Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy

Integrated machine learning algorithms reveal a bone metastasis-related signature of circulating tumor cells in prostate cancer

Bone metastasis is an essential factor affecting the prognosis of prostate cancer (PCa), and circulating tumor cells (CTCs) are closely related to distant tumor metastasis. Here, the protein-protein interaction (PPI) networks and Cytoscape application were used to identify diagnostic markers for metastatic events in PCa. We screened ten hub genes, eight of which had area under the ROC curve (AUC) values > 0.85. Subsequently, we aim to develop a bone metastasis-related model relying on differentially expressed genes in CTCs for accurate risk stratification. We developed an integrative program based on machine learning algorithm combinations to construct reliable bone metastasis-related genes prognostic index (BMGPI). On the basis of BMGPI, we carefully evaluated the prognostic outcomes, functional status, tumor immune microenvironment, somatic mutation, copy number variation (CNV), response to immunotherapy and drug sensitivity in different subgroups. BMGPI was an independent risk factor for disease-free survival in PCa. The high risk group demonstrated poor survival as well as higher immune scores, higher tumor mutation burden (TMB), more frequent co-occurrence mutation, and worse efficacy of immunotherapy. This study highlights a new prognostic signature, the BMGPI. BMGPI is an independent predictor of prognosis in PCa patients and is closely associated with the immune microenvironment and the efficacy of immunotherapy.

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66-6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05-14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

Epigenetic-related gene-based prognostic model construction and validation in prostate adenocarcinoma

Prostate adenocarcinoma (PRAD), driven by both genetic and epigenetic factors, is a common malignancy that affects men worldwide. We aimed to identify and characterize differentially expressed epigenetic-related genes (ERGs) in PRAD and investigate their potential roles in disease progression and prognosis. We used PRAD samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to identify prognosis-associated ERGs. Thirteen ERGs with two distinct expression profiles were identified through consensus clustering. Gene set variation analysis highlighted differences in pathway activities, particularly in the Hedgehog and Notch pathways. Higher epigenetic scores correlated with favorable prognosis and improved immunotherapeutic response. Experimental validation underscored the importance of CBX3 and KAT2A, suggesting their pivotal roles in PRAD. This study provides crucial insights into the epigenetic scoring approach and presents a promising prognostic tool, with CBX3 and KAT2A as key players. These findings pave the way for targeted and personalized interventions for the treatment of PRAD.

Combating anoikis resistance: bioactive compounds transforming prostate cancer therapy

The study aims to discuss the challenges associated with treating prostate cancer (PCa), which is known for its complexity and drug resistance. It attempts to find differentially expressed genes (DEGs), such as those linked to anoikis resistance and circulating tumor cells, in PCa samples. This study involves analyzing the functional roles of these DEGs using gene enrichment analysis, and then screening of 102 bioactive compounds to identify a combination that can control the expression of the identified DEGs. In this study, 53 DEGs were identified from PCa samples including anoikis-resistant PCa cells and circulating tumor cells in PCa. Gene enrichment analysis with regards to functional enrichment of DEGs was performed. An inclusive screening process was carried out among 102 bioactive compounds to identify a combination capable of affecting and regulating the expression of selected DEGs. Eventually, gastrodin, nitidine chloride, chenodeoxycholic acid, and bilobalide were selected, as their combination demonstrated ability to modulate expression of 50 out of the 53 genes targeted. The subsequent analysis focused on investigating the biological pathways and processes influenced by this combination. The findings revealed a multifaceted and multidimensional approach to tumor regression. The combination of bioactive compounds exhibited effects on various genes including those related to production of inflammatory cytokines, cell proliferation, autophagy, apoptosis, angiogenesis, and metastasis. The current study has made a valuable contribution to the development of a combination of bioactive natural compounds that can significantly impede the development of treatment resistance in prostate tumor while countering the tumors' evasion of the immune system. The implications of this study are highly significant as it suggests the creation of an enhanced immunotherapeutic, natural therapeutic concoction with combinatorial potential.

Advances in sequencing and omics studies in prostate cancer: unveiling molecular pathogenesis and clinical applications

Background

Prostate cancer (PCa) is one of the most threatening health problems for the elderly males. However, our understanding of the disease has been limited by the research technology for a long time. Recently, the maturity of sequencing technology and omics studies has been accelerating the studies of PCa, establishing themselves as an essential impetus in this field.

Methods

We assessed Web of Science (WoS) database for publications of sequencing and omics studies in PCa on July 3rd, 2023. Bibliometrix was used to conduct ulterior bibliometric analysis of countries/affiliations, authors, sources, publications, and keywords. Subsequently, purposeful large amounts of literature reading were proceeded to analyze research hotspots in this field.

Results

3325 publications were included in the study. Research associated with sequencing and omics studies in PCa had shown an obvious increase recently. The USA and China were the most productive countries, and harbored close collaboration. CHINNAIYAN AM was identified as the most influential author, and CANCER RESEARCH exhibited huge impact in this field. Highly cited publications and their co-citation relationships were used to filtrate literatures for subsequent literature reading. Based on keyword analysis and large amounts of literature reading, 'the molecular pathogenesis of PCa' and 'the clinical application of sequencing and omics studies in PCa' were summarized as two research hotspots in the field.

Conclusion

Sequencing technology had a deep impact on the studies of PCa. Sequencing and omics studies in PCa helped researchers reveal the molecular pathogenesis, and provided new possibilities for the clinical practice of PCa.

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

Background

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

Methods

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

Results

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

Conclusion

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

Molecular panorama of therapy resistance in prostate cancer: a pre-clinical and bioinformatics analysis for clinical translation

Prostate cancer (PCa) is a malignant disorder of prostate gland being asymptomatic in early stages and high metastatic potential in advanced stages. The chemotherapy and surgical resection have provided favourable prognosis of PCa patients, but advanced and aggressive forms of PCa including CRPC and AVPC lack response to therapy properly, and therefore, prognosis of patients is deteriorated. At the advanced stages, PCa cells do not respond to chemotherapy and radiotherapy in a satisfactory level, and therefore, therapy resistance is emerged. Molecular profile analysis of PCa cells reveals the apoptosis suppression, pro-survival autophagy induction, and EMT induction as factors in escalating malignant of cancer cells and development of therapy resistance. The dysregulation in molecular profile of PCa including upregulation of STAT3 and PI3K/Akt, downregulation of STAT3, and aberrant expression of non-coding RNAs are determining factor for response of cancer cells to chemotherapy. Because of prevalence of drug resistance in PCa, combination therapy including co-utilization of anti-cancer drugs and nanotherapeutic approaches has been suggested in PCa therapy. As a result of increase in DNA damage repair, PCa cells induce radioresistance and RelB overexpression prevents irradiation-mediated cell death. Similar to chemotherapy, nanomaterials are promising for promoting radiosensitivity through delivery of cargo, improving accumulation in PCa cells, and targeting survival-related pathways. In respect to emergence of immunotherapy as a new tool in PCa suppression, tumour cells are able to increase PD-L1 expression and inactivate NK cells in mediating immune evasion. The bioinformatics analysis for evaluation of drug resistance-related genes has been performed.

Establishment of novel ferroptosis-related prognostic subtypes correlating with immune dysfunction in prostate cancer patients

Background

We aimed to identify two new prognostic subtypes and create a predictive index for prostate cancer (PCa) patients based on ferroptosis database.

Methods

The nonnegative matrix factorization approach was used to identify molecular subtypes. We investigate the differences between cluster 1 and cluster 2 in terms of clinical features, functional pathways, tumour stemness, tumour heterogeneity, gene mutation and tumour immune microenvironment score after identifying the two molecular subtypes. Colony formation assay and flow cytometry assay were performed.

Results

The stratification of two clusters was closely connected to BCR-free survival using the nonnegative matrix factorization method, which was validated in the other three datasets. Furthermore, multivariate Cox regression analysis revealed that this classification was an independent risk factor for patients with PCa. Ribosome, aminoacyl tRNA production, oxidative phosphorylation, and Parkinson's disease-related pathways were shown to be highly enriched in cluster 1. In comparison to cluster 2, patients in cluster 1 exhibited significantly reduced CD4+ T cells, CD8+ T cells, neutrophils, dendritic cells and tumor immune microenvironment scores. Only HHLA2 was more abundant in cluster 1. Moreover, we found that P4HB downregulation could significantly inhibit the colony formation ability and contributed to cell apoptosis of C4-2B and DU145 cell lines.

Conclusions

We discovered two new prognostic subtypes associated with immunological dysfunction in PCa patients based on ferroptosis-related genes and found that P4HB downregulation could significantly inhibit the colony formation ability and contributed to cell apoptosis of PCa cell lines.

