The relationship between Prostate CAncer gene 3 (PCA3) and prostate cancer significance

P53-associated lncRNAs regulate immune functions and RNA-modifiers in gastric cancer

TP53, a guardian of the genome, suppresses or enhances tumors through various regulatory pathways. However, the role of p53-related long non-coding RNAs (lncRNAs) in immune regulation of tumor microenvironment and prognosis of gastric cancer (GC) is so far unelucidated. We analyzed the role of TP53-associated lncRNAs (obtained from the TP53LNC-DB database) in immune regulation, immune cell infiltration and RNA modification in gastric cancer. Firstly, using multivariate COX regression analysis, we identified eight lncRNAs related to the prognosis of GC. Furthermore, based on the expression of the lncRNA signature and risk score, the GC patients were divided into high-risk and low-risk groups. We found that M2-macrophages have significantly higher infiltration in the high-risk group. Similarly, significant differences in immune function (APC_co_stimulation, CCR, and checkpoint) and m6A modification (FTO, ZC3H13, YTHDC1, and RBM15), and m5C modification (NOP2 and TET1) between both groups were also observed. These signature lncRNAs were also positively associated with oxidative stress-related genes (MPO, MAPK14, HMOX1, and APP). Additionally, we found that high expression of GAS5 and low expression of MALAT1 in Helicobacter pylori (H-pylori) positive GC patients. Finally, GC patients in the low-risk group showed higher resistance to immunotherapy while patients in the high-risk group were more sensitive to various chemotherapy drugs. Based on these findings, we conclude that p53-associated lncRNAs signature could potentially predict the immune status and overall survival, and may also be used for risk management and planning immunotherapy for gastric cancer patients.

Molecular diagnostics of prostate cancer: impact of molecular tests

ABSTRACT

Prostate cancer (PCa) is the second leading cause of cancer-related death among men. Prostate-specific antigen (PSA) testing is used in screening programs for early detection with a consequent reduction of PCa-specific mortality at the cost of overdiagnosis and overtreatment of the nonaggressive PCa. Recently, several assays have been commercially developed to implement PCa diagnosis, but they have not been included in both screening and diagnosis of PCa. This review aims to describe the actual and novel commercially available molecular biomarkers that can be used in PCa management to implement and tailor the screening and diagnosis of PCa.

Prostate cancer knowledge and barriers to screening among men at risk in northern Tanzania: A community-based study

BACKGROUND

Although prostate cancer (Pca) screening plays important role in early diagnosis and reduction of mortality, Tanzanian men are relatively unscreened. We aimed to investigate Pca knowledge level and barriers to screening among at-risk men in northern Tanzania.

METHODS

This community-based survey was conducted in northern Tanzania from May to September 2022, involving men age ≥40 years. Participants were invited by announcing in local churches, mosques, brochures, and social media groups. Participants attended a nearby health facility where survey questionnaires were administered. Knowledge level was measured on the Likert scale and scored as poor (<50 %) or good (≥50 %).

RESULTS

A total of 6205 men with a mean age of 60.23 ± 10.98 years were enrolled in the study. Of these, 586 (9.5 %) had ever been screened for Pca. Overall, 1263 men (20.4 %) had good knowledge of Pca. Having health insurance, knowing at least 1 risk factor or symptoms of Pca, and hospital as the source of Pca information were significantly associated with ever being screened. The most common reasons for not being screened were a belief that they are healthy (n = 2983; 53.1 %), that Pca is not a serious disease (n = 3908; 69.6 %), and that digital rectal examination (DRE) as an embarrassing (n = 3634; 64.7 %) or harmful (n = 3047; 54.3 %) procedure.

CONCLUSION

Having Pca knowledge, health insurance and hospital source of information were correlated with increased screening. False beliefs about DRE and the seriousness of Pca had negative effects on screening. Increasing community knowledge and universal health coverage would improve uptake of Pca screening.

Long non-coding RNAs ANRIL, THRIL, and NEAT1 as potential circulating biomarkers of SARS-CoV-2 infection and disease severity

The current outbreak of coronavirus disease 2019 (COVID-19) is a global emergency, as its rapid spread and high mortality rate, which poses a significant threat to public health. Innate immunity plays a crucial role in the primary defense against infections, and recent studies have highlighted the pivotal regulatory function of long non-coding RNAs (lncRNAs) in innate immune responses. This study aims to assess the circulating levels of lncRNAs namely ANRIL, THRIL, NEAT1, and MALAT1 in the blood of moderate and severe SARS-CoV-2 infected patients, in comparison to healthy individuals. Additionally, it aims to explore the potential of these lncRNAs as biomarkers for determining the severity of the disease. The blood samples were collected from a total of 38 moderate and 25 severe COVID-19 patients, along with 30 healthy controls. The total RNA was extracted and qPCR was performed to evaluate the blood levels of the lncRNAs. The results indicate significantly higher expression levels of lncRNAs ANRIL and THRIL in severe patients when compared to moderate patients (P value = 0.0307, P value = 0.0059, respectively). Moreover, the expression levels of lncRNAs ANRIL and THRIL were significantly up-regulated in both moderate and severe patients in comparison to the control group (P value < 0.001, P value < 0.001, P value = 0.001, P value < 0.001, respectively). The expression levels of lncRNA NEAT1 were found to be significantly higher in both moderate and severe COVID-19 patients compared to the healthy group (P value < 0.001, P value < 0.001, respectively), and there was no significant difference in the expression levels of NEAT1 between moderate and severe patients (P value = 0.6979). The expression levels of MALAT1 in moderate and severe patients did not exhibit a significant difference compared to the control group (P value = 0.677, P value = 0.764, respectively). Furthermore, the discriminative power of ANRIL and THRIL was significantly higher in the severe patient group than the moderate group (Area under curve (AUC) = 0.6879; P-value = 0.0122, AUC = 0.6947; P-value = 0.0093, respectively). In conclusion, the expression levels of the lncRNAs ANRIL and THRIL are correlated with the severity of COVID-19 and can be regarded as circulating biomarkers for disease progression.

