genes in ccRCC from The Cancer Genome Atlas and their alterations in genetics and transcription. An ROS-related gene signature was constructed and verified in three datasets and immunohistochemical staining (IHC) analysis. The immune characteristics of the two risk groups divided by the signature were clarified. The sensitivity to immunotherapy and targeted therapy was investigated. Results: Our signature was constructed on the basis of glutamate-cysteine ligase modifier subunit (GCLM), interaction protein for cytohesin exchange factors 1 (ICEF1), methionine sulfoxide reductase A (MsrA), and strawberry notch homolog 2 (SBNO2) genes. More importantly, protein expression levels of GCLM, MsrA, and SBNO2 were detected by IHC in our own ccRCC samples. The high-risk group of patients with ccRCC suffered lower overall survival rates. As an independent predictor of prognosis, our signature exhibited a strong association with clinicopathological features. An accurate nomogram for improving the clinical applicability of our signature was constructed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the signature was closely related to immune response, immune activation, and immune pathways. The comprehensive results revealed that the high-risk group was associated with high infiltration of regulatory T cells and CD8+ T cells and more benefited from targeted therapy. In addition, immunotherapy had better therapeutic effects in the high-risk group. Conclusion: Our signature paved the way for assessing prognosis and developing more effective strategies of immunotherapy and targeted therapy in ccRCC.

Integrated high-throughput analysis identifies super enhancers in metastatic castration-resistant prostate cancer.

Background: Metastatic castration-resistant prostate cancer (mCRPC) is a highly aggressive stage of prostate cancer, and non-mutational epigenetic reprogramming plays a critical role in its progression. Super enhancers (SE), epigenetic elements, are involved in multiple tumor-promoting signaling pathways. However, the SE-mediated mechanism in mCRPC remains unclear. Methods: SE-associated genes and transcription factors were identified from a cell line (C4-2B) of mCRPC by the CUT&Tag assay. Differentially expressed genes (DEGs) between mCRPC and primary prostate cancer (PCa) samples in the GSE35988 dataset were identified. What's more, a recurrence risk prediction model was constructed based on the overlapping genes (termed SE-associated DEGs). To confirm the key SE-associated DEGs, BET inhibitor JQ1 was applied to cells to block SE-mediated transcription. Finally, single-cell analysis was performed to visualize cell subpopulations expressing the key SE-associated DEGs. Results: Nine human TFs, 867 SE-associated genes and 5417 DEGs were identified. 142 overlapping SE-associated DEGs showed excellent performance in recurrence prediction. Time-dependent receiver operating characteristic (ROC) curve analysis showed strong predictive power at 1 year (0.80), 3 years (0.85), and 5 years (0.88). The efficacy of his performance has also been validated in external datasets. In addition, FKBP5 activity was significantly inhibited by JQ1. Conclusion: We present a landscape of SE and their associated genes in mCPRC, and discuss the potential clinical implications of these findings in terms of their translation to the clinic.

Development and validation of a tobacco smoking-related index for predicting overall survival and immunotherapy response in bladder cancer.

Bladder cancer is one of the top five most prevalent cancers in the United States and a major cause of cancer-related mortality worldwide. Meanwhile, tobacco smoking is a well-established modifiable risk factor for bladder cancer, with a population-attributable risk of approximately 50%. But the relationship between the prognosis of bladder cancer and tobacco smoking remains unclear. To further explore the potential relationship between tobacco smoking and bladder cancer prognosis, the bladder cancer dataset from The Cancer Genome Atlas Program was used to build a tobacco smoking-related signature known as the "smoker index" for prognosis prediction. Additionally, we validated the efficacy of the signature with some external datasets. Finally, we preliminarily verified the role of CGB5, the hub gene in the smoker index, through pan-cancer analysis and in vitro assays. The study digs into the underlying connection between tobacco smoking and the prognosis of bladder cancer from a multi-omics perspective.

Identification and verification of an ALYREF-involved 5-methylcytosine based signature for stratification of prostate cancer patients and prediction of clinical outcome and response to therapies.