Serine/threonine kinase 36 induced epithelial-mesenchymal transition promotes docetaxel resistance in prostate cancer

To investigate the role and potential mechanism of serine/threonine kinase 36 (STK36) in docetaxel resistance-prostate cancer (PCa). The expression of STK36 in PCa and the correlation with clinicopathological characteristics of PCa patients were analyzed using the data from different databases and tissue microarrays. To investigate the role of STK36 on cell proliferation, invasion, and migration, STK36 was overexpressed and silenced in DU-145 and PC-3 cell lines. Cell counting kit-8 (CCK8) was used to test cell proliferation. Cell invasion and migration were detected by cell wound scratch assay and trans well, respectively. The expression profile of STK36, E-Cadherin, and Vimentin was analyzed by Western blot. Cell apoptosis was detected by the TUNEL assay. STK36 expression was upregulated in PCa tissue compared with adjacent benign PCa tissue; it was higher in patients with advanced stages compared with lower stages and was significantly correlated with decreased overall survival. Up-regulation of STK36 significantly promoted the proliferation, invasion, and migration of DU-145 and PC-3 cells and compensated for the suppression caused by docetaxel treatment in vitro. A striking apoptosis inhibition could be observed when dealing with docetaxel, although the apoptosis of DU-145 and PC-3 cells was not affected by the STK36 exclusive overexpression. Besides, E-Cadherin expression was restrained while the expression levels of vimentin were all enhanced. The knockdown of STK36 reversed the above process. STK36 up-regulation could accelerate the biological behavior and docetaxel resistance of PCa by epithelial-mesenchymal transition (EMT) activation. STK36 may be potentially used as a target in PCa resolvent with docetaxel.

Identifying multi-target drugs for prostate cancer using machine learning-assisted transcriptomic analysis

Prostate cancer is a leading cause of cancer death in men, and the development of effective treatments is of great importance. This study explored to identify the candidate drugs for prostate cancer by transcriptomic data and CMap database analysis. After integrating the results of omics analysis, bisoprolol is confirmed as a promising drug. Moreover, cell experiment reveals its potential inhibitory effect on the proliferation of prostate cancer cells. Importantly, machine learning methods are employed to predict the targets of bisoprolol, and the dual-target ADRB3 and hERG are explored by dynamic simulation. The findings of this study demonstrate the potential of bisoprolol as a multi-target drug for prostate cancer treatment and the feasibility of using beta-adrenergic receptor inhibitors in prostate cancer treatment. In addition, the proposed research approach is promising for discovering potential drugs for cancer treatment by leveraging the concept of drug side effects leading to anticancer effects. Further research is necessary to investigate the pharmacological action, potential toxicity, and underlying mechanisms of bisoprolol in treating prostate cancer with ADRB3.Communicated by Ramaswamy H. Sarma.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Development of a novel, clinically relevant anoikis-related gene signature to forecast prognosis in patients with prostate cancer

Introduction: Anoikis is a specific form of programmed cell death and is related to prostate cancer (PC) metastasis. This study aimed to develop a reliable anoikis-related gene signature to accurately forecast PC prognosis. Methods: Based on anoikis-related genes and The Cancer Genome Atlas (TCGA) data, anoikis-related molecular subtypes were identified, and their differences in disease-free survival (DFS), stemness, clinical features, and immune infiltration patterns were compared. Differential expression analysis of the two subtypes and weighted gene co-expression network analysis (WGCNA) were employed to identify clinically relevant anoikis-related differentially expressed genes (DEGs) between subtypes, which were then selected to construct a prognostic signature. The clinical utility of the signature was verified using the validation datasets GSE116918 and GSE46602. A nomogram was established to predict patient survival. Finally, differentially enriched hallmark gene sets were revealed between the different risk groups. Results: Two anoikis-related molecular subtypes were identified, and cluster 1 had poor prognosis, higher stemness, advanced clinical features, and differential immune cell infiltration. Next, 13 clinically relevant anoikis-related DEGs were identified, and five of them (CKS2, CDC20, FMOD, CD38, and MSMB) were selected to build a prognostic signature. This gene signature had a high prognostic value. A nomogram that combined Gleason score, T stage, and risk score could accurately predict patient survival. Furthermore, gene sets closely related with DNA repair were differentially expressed in the different risk groups. Conclusion: A novel, clinically relevant five-anoikis-related gene signature was a powerful prognostic biomarker for PC.

Senescence-associated secretory phenotype constructed detrimental and beneficial subtypes and prognostic index for prostate cancer patients undergoing radical prostatectomy

BACKGROUND

Cellular senescence is growing in popularity in cancer. A dual function is played by the senescence-associated secretory phenotype (SASP) that senescent cells produce in the development of pro-inflammatory niches, tissue regeneration or destruction, senescence propagation, and malignant transformation. In this study, we conducted thorough bioinformatic analysis and meta-analysis to discover detrimental and beneficial subtypes and prognostic index for prostate cancer (PCa) patients using the experimentally confirmed SASP genes.

METHODS

We identified differentially expressed and prognosis-related SASP genes and used them to construct two molecular subtypes and risk score. Another two external cohorts were used to confirm the prognostic effect of the above subtypes and risk score and meta-analysis was further conducted. Additionally, functional analysis, tumor stemness and heterogeneity and tumor microenvironment were also evaluated. We completed analyses using software R 3.6.3 and its suitable packages. Meta-analysis was performed by software Stata 14.0.

RESULTS

Through multivariate Cox regression analysis and consensus clustering analysis, we used VGF, IGFBP3 and ANG to establish detrimental and beneficial subtypes in the TCGA cohort, which was validated through other two independent cohorts. Meta-analysis showed that detrimental SASP group had significantly higher risk of biochemical recurrence (BCR) than beneficial SASP group (HR: 2.48). Moreover, we also constructed and validated risk score based on these genes to better guide clinical practice. DNA repair, MYC target, oxidative phosphorylation, proteasome and ribosome were highly enriched in detrimental SASP group. Detrimental SASP group had significantly higher levels of B cells, CD8+ T cells, homologous recombination deficiency, loss of heterozygosity, microsatellite instability, purity, tumor mutation burden, mRNAsi, differentially methylated probes and epigenetically regulated RNA expression than beneficial SASP group. The top mutation genes between detrimental and beneficial SASP groups were SPOP, FOXA1, KMT2C, APC, BSN, DNAH17, MYH6, EPPK1, ZNF536 and ZC3H13 with statistical significance.

CONCLUSIONS

From perspective of SASP, we found detrimental and beneficial tumor subtypes which were closely associated with BCR-free survival for PCa patients, which might be important for the furture research in the field of PCa.

Prolyl 4-hydroxylase subunit beta (P4HB) could serve as a prognostic and radiosensitivity biomarker for prostate cancer patients

BACKGROUND

Prolyl 4-hydroxylase subunit beta (P4HB) has been reported as a suppressor in ferroptosis. However, no known empirical research has focused on exploring relationships between P4HB and prostate cancer (PCa). In this research, we initially examine the function of P4HB in PCa by thorough analysis of numerous databases and proliferation experiment.

METHODS

We analyzed the correlations of P4HB expression with prognosis, clinical features, mutation genes, tumor heterogeneity, stemness, tumor immune microenvironment and PCa cells using multiple databases and in vitro experiment with R 3.6.3 software and its suitable packages.

RESULTS

P4HB was significantly upregulated in tumor tissues compared to normal tissues and was closely related to biochemical recurrence-free survival. In terms of clinical correlations, we found that higher P4HB expression was significantly related to older age, higher Gleason score, advanced T stage and residual tumor. Surprisingly, P4HB had highly diagnostic accuracy of radiotherapy resistance (AUC 0.938). TGF beta signaling pathway and dorso ventral axis formation were upregulated in the group of low-expression P4HB. For tumor stemness, P4HB expression was positively related to EREG.EXPss and RNAss, but was negatively associated with ENHss and DNAss with statistical significance. For tumor heterogeneity, P4HB expression was positively related to MATH, but was negatively associated with tumor ploidy and microsatellite instability. For the overall assessment of TME, we observed that P4HB expression was negatively associated with all parameters, including B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score and ESTIMATE score. Spearman analysis showed that P4HB expression was negatively related to TIDE score with statistical significance. In vitro experiment, RT-qPCR and western blot showed that three siRNAs of P4HB were effective on the knockdown of P4HB expression. Furthermore, we observed that the downregulation of P4HB had significant influence on the cell proliferation of six PCa cell lines, including LNCap, C4-2, C4-2B, PC3, DU145 and 22RV1 cells.

CONCLUSIONS

In this study, we found that P4HB might serve as a prognostic biomarker and predict radiotherapy resistance for PCa patients. Downregulation of P4HB expression could inhibit the cell proliferation of PCa cells.