A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care

Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.

JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.

Identification and Validation of an Immune-Related lncRNA Signature to Facilitate Survival Prediction in Gastric Cancer

Background

Long noncoding RNAs (lncRNAs) are versatile in functions and can regulate cancer development, including the modulation of cancer immunity. Immune-related lncRNA signatures predicting prognosis have been reported in multiple cancers, but relevant studies in gastric cancer (GC) are still lacking.

Methods

We performed a comprehensive analysis using TCGA and Immport databases and identified an immune-related lncRNA signature by univariate and multivariate Cox regression analysis. qRT-PCR and immunohistochemistry assays were used for further validation. KEGG and GO analysis and ceRNA network establishment were carried out to explore the regulatory functions.

Results

We first identified an immune-related lncRNA signature, which can stratify gastric cancer patients into high- and low-risk subgroups and the high-risk cases frequently suffered from shorter overall survival time. Next, we validated the reliability of the lncRNA signature in an independent 75 gastric cancer samples and demonstrated that the three-year survival rate in high-risk patients was only 30.8% versus 66.5% in low-risk counterparts. Functional exploration indicated that the lncRNA signature might participate in multiple cancer-associated processes including cell adhesion and migration, cytokine-receptor interaction and immune evasion. Additionally, we observed that high-risk samples tended to form an immunosuppressive microenvironment, which had more M2-polarized macrophages and Tregs, but fewer CD8 effector T cells within tumors. Moreover, we found that PD-1 and PD-L1 were dramatically upregulated in a subset of high-risk patients with abundant M2 and Treg infiltration, implying these patients may benefit from anti-PD-1 and PD-L1 immunotherapy.

Conclusions

These results showed that the immune-related lncRNA signature had a prominent capacity to predict overall survival and the immune status of microenvironment in gastric cancer. Our findings may be useful for the risk-stratification management and provide a valuable clue to identify proper patients potentially benefit from immune checkpoint therapy in gastric cancer.

LPI-deepGBDT: a multiple-layer deep framework based on gradient boosting decision trees for lncRNA-protein interaction identification

Background

Long noncoding RNAs (lncRNAs) play important roles in various biological and pathological processes. Discovery of lncRNA–protein interactions (LPIs) contributes to understand the biological functions and mechanisms of lncRNAs. Although wet experiments find a few interactions between lncRNAs and proteins, experimental techniques are costly and time-consuming. Therefore, computational methods are increasingly exploited to uncover the possible associations. However, existing computational methods have several limitations. First, majority of them were measured based on one simple dataset, which may result in the prediction bias. Second, few of them are applied to identify relevant data for new lncRNAs (or proteins). Finally, they failed to utilize diverse biological information of lncRNAs and proteins.

Results

Under the feed-forward deep architecture based on gradient boosting decision trees (LPI-deepGBDT), this work focuses on classify unobserved LPIs. First, three human LPI datasets and two plant LPI datasets are arranged. Second, the biological features of lncRNAs and proteins are extracted by Pyfeat and BioProt, respectively. Thirdly, the features are dimensionally reduced and concatenated as a vector to represent an lncRNA–protein pair. Finally, a deep architecture composed of forward mappings and inverse mappings is developed to predict underlying linkages between lncRNAs and proteins. LPI-deepGBDT is compared with five classical LPI prediction models (LPI-BLS, LPI-CatBoost, PLIPCOM, LPI-SKF, and LPI-HNM) under three cross validations on lncRNAs, proteins, lncRNA–protein pairs, respectively. It obtains the best average AUC and AUPR values under the majority of situations, significantly outperforming other five LPI identification methods. That is, AUCs computed by LPI-deepGBDT are 0.8321, 0.6815, and 0.9073, respectively and AUPRs are 0.8095, 0.6771, and 0.8849, respectively. The results demonstrate the powerful classification ability of LPI-deepGBDT. Case study analyses show that there may be interactions between GAS5 and Q15717, RAB30-AS1 and O00425, and LINC-01572 and P35637.

Conclusions

Integrating ensemble learning and hierarchical distributed representations and building a multiple-layered deep architecture, this work improves LPI prediction performance as well as effectively probes interaction data for new lncRNAs/proteins.

Tissue- and Liquid-Based Biomarkers in Prostate Cancer Precision Medicine

Worldwide, prostate cancer (PC) is the second-most-frequently diagnosed male cancer and the fifth-most-common cause of all cancer-related deaths. Suspicion of PC in a patient is largely based upon clinical signs and the use of prostate-specific antigen (PSA) levels. Although PSA levels have been criticised for a lack of specificity, leading to PC over-diagnosis, it is still the most commonly used biomarker in PC management. Unfortunately, PC is extremely heterogeneous, and it can be difficult to stratify patients whose tumours are unlikely to progress from those that are aggressive and require treatment intensification. Although PC-specific biomarker research has previously focused on disease diagnosis, there is an unmet clinical need for novel prognostic, predictive and treatment response biomarkers that can be used to provide a precision medicine approach to PC management. In particular, the identification of biomarkers at the time of screening/diagnosis that can provide an indication of disease aggressiveness is perhaps the greatest current unmet clinical need in PC management. Largely through advances in genomic and proteomic techniques, exciting pre-clinical and clinical research is continuing to identify potential tissue, blood and urine-based PC-specific biomarkers that may in the future supplement or replace current standard practices. In this review, we describe how PC-specific biomarker research is progressing, including the evolution of PSA-based tests and those novel assays that have gained clinical approval. We also describe alternative diagnostic biomarkers to PSA, in addition to biomarkers that can predict PC aggressiveness and biomarkers that can predict response to certain therapies. We believe that novel biomarker research has the potential to make significant improvements to the clinical management of this disease in the near future.

