Immunotherapy and endothelin receptor antagonists for treatment of castration-resistant prostate cancer

A tumour-associated macrophage-based signature for deciphering prognosis and immunotherapy response in prostate cancer

For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

Development and validation of [18 F]-PSMA-1007 PET-based radiomics model to predict biochemical recurrence-free survival following radical prostatectomy

PURPOSE

Biochemical recurrence (BCR) following radical prostatectomy (RP) is a significant concern for patients with prostate cancer. Reliable prediction models are needed to identify patients at risk for BCR and facilitate appropriate management. This study aimed to develop and validate a clinical-radiomics model based on preoperative [18 F]PSMA-1007 PET for predicting BCR-free survival (BRFS) in patients who underwent RP for prostate cancer.

MATERIALS AND METHODS

A total of 236 patients with histologically confirmed prostate cancer who underwent RP were retrospectively analyzed. All patients had a preoperative [18 F]PSMA-1007 PET/CT scan. Radiomics features were extracted from the primary tumor region on PET images. A radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression model. The performance of the radiomics signature in predicting BRFS was assessed using Harrell's concordance index (C-index). The clinical-radiomics nomogram was constructed using the radiomics signature and clinical features. The model was externally validated in an independent cohort of 98 patients.

RESULTS

The radiomics signature comprised three features and demonstrated a C-index of 0.76 (95% CI: 0.60-0.91) in the training cohort and 0.71 (95% CI: 0.63-0.79) in the validation cohort. The radiomics signature remained an independent predictor of BRFS in multivariable analysis (HR: 2.48, 95% CI: 1.47-4.17, p < 0.001). The clinical-radiomics nomogram significantly improved the prediction performance (C-index: 0.81, 95% CI: 0.66-0.95, p = 0.007) in the training cohort and (C-index: 0.78 95% CI: 0.63-0.89, p < 0.001) in the validation cohort.

CONCLUSION

We developed and validated a novel [18 F]PSMA-1007 PET-based clinical-radiomics model that can predict BRFS following RP in prostate cancer patients. This model may be useful in identifying patients with a higher risk of BCR, thus enabling personalized risk stratification and tailored management strategies.

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.

A review focusing on the role of pyroptosis in prostate cancer

As one of the types of programmed cell death, pyroptosis has become a focus of research in recent years. Numerous studies have shown that pyroptosis plays a regulatory role in tumor cell invasiveness, differentiation, proliferation, and metastasis. It has been demonstrated that pyroptosis is involved in the regulation of signaling pathways implicated in the pathogenesis of prostate cancer (PCa). Furthermore, the loss of expression of pyroptosis-related genes in PCa has been reported, and pyroptosis-related genes have demonstrated a considerable ability in predicting the prognosis of PCa. Therefore, the potential role of pyroptosis in regulating the development of PCa warrants further investigation and attention. In this review, we summarize the basics of the role of pyroptosis and also discuss research into the mechanisms of action associated with pyroptosis in PCa. It is hoped that by exploring the potential of the pyroptosis pathway in intervening in PCa, it will provide a viable direction for the diversification of PCa treatment.

Identification of a novel stemness-related signature with appealing implications in discriminating the prognosis and therapy responses for prostate cancer

PURPOSE

Cancer stemness represents the tumor-initiation and self-renewal potentials of cancer stem cells. It is involved in prostate cancer progression and resistance to therapy. Herein, we aimed to unveil the stemness features, establish a novel prognostic model, and identify potential therapeutic targets.

METHODS

26 stemness-related signatures were obtained from StemChecker. The expression profiles and clinical traits of TCGA-PRAD were obtained from TCGA and cBioPortal, respectively. GSE5446 and GSE70769 cohorts were acquired from GEO. PRAD_MSKCC cohort was also retrieved via the cBioPortal. The consensus clustering method was used for stemness subclusters classification. WGCNA was used to identify hub genes related to the stemness subcluster. The most important feature was explored in vitro.

RESULTS

Prostate cancer patients of TCGA-PRAD were divided into two subclusters (C1 and C2) based on the enrichment scores of the 26 stemness-related signatures. C1 was characterized by decreased survival, rich infiltrations of M0 macrophages and regulatory T cells, minimum sensitivity to chemotherapy, and a low response to immunotherapy. Hub genes of the red module with the highest correlation with C1 were subsequently identified by WGCNA and subjected to stemness-related risk model construction based on the machine-learning framework. Prostate cancer patients with high stemness scores had unfavorable prognosis, immunosuppressive tumor microenvironment, minimum sensitivity to chemotherapy, and a low response to immunotherapy. MXD3, the most important factor of the model, can regulate the stemness traits of prostate cancer cells.