Prediction of prostate cancer biochemical recurrence by using discretization supports the critical contribution of the extra-cellular matrix genes

Due to its complexity, much effort has been devoted to the development of biomarkers for prostate cancer that have acquired the utmost clinical relevance for diagnosis and grading. However, all of these advances are limited due to the relatively large percentage of biochemical recurrence (BCR) and the limited strategies for follow up. This work proposes a methodology that uses discretization to predict prostate cancer BCR while optimizing the necessary variables. We used discretization of RNA-seq data to increase the prediction of biochemical recurrence and retrieve a subset of ten genes functionally known to be related to the tissue structure. Equal width and equal frequency data discretization methods were compared to isolate the contribution of the genes and their interval of action, simultaneously. Adding a robust clinical biomarker such as prostate specific antigen (PSA) improved the prediction of BCR. Discretization allowed classifying the cancer patients with an accuracy of 82% on testing datasets, and 75% on a validation dataset when a five-bin discretization by equal width was used. After data pre-processing, feature selection and classification, our predictions had a precision of 71% (testing dataset: MSKCC and GSE54460) and 69% (Validation dataset: GSE70769) should the patients present BCR up to 24 months after their final treatment. These results emphasize the use of equal width discretization as a pre-processing step to improve classification for a limited number of genes in the signature. Functionally, many of these genes have a direct or expected role in tissue structure and extracellular matrix organization. The processing steps presented in this study are also applicable to other cancer types to increase the speed and accuracy of the models in diverse datasets.

Establishment of cancer-associated fibroblasts-related subtypes and prognostic index for prostate cancer through single-cell and bulk RNA transcriptome

Current evidence indicate that cancer-associated fibroblasts (CAFs) play an important role in prostate cancer (PCa) development and progression. In this study, we identified CAF-related molecular subtypes and prognostic index for PCa patients undergoing radical prostatectomy through integrating single-cell and bulk RNA sequencing data. We completed analyses using software R 3.6.3 and its suitable packages. Through single-cell and bulk RNA sequencing analysis, NDRG2, TSPAN1, PTN, APOE, OR51E2, P4HB, STEAP1 and ABCC4 were used to construct molecular subtypes and CAF-related gene prognostic index (CRGPI). These genes could clearly divide the PCa patients into two subtypes in TCGA database and the BCR risk of subtype 1 was 13.27 times higher than that of subtype 2 with statistical significance. Similar results were observed in MSKCC2010 and GSE46602 cohorts. In addtion, the molucular subtypes were the independent risk factor of PCa patients. We orchestrated CRGPI based on the above genes and divided 430 PCa patients in TCGA database into high- and low- risk groups according to the median value of this score. We found that high-risk group had significant higher risk of BCR than low-risk group (HR: 5.45). For functional analysis, protein secretion was highly enriched in subtype 2 while snare interactions in vesicular transport was highly enriched in subtype 1. In terms of tumor heterogeneity and stemness, subtype 1 showd higher levels of TMB than subtype 2. In addition, subtype 1 had significant higher activated dendritic cell score than subtype 2. Based on eight CAF-related genes, we developed two prognostic subtypes and constructed a gene prognostic index, which could predict the prognosis of PCa patients very well.

Targeting PHB1 to inhibit castration-resistant prostate cancer progression in vitro and in vivo

BACKGROUND

Castration-resistant prostate cancer (CRPC) is currently the main challenge for prostate cancer (PCa) treatment, and there is an urgent need to find novel therapeutic targets and drugs. Prohibitin (PHB1) is a multifunctional chaperone/scaffold protein that is upregulated in various cancers and plays a pro-cancer role. FL3 is a synthetic flavagline drug that inhibits cancer cell proliferation by targeting PHB1. However, the biological functions of PHB1 in CRPC and the effect of FL3 on CRPC cells remain to be explored.

METHODS

Several public datasets were used to analyze the association between the expression level of PHB1 and PCa progression as well as outcome in PCa patients. The expression of PHB1 in human PCa specimens and PCa cell lines was examined by immunohistochemistry (IHC), qRT-PCR, and Western blot. The biological roles of PHB1 in castration resistance and underlying mechanisms were investigated by gain/loss-of-function analyses. Next, in vitro and in vivo experiments were conducted to investigate the anti-cancer effects of FL3 on CRPC cells as well as the underlying mechanisms.

RESULTS

PHB1 expression was significantly upregulated in CRPC and was associated with poor prognosis. PHB1 promoted castration resistance of PCa cells under androgen deprivation condition. PHB1 is an androgen receptor (AR) suppressive gene, and androgen deprivation promoted the PHB1 expression and its nucleus-cytoplasmic translocation. FL3, alone or combined with the second-generation anti-androgen Enzalutamide (ENZ), suppressed CRPC cells especially ENZ-sensitive CRPC cells both in vitro and in vivo. Mechanically, we demonstrated that FL3 promoted trafficking of PHB1 from plasma membrane and mitochondria to nucleus, which in turn inhibited AR signaling as well as MAPK signaling, yet promoted apoptosis in CRPC cells.

CONCLUSION

Our data indicated that PHB1 is aberrantly upregulated in CRPC and is involved in castration resistance, as well as providing a novel rational approach for treating ENZ-sensitive CRPC.

Membrane tension-mediated stiff and soft tumor subtypes closely associated with prognosis for prostate cancer patients

BACKGROUND

Prostate cancer (PCa) is usually considered as cold tumor. Malignancy is associated with cell mechanic changes that contribute to extensive cell deformation required for metastatic dissemination. Thus, we established stiff and soft tumor subtypes for PCa patients from perspective of membrane tension.

METHODS

Nonnegative matrix factorization algorithm was used to identify molecular subtypes. We completed analyses using software R 3.6.3 and its suitable packages.

RESULTS

We constructed stiff and soft tumor subtypes using eight membrane tension-related genes through lasso regression and nonnegative matrix factorization analyses. We found that patients in stiff subtype were more prone to biochemical recurrence than those in soft subtype (HR 16.18; p < 0.001), which was externally validated in other three cohorts. The top ten mutation genes between stiff and soft subtypes were DNAH, NYNRIN, PTCHD4, WNK1, ARFGEF1, HRAS, ARHGEF2, MYOM1, ITGB6 and CPS1. E2F targets, base excision repair and notch signaling pathway were highly enriched in stiff subtype. Stiff subtype had significantly higher TMB and T cells follicular helper levels than soft subtype, as well as CTLA4, CD276, CD47 and TNFRSF25.

CONCLUSIONS

From the perspective of cell membrane tension, we found that stiff and soft tumor subtypes were closely associated with BCR-free survival for PCa patients, which might be important for the future research in the field of PCa.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Tumor immune contexture predicts recurrence after prostatectomy and efficacy of androgen deprivation and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with notable interpatient heterogeneity. Implications of the immune microenvironment in predicting the biochemical recurrence-free survival (BCRFS) after radical prostatectomy and the efficacy of systemic therapies in prostate cancer remain ambiguous.

METHODS

The tumor immune contexture score (TICS) involving eight immune contexture-related signatures was developed using seven cohorts of 1120 patients treated with radical prostatectomy (training: GSE46602, GSE54460, GSE70769, and GSE94767; validation: GSE70768, DKFZ2018, and TCGA). The association between the TICS and treatment efficacy was investigated in GSE111177 (androgen deprivation therapy [ADT]) and EGAS00001004050 (ipilimumab).

RESULTS

A high TICS was associated with prolonged BCRFS after radical prostatectomy in the training (HR = 0.32, 95% CI 0.24-0.45, P < 0.001) and the validation cohorts (HR = 0.45, 95% CI 0.32-0.62, P < 0.001). The TICS showed stable prognostic power independent of tumor stage, surgical margin, pre-treatment prostatic specific antigen (PSA), and Gleason score (multivariable HR = 0.50, 95% CI 0.39-0.63, P < 0.001). Adding the TICS into the prognostic model constructed using clinicopathological features significantly improved its 1/2/3/4/5-year area under curve (P < 0.05). A low TICS was associated with high homologous recombination deficiency scores, abnormally activated pathways concerning DNA replication, cell cycle, steroid hormone biosynthesis, and drug metabolism, and fewer tumor-infiltrating immune cells (P < 0.05). The patients with a high TICS had favorable BCRFS with ADT (HR = 0.25, 95% CI 0.06-0.99, P = 0.034) or ipilimumab monotherapy (HR = 0.23, 95% CI 0.06-0.81, P = 0.012).

CONCLUSIONS

Our study delineates the associations of tumor immune contexture with molecular features, recurrence after radical prostatectomy, and the efficacy of ADT and immunotherapy. The TICS may improve the existing risk stratification systems and serve as a patient-selection tool for ADT and immunotherapy in prostate cancer.