The potential of long noncoding RNAs for precision medicine in human cancer

Precision medicine promises to better classify patients by individual clinical and biological biomarkers, which may provide an accurate assessment of disease risk, diagnosis, prognosis and treatment response. Cancer frequently displays substantial inter-tumor and intra-tumor heterogeneity and hence oncology is well suited for application of precision approaches. Recent studies have demonstrated that dysregulated lncRNAs play pivotal roles in tumor heterogeneity. In this review, attention is focused on the potential applications of lncRNAs as biomarker candidates for cancer risk evaluation, detection, surveillance and prognosis. LncRNAs are often stable in clinical samples and easily detected. The functional implications and therapeutic potential of targeting lncRNAs in human cancer are further discussed. Finally, existing deficiencies and future perspectives in translating fundamental lncRNA knowledge into clinical practice are highlighted.

Emerging impact of the long noncoding RNA MIR22HG on proliferation and apoptosis in multiple human cancers

An increasing number of studies have shown that long noncoding RNAs (lncRNAs) play important roles in diverse cellular processes, including proliferation, apoptosis, migration, invasion, chromatin remodeling, metabolism and immune escape. Clinically, the expression of MIR22HG is increased in many human tumors (colorectal cancer, gastric cancer, hepatocellular carcinoma, lung cancer, and thyroid carcinoma), while in others (esophageal adenocarcinoma and glioblastoma), it is significantly decreased. Moreover, MIR22HG has been reported to function as a competitive endogenous RNA (ceRNA), be involved in signaling pathways, interact with proteins and interplay with miRNAs as a host gene to participate in tumorigenesis and tumor progression. In this review, we describe the biological functions of MIR22HG, reveal its underlying mechanisms for cancer regulation, and highlight the potential role of MIR22HG as a novel cancer prognostic biomarker and therapeutic target that can increase the efficacy of immunotherapy and targeted therapy for cancer treatment.

Expression, Localization, and Function of the Nucleolar Protein BOP1 in Prostate Cancer Progression

Differentiating between indolent and aggressive prostate cancers (CaP) is important to decrease overtreatment and increase survival for men with the aggressive disease. Nucleolar prominence is a histologic hallmark of CaP; however, the expression, localization, and functional significance of specific nucleolar proteins have not been investigated thoroughly. The nucleolar protein block of proliferation 1 (BOP1) is associated with multiple cancers but has not been implicated in CaP thus far. Meta-analysis of publicly available data showed increased BOP1 expression in metastatic CaP and recurrent CaP, and was inversely associated with overall survival. Multiplexed immunohistochemistry was used to analyze expression and localization of BOP1 and nucleolar protein 56 in human tissue samples from various stages of CaP progression. Here, increased BOP1 expression was observed at later stages of CaP progression, coinciding with a localization change from nuclear to cytoplasmic. In patient samples, cytoplasmic BOP1 was also inversely associated with overall survival. In models of prostate cancer progression, BOP1 expression showed expression and localization similar to that in human patient samples. The functional significance of BOP1 in metastatic CaP was assessed by genetic knockdown, where BOP1 knockdown resulted in decreased proliferation and motility compared with control. Taken together, these data suggest prognostic significance of BOP1 expression and localization in CaP progression and provide a foundation for further investigation into the functional role of nucleolar proteins in advanced CaP.

An up-to-date catalogue of urinary markers for the management of prostate cancer

PURPOSE OF REVIEW

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. Poor specificity and sensitivity of total PSA often results in over and sometimes underdetection/treatment. Therefore, more specific and sensitive biomarkers for the detection and monitoring especially of clinically significant PCa as well as treatment-specific markers are much sought after. In this field, urine has emerged as a promising noninvasive source of biomarkers.

RECENT FINDINGS

RNA-based biomarkers are the most extensively studied type of urinary nucleic acids. ERG-Score/MiPS (Mi-Prostate Score) and SelectMDx might be considered as additional parameters together with clinical and imaging modalities to decrease unnecessary biopsies. miR Sentinel Tests could make it possible to accurately detect the presence of cancer and to distinguish low-grade from high-grade disease. In men with previous negative biopsies, PCA3 may suggest the need to repeat biopsy.

SUMMARY

The definitive role of these markers and their clinical benefit needs future validation.

Computational models for lncRNA function prediction and functional similarity calculation

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

The long non-coding RNA PCA3: an update of its functions and clinical applications as a biomarker in prostate cancer

Prostate cancer antigen 3 (PCA3) is an overexpressed prostate long non-coding RNA (lncRNA), transcribed from an intronic region at the long arm of human chromosome 9q21–22. It has been described that PCA3 modulates prostate cancer (PCa) cell survival through modulating androgen receptor (AR) signaling, besides controlling the expression of several androgen responsive and cancer-related genes, including epithelial–mesenchymal transition (EMT) markers and those regulating gene expression and cell signaling. Also, PCA3 urine levels have been successfully used as a PCa diagnostic biomarker. In this review, we have highlighted recent findings regarding PCA3, addressing its gene structure, putative applications as a biomarker, a proposed origin of this lncRNA, roles in PCa biology and expression patterns. We also updated data regarding PCA3 interactions with cancer-related miRNAs and expression in other tissues and diseases beyond the prostate. Altogether, literature data indicate aberrant expression and dysregulated activity of PCA3, suggesting PCA3 as a promising relevant target that should be even further evaluated on its applicability for PCa detection and management.

LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs

Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA-disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA-disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred's ability to discover potential lncRNA-disease associations.