CONCLUSIONS

Our study depicted the stemness landscapes of prostate cancer and characterized two subclusters with diverse prognoses and tumor immune microenvironments. A stemness-risk signature was developed and demonstrated prospective implications in predicting prognosis and precision medicine.

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.

Identification of ZDHHC1 as a Pyroptosis Inducer and Potential Target in the Establishment of Pyroptosis-Related Signature in Localized Prostate Cancer

Pyroptosis or cellular inflammatory necrosis is a programmed cell death kind. Accumulating evidence shows that pyroptosis plays a crucial role in the invasion, metastasis, and proliferation of tumor cells, thus affecting the prognosis of tumors and therapeutic effects. Prostate cancer (PCa), a common malignancy among men, is associated with inflammation. Pathophysiological effects of pyroptosis on tumor development and progression, as well as the mediation of PCa, are known, but its effects on the potential prognosis for PCa warrant in-depth investigation. Herein, we built a risk model of six pyroptosis-related genes and verified their predictive abilities for prognostic and therapeutic effects. Higher risk scores indicated a higher probability of biochemical recurrence (BCR), higher immune infiltration, and worsened clinicopathological features. To derive scientific and reliable predictions for BCR in patients having PCa, the findings of the current study were verified in the Gene Expression Omnibus (GEO) cohort following evaluation in The Cancer Genome Atlas (TCGA) dataset. Additionally, after evaluating the six genes in the model, ZDHHC1 was found to be an important component. Its antitumor role was further assessed through in vivo and in vitro experiments, and its promoting effect on pyroptosis was further evaluated and verified. The above results provided a new perspective for further studies on pyroptosis and its clinical utility for PCa.

The cGAS-STING pathway-related gene signature can predict patient prognosis and immunotherapy responses in prostate adenocarcinoma

The cyclic GMP-AMP synthase-stimulator of the interferon genes (cGAS-STING) pathway is essential in inflammation-driven tumor occurrence and progression. However, the prognostic roles and immune functions of cGAS-STING pathway-related genes in patients with prostate adenocarcinoma (PRAD) remain unclear. cGAS-STING pathway-related genes were obtained from the gene set enrichment analysis (GSEA) website. Univariate Cox regression analysis was performed to screen the prognosis-related hub genes in the cancer genome atlas (TCGA) and GSE116918 datasets. Unsupervised clustering analysis was performed to identify different clusters. The least absolute shrinkage and selection operator and multivariate Cox regression analyses were applied to develop a prognostic risk model. The prognostic values and predictive performance of risk signature were assessed by the Kaplan-Meier curve and receiver operating characteristic curve. The IMvigor210 cohort was used to investigate the potential values of the risk score in immunotherapeutic responses. Two clusters were identified based on the expression matrix of 12 prognosis-related genes. Specifically, better overall survival was observed in cluster 2 than cluster 1 in both datasets. Inflammation-related pathway enrichment and immune cell infiltration levels were altered between 2 clusters. Moreover, 6 genes (CASP8, GRK6, IL3RA, PLCB1, TBKBP1, and TNFSF10) were identified to generate a cGAS-STING pathway-related signature (CPRS). Survival analysis showed that patients in the high-risk group showed a more dismal survival than those in the low-risk group in TCGA and GSE116918 datasets. Notably, the CPRS can differentiate responsive patients from non-responsive individuals treated with PD-L1 blockades in an independent cohort. In addition, higher CPRS was associated with a more favorable prognosis. The proposed risk model was developed based on 6 cGAS-STING pathway related-genes, which can be used as a promising predictor for patient survival and immunotherapeutic responses in PRAD, contributing to treatment strategy-related decision-making.

Cancer-associated fibroblast-derived gene signatures predict radiotherapeutic survival in prostate cancer patients

BACKGROUND

Cancer-associated fibroblasts (CAFs) play multiple roles in regulating tumor metastasis and treatment response. Current clinical indicators are insufficient to accurately assess disease risk and radiotherapy response, emphasizing the urgent need for additional molecular prognostic markers.