An autophagy-related gene prognostic index predicting biochemical recurrence, metastasis, and drug resistance for prostate cancer

Given the dual role of autophagy presenting in tumorigenesis and inhibition, we established an autophagy-related gene prognostic index (ARGPI) with validation to well predict the biochemical recurrence (BCR), metastasis, as well as chemoresistance for patients with prostate cancer (PCa) who underwent radical radiotherapy or prostatectomy. Then, Lasso and COX regression was used to develop the ARGPI. We performed the whole analyses through R packages (version 3.6.3). Secreted phosphoprotein 1 (SPP1), single-minded 2 (SIM2), serine protease inhibitor b5 (SERPINB5), aldehyde dehydrogenase 2 (ALDH2), and acyl-CoA synthetase long-chain 3 (ACSL3) were eventually used to establish the ARGPI score. Patients were divided into two different-risk groups based on the median ARGPI score, high-risk patients with a higher risk of BCR than low-risk patients (hazard ratio [HR]: 5.46, 95% confidence interval [CI]: 3.23-9.24). The risk of metastasis of high-risk patients was higher than low-risk patients (HR: 11.31, 95% CI: 4.89-26.12). In The Cancer Genome Atlas (TCGA) dataset, we observed similar prognostic value of ARGPI in terms of BCR-free survival (HR: 1.79, 95% CI: 1.07-2.99) and metastasis-free survival (HR: 1.80, 95% CI: 1.16-2.78). ARGPI score showed a diagnostic accuracy of 0.703 for drug resistance. Analysis of gene set enrichment analysis (GSEA) indicated that patients in the high-risk group were significantly positively related to interleukin (IL)-18 signaling pathway. Moreover, ARGPI score was significantly related to cancer-related fibroblasts (CAFs; r = 0.36), macrophages (r = 0.28), stromal score (r = 0.38), immune score (r = 0.35), estimate score (r = 0.39), as well as tumor purity (r = -0.39; all P < 0.05). Drug analysis showed that PI-103 was the common sensitive drug and cell line analysis indicated that PC3 was the common cell line of PI-103 and the definitive gene. In conclusion, we found that ARGPI could predict BCR, metastasis, and chemoresistance in PCa patients who underwent radical radiotherapy or prostatectomy.

Identification of senescence-related molecular subtypes and key genes for prostate cancer

We identified distinct senescence-related molecular subtypes and critical genes among prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT). We conducted all analyses using R software and its suitable packages. Twelve genes, namely, secreted frizzled-related protein 4 (SFRP4), DNA topoisomerase II alpha (TOP2A), pleiotrophin (PTN), family with sequence similarity 107 member A (FAM107A), C-X-C motif chemokine ligand 14 (CXCL14), prostate androgen-regulated mucin-like protein 1 (PARM1), leucine zipper protein 2 (LUZP2), cluster of differentiation 38 (CD38), cartilage oligomeric matrix protein (COMP), vestigial-like family member 3 (VGLL3), apolipoprotein E (APOE), and aldehyde dehydrogenase 2 family member (ALDH2), were eventually used to subtype PCa patients from The Cancer Genome Atlas (TCGA) database and GSE116918, and the molecular subtypes showed good correlations with clinical features. In terms of the tumor immune environment (TME) analysis, compared with cluster 1, cancer-associated fibroblasts (CAFs) scored significantly higher, while endothelial cells scored lower in cluster 2 in TCGA database. There was a statistically significant correlation between both CAFs and endothelial cells with biochemical recurrence (BCR)-free survival for PCa patients undergoing RP. For the GSE116918 database, cluster 2 had significantly lower levels of CAFs and tumor purity and higher levels of stromal, immune, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) scores than cluster 1; in addition, patients with high levels of CAFs, stromal scores, immune scores, and ESTIMATE scores and low levels of tumor purity tended to suffer from BCR. Based on the median of differentially expressed checkpoints, high expression of CD96, hepatitis A virus cellular receptor 2 (HAVCR2), and neuropilin 1 (NRP1) in GSE116918 and high expression of CD160 and tumor necrosis factor (ligand) superfamily member 18 (TNFSF18) in TCGA database were associated with a significantly higher risk of BCR than their counterparts. In conclusion, we first constructed distinct molecular subtypes and critical genes for PCa patients undergoing RP or RT from the fresh perspective of senescence.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer

Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein-protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60-80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.

FASN multi-omic characterization reveals metabolic heterogeneity in pancreatic and prostate adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) and prostate cancer (PCa) are among the most prevalent malignant tumors worldwide. There is now a comprehensive understanding of metabolic reprogramming as a hallmark of cancer. Fatty acid synthase (FASN) is a key regulator of the lipid metabolic network, providing energy to favor tumor proliferation and development. Whereas the biological role of FASN is known, its response and sensitivity to inhibition have not yet been fully established in these two cancer settings.

METHODS

To evaluate the association between FASN expression, methylation, prognosis, and mutational profile in PDAC and PCa, we interrogated public databases and surveyed online platforms using TCGA data. The STRING database was used to investigate FASN interactors, and the Gene Set Enrichment Analysis platform Reactome database was used to perform an enrichment analysis using data from RNA sequencing public databases of PDAC and PCa. In vitro models using PDAC and PCa cell lines were used to corroborate the expression of FASN, as shown by Western blot, and the effects of FASN inhibition on cell proliferation/cell cycle progression and mitochondrial respiration were investigated with MTT, colony formation assay, cell cycle analysis and MitoStress Test.

RESULTS

The expression of FASN was not modulated in PDAC compared to normal pancreatic tissues, while it was overexpressed in PCa, which also displayed a different level of promoter methylation. Based on tumor grade, FASN expression decreased in advanced stages of PDAC, but increased in PCa. A low incidence of FASN mutations was found for both tumors. FASN was overexpressed in PCa, despite not reaching statistical significance, and was associated with a worse prognosis than in PDAC. The biological role of FASN interactors correlated with lipid metabolism, and GSEA indicated that lipid-mediated mitochondrial respiration was enriched in PCa. Following validation of FASN overexpression in PCa compared to PDAC in vitro, we tested TVB-2640 as a FASN inhibitor. PCa proliferation arrest was modulated by FASN inhibition in a dose- and time-dependent manner, whereas PDAC proliferation was not altered. In line with this finding, mitochondrial respiration was found to be more affected in PCa than in PDAC. FASN inhibition interfered with metabolic signaling causing lipid accumulation and affecting cell viability with an impact on the replicative processes.

CONCLUSIONS

FASN exhibited differential expression patterns in PDAC and PCa, suggesting a different evolution during cancer progression. This was corroborated by the fact that both tumors responded differently to FASN inhibition in terms of proliferative potential and mitochondrial respiration, indicating that its use should reflect context specificity.

The m6A methylation landscape, molecular characterization and clinical relevance in prostate adenocarcinoma

Background

Despite the recent progress of therapeutic strategies in treating prostate cancer (PCa), the majority of patients still eventually relapse, experiencing dismal outcomes. Therefore, it is of utmost importance to identify novel viable targets to increase the effectiveness of treatment. The present study aimed to investigate the potential relationship between N6-methyladenosine (m6A) RNA modification and PCa development and determine its clinical relevance.

Methods

Through systematic analysis of the TCGA database and other datasets, we analyzed the gene expression correlation and mutation profiles of m6A-related genes between PCa and normal tissues. Patient samples were divided into high- and low-risk groups based on the results of Least Absolute Shrinkage and Selection Operator (LASSO) Cox analysis. Subsequently, differences in biological processes and genomic characteristics of the two risk groups were determined, followed by functional enrichment analysis and gene set enrichment (GSEA) analysis. Next, we constructed the protein-protein interaction (PPI) network of differentially expressed genes between patients in high- and low-risk groups, along with the mRNA-miRNA-lncRNA network. The correlation analysis of tumor-infiltrating immune cells was further conducted to reveal the differences in immune characteristics between the two groups.

Results

A variety of m6A-related genes were identified to be differentially expressed in PCa tissues as compared with normal tissues. In addition, the PPI network contained 278 interaction relationships and 34 m6A-related genes, and the mRNA-miRNA-lncRNA network contained 17 relationships, including 91 miRNAs. Finally, the immune characteristics analysis showed that compared with the low-risk group, the levels of M1 and M2 macrophages in the high-risk group significantly increased, while the levels of mast cells resting and T cells CD4 memory resting significantly decreased.

Conclusions

This study provides novel findings that can further the understanding of the role of m6A methylation during the progression of PCa, which may facilitate the invention of targeted therapeutic drugs.

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.

The Development of a Gleason Score-Related Gene Signature for Predicting the Prognosis of Prostate Cancer

The recurrence of prostate cancer (PCa) is intrinsically linked to increased mortality. The goal of this study was to develop an efficient and reliable prognosis prediction signature for PCa patients. The training cohort was acquired from The Cancer Genome Atlas (TCGA) dataset, while the validation cohort was obtained from the Gene Expression Omnibus (GEO) dataset (GSE70769). To explore the Gleason score (GS)-based prediction signature, we screened the differentially expressed genes (DEGs) between low- and high-GS groups, and then univariate Cox regression survival analysis and multiple Cox analyses were performed sequentially using the training cohort. The testing cohort was used to evaluate and validate the prognostic model's effectiveness, accuracy, and clinical practicability. In addition, the correlation analyses between the risk score and clinical features, as well as immune infiltration, were performed. We constructed and optimized a valid and credible model for predicting the prognosis of PCa recurrence using four GS-associated genes (SFRP4, FEV, COL1A1, SULF1). Furthermore, ROC and Kaplan-Meier analysis revealed a higher predictive efficiency for biochemical recurrence (BCR). The results showed that the risk model was an independent prognostic factor. Moreover, the risk score was associated with clinical features and immune infiltration. Finally, the risk model was validated in a testing cohort. Our data support that the GS-based four-gene signature acts as a novel signature for predicting BCR in PCa patients.