A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA-disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leave-one-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004, and 0.9292 for known lncRNA-disease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.

[What primary care physicians should know about new markers in prostate cancer]

The use of prostate-specific antigen as a diagnostic tool in the screening of prostate cancer is reflected in an increase in the incidence, an increase in diagnosis at initial stages, and an increase in radical therapies, even at the expense of over-treatment in some cases. It is known from the data collected in the literature that not every patient with high prostate-specific antigen needs a biopsy, and that not every patient diagnosed with prostate cancer needs treatment. With the new emerging prostate markers, we will try to improve the specificity of prostate-specific antigen in the grey area (4-10 ng/ml) should be improved. This should avoid unnecessary biopsies. The sensitivity in the detection of significant prostate cancer with low prostate-specific antigen should also be improved in an attempt to reduce the risk of over-treatment. On the other hand, prognostic biomarkers with genomic tests will help to choose the best therapeutic option for the patient.

The role of prostate cancer antigen 3 (PCA3) in prostate cancer detection

INTRODUCTION

The prostate cancer antigen 3 (PCA3) score has been the first urine assay to obtain the Food and Drug Administration approval for guiding decisions regarding additional biopsies. Different aspects of this urinary assay (diagnostic performance, prognostic value, cost/benefit balance, integration with other molecular and imaging modalities) have now been well evaluated. Areas covered: This expert review will summarize current achievements and future perspectives provided by this urine biomarker. Expert commentary: The clinical benefit of the PCA3 score, in addition to the other established factors has been demonstrated before regarding biopsy decision making in men with persistent risk of prostate cancer. Its potential prognostic value also suggests its usefulness in selecting low risk patients for active surveillance protocols, however future daily-practice changing studies are needed. Economics assessment and additional value compared with other biomolecular and imaging modalities are still under investigation.

Correlation of mRNA-PCA3 urine levels with the new grading system in prostate cancer

PURPOSE

To evaluate the PCA3 (Prostate Cancer 3 gene) as a tool to improve prostate cancer (PCa) screening and its capability to predict PCa aggressiveness.

PATIENTS AND METHODS

A retrospective study with data from consecutive patients with suspected PCa seen in the urology department between November 2009 and April 2016 and who were candidates for prostate biopsy. A total of 1038 urine samples were tested in our laboratory with a kit that generated a PCA3 score (s-PCA3). A prostate biopsy was recommended only in those patients with s-PCA3≥35. Associations between variables were analyzed using the R software.

RESULTS

In patients with a positive s-PCA3 (44.5%), a subsequent biopsy was recommended. Of a total of 151 biopsies studied, 56.3% yielded a diagnosis of PCa. The probability of a positive biopsy increased as the s-PCA3 increased (p=0.041). The percentage of affected cylinders increased as the s-PCA3 increased (p=0.015). A statistically significant relationship was observed between s-PCA3 and both the Gleason score and the Grade Group (p=0.001 and 0.008, respectively). The best log-linear models and a logistic model confirmed the relationships shown previously with Fisher's exact tests.

CONCLUSIONS

S-PCA3 may serve as an additional marker to reduce the indication for biopsies and avoid overdiagnosis and overtreatment of patients with suspected PCa. The prognostic significance of s-PCA3 was confirmed, as it was associated with tumor volume and Gleason score. Importantly, to our knowledge this is the first time that an association has been demonstrated between s-PCA3 and the new Grade Group.

The Non-Coding Transcriptome of Prostate Cancer: Implications for Clinical Practice

Prostate cancer (PCa) is the most common type of cancer and the second leading cause of cancer-related death in men. Despite extensive research, the molecular mechanisms underlying PCa initiation and progression remain unclear, and there is increasing need of better biomarkers that can distinguish indolent from aggressive and life-threatening disease. With the advent of advanced genomic technologies in the last decade, it became apparent that the human genome encodes tens of thousands non-protein-coding RNAs (ncRNAs) with yet to be discovered function. It is clear now that the majority of ncRNAs exhibit highly specific expression patterns restricted to certain tissues and organs or developmental stages and that the expression of many ncRNAs is altered in disease and cancer, including cancer of the prostate. Such ncRNAs can serve as important biomarkers for PCa diagnosis, prognosis, or prediction of therapy response. In this review, we give an overview of the different types of ncRNAs and their function, describe ncRNAs relevant for the diagnosis and prognosis of PCa, and present emerging new aspects of ncRNA research that may contribute to the future utilization of ncRNAs as clinically useful therapeutic targets.

Combination of Prostate Cancer Antigen 3 and Prostate-Specific Antigen Improves Diagnostic Accuracy in Men at Risk of Prostate Cancer

Context.—

Prostate cancer antigen 3 (PCA3) is a noncoding RNA that is highly overexpressed in prostate cancer (PCa) tissue and excreted in urine in patients with PCa.

Objective.—

To assess the clinical utility of urinary PCA3 in men at risk of PCa.

Design.—

We retrospectively reviewed a cohort of 271 men (median age, 63 years) with elevated prostate-specific antigen (PSA), and/or strong family history, and/or abnormal digital rectal examination findings. Diagnostic sensitivity, specificity, positive and negative predictive values (PPV, NPV), positive and negative likelihood ratios (LR+, LR−), and diagnostic odds ratio (DOR), and area under the receiver-operating characteristic curves (AUC) were evaluated.

Results.—

PCA3 score was a significant predictor of prostate biopsy outcome (P &lt; .001). A PCA3 score of 30 was the optimal cutoff for our study cohort, with a diagnostic sensitivity of 72.7%, specificity of 67.5%, PPV of 47.1%, NPV of 86.2%, LR+ of 2.24, LR− of 0.40, and DOR of 5.55. At this cutoff score, the PCA3 assay could avoid 57.4% of unnecessary invasive biopsies in the overall study cohort and 70.3% in the subgroup with PSA level in the “gray zone” (4–10 ng/mL). A logistic regression algorithm combining PCA3 with PSA increased the AUC from 0.571 for PSA-only to 0.729 (P &lt; .001). The logistic combined marker gained the ability to discriminate low-grade from high-grade cancers.