METHODS

In order to investigate CAF-related genes associated with radiotherapy and construct prognostic CAF-related gene signatures for prostate cancer, we firstly established a radio-resistant prostate CAF cell subline (referred to as CAFR) from Mus-CAF (referred to as CAF) through fractionated irradiation using X-rays. Transcriptome sequencing for CAF and CAFR was conducted, and 2626 CAF-related differentially expressed genes (DEGs) associated with radiotherapy were identified. Human homologous genes of mouse CAF-related DEGs were then obtained.

RESULTS

Functional enrichment analysis revealed that these CAF-related DEGs were significantly enriched ECM- and immune-related functions and pathways. Based on GSE116918 dataset, 186 CAF-related DEGs were correlated with biochemical recurrence-free survival (BCRFS) of prostate cancer patients, 16 of which were selected to construct a BCRFS-related CAF signature, such as ACPP, THBS2, and KCTD14; 142 CAF-related DEGs were correlated with metastasis-free survival (MFS), 16 of which were used to construct a MFS-related CAF signature, such as HOPX, TMEM132A, and ZNF467. Both Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets confirmed that the two CAF signatures accurately predicted BCRFS and MFS of prostate cancer patients. The risk scores were higher in patients with higher gleason grades and higher clinical T stages. Moreover, the BCRFS-related CAF signature was an independent prognostic factor and a nomogram consisting of BCRFS-related CAF signature and various clinical factors accurately predicted 2-, 3-, and 5-year survival time of prostate cancer patients. Furthermore, the risk score was positively correlated with multiple immune checkpoints.

CONCLUSIONS

Our established CAF signatures could accurately predict BCRFS and MFS in prostate cancer patients undergoing radiotherapy.

Gene Expression Analysis Reveals Prognostic Biomarkers of the Tyrosine Metabolism Reprogramming Pathway for Prostate Cancer

Background

Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers.

Aims

To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes.

Results

Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups.

Conclusions

Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Construction and validation of N6-methyladenosine long non-coding RNAs signature of prognostic value for early biochemical recurrence of prostate cancer

PURPOSE

Early biochemical recurrence (eBCR) indicated a high risk for potential recurrence and metastasis in prostate cancer. The N6-methyladenosine (m6A) methylation modification played an important role in prostate cancer progression. This study aimed to develop a m6A lncRNA signature to accurately predict eBCR in prostate cancer.

METHODS

Pearson correlation analysis was first conducted to explore m6A lncRNAs and univariate Cox regression analysis was further performed to identify m6A lncRNAs of prognostic roles for predicting eBCR in prostate cancer. The m6A lncRNA signature was constructed by least absolute shrinkage and selection operator analysis (LASSO) in training cohort and further validated in test cohort. Furthermore, half maximal inhibitory concentration (IC50) values were utilized to explore potential effective drugs for high-risk group in this study.

RESULTS

Five hundred and thirty-eighth m6A lncRNAs were searched out through Pearson correlation analysis and 25 out of 538 m6A lncRNAs were identified to pose prediction roles for eBCR in prostate cancers. An m6A lncRNA signature including 5 lncRNAs was successfully built in training cohort. The high-risk group derived from m6A lncRNA signature could efficiently predict eBCR occurrence in both training (p < 0.001) and test cohort (p = 0.002). ROC analysis also confirmed that lncRNA signature in this study posed more accurate prediction roles for eBCR occurrence when compared with PSA, TNM stages and Gleason scores. Drug sensitivity analysis further discovered that various drugs could be potentially utilized to treat high-risk samples in this study.

CONCLUSIONS

The m6A lncRNA signature in this study could be utilized to efficiently predict eBCR occurrence, various clinical characteristic and immune microenvironment for prostate cancer.

Risk subtyping and prognostic assessment of prostate cancer based on consensus genes

Prostate cancer (PCa) is the most frequent malignancy in male urogenital system around worldwide. We performed molecular subtyping and prognostic assessment based on consensus genes in patients with PCa. Five cohorts containing 1,046 PCa patients with RNA expression profiles and recorded clinical follow-up information were included. Univariate, multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were used to select prognostic genes and establish the signature. Immunohistochemistry staining, cell proliferation, migration and invasion assays were used to assess the biological functions of key genes. Thirty-nine intersecting consensus prognostic genes from five independent cohorts were identified. Subsequently, an eleven-consensus-gene classifier was established. In addition, multivariate Cox regression analyses showed that the classifier served as an independent indicator of recurrence-free survival in three of the five cohorts. Combined receiver operating characteristic (ROC) analysis achieved synthesized effects by combining the classifier with clinicopathological features in four of five cohorts. SRD5A2 inhibits cell proliferation, while ITGA11 promotes cell migration and invasion, possibly through the PI3K/AKT signaling pathway. To conclude, we established and validated an eleven-consensus-gene classifier, which may add prognostic value to the currently available staging system.