OBJECTIVES: Due to the heterogeneity of PCa, the clinical indicators used for PCa can't satisfy risk prognostication and personalized treatment. It is imperative to develop novel biomarkers for prognosis prediction and therapy response in PCa. Accumulating evidence shows that non-mutational epigenetic reprogramming, independent from genomic instability and mutation, serves as a newly added hallmark in cancer progression. METHODS: In this study, we integrated multi-center cohorts (N > 1300) to develop a RNA 5-methylcytosine regulator-based signature, the m5C score. We performed unsupervised clustering and LASSO regression to identify novel m5C-related subtypes and calculate the m5C score. Then we assessed the role of m5C cluster and m5C score in several clinical aspects such as prognosis in various molecular subtypes, responses to chemotherapy, androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy in PCa. Finally, we validated the cancer-promoting performance of ALYREF through clinical data analysis and experiments in vivo and in vitro. RESULTS: The investigation revealed that the m5C score could accurately predict the biochemical recurrence (BCR) in different subtypes (the PAM50 subtypes and immunophenotypes) and the responses to chemotherapy, ARSI therapy, and immunotherapy (PD1/PD-L1). A high m5C score indicated a poor BCR prognosis in every subtype of PCa, unfavorable responses in ARSI therapy and immunotherapy (PD1/PD-L1). Moreover, the m5C reader gene termed ALYREF, yielding the highest weighed coefficient, promoted PCa progression through in silico analysis and experimental validations (in vivo and in vitro). CONCLUSIONS: The m5C signature can function in many aspects of PCa, such as the development and prognosis of the disease, and multiple therapy responses. Further, the m5C reader, ALYREF, was identified as a prognostic biomarker and a potential therapeutic target for PCa. The m5C signature could act as a brand-new tool for predicting the prognosis of patients in different molecular subtypes and patients' therapy responses and promoting customized treatments.

Prognostic implication of heterogeneity and trajectory progression induced by enzalutamide in prostate cancer.

Background: Enzalutamide, as a second-generation endocrine therapy drug for prostate cancer (PCa), is prominent representative among the synthetic androgen receptor antagonists. Currently, there is lack of enzalutamide-induced signature (ENZ-sig) for predicting progression and relapse-free survival (RFS) in PCa. Methods: Enzalutamide-induced candidate markers were derived from single-cell RNA sequencing analysis integrating three enzalutamide-stimulated models (0-, 48-, and 168-h enzalutamide stimulation). ENZ-sig was constructed on the basis of candidate genes that were associated with RFS in The Cancer Genome Atlas leveraging least absolute shrinkage and selection operator method. The ENZ-sig was further validated in GSE70768, GSE94767, E-MTAB-6128, DFKZ, GSE21034, and GSE70769 datasets. Biological enrichment analysis was used to discover the underlying mechanism between high ENZ-sig and low ENZ-sig in single-cell RNA sequencing and bulk RNA sequencing. Results: We identified a heterogenous subgroup that induced by enzalutamide stimulation and found 53 enzalutamide-induced candidate markers that are related to trajectory progression and enzalutamide-stimulated. The candidate genes were further narrowed down into 10 genes that are related to RFS in PCa. A 10-gene prognostic model (ENZ-sig)-IFRD1, COL5A2, TUBA1A, CFAP69, TMEM388, ACPP, MANEA, FOSB, SH3BGRL, and ST7-was constructed for the prediction of RFS in PCa. The effective and robust predictability of ENZ-sig was verified in six independent datasets. Biological enrichment analysis revealed that differentially expressed genes in high ENZ-sig were more activated in cell cycle-related pathway. High-ENZ-sig patients were more sensitive to cell cycle-targeted drugs (MK-1775, AZD7762, and MK-8776) than low-ENZ-sig patients in PCa. Conclusions: Our results provided evidence and insight on the potential utility of ENZ-sig in PCa prognosis and combination therapy strategy of enzalutamide and cell cycle-targeted compounds in treating PCa.

A prognostic signature consisting of N6-methyladenosine modified mRNAs demonstrates clinical potential in prediction of biochemical recurrence and guidance on precision therapy in prostate cancer.