Conclusions.—

Our data suggest that PCA3 improves the diagnostic sensitivity and specificity of PSA and that the combination of PCA3 with PSA gives better overall performance in identification of PCa than serum PSA alone in the high-risk population.

Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

Association Between Combined TMPRSS2:ERG and PCA3 RNA Urinary Testing and Detection of Aggressive Prostate Cancer

Importance

Potential survival benefits from treating aggressive (Gleason score, ≥7) early-stage prostate cancer are undermined by harms from unnecessary prostate biopsy and overdiagnosis of indolent disease.

Objective

To evaluate the a priori primary hypothesis that combined measurement of PCA3 and TMPRSS2:ERG (T2:ERG) RNA in the urine after digital rectal examination would improve specificity over measurement of prostate-specific antigen alone for detecting cancer with Gleason score of 7 or higher. As a secondary objective, to evaluate the potential effect of such urine RNA testing on health care costs.

Design, Setting, and Participants

Prospective, multicenter diagnostic evaluation and validation in academic and community-based ambulatory urology clinics. Participants were a referred sample of men presenting for first-time prostate biopsy without preexisting prostate cancer: 516 eligible participants from among 748 prospective cohort participants in the developmental cohort and 561 eligible participants from 928 in the validation cohort.

Interventions/Exposures

Urinary PCA3 and T2:ERG RNA measurement before prostate biopsy.

Main Outcomes and Measures

Presence of prostate cancer having Gleason score of 7 or higher on prostate biopsy. Pathology testing was blinded to urine assay results. In the developmental cohort, a multiplex decision algorithm was constructed using urine RNA assays to optimize specificity while maintaining 95% sensitivity for predicting aggressive prostate cancer at initial biopsy. Findings were validated in a separate multicenter cohort via prespecified analysis, blinded per prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) criteria. Cost effects of the urinary testing strategy were evaluated by modeling observed biopsy results and previously reported treatment outcomes.

Results

Among the 516 men in the developmental cohort (mean age, 62 years; range, 33-85 years) combining testing of urinary T2:ERG and PCA3 at thresholds that preserved 95% sensitivity for detecting aggressive prostate cancer improved specificity from 18% to 39%. Among the 561 men in the validation cohort (mean age, 62 years; range, 27-86 years), analysis confirmed improvement in specificity (from 17% to 33%; lower bound of 1-sided 95% CI, 0.73%; prespecified 1-sided P = .04), while high sensitivity (93%) was preserved for aggressive prostate cancer detection. Forty-two percent of unnecessary prostate biopsies would have been averted by using the urine assay results to select men for biopsy. Cost analysis suggested that this urinary testing algorithm to restrict prostate biopsy has greater potential cost-benefit in younger men.

Conclusions and Relevance

Combined urinary testing for T2:ERG and PCA3 can avert unnecessary biopsy while retaining robust sensitivity for detecting aggressive prostate cancer with consequent potential health care cost savings.

Prostate Cancer Antigen 3 Score Does Not Predict for Adverse Pathologic Features at Radical Prostatectomy or for Progression-free Survival in Clinically Localized, Intermediate- and High-risk Prostate Cancer

OBJECTIVE

To evaluate whether preoperative urinary prostate cancer antigen 3 (PCA3) scores predict for adverse pathologic features (APFs) or progression-free survival (PFS) in men with intermediate- or high-risk prostate cancer (PCa) undergoing radical prostatectomy (RP).

MATERIALS AND METHODS

One hundred nine men with intermediate- (n = 52) or high-risk (n = 57) PCa who underwent RP were retrospectively identified. Logistic regression analysis was performed to evaluate the association of PCA3 score with various APFs (eg, extracapsular extension, seminal vesicle invasion, etc.). Among 78 men with ≥1 year of follow-up, the association between PCA3 score and PFS was assessed using Cox regression analysis.

RESULTS

At RP, 52% of patients had at least 1 APF, and with median follow-up of 2.3 years, overall 3-year PFS was 70%. PCA3 was not a significant predictor of any APF on multivariate analysis (MVA), whereas canonical predictors (eg, biopsy Gleason score and initial prostate-specific antigen) remained predictive of various APFs. No significant predictors for PFS were found on MVA, although certain canonical predictors (eg, National Comprehensive Cancer Network risk group) were significant predictors of PFS on univariate analysis (UVA). PCA3 score was not a significant predictor of PFS on either UVA or MVA.

CONCLUSION

Unlike in lower risk cohorts, increasing PCA3 score was not associated with any APF in this higher risk cohort, despite enrichment for APFs, nor was it associated with PFS. Notably, multiple known preoperative predictors for APFs were significant on MVA, and multiple predictors were associated with PFS on UVA. Therefore, PCA3 may not be a useful adjunct predictive marker in men with intermediate- or high-risk PCa.

Insights from Global Analyses of Long Noncoding RNAs in Breast Cancer

PURPOSE OF REVIEW

The goal of this review was to compare and contrast the results and implications from several recent transcriptomic studies that analyzed the expression of lncRNAs in breast cancer. How many lncRNAs are dysregulated in breast cancer? Do dysregulated lncRNAs contribute to breast cancer etiology? Are lncRNAs viable biomarkers in breast cancer?

RECENT FINDINGS

Transcriptomic profiling of breast cancer tissues, mostly from The Cancer Genome Atlas, identified thousands of long noncoding RNAs that are expressed and dysregulated in breast cancer. The expression of lncRNAs alone can divide patients into molecular subtypes. Subsequent functional studies demonstrated that several of these lncRNAs have important roles in breast cancer cell biology.