By analysis of gene expression profiles of prostate cancer patients from multiple platforms, an eleven-consensus-gene classifier is constructed to provide a robust tool for the prediction of recurrence-free survival.

MiR-5195-3p functions as a tumor suppressor in prostate cancer via targeting CCNL1

BACKGROUND

Accumulating evidence indicates that miR-5195-3p exerts tumor-suppressive roles in several tumors. However, the clinical significance and biological function of miR-5195-3p in prostate cancer (PCa) have not been reported yet.

METHODS

The expression levels of miR-5195-3p and Cyclin L1 (CCNL1) were determined using quantitative real-time PCR in clinical specimens and cell lines. The clinical significance of miR-5195-3p in patients with PCa was evaluated using Kaplan-Meier survival analysis and Cox regression models. Cell proliferation and cell cycle distribution were measured by CCK-8 assay and flow cytometry, respectively. The association between miR-5195-3p and CCNL1 was analyzed by luciferase reporter assay.

RESULTS

MiR-5195-3p expression levels were significantly downregulated in 69 paired PCa tissues compared with matched adjacent normal tissues. The decreased miR-5195-3p expression was associated with Gleason score and TNM stage, as well as worse survival prognosis. The in vitro experiments showed that miR-5195-3p overexpression suppressed the proliferation and cell cycle G1/S transition in PC-3 and DU145 cells. Elevated miR-5195-3p abundance obviously impaired tumor formation in vivo using PC-3 xenografts. Mechanistically, CCNL1 was a direct target of miR-5195-3p in PCa cells, which was inversely correlated with miR-5195-3p in PCa tissues. Importantly, CCNL1 knockdown imitated, while overexpression reversed, the effects of miR-5195-3p overexpression on PCa cell proliferation and cell cycle G1/S transition.

CONCLUSIONS

Our data suggest that miR-5195-3p functions as a tumor suppressor by targeting CCNL1 in PCa.

A novel defined risk signature based on pyroptosis-related genes can predict the prognosis of prostate cancer

Background

Pyroptosis can not only inhibit the occurrence and development of tumors but also develop a microenvironment conducive to cancer growth. However, pyroptosis research in prostate cancer (PCa) has rarely been reported.

Methods

The expression profile and corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients were divided into different clusters using consensus clustering analysis, and differential genes were obtained. We developed and validated a prognostic biomarker for biochemical recurrence (BCR) of PCa using univariate Cox analysis, Lasso-Cox analysis, Kaplan–Meier (K–M) survival analysis, and time-dependent receiver operating characteristics (ROC) curves.

Results

The expression levels of most pyroptosis-related genes (PRGs) are different not only between normal and tumor tissues but also between different clusters. Cluster 2 patients have a better prognosis than cluster 1 patients, and there are significant differences in immune cell content and biological pathway between them. Based on the classification of different clusters, we constructed an eight genes signature that can independently predict the progression-free survival (PFS) rate of a patient, and this signature was validated using a GEO data set (GSE70769). Finally, we established a nomogram model with good accuracy.

Conclusions

In this study, PRGs were used as the starting point and based on the expression profile and clinical data, a prognostic signature with a high predictive value for biochemical recurrence (BCR) following radical prostatectomy (RP) was finally constructed, and the relationship between pyroptosis, immune microenvironment, and PCa was explored, providing important clues for future research on pyroptosis and immunity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01172-5.