Novel biomarkers are urgently needed to improve the prediction of clinical outcomes and guide personalized treatment for prostate cancer (PCa) patients. However, the role of N6-methyladenosine (m6A) modifications in PCa initiation and progression remains largely elusive. In our study, we collected benign Prostate Hyperplasia (BPH), localized PCa, and metastatic PCa samples from patients and performed methylated RNA immunoprecipitation sequencing (MeRIP-Seq) to map m6A-methylated mRNAs. Furthermore, we developed a prognostic signature based on 239 differentially methylated RNAs and the TCGA-PRAD dataset, which can be used to calculate an m6A-modified mRNA (MMM) score for a PCa patient, validated by independent multi-center cohorts. Our findings revealed that differential m6A modifications were positively correlated with altered expressions of mapped m6A-modified mRNAs. Higher MMM scores were associated with shorter times to biochemical recurrence (BCR) in PCa patients, and the MMM scoring system outperformed three well-established signatures in nine independent validation cohorts, as demonstrated by Kaplan-Meier survival analysis, C-index and ROC. Patients who did not respond to androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy were found to have high MMM scores. Two hub genes, TLE1 and PFKL, were confirmed to have m6A sites through MeRIP-qPCR, and their knockdown promoted PCa cell invasion. Bioinformatics analysis of single-cell databases identified cell types with high transcript abundance levels of these two genes. In summary, our study is the first to perform transcriptome-wide m6A mapping in prostate tissues. The translational potential of a prognostic signature, comprising m6A-methylated mRNAs, in predicting clinical outcomes and therapy responses for PCa patients, is demonstrated.

Identification and Validation of FGF-Related Prognostic Signatures in Prostate Cancer.

Background: FGF signaling is critical to controlling various cancers. Nevertheless, the functions of FGF-related genes in PCa are still unknown. Objective: The objective of this study is to build a FGF-related signature that was capable of accurately predicting PCa survival and prognosis for BCR. Methods: The univariate and multivariate Cox regression, infiltrating immune cells, LASSO, and GSEA analyses were carried out to build a prognostic model. Results: A FGF-related signature that consists of PIK3CA and SOS1 was developed for the purpose of predicting PCa prognosis, and all patients were categorized into low- and high-risk groups. In comparison to the low-risk group, high-risk score patients had poorer BCR survival. This signature's predictive power has been investigated utilizing the AUC of the ROC curves. The risk score has been shown to be an independent prognostic factor by multivariate analysis. The four enriched pathways of the high-risk group were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of PCa, including focal adhesion, TGF-ß signaling pathway, adherens junction, and ECM receptor interaction. The high-risk groups had considerably higher levels of immune status and tumor immune cell infiltration, suggesting a more favorable response to immune checkpoint inhibitors. IHC found that the expression of the two FGF-related genes in the predictive signature was extremely different in PCa tissues. Conclusion: To summarize, our FGF-related risk signature may effectively predict and diagnose PCa, indicating that in PCa patients, they are potential therapeutic targets and promising prognostic biomarkers.

Molecular predictors of metastasis in patients with prostate cancer.

INTRODUCTION: Prostate cancer is a serious threat to the health of older adults worldwide. The quality of life and survival time of patients sharply decline once metastasis occurs. Thus, early screening for prostate cancer is very advanced in developed countries. The detection methods used include Prostate-specific antigen (PSA) detection and digital rectal examination. However, the lack of universal access to early screening in some developing countries has resulted in an increased number of patients presenting with metastatic prostate cancer. In addition, the treatment methods for metastatic and localized prostate cancer are considerably different. In many patients, early-stage prostate cancer cells often metastasize due to delayed observation, negative PSA results, and delay in treatment time. Therefore, the identification of patients who are prone to metastasis is important for future clinical studies. AREAS COVERED: this review introduced a large number of predictive molecules related to prostate cancer metastasis. These molecules involve the mutation and regulation of tumor cell genes, changes in the tumor microenvironment, and the liquid biopsy. EXPERT OPINION: In next decade, PSMA PET/CT and liquid biopsy will be the excellent predicting tools, while 177 Lu- PSMA-RLT will be showed excellent anti-tumor efficacy in mPCa patients.

[Corrigendum] miR­195 inhibits cell proliferation and angiogenesis in human prostate cancer by downregulating PRR11 expression.

Subsequently to the publication of the above paper, an interested reader drew to the authors' attention that a pair of data panels featured in Figs. 1B and 4C contained overlapping sections, such that data that were intended to show the results from differently performed experiments appeared to have been derived from the same original source (specifically, the 'LNCaP / miR­NC' panel in Fig. 1B and the 'LNCaP / miR­195+ PRR11' panel in Fig. 4C were overlapping). The authors were able to re­examine their original data files, and realized that this figure had been inadverently assembled incorrectly. The revised version of Fig. 1, containing the correct data for Fig. 1B (wherein the error was contained), is shown on the next page. Note that the revisions made to this figure do not affect the overall conclusions reported in the paper. The authors are grateful to the Editor of Oncology Reports for allowing them the opportunity to publish this Corrigendum, and apologize to the readership for any inconvenience caused. [Oncology Reports 39: 1658­1670, 2018; DOI: 10.3892/or.2018.6240].