Integrated analysis to identify the AC005154.6/hsa-miR-29c-3p/CCNL2 axis as a novel prognostic biomarker associated with immune infiltration in prostate cancer

Background

Prostate cancer (PCa) is currently the major malignancy in men. It is becoming increasingly clear that competitive endogenous RNA (ceRNA) regulation networks are important in a wide variety of cancers. Nevertheless, there is still much to learn about the biological functions of the ceRNA network in prostate cancer.

Methods

The ceRNA network was constructed using the "GDCRNATools" package. Based on survival analysis, we obtained AC005154.6/hsa-miR-29c-3p/CCNL2 for further analysis. The prognostic model based on this ceRNA network was constructed by univariate and multivariate Cox regression methods. Furthermore, functional enrichment analysis, mutation landscape analysis, immune infiltration analysis, drug sensitivity analysis, methylation analysis, pan-cancer analysis, and molecular experiments of CCNL2 were carried out to investigate the role of CCNL2 in tumorigenesis.

Results

We identified the AC005154.6/CCNL2 axis as a risk factor that can promote the progression of prostate cancer by bioinformatics analysis and molecular experiments. Immune infiltration analysis suggested that CCNL2 may act as a novel biomarker for treatment decisions. The methylation level of CCNL2 was significantly decreased in tumor samples, possibly contributing to the upregulation of CCNL2 in prostate cancer. Moreover, CCNL2 is differentially expressed in multiple cancers and is tightly correlated with immune infiltration.

Conclusion

The current study constructed a ceRNA network, AC005154.6/hsa-miR-29c-3p/CCNL2. Potentially, this biomarker can be used for early diagnosis and decision-making about prostate cancer treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02779-5.

Study on the role of SLC14A1 gene in biochemical recurrence of prostate cancer

Prostate cancer (PCa) is a common malignant disease among men and biochemical recurrence (BCR) is considered to be a decisive risk factor for clinical recurrence and PCa metastasis. Clarifying the genes related to BCR and its possible pathways is vital for providing diagnosis and treatment methods to delay the progress of BCR. An analysis of data concerning PCa from previous datasets of The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) was performed. Immunohistochemical (IHC) staining were used to evaluate the expression of SLC14A1 in prostate tissues. Kaplan-Meier analysis, Pearson correlation, and single sample Gene Set Enrichment Analysis (ssGSEA) were used to identify the potential pathway and molecular mechanism of the function of SLC14A1 in BCR of PCa. The expression of SLC14A1 is significantly reduced in prostate cancer cells and tissue comparing to normal prostate epithelial cell and para-cancerous tissue. As indicated by Kaplan-Meier analysis, High expression of SLC14A1 could increase the BCR-free survival time of PCa patients. This effect might be related to the interaction with miRNAs (has-miR-508, has-mir-514a2, and has-mir-449a) and the infiltration of B cells. SLC14A1 is a novel important gene associated with BCR of PCa, and further studies of its molecular mechanism may delay the progress of BCR.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

Leucine zipper protein 2 serves as a prognostic biomarker for prostate cancer correlating with immune infiltration and epigenetic regulation

Background

We sought to determine whether leucine zipper protein 2 (LUZP2) could benefit men with prostate cancer (PCa) undergoing radical radiotherapy (RT) or prostatectomy (RP).

Methods

Analysis was done on differentiating expression, clinical prognosis, co-expressed genes, immune infiltration, and epigenetic changes. All of our analyses were done using the R software (version 3.6.3) and the appropriate packages.

Results

In terms of PCa, tumor samples expressed LUZP2 more than normal samples did. In the TCGA database and GSE116918, we found that LUZP2 was the only independent risk factor for PCa. The shared enriched pathways for patients undergoing RP or RT were cell-cell adhesion, regulation of filopodium assembly, and extracellular matrix containing collagen. With the exception of TNFRSF14, we discovered that LUZP2 was negatively correlated with 21 immune checkpoints in PCa patients receiving RT. We found a significant inverse relationship between LUZP2 expression and the tumor immune environment, which included B cells, CD4+ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score, and estimate score, in patients receiving RP or RT. Additionally, tumor purity was positively correlated with LUZP2. We found that the drug bortezomib may be susceptible to the LUZP2. DNA methylation was significantly associated with the mRNA expression of LUZP2 in PCa patients from the TCGA database, and LUZP2 methylation was positively correlated with immune cells. The proliferative activity of various PCa cells, which correlated to different stages of this disease, was also found to be significantly reduced by LUZP2 reduction, according to the results of our experimental work.

Conclusions

We proposed a relatively comprehensive understanding of the roles of LUZP2 on PCa from the fresh perspective of senescence.

Mitochondrial Aldehyde Dehydrogenase 2 Represents a Potential Biomarker of Biochemical Recurrence in Prostate Cancer Patients

BACKGROUND

We aimed to explore the role of mitochondrial aldehyde dehydrogenase 2 (ALDH2) in prostate cancer (PCa) patients and provide insights into the tumor immune microenvironment (TME) for those patients undergoing radical radiotherapy.

METHODS

We performed all analyses using R version 3.6.3 and its suitable packages. Cytoscape 3.8.2 was used to establish network of competing endogenous RNAs (ceRNAs).

RESULTS

Downregulation of ADLH2 was significantly associated with higher risk of BCR-free survival (HR: 0.40, 95%CI: 0.24-0.68, p = 0.001) and metastasis-free survival (HR: 0.21, 95%CI: 0.09-0.49, p = 0.002). Additionally, ALDH2 repression contributed to significantly shorter BCR-free survival in the TCGA database (HR: 0.55, 95%CI: 0.33-0.93, p = 0.027). For immune checkpoints, patients that expressed a higher level of CD96 had a higher risk of BCR than their counterparts (HR: 1.79, 95%CI: 1.06-3.03, p = 0.032), as well as NRP1 (HR: 2.18, 95%CI: 1.29-3.69, p = 0.005). In terms of the TME parameters, the spearman analysis showed that ALDH was positively associated with B cells (r: 0.13), CD8+ T cells (r: 0.19), neutrophils (r: 0.13), and macrophages (r: 0.17). Patients with higher score of neutrophils (HR: 1.75, 95%CI: 1.03-2.95, p = 0.038), immune score (HR: 1.92, 95%CI: 1.14-3.25, p = 0.017), stromal score (HR: 2.52, 95%CI: 1.49-4.26, p = 0.001), and estimate score (HR: 1.81, 95%CI: 1.07-3.06, p = 0.028) had higher risk of BCR than their counterparts. Our ceRNA network found that PART1 might regulate the expression of ALDH via has-miR-578 and has-miR-6833-3p. Besides, PHA-793887, PI-103, and piperlongumine had better correlations with ALDH2.

CONCLUSIONS

We found that ALDH2 might serve as a potential biomarker predicting biochemical recurrence for PCa patients.

Long non‑coding RNA CASC11 interacts with YBX1 to promote prostate cancer progression by suppressing the p53 pathway

Prostate cancer (PCa) is one of the principal causes of cancer‑related death worldwide. The roles and mechanisms of long non‑coding RNA (lncRNA) involved in the development of PCa remain incompletely understood. The present study aimed to investigate the role and mechanism of lncRNA in PCa tumorigenesis. In the present study, lncRNA cancer susceptibility candidate 11 (CASC11) was revealed to be a crucial regulator of PCa progression. The expression profiles of CASC11 in PCa were identified through analysis of The Cancer Genome Atlas and Gene Expression Omnibus datasets, and validated in human PCa specimens and cell lines. Gain‑ and loss‑of‑function assays were utilized to explore the biological role of CASC11 in PCa initiation and progression. RNA‑sequencing, RNA pull‑down and RNA immunoprecipitation analyses were used to explore potential mechanisms with which CASC11 may be associated. Rescue experiments were further conducted to confirm this association. The present results revealed that CASC11 was dominantly distributed in the nuclei of PCa cells, and was highly expressed in PCa tissues and cells. Overexpression of CASC11 was markedly associated with increased tumor proliferation and migratory ability. Functionally, decreased proliferation and migration, as well as inhibited xenograft tumor growth, were observed in CASC11‑silenced PCa cells, whereas the opposite effects were detected in CASC11‑overexpressing cells. Mechanistically, CASC11 promoted progression of the cell cycle and competitively interacted with Y‑box binding protein 1 (YBX1) to block the p53 pathway. Given this, poly (β‑amino ester) (PBAE)/small interfering RNA‑CASC11 (si‑CASC11) nanoparticles were applied to inhibit CASC11 expression and enhance the antitumor effect in vivo. The results revealed that PBAE/si‑CASC11 nanoparticles augmented the antitumor efficacy of CASC11 knockdown in vivo. In conclusion, the present study suggested that CASC11 may regulate PCa progression and elucidated a novel CASC11/YBX1/p53 signaling axis, providing a potential lncRNA‑directed therapeutic strategy particularly for the treatment of patients with PCa.