A Novel Set of Immune-associated Gene Signature predicts Biochemical Recurrence in Localized Prostate Cancer Patients after Radical Prostatectomy

Background: Decision-making regarding biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) mainly relies on clinicopathological parameters with a low predictive accuracy. Currently, accumulating evidence suggests that immune-associated genes (IAGs) play irreplaceable roles in tumorigenesis, progression and metastasis. Considering the critical role of immune in PCa, we therefore attempted to identify the novel IAGs signature and validate its prognostic value that can better forecast the risk for BCR and guide clinical treatment. Methods: RNA-sequencing and corresponding clinicopathological data were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was utilized to screen out the candidate module closely related to BCR, and univariate and LASSO Cox regression analyses were performed to build the gene signature. Kaplan-Meier (KM) survival analysis, time-dependent receiver operating curve (ROC), independent prognostic analysis and nomogram were also applied to evaluate the prognostic value of the signature. Besides, Gene ontology analysis (GO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore potential biological pathways. Results: A total of six IAGs (SSTR1, NFATC3, NRP1, TUBB3, IL1R1, GDF15) were eventually identified and used to establish a novel IAGs signature. The Kaplan-Meier analysis revealed that patients with low-risk scores had longer recurrence-free survival (RFS) than those with high-risk scores in both GSE70769 and TCGA cohorts. Further, our signature was also proven to be a valuable independent prognostic factor for BCR. We also constructed a nomogram based on the gene signature and related clinicopathologic features, which excellently predict 1-year, 3-year and 5-year prognosis of localized PCa patients after RP. Moreover, functional enrichment analysis demonstrated the vital biological processes, and stratified GSEA revealed that a crucial immune-related pathway (T cell receptor signaling pathway) was notably enriched in the high-risk group. Conclusions: We successfully developed a novel robust IAGs signature that is powerful in BCR prediction in localized PCa patients after RP, and created a prognostic nomogram. In addition, the signature might help clinicians in selecting high-risk subpopulation, predicting survival status of patients and promoting more individualized therapies than traditional clinical factors.

Identification of seven long noncoding RNAs signature for prediction of biochemical recurrence in prostate cancer

Accumulating evidence suggested that long noncoding RNAs (lncRNAs) possess a potential role in prostate cancer (PCa) diagnosis and prognosis. Rapid biochemical recurrence (BCR) is considered as a sign for clinical recurrence metastasis and PCa-specific mortality. Hence, the aim of the present study was to identify a lncRNA signature that can predict BCR of PCa accurately. Bioinformatics analysis, Kaplan–Meier analyses, Cox regression analyses, and Gene Set Enrichment Analysis (GSEA) were performed in a publicly available database with 499 PCa tissues and 52 matched normal tissues. A signature was identified. All these lncRNAs were differentially expressed between tumor and normal tissues and differentially expressed between high Gleason score and low Gleason score tissues. Furthermore, we developed a seven lncRNAs signature that can predict PCa BCR. Patients classified into low-risk group showed better BCR survival significantly than the patients in the high-risk group (hazard ratio = 0.32, 95% CI: 0.20–0.52, concordance index = 0.63). The area under the curve was 0.68 for BCR. The signature also had good discrimination for BCR in men with Gleason 7 PCa. In conclusion, our results suggest that the seven lncRNAs signature is a new biomarker of BCR and high risk in PCa. In addition, the individual lncRNA warrants further study to uncover the associated mechanisms of PCa progression and the signature could be used to design direct clinical trials for adjuvant therapy.

A novel gene signature to predict immune infiltration and outcome in patients with prostate cancer

Prostate cancer (PCa) is one of the most common malignancies in male. We aim to establish a novel gene signature for immune infiltration and outcome (biochemical recurrence (BCR) and overall survival (OS)) of patients with prostate cancer (PCa) to augment Gleason patterns for evaluating prognosis and managing patients undergoing radical prostatectomy (RP). Combined with our microarray data and the Cancer Genome Atlas Project (TCGA) database (discovery set), we identified a six-gene signature. The Gene Expression Omnibus (GEO) database served as the test set. The databases of Fudan University Shanghai Cancer Center (FUSCC) and Third Affiliated Hospital of Nantong University (TAHNU) served as an external validation set. Immunohistochemistry was used to investigate the relationship between risk groups and the immune infiltrate. We identified a six-gene signature to predict immune cell infiltration and outcome of PCa patients. The AUC values used to predict early BCR in the discovery, test, FUSCC, and TAHNU sets were 0.73, 0.76, 0.72, and 0.81, respectively. Low-risk score patients in each dataset experienced significantly longer OS (P = .01, 0.04, 0.02, respectively). The signature also predicted high regulatory T cells (Tregs) and M2-polarized macrophages infiltration in high-risk score patients with PCa. Additionally, high mutation load, related signal pathways, and sensitivity to anticancer drugs that correlated with high-risk score of cancer progression and death were also identified. The six-gene signature may improve prognostic information, serve as a prognostic tool to manage patients after RP, and advance basic studies of PCa.