SUMMARY

Thousands of lncRNAs are dysregulated in breast cancer that can be developed as biomarkers for prognostic or therapeutic purposes. The reviewed reports provide a roadmap to guide functional studies to discover lncRNAs with critical biological functions relating to breast cancer development and progression.

Active surveillance for prostate cancer

It is worth distinguishing between the two strategies of expectant management for prostate cancer. Watchful waiting entails administering non-curative androgen deprivation therapy to patients on development of symptomatic progression, whereas active surveillance entails delivering curative treatment on signs of disease progression. The objectives of the two management strategies and the patients enrolled in either are different: (i) to review the role of active surveillance as a management strategy for patients with low-risk prostate cancer; and (ii) review the benefits and pitfalls of active surveillance. We carried out a systematic review of active surveillance for prostate cancer in the literature using the National Center for Biotechnology Information's electronic database, PubMed. We carried out a search in English using the terms: active surveillance, prostate cancer, watchful waiting and conservative management. Selected studies were required to have a comprehensive description of the demographic and disease characteristics of the patients at the time of diagnosis, inclusion criteria for surveillance, and a protocol for the patients' follow up. Review articles were included, but not multiple papers from the same datasets. Active surveillance appears to reduce overtreatment in patients with low-risk prostate cancer without compromising cancer-specific survival at 10 years. Therefore, active surveillance is an option for select patients who want to avoid the side-effects inherent to the different types of immediate treatment. However, inclusion criteria for active surveillance and the most appropriate method of monitoring patients on active surveillance have not yet been standardized.

Molecular Updates in Prostate Cancer

A wide array of molecular markers and genomic signatures, reviewed in this article, may soon be used as adjuncts to currently established screening strategies, prognostic parameters, and early detection markers. Markers of genetic susceptibility to PCA, recurrent epigenetic and genetic alterations, including ETS gene fusions, PTEN alterations, and urine-based early detection marker PCA3, are discussed. Impact of recent genome-wide assessment on our understanding of key pathways of PCA development and progression and their potential clinical implications are highlighted.

Addressing the need for repeat prostate biopsy: new technology and approaches

No guidelines currently exist that address the need for rebiopsy in patients with a negative diagnosis of prostate cancer on initial biopsy sample analysis. Accurate diagnosis of prostate cancer in these patients is often complicated by continued elevation of serum PSA levels that are suggestive of prostate cancer, resulting in a distinct management challenge. Following negative initial findings of biopsy sample analysis, total serum PSA levels and serum PSA kinetics are ineffective indicators of a need for a repeat biopsy; therefore, patients suspected of having prostate cancer might undergo several unnecessary biopsy procedures. Several alternative strategies exist for identifying men who might be at risk of prostate cancer despite negative findings of biopsy sample analysis. Use of other serum PSA-related measurements enables more sensitive and specific diagnosis and can be combined with knowledge of clinicopathological features to improve outcomes. Other options include the FDA-approved Progensa(®) test and prostate imaging using MRI. Newer tissue-based assays that measure methylation changes in normal prostate tissue are currently being developed. A cost-effective strategy is proposed in order to address this challenging clinical scenario, and potential directions of future studies in this area are also described.

Constructing lncRNA functional similarity network based on lncRNA-disease associations and disease semantic similarity

Increasing evidence has indicated that plenty of lncRNAs play important roles in many critical biological processes. Developing powerful computational models to construct lncRNA functional similarity network based on heterogeneous biological datasets is one of the most important and popular topics in the fields of both lncRNAs and complex diseases. Functional similarity network consturction could benefit the model development for both lncRNA function inference and lncRNA-disease association identification. However, little effort has been attempted to analysis and calculate lncRNA functional similarity on a large scale. In this study, based on the assumption that functionally similar lncRNAs tend to be associated with similar diseases, we developed two novel lncRNA functional similarity calculation models (LNCSIM). LNCSIM was evaluated by introducing similarity scores into the model of Laplacian Regularized Least Squares for LncRNA–Disease Association (LRLSLDA) for lncRNA-disease association prediction. As a result, new predictive models improved the performance of LRLSLDA in the leave-one-out cross validation of various known lncRNA-disease associations datasets. Furthermore, some of the predictive results for colorectal cancer and lung cancer were verified by independent biological experimental studies. It is anticipated that LNCSIM could be a useful and important biological tool for human disease diagnosis, treatment, and prevention.

[Prostate cancer]

The Vall d'Hebron multidisciplinary prostate cancer (PC) team reviews recent advances in the management of this neoplasm. Screening studies with long follow-up show a reduction in mortality, whereas active surveillance is emerging as a therapeutic approach of non-aggressive cancers. New markers increase the specificity of PSA and also allow targeting suspected aggressive cancers. Multiparametric magnetic resonance (mMRI) has emerged as the most effective method in the selection of patients for biopsy and also for local tumor staging. The paradigm of random prostatic biopsy is changing through the fusion techniques that allow guiding ultrasonography-driven biopsy of suspicious areas detected in mMRI. Radical prostatectomy (RP) and radiotherapy (RT) are curative treatments of localized PC and both have experienced significant technological improvements. RP is highly effective and the incorporation of robotic surgery is reducing morbidity. Modern RT allows the possibility of high tumor dose with minimal adjacent dose reducing its toxicity. Androgen deprivation therapy with LHRH analogues remains the treatment of choice for advanced PC, but should be limited to this indication. The loss of bone mass and adverse metabolic effects increases the frequency of fractures and cardiovascular morbimortality. After castration resistance in metastatic disease, new hormone-based drugs have demonstrated efficacy even after chemotherapy resistance.