A cellular senescence-related gene prognostic index for biochemical recurrence and drug resistance in patients with prostate cancer

In this study, we aimed to establish a novel cellular senescence-related gene prognostic index (CSG PI) to predict biochemical recurrence (BCR) and drug resistance in patients with prostate cancer (PCa) undergoing radical radiotherapy or prostatectomy. We performed all analyses using R version 3.6.3 and its suitable packages. Cytoscape 3.8.2 was used to establish a network of transcription factors and competing endogenous RNAs. Three cellular senescence-related genes were used to establish the CSGPI. We observed that CSGPI was an independent risk factor for BCR in PCa patients (HR: 2.62; 95% CI: 1.55-4.44), consistent with the results of external validation (HR: 1.88; 95% CI: 1.12-3.14). The CSGPI had a moderate diagnostic effect on drug resistance (AUC: 0.812, 95% CI: 0.586-1.000). The lncRNA PART1 was significantly associated with BCR (HR: 0.46; 95% CI: 0.27-0.77), and might modulate the mRNA expression of definitive genes through interactions with 57 miRNAs. Gene set enrichment analysis indicated that CSGPI was closely related to ECM receptor interaction, focal adhesion, TGF beta signaling pathway, pathway in cancer, regulation of actin cytoskeleton, and so on. Immune checkpoint analysis showed that PDCD1LG2 and CD96 were significantly higher in the BCR group compared to non-BCR group, and patients with higher expression of CD96 were more prone to BCR than their counterparts (HR: 1.79; 95% CI: 1.06-3.03). In addition, the CSGPI score was significantly associated with the mRNA expression of HAVCR2, CD96, and CD47. Analysis of mismatch repair and methyltransferase genes showed that DNMT3B was more highly expressed in the BCR group and that patients with higher expression of DNMT3B experienced a higher risk of BCR (HR: 2.08; 95% CI: 1.23-3.52). We observed that M1 macrophage, CD8+ T cells, stromal score, immune score, and ESTIMATE score were higher in the BCR group. In contrast, tumor purity was less scored in the BCR group. Spearman analysis revealed a positive relationship between CSGPI and M1 macrophages, CD4+ T cells, dendritic cells, stromal score, immune score, and ESTIMATE score. In conclusion, we found that the CSGPI might serve as a biomarker to predict BCR and drug resistance in PCa patients. Moreover, CD96 and DNMT3B might be potential treatment targets, and immune evasion might contribute to the BCR process of PCa.

FAM107A Inactivation Associated with Promoter Methylation Affects Prostate Cancer Progression through the FAK/PI3K/AKT Pathway

Simple Summary

Prostate cancer (PCa) is a common male malignancy. FAM107A, or actin-associated protein, is commonly downregulated in PCa and is associated with a poor patient prognosis. We investigated the role of FAM107A in PCa and found that downregulation of FAM107A expression was caused by hypermethylation of CpG islands, and DNA methyltransferase 1 (DNMT1) was involved in maintaining hypermethylation. Mechanistically, FAM107A regulated PCa cell growth through the FAK/PI3K/AKT signaling pathway. Therefore, FAM107A overexpression may represent a potential treatment for PCa, while therapies targeting epigenetic events that regulate FAM107A expression may also be an effective strategy for PCa treatment.

Abstract

Prostate cancer (PCa) is one of the most common cancers and is the second leading cause of mortality in men. Studies exploring novel therapeutic methods are urgently needed. FAM107A, a coding gene located in the short arm of chromosome3, is generally downregulated in PCa and is associated with a poor prognosis. However, the downregulation of FAM107A in PCa and the mechanism of its action remain challenging to determine. This investigation found that downregulation of FAM107A expression in PCa was caused by hypermethylation of CpG islands. Furthermore, DNA methyltransferase 1 (DNMT1) was involved in maintaining hypermethylation. Mechanistically, overexpression of FAM107A inhibits tumor cell proliferation, migration, invasion and promotes apoptosis through the FAK/PI3K/AKT signaling pathway, indicating that FAM107A may be a molecular brake of FAK/PI3K/AKT signaling, thus limiting the active state of the FAK/PI3K/AKT pathway. These findings will contribute to a better understanding of the effect of FAM107A in PCa, and FAM107A may represent a new therapeutic target for PCa.

Utilization of hypoxia-derived gene signatures to predict clinical outcomes and immune checkpoint blockade therapy responses in prostate cancer

Background: Increasing evidences show a clinical significance in the interaction between hypoxia and prostate cancer. However, reliable prognostic signatures based on hypoxia have not been established yet.

Methods: We screened hypoxia-related gene modules by weighted gene co-expression network analysis (WGCNA) and established a hypoxia-related prognostic risk score (HPRS) model by univariate Cox and LASSO-Cox analyses. In addition, enriched pathways, genomic mutations, and tumor-infiltrating immune cells in HPRS subgroups were analyzed and compared. HPRS was also estimated to predict immune checkpoint blockade (ICB) therapy response.

Results: A hypoxia-related 22-gene prognostic model was established. Furthermore, three independent validation cohorts showed moderate performance in predicting biochemical recurrence-free (BCR-free) survival. HPRS could be a useful tool in selecting patients who can benefit from ICB therapy. The CIBERSORT results in our study demonstrated that hypoxia might act on multiple T cells, activated NK cells, and macrophages M1 in various ways, suggesting that hypoxia might exert its anti-tumor effects by suppressing T cells and NK cells.

Conclusion: Hypoxia plays an important role in the progression of prostate cancer. The hypoxia-derived signatures are promising biomarkers to predict biochemical recurrence-free survival and ICB therapy responses in patients with prostate cancer.

LTBP2 inhibits prostate cancer progression and metastasis via the PI3K/AKT signaling pathway

Biochemical recurrence (BCR) is a cause of concern in advanced prostate cancer (PCa). Thus, novel diagnostic biomarkers are required to improve clinical care. However, research on PCa immunotherapy is also scarce. Hence, the present study aimed to explore promising BCR-related diagnostic biomarkers, and their expression pattern, prognostic value, immune response effects, biological functions, and possible molecular mechanisms were evaluated. GEO datasets (GSE46602, GSE70768, and GSE116918) were downloaded and merged as the training cohort, and differential expression analysis was performed. Lasso regression and SVM-RFE algorithm, as well as PPI analysis and MCODE algorithm, were then applied to filter BCR-related biomarker genes. The CIBERSORT and estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) methods were used to calculate the fractions of tumor-infiltrating immune cells. GO/DO enrichment analyses were used to identify the biological functions. The expression of latent transforming growth factor β-binding protein 2 (LTBP2) was determined by RT-qPCR and western blotting. The role of LTBP2 in PCa was determined by CCK-8, Transwell, and the potential mechanism was investigated by KEGG and GSEA and confirmed by western blotting. In total, 44 BCR-related differentially expressed genes (DEGs) in the training cohort were screened. LTBP2 was found to be a diagnostic biomarker of BCR in PCa and was associated with CD4⁺ T-cell infiltration and response to anti-PD-1/PD-L1 immunotherapy. Subsequently, using the ESTIMATE algorithm, it was identified that LTBP2 was associated with the tumor microenvironment and could be a predictor of the clinical benefit of immune checkpoint blockade. Finally, the expression and biological function of LTBP2 were evaluated via cellular experiments. The results showed that LTBP2 was downregulated in PCa cells and inhibited PCa proliferation and metastasis via the PI3K/AKT signaling pathway in vitro. In conclusion, LTBP2 was a promising diagnostic biomarker of BCR of PCa and had an important role in CD4⁺ T-cell recruitment. Moreover, it was associated with immunotherapy in patients with PCa who developed BCR, and it inhibited PCa proliferation and metastasis via the PI3K/AKT signaling pathway in vitro.

The genes controlling normal function of citrate and spermine secretion are lost in aggressive prostate cancer and prostate model systems

High secretion of the metabolites citrate and spermine is a unique hallmark for normal prostate epithelial cells, and is reduced in aggressive prostate cancer. However, the identity of the genes controlling this biological process is mostly unknown. In this study, we have created a gene signature of 150 genes connected to citrate and spermine secretion in the prostate. We have computationally integrated metabolic measurements with multiple transcriptomics datasets from the public domain, including 3826 tissue samples from prostate and prostate cancer. The accuracy of the signature is validated by its unique enrichment in prostate samples and prostate epithelial tissue compartments. The signature highlights genes AZGP1, ANPEP and metallothioneins with zinc-binding properties not previously studied in the prostate, and the expression of these genes are reduced in more aggressive cancer lesions. However, the absence of signature enrichment in common prostate model systems can make it challenging to study these genes mechanistically.