HMGB1 Promotes Prostate Cancer Development and Metastasis by Interacting with Brahma-Related Gene 1 and Activating the Akt Signaling Pathway

Background and Aim: We have previously shown that high-mobility group box 1 (HMGB1) is an independent biomarker for shortened survival of prostate cancer (PCa) patients. However, the specific role of HMGB1 in tumor development and progression remains largely unknown. In this study, we investigated the molecular mechanisms of HMGB1 in PCa tumorigenesis.

Methods: Gain-of-function and loss-of-function experiments were used to determine the biological functions of HMGB1 both in vitro and in vivo. Bioinformatic analysis, immunoprecipitation, and immunofluorescence assays were applied to discern and examine the relationship between HMGB1 and its potential targets. Specimens from 64 patients with PCa were analyzed for the expression of HMGB1 and its relationship with Brahma-related gene 1 (BRG1) was examined by immunohistochemistry.

Results: The results demonstrated that ectopic expression of HMGB1 facilitated growth and metastasis of PCa by enhancing Akt signaling pathway and promoting epithelial-mesenchymal transition (EMT), while silencing of HMGB1 showed the opposite effects. Mechanistically, HMGB1 exerted these functions through its interaction with BRG1 which may augment BRG1 function and activate the Akt signaling pathway thereby promoting EMT. Importantly, both HMGB1 and BRG1 expression was markedly increased in human PCa tissues.

Conclusions: Taken together, these findings indicate that upregulation of HMGB1 promotes PCa development via activation of Akt and accelerates metastasis through regulating BRG1-mediated EMT. HMGB1 could be used as a novel potential target for the treatment of PCa.

Endothelin-Receptor Antagonists beyond Pulmonary Arterial Hypertension: Cancer and Fibrosis

The endothelin axis and in particular the two endothelin receptors, ETA and ETB, are targets for therapeutic intervention in human diseases. Endothelin-receptor antagonists are in clinical use to treat pulmonary arterial hypertension and have been under clinical investigation for the treatment of several other diseases, such as systemic hypertension, cancer, vasospasm, and fibrogenic diseases. In this Perspective, we review the molecules that have been evaluated in human clinical trials for the treatment of pulmonary arterial hypertension, as well as other cardiovascular diseases, cancer, and fibrosis. We will also discuss the therapeutic consequences of receptor selectivity with regard to ETA-selective, ETB-selective, or dual ETA/ETB antagonists. We will also consider which chemical characteristics are relevant to clinical use and the properties of molecules necessary for efficacy in treating diseases against which known molecules displayed suboptimal efficacy.

A Meta-Analysis and Indirect Comparison of Endothelin A Receptor Antagonist for Castration-Resistant Prostate Cancer

Background

Endothelin A (ET-A) receptor antagonists including zibotentan and atrasentan, have been suggested as a treatment for castration-resistant prostate cancer (CRPC). Our aim was to conduct a meta-analysis and indirect comparison to assess the efficacy and safety of ET-A receptor antagonists for treatment of CRPC.

Methods

We systematically searched PubMed, EMBASE, the Cochrane Library, and Web of Science from inception to November 2014 to identify randomized controlled trials (RCTs) which assessed ET-A receptor antagonists for treatment of CRPC. Meta-analysis was conducted by STATA version 12.0 software.

Results

Eight RCTs were identified, involving 6,065 patients. The results of direct comparison showed that compared with placebo, there was no statistically significant difference in the improvement of progression-free survival (PFS), overall survival (OS), time to disease progression (TTP), and total adverse events (AEs) with ET-A receptor antagonist treatment for CRPC. The results of ET-A receptor antagonists plus docetaxel versus docetaxel alone were similar. The indirect comparisons showed that there were no significant differences between zibotentan plus docetaxel versus atrasentan plus docetaxel when compared with docetaxel alone or zibotentan versus atrasenta compared with placebo in the improvement of PFS, OS, TTP, and total adverse events.

Conclusions

There were no significant benefits for ET-A receptor antagonists with or without docetaxel in the improvement of PFS, OS, TTP, and overall AEs. And there were no significant differences between zibotentan and atrasentan. Single-agent docetaxel should remain as one of the standard treatments.