PCA3 in prostate cancer and tumor aggressiveness detection on 407 high-risk patients: a National Cancer Institute experience

BACKGROUND

Prostate cancer (PCa) is the most common male cancer in Europe and the US. The early diagnosis relies on prostate specific antigen (PSA) serum test, even if it showed clear limits. Among the new tests currently under study, one of the most promising is the prostate cancer gene 3 (PCA3), a non-coding mRNA whose level increases up to 100 times in PCa tissues when compared to normal tissues. With the present study we contribute to the validation of the clinical utility of the PCA3 test and to the evaluation of its prognostic potential.

METHODS

407 Italian men, with two or more PCa risk factors and at least a previous negative biopsy, entering the Urology Unit of Regina Elena National Cancer Institute, were tested for PCA3, total PSA (tPSA) and free PSA (fPSA and f/tPSA) tests. Out of the 407 men enrolled, 195 were positive for PCa and 114 of them received an accurate staging with evaluation of the Gleason score (Gs). Then, the PCA3 score was correlated to biopsy outcome, and the diagnostic and prognostic utility were evaluated.

RESULTS

Out of the 407 biopsies performed after the PCA3 test, 195 (48%) resulted positive for PCa; the PCA3 score was significantly higher in this population (p < 0.0001) differently to tPSA (p = 0.87). Moreover, the PCA3 test outperformed the f/tPSA (p = 0.01). The sensitivity (94.9) and specificity (60.1) of the PCA3 test showed a better balance for a threshold of 35 when compared to 20, even if the best result was achieved considering a cutoff of 51, with sensitivity and specificity of 82.1% and 79.3%, respectively. Finally, comparing values of the PCA3 test between two subgroups with increasing Gs (Gs ≤ 6 versus Gs ≥ 7) a significant association between PCA3 score and Gs was found (p = 0.02).

CONCLUSIONS

The PCA3 test showed the best diagnostic performance when compared to tPSA and f/tPSA, facilitating the selection of high-risk patients that may benefit from the execution of a saturation prostatic biopsy. Moreover, the PCA3 test showed a prognostic value, as higher PCA3 score values are associated to a greater tumor aggressiveness.

Projecting Benefits and Harms of Novel Cancer Screening Biomarkers: A Study of PCA3 and Prostate Cancer

BACKGROUND

New biomarkers for early detection of cancer must pass through several phases of development. Early phases provide information on diagnostic properties but not on population benefits and harms. Prostate cancer antigen 3 (PCA3) is a promising prostate cancer biomarker still in early development. We use simulation modeling to project the impact of adding PCA3 to prostate-specific antigen (PSA) screening on prostate cancer detection and mortality in the United States.

METHODS

We used data from a recent study of PCA3 in men referred for prostate biopsy to extend an existing simulation model of PSA growth, disease progression, and survival. We specified several PSA-PCA3 strategies designed to improve specificity and reduce overdiagnosis. Using these strategies to screen a cohort of men biennially between ages 50 and 74, we projected true- and false-positive tests, overdiagnoses, and lives saved relative to a PSA-based strategy with a cutoff of 4.0 ng/mL for biopsy referral.

RESULTS

We identified several PSA-PCA3 strategies that substantially reduced false-positive tests and overdiagnoses while preserving the majority of lives saved. PCA3>35 for biopsy referral in men with PSA between 4.0 and 10.0 ng/mL retained 85% of lives saved while approximately halving false positives and reducing overdiagnoses by 25%.

CONCLUSIONS

Adding PCA3 to PSA screening can significantly reduce adverse screening outcomes. Strategies can be identified that preserve most of the lives saved relative to PSA-based screening.

IMPACT

Simulation modeling provides advance projections of population outcomes of new screening biomarkers and may help guide early detection research.

Circulating biomarkers for discriminating indolent from aggressive disease in prostate cancer active surveillance

PURPOSE OF REVIEW

To review research on the use of circulating biomarkers to predict unfavorable tumor pathology in the setting of active surveillance, or in clinical contexts that are informative for active surveillance, such as men with low-risk prostate cancer evaluated for upgrading or upstaging at surgery.

RECENT FINDINGS

Biomarkers have been evaluated in serum, plasma, urine, and expressed prostatic secretions. Only a small number of biomarkers have been evaluated in multiple studies: %free prostate-specific antigen (PSA), PSA velocity, PSA doubling time, proPSA, PCA3, TMPRSS2-ERG. Single studies with relevance to active surveillance have evaluated microRNAs, circulating tumor cells, and exosomes. The most consistent significant associations with unfavorable tumor pathology have been with %free PSA. Associations with [-2]proPSA and Prostate Health Index have also been consistent; however, three of four studies come from the same active surveillance patient cohort.

SUMMARY

Circulating biomarkers represent a promising approach to identify men with apparently low-risk biopsy pathology, but who harbor potentially aggressive tumors unsuitable for active surveillance. Research is still at an early stage; existing biomarkers need rigorous validation with consistent methodology, and additional biomarkers need to be evaluated. Successful clinical translation would reduce the frequency of surveillance biopsies, and may enhance acceptance of active surveillance.

Function of PCA3 in prostate tissue and clinical research progress on developing a PCA3 score

Prostate cancer gene 3 (PCA3, also known as DD3) is a new biomarker that could improve the accuracy of prostate cancer diagnosis. It is a great biomarker with fairly high specificity and sensitivity. The incidence of prostate cancer is rising steadily in most countries. The commonly used prostate-specific antigen (PSA) test once gave people hope for early diagnosis of prostate cancer. However, the low specificity of the PSA test has resulted in a large number of unnecessary biopsies and overtreatment. During the past decade, many new prostate cancer biomarkers have been found. Among these, PCA3 is the most promising. Due to its great performance in distinguishing prostate cancer from other prostate conditions, PCA3 could likely be applied for early diagnosis of prostate cancer, patient follow-up, prognosis prediction, and targeted therapy. After years of research, we have obtained some knowledge about the sequence of PCA3 gene. We have also determined the relationship between PCA3 and the proliferation of prostate cancer cells and learned some information about how PCA3 affects tumor-related genes and proteins. A PCA3 score has been created, and it has been used in a variety of studies. Some researchers have even applied PCA3 to targeted therapy and obtained a good effect in vitro. This review describes the current state of research, and explores the future prospects for PCA3.