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Novel 150 gene signature reflecting prostatic citrate and spermine secretion

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Identified several zinc-binding proteins not previously investigated in the prostate

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The signature is absent in prostate model systems

Identification of novel biomarkers in prostate cancer diagnosis and prognosis

Prostate cancer (PCa) is a common urinary malignancy. The lack of specific and sensitive biomarkers for the early diagnosis and prognosis of PCa makes it important to seek alternatives. R software was used to analyze the PCa expression profile from data sets in Gene Expression Omnibus. Core differential genes were identified by String and Cytoscape and further validated by Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas (HPA). Gene Ontology analysis was done in the DIVID database and visualization analysis was conducted by Hiplot. Pathway enrichment was analyzed by IPA. To identify potential competitive endogenous RNAs (ceRNA) networks, the experimentally validated microRNA-target interactions database (miRTarBase), The Encyclopedia of RNA Interactomes (StarBase), lncBase, and GEPIA were used. The lncLocator was utilized to perform subcellular localization of long noncoding RNAs (lncRNAs). Both miRTarBase and StarBase were used to find the binding site of mRNAs-miRNAs and miRNAs-lncRNAs. Visualization of the ceRNA network was performed with Cytoscape. Nine genes closely related to the diagnosis and prognosis of PCa were obtained, including four identified biomarkers by HPA, CENPF, TPX2, TK1, and CCNB1, and five novel PCa biomarkers, RRM2, UBE2C, TOP2A, BIRC5, and ZWINT. Pathway analysis indicated that PCa carcinogenesis was highly correlated with liver fibrosis pathways, ILK signaling, and NRF2-mediated oxidative stress response. Two sets of ceRNA networks, BIRC5/hsa-miR-218-5p/NEAT1 and UBE2C/hsa-miR-483-3p/NEAT1 were found to be novel biomarkers for the identification of PCa. The quantitative real-time polymerase chain reaction results verified that UBE2C, BIRC5, and NEAT1 were upregulated and hsa-miR-218-5p and hsa-miR-483-3p were downregulated in human PCa cells compared with normal prostate epithelial cells. The novel identified biomarkers in this study would be valuable for the diagnosis and prognosis of PCa.

Energy Metabolism-Related Gene Prognostic Index Predicts Biochemical Recurrence for Patients With Prostate Cancer Undergoing Radical Prostatectomy

Background

We aimed to construct and validate an energy metabolism-related gene prognostic index (EMRGPI) to predict biochemical recurrence (BCR) in patients undergoing radical prostatectomy.

Methods

We used Lasso and COX regression analysis to orchestrate the EMRGPI in the TCGA database, and the prognostic value of EMRGPI was further validated externally using the GSE46602. All analyses were conducted with R version 3.6.3 and its suitable packages.

Results

SDC1 and ADH1B were finally used to construct the risk formula. We classified the 430 tumor patients in the TCGA database into two groups, and patients in the high-risk group had a higher risk of BCR than those in the low-risk group (HR: 1.98, 95%CI: 1.18-3.32, p=0.01). Moreover, in the GSE46602, we confirmed that the BCR risk in the high-risk group was 3.86 times higher than that in the low-risk group (95%CI: 1.61-9.24, p=0.001). We found that patients in the high-risk group had significantly higher proportions of residual tumor, older age, and T stage. SDC1 and ADH1B were significantly expressed low in the normal tissues when compared to the tumor tissues, which were opposite at the protein level. The spearman analysis showed that EMRGPI was significantly associated with B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score, and estimate score. In addition, the EMRGPI was positively associated with the 54 immune checkpoints, among which CD80, ADORA2A, CD160, and TNFRSF25 were significantly related to the BCR-free survival of PCa patients undergoing RP.

Conclusions

The EMRGPI established in this study might serve as an independent risk factor for PCa patients undergoing radical prostatectomy.

Mitochondria Dysfunction-Mediated Molecular Subtypes and Gene Prognostic Index for Prostate Cancer Patients Undergoing Radical Prostatectomy or Radiotherapy

Background

Given the age relevance of prostate cancer (PCa) and the role of mitochondrial dysfunction (MIDS) in aging, we orchestrated molecular subtypes and identified key genes for PCa from the perspective of MIDS.

Methods

Cluster analysis, COX regression analysis, function analysis, and tumor immune environment were conducted. We performed all analyses using software R 3.6.3 and its suitable packages.

Results

CXCL14, SFRP4, and CD38 were eventually identified to classify the PCa patients in The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) dataset into two distinct clusters. Patients in the cluster 2 had shorter BCR-free survival than those in the cluster 1 in terms of both TCGA database and GEO dataset. We divided the patients from the TCGA database and the GEO dataset into high- and low-risk groups according to the median of MIDS-related genetic prognostic index. For patients in the TCGA database, the biochemical recurrence (BCR) risk in high-risk group was 2.34 times higher than that in low-risk group. Similarly, for patients in the GEO dataset, the risk of BCR and metastasis in high-risk group was 2.35 and 3.04 times higher than that in low-risk group, respectively. Cluster 2 was closely associated with advanced T stage and higher Gleason score for patients undergoing radical prostatectomy or radiotherapy. For patients undergoing radical prostatectomy, the number of CD8⁺ T cells was significantly lower in cluster 2 than in cluster 1, while cluster 2 had significantly higher stromal score than cluster 1. For patients undergoing radical radiotherapy, cluster 2 had significantly higher level of CD8⁺ T cells, neutrophils, macrophages, dendritic cells, stromal score, immune score, and estimate score, but showed lower level of tumor purity than cluster 1.

Conclusions

We proposed distinctly prognosis-related molecular subtypes at genetic level and related formula for PCa patients undergoing radical prostatectomy or radiotherapy, mainly to provide a roadmap for precision medicine.

Identification of a Gene Signature to Aid Treatment Decisions by Integrated Analysis of Mutated Genes Between Primary and Metastatic Prostate Cancer

Prostate cancer is one of the most common malignancies in males. Despite the recent development of advanced diagnostic platforms and treatment, patients with metastatic disease still have a poor five-year survival rate. Cancer metastasis is correlated with the characteristics of the tumor microenvironment and is significantly associated with patient prognosis. In this study, we obtained mutated genes with significant differences between primary and metastatic prostate cancer from the COSMIC database. Unsupervised consensus clustering was used based on the 1,051 genes obtained, and two PCa clusters were identified, which exhibited different prognostic outcomes and immune characteristics. Next, we generated a scoring system and evaluated the prognostic value of riskscore and its potential to aid treatment decisions in clinical practice. The riskscore could be applied to predict patients’ response to immunotherapy and sensitivity to Docetaxel. In conclusion, this study performed an integrated analysis of mutated genes between primary and metastatic prostate cancer and provides a novel assessment scheme to precisely select treatment strategies.

Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

Prostate cancer (PCa) is the most prevalent cancer (20%) in males and is accountable for a fifth (6.8%) cancer-related deaths in males globally. Smoking, obesity, race/ethnicity, diet, age, chemicals and radiation exposure, sexually transmitted diseases, etc. are among the most common risk factors for PCa. However, the basic change at the molecular level is the manifested confirmation of PCa. Thus, this study aims to evaluate the molecular signature for PCa in comparison to benign prostatic hyperplasia (BPH). Additionally, representation of differentially expressed genes (DEGs) are conducted with the help of some bioinformatics tools like DAVID, STRING, GEPIA, Cytoscape. The gene expression profile for the four data sets GSE55945, GSE104749, GSE46602, and GSE32571 was downloaded from NCBI, Gene Expression Omnibus (GEO). For the extracted DEGs, different types of analysis including functional and pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis and transcription factor (TF) prediction were conducted. We obtained 633 most significant upregulated genes and 1219 downregulated genes, and a sum total of 1852 DEGs were found from all four datasets after assessment. The key genes, including EGFR, MYC, VEGFA, and PTEN, are targeted by TF such as AR, Sp1, TP53, NF-KB1, STAT3, RELA. Moreover, miR-21-5p also found significantly associated with all the four key genes. Further, The Cancer Genome Atlas data (TCGA) independent database was used for validation of key genes EGFR, MYC, VEGFA, PTEN expression in prostate adenocarcinoma. All four key genes were found to be significantly correlated with overall survival in PCa. Therefore, the therapeutic target may be determined by the information of these key gene’s findings for the diagnosis, prognosis and treatment of PCa.

Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

Background

Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study.

Methods

High-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa.

Results

A total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa.

Conclusion

This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.

Indolethylamine-N-Methyltransferase Inhibits Proliferation and Promotes Apoptosis of Human Prostate Cancer Cells: A Mechanistic Exploration

Indolethylamine-N-methyltransferase (INMT) is a methyltransferase downregulated in lung cancer, meningioma, and prostate cancer; however, its role and mechanism in prostate cancer remain unclear. By analyzing The Cancer Genome Atlas (TCGA)-PRAD, we found that the expression of INMT in prostate cancer was lower than that of adjacent non-cancerous prostate tissues and was significantly correlated with lymph node metastasis Gleason score, PSA expression, and survival. Combined with the GSE46602 cohorts for pathway enrichment analysis, we found that INMT was involved in regulating the MAPK, TGFβ, and Wnt signaling pathways. After overexpression of INMT in prostate cancer cell lines 22Rv1 and PC-3, we found an effect of INMT on these tumor signal pathways; overexpression of INMT inhibited the proliferation of prostate cancer cells and promoted apoptosis. Using the ESTIMATE algorithm, we found that with the increase of INMT expression, immune and stromal scores in the tumor microenvironment increased, immune response intensity increased, and tumor purity decreased. The difference in INMT expression affected the proportion of several immune cells. According to PRISM and CTRP2.0, the potential therapeutic agents associated with the INMT expression subgroup in TCGA were predicted. The area under the curve (AUC) values of 26 compounds positively correlated with the expression of INMT, while the AUC values of 14 compounds were negatively correlated with the expression of INMT. These findings suggest that INMT may affect prostate cancer’s occurrence, development, and drug sensitivity via various tumor signaling pathways and tumor microenvironments.

A Gene Prognostic Index Associated With Epithelial-Mesenchymal Transition Predicting Biochemical Recurrence and Tumor Chemoresistance for Prostate Cancer

Background

We aimed to establish a novel epithelial-mesenchymal transition (EMT)-related gene prognostic index (EMTGPI) associated with biochemical recurrence (BCR) and drug resistance for prostate cancer (PCa).

Methods

We used Lasso and Cox regression analysis to establish the EMTGPI. All analyses were conducted with R version 3.6.3 and its suitable packages.

Results

We established the EMTGPI based on SFRP4 and SPP1. Patients in high-risk group had 2.23 times of BCR risk than those in low-risk group (p = 0.003), as well as 2.36 times of metastasis risk (p = 0.053). In external validation, we detected similar diagnostic efficacy and prognostic value in terms of BCR free survival. For drug resistance, we observe moderately diagnostic accuracy of EMTGPI score (AUC: 0.804). We found that PDCD1LG2 (p = 0.04) and CD96 (p = 0.01) expressed higher in BCR patients compared with their counterpart. For TME analysis, we detected that CD8+ T cells and M1 macrophages expressed higher in BCR group. Moreover, stromal score (p = 0.003), immune score (p = 0.01), and estimate score (p = 0.003) were higher in BCR patients. We found that EMTGPI was significantly related to HAVCR2 (r: 0.34), CD96 (r: 0.26), CD47 (r: 0.22), KIR3DL1 (r: −0.21), KLRD1 (r: −0.21), and CD2 (r: 0.21). In addition, we observed that EMTGPI was significantly associated with M1 macrophages (r: 0.6), M2 macrophages (r: −0.33), monocytes (r: −0.18), neutrophils (r: −0.43), CD8+ T cells (r: 0.13), and dendritic cells (r: 0.37). PHA-793887 was the common drug sensitive to SPP1 and SFRP4, and PC3 and DU145 were the common PCa-related cell lines of SPP1, SFRP4, and PHA-793887.

Conclusions

We concluded that the EMTGPI score based on SFRP4 and SPP1 could be used to predict BCR for PCa patients. We confirmed the impact of immune evasion on the BCR process of PCa.

Integrative Multiomics Evaluation of IIDH1 Metabolic Enzyme as a Candidate Oncogene That is Correlated with Poor Prognosis and Immune Infiltration in Prostate Adenocarcinoma

Mutations in the isocitrate dehydrogenase gene (IDH1) are involved in the progression of tumors. Although IDH1 has a role in various tumors, its clinical relevance and its expression in response to the immune response have not been investigated in prostate adenocarcinoma (PRAD). In the present study, we investigated the utility of IDH1 as a prognostic biomarker for PRAD by analyzing IDH1 mRNA expression and its association with patient survival and immune cell infiltration. IDH1 mRNA expression was significantly higher in PRAD tissue than in normal tissue, and Kaplan–Meier survival analysis showed that IDH1 expression was significantly associated with poor prognosis in PRAD patients. To elucidate the mechanisms involved, the correlation between IDH1 expression and the level of immune cell infiltration, in particular of immunosuppressive cells such as CD8+ T-cells, CD4+ T-cells, and macrophages, was further analyzed by single-cell RNA sequencing. We also screened a pharmacogenetic database for IDH1-specific drugs that inhibited high expression in PRAD. In the present study, we used a combination of databases to identify a significant correlation between IDH1 expression and cellular infiltration and to explain the mechanism by which IDH1 confers poor prognosis in PRAD, thus demonstrating the relevance of IDH1 expression as a prognostic biomarker with clinical utility in PRAD patients.

Impact of genetically predicted atrial fibrillation on cancer risks: A large cardio-oncology Mendelian randomization study using UK biobank

Background

Increasing incidences of both atrial fibrillation (AF) and cancer have been observed in recent years. However, the casual association of both serious conditions has been scarcely evaluated and is considered to be a blank slate in cardio-oncology. Thus, we introduced Mendelian randomization (MR) methods to estimate the effects of AF on cancer risks.

Methods

We performed univariable and multivariable two-sample MR analyses to evaluate the effects of AF on the risk of 19 site-specific types of cancer. This MR study was conducted based on 111 independent AF-associated genetic instruments from genome-wide association studies and summarized-level data from corresponding cancer consortia. Multiple sensitivity analyses, including the leave-one-out analysis, MR-Egger regression, and MR-PRESSO tests, were further performed to examine the potential directional pleiotropic effects. Functional annotation was performed for common differentially expressed genes of AF and prostate cancer (PCA).

Results

A total of 6,777,155 European-descent people, including 533,725 cases and 6,243,430 controls, were included in the present MR analysis. Univariable MR analyses demonstrated a causal effect of AF on the incidence of PCA [odds ratio (OR): 0.96; 95% confidence interval (CI) 0.92-0.99, p = 0.01], and the causal effect remained significant (OR: 0.65; 95% CI 0.47-0.90, p = 0.01) after adjusting for potential confounders through the multivariable MR approach. However, no casual associations between AF and the other 18 site-specific cancer risks were observed (all p-values were > 0.05). The consistency of outcomes across complementary sensitivity MR methods further supported the causality. The functional analysis emphasized the essential role of antioxidant and xenobiotic catabolic processes in AF and PCA.

Conclusion

Contrary to the findings of several previous observational studies, our comprehensive MR analyses did not corroborate a causal role for AF in increasing the risk of various types of cancer. They did, however, demonstrate that AF may decrease the risk of PCA. Studies from larger sample sizes and individuals with different ethnic backgrounds are required to further support our conclusions.

A novel ferroptosis-related gene prognostic index for prognosis and response to immunotherapy in patients with prostate cancer

BACKGROUND

Prostate cancer (PCa) is among the leading causes of cancer death worldwide. Ferroptosis refers to an iron-dependent form of regulated cell death and is involved in prostate tumorigenesis. A few ferroptosis-related gene signatures have been developed to predict the prognosis for PCa patients. However, previous signatures were typically established based on biochemical recurrence-free survival, which has proven not to be a good surrogate for overall survival (OS). This study aimed to construct a novel ferroptosis-related gene prognostic index (FRGPI) to predict disease-free survival (DFS) and response to immunotherapy for PCa patients after radical prostatectomy.

METHODS

Gene expression and clinicopathological data on PCa patients were obtained from the TCGA database. Ferroptosis-related hub genes associated with DFS of PCa patients were identified by an in-depth bioinformatics analysis using a novel and comprehensive algorithm based on functional enrichment, consensus clustering, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. The FRGPI was established on the basis of the genes selected using multivariate cox regression analysis and further validated in two additional PCa cohorts. Next, the clinicopathological, molecular, and immune profiles were characterized and compared between FRGPI-high and FRGPI-low subgroups. Finally, the predictive role of the FRGPI in response to immunotherapy was estimated using a metastatic urothelial cancer cohort treated with an anti-PD-L1 agent.

RESULTS

The FRGPI was constructed based on four genes (E2F1, CDC20, TYMS, and NUP85), and FRGPI-high patients had worse DFS than FRGPI-low patients. Multivariate cox regression analysis revealed that FRGPI could act as an independent prognostic factor for PCa patients after radical prostatectomy. A prognostic nomogram comprising the FRGPI and other clinicopathological parameters was established to predict the DFS for PCa patients quantitatively. In addition, comprehensive results demonstrated that high FRGPI scores showed a significantly positive correlation with worse clinicopathological features, higher mutation counts, increased frequency of copy number variations (CNVs), higher homologous recombination deficiency (HRD) and immune scores, higher mRNAsi, and more importantly, enhanced sensitivity to immunotherapy.

CONCLUSIONS

FRGPI is not only a promising and robust prognostic biomarker, but also a potential indicator of immunotherapeutic outcomes for PCa patients after radical prostatectomy.