Comparative evaluation of urinary PCA3 and TMPRSS2: ERG scores and serum PHI in predicting prostate cancer aggressiveness

It has been suggested that urinary PCA3 and TMPRSS2:ERG fusion tests and serum PHI correlate to cancer aggressiveness-related pathological criteria at prostatectomy. To evaluate and compare their ability in predicting prostate cancer aggressiveness, PHI and urinary PCA3 and TMPRSS2:ERG (T2) scores were assessed in 154 patients who underwent radical prostatectomy for biopsy-proven prostate cancer. Univariate and multivariate analyses using logistic regression and decision curve analyses were performed. All three markers were predictors of a tumor volume≥0.5 mL. Only PHI predicted Gleason score≥7. T2 score and PHI were both independent predictors of extracapsular extension(≥pT3), while multifocality was only predicted by PCA3 score. Moreover, when compared to a base model (age, digital rectal examination, serum PSA, and Gleason sum at biopsy), the addition of both PCA3 score and PHI to the base model induced a significant increase (+12%) when predicting tumor volume>0.5 mL. PHI and urinary PCA3 and T2 scores can be considered as complementary predictors of cancer aggressiveness at prostatectomy.

The development of nomograms for stratification of men at risk of prostate cancer prior to prostate biopsy

A main limitation of early prostate cancer (PCa) detection due to elevated PSA levels is caused by the low specificity of PSA, which is associated with a high proportion of men detected with nonmalignant findings at first or subsequent prostate biopsy (PBX). Multivariate prediction models, such as nomograms, have been developed, providing a more accurate method to prospectively determine the risk of a positive PBX. Combining established clinical risk factors with novel diagnostic markers of PCa appears promising to further improve predictive accuracy estimates. Ideally, these nomograms should be capable of identifying PCa at PBX without missing men with high-grade PCa, and preventing a significant proportion of men without, or with insignificant, PCa from undergoing PBX. The intention is to reduce disease morbidity and mortality by detecting significant PCa at an early stage, and at the same time to avoid overdiagnosis as well as overintervention.

Curated microRNAs in urine and blood fail to validate as predictive biomarkers for high-risk prostate cancer

Purpose

The purpose of this study was to determine if microRNA profiling of urine and plasma at radical prostatectomy can distinguish potentially lethal from indolent prostate cancer.

Materials and Methods

A panel of microRNAs was profiled in the plasma of 70 patients and the urine of 33 patients collected prior to radical prostatectomy. Expression of microRNAs was correlated to the clinical endpoints at a follow-up time of 3.9 years to identify microRNAs that may predict clinical response after radical prostatectomy. A machine learning approach was applied to test the predictive ability of all microRNAs profiled in urine, plasma, and a combination of both, and global performance assessed using the area under the receiver operator characteristic curve (AUC). Validation of urinary expression of miRNAs was performed on a further independent cohort of 36 patients.

Results

The best predictor in plasma using eight miRs yielded only moderate predictive performance (AUC = 0.62). The best predictor of high-risk disease was achieved using miR-16, miR-21 and miR-222 measured in urine (AUC = 0.75). This combination of three microRNAs in urine was a better predictor of high-risk disease than any individual microRNA. Using a different methodology we found that this set of miRNAs was unable to predict high-volume, high-grade disease.

Conclusions

Our initial findings suggested that plasma and urinary profiling of microRNAs at radical prostatectomy may allow prognostication of prostate cancer behaviour. However we found that the microRNA expression signature failed to validate in an independent cohort of patients using a different platform for PCR. This highlights the need for independent validation patient cohorts and suggests that urinary microRNA signatures at radical prostatectomy may not be a robust way to predict the course of clinical disease after definitive treatment for prostate cancer.

Diagnostic and predictive value of urine PCA3 gene expression for the clinical management of patients with altered prostatic specific antigen

OBJECTIVE

Analyze the impact of the introduction of the study of PCA3 gene in post-prostatic massage urine in the clinical management of patients with PSA altered, evaluating its diagnostic ability and predictive value of tumor aggressiveness.

METHODS

Observational, prospective, multicenter study of patients with suspected prostate cancer (PC) candidates for biopsy. We present a series of 670 consecutive samples of urine collected post-prostatic massage for three years in which we determined the "PCA3 score" (s-PCA3). Biopsy was only indicated in cases with s-positive PCA3.

RESULTS

The s-PCA3 was positive in 43.7% of samples. In the 124 biopsies performed, the incidence of PC or atypical small acinar proliferation was 54%, reaching 68,6% in s-PCA3≥100. Statistically significant relationship between the s-PCA3 and tumor grade was demonstrated. In cases with s-PCA3 between 35 and 50 only 23% of PC were high grade (Gleason≥7), compared to 76.7% in cases with s-PCA3 over 50. There was a statistically significant correlation between s-PCA3 and cylinders affected. Both relationships were confirmed by applying a log-linear model.

CONCLUSIONS

The incorporation of PCA3 can avoid the need for biopsies in 54% of patients. s-PCA3 positivity increases the likelihood of a positive biopsy, especially in higher s-PCA3 100 (68.6%). s-PCA3 is also an indicator of tumor aggressiveness and provides essential information in making treatment decisions.