Emerging biomarkers of prostate cancer (Review)

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

Histologically benign PI-RADS 4 and 5 lesions contain cancer-associated epigenetic alterations

BACKGROUND

The detection rate of clinically significant prostate cancer has improved with the use of multiparametric magnetic resonance imaging (mpMRI). Yet, even with MRI-guided biopsy 15%-35% of high-risk lesions (Prostate Imaging-Reporting and Data System [PI-RADS] 4 and 5) are histologically benign. It is unclear if these false positives are due to diagnostic/sampling errors or pathophysiological alterations. To better understand this, we tested histologically benign PI-RAD 4 and 5 lesions for common malignant epigenetic alterations.

MATERIALS AND METHODS

MRI-guided in-bore biopsy samples were collected from 45 patients with PI-RADS 4 (n = 31) or 5 (n = 14) lesions. Patients had a median clinical follow-up of 3.8 years. High-risk mpMRI patients were grouped based on their histology into biopsy positive for tumor (BPT; n = 28) or biopsy negative for tumor (BNT; n = 17). From these biopsy samples, DNA methylation of well-known tumor suppressor genes (APC, GSTP1, and RARβ2) was quantified.

RESULTS

Similar to previous work we observed high rates of promoter methylation at GSTP1 (92.7%), RARβ2 (57.3%), and APC (37.8%) in malignant BPT samples but no methylation in benign TURP chips. Interestingly, similar to the malignant samples the BNT biopsies also had increased methylation at the promoter of GSTP1 (78.8%) and RARβ2 (34.6%). However, despite these epigenetic alterations none of these BNT patients developed prostate cancer, and those who underwent repeat mpMRI (n = 8) demonstrated either radiological regression or stability.

CONCLUSIONS

Histologically benign PI-RADS 4 and 5 lesions harbor prostate cancer-associated epigenetic alterations.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Multiplexed Prostate Cancer Companion Diagnostic Devices

Prostate cancer (PCa) remains one of the most prominent forms of cancer for men. Since the early 1990s, Prostate-Specific Antigen (PSA) has been a commonly recognized PCa-associated protein biomarker. However, PSA testing has been shown to lack in specificity and sensitivity when needed to diagnose, monitor and/or treat PCa patients successfully. One enhancement could include the simultaneous detection of multiple PCa-associated protein biomarkers alongside PSA, also known as multiplexing. If conventional methods such as the enzyme-linked immunosorbent assay (ELISA) are used, multiplexed detection of such protein biomarkers can result in an increase in the required sample volume, in the complexity of the analytical procedures, and in adding to the cost. Using companion diagnostic devices such as biosensors, which can be portable and cost-effective with multiplexing capacities, may address these limitations. This review explores recent research for multiplexed PCa protein biomarker detection using optical and electrochemical biosensor platforms. Some of the novel and potential serum-based PCa protein biomarkers will be discussed in this review. In addition, this review discusses the importance of converting research protocols into multiplex point-of-care testing (xPOCT) devices to be used in near-patient settings, providing a more personalized approach to PCa patients’ diagnostic, surveillance and treatment management.

Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

Clinical proteomics for prostate cancer: understanding prostate cancer pathology and protein biomarkers for improved disease management

Following the introduction of routine Prostate Specific Antigen (PSA) screening in the early 1990's, Prostate Cancer (PCa) is often detected at an early stage. There are also a growing number of treatment options available and so the associated mortality rate is generally low. However, PCa is an extremely complex and heterogenous disease and many patients suffer disease recurrence following initial therapy. Disease recurrence commonly results in metastasis and metastatic PCa has an average survival rate of just 3-5 years. A significant problem in the clinical management of PCa is being able to differentiate between patients who will respond to standard therapies and those who may benefit from more aggressive intervention at an earlier stage. It is also acknowledged that for many men the disease is not life threatenting. Hence, there is a growing desire to identify patients who can be spared the significant side effects associated with PCa treatment until such time (if ever) their disease progresses to the point where treatment is required. To these important clinical needs, current biomarkers and clinical methods for patient stratification and personlised treatment are insufficient. This review provides a comprehensive overview of the complexities of PCa pathology and disease management. In this context it is possible to review current biomarkers and proteomic technologies that will support development of biomarker-driven decision tools to meet current important clinical needs. With such an in-depth understanding of disease pathology, the development of novel clinical biomarkers can proceed in an efficient and effective manner, such that they have a better chance of improving patient outcomes.

An Update on the Prognostic and Predictive Serum Biomarkers in Metastatic Prostate Cancer

Serum biomarkers are molecules produced by normal and abnormal cells. Prostate specific antigen (PSA) is an example of a serum biomarker used widely in the diagnosis and prognostication of prostate cancer. PSA has its limitations as it is organ- but not cancer-specific. The aim of this review is to summarize the current published data on the potential prognostic and predictive biomarkers in metastatic prostate cancer (mPC) that can be used in conjunction with PSA. These biomarkers include microRNAs, androgen receptor variants, bone metabolism, neuroendocrine and metabolite biomarkers, and could guide treatment selection and sequence in an era where we strive to personalized therapy.

Analysis of Differential Expression of microRNAs and Their Target Genes in Prostate Cancer: A Bioinformatics Study on Microarray Gene Expression Data

Early diagnosis of prostate cancer (PCa) as the second most common cancer in men is not associated with precise and specific results. Thus, alternate methods with high specificity and sensitivity are needed for accurate and timely detection of PCa. MicroRNAs regulate the molecular pathways involved in cancer by targeting multiple genes. The aberrant expression of the microRNAs has been reported in different cancer types including PCa. In this bioinformatics study, we studied differential expression profiles of microRNAs and their target genes in four PCa gene expression omnibus (GEO) databases. PCa diagnostic biomarker candidates were investigated using bioinformatics tools for analysis of gene expression data, microRNA target prediction, pathway and GO annotation, as well as ROC curves. The results of this study revealed significant changes in the expression of 14 microRNAs and 40 relevant target genes, which ultimately composed four combination panels (miR- 375+96+663/ miR- 133b+143- 3p + 205/ C2ORF72 + ENTPD5 + GLYAT11/LAMB3 + NTNG2+TSLP) as candidate biomarkers capable to distinguish between PCa tumor samples and normal prostate tissue samples. These biomarkers may be suggested for a more accurate early diagnosis of PCa patients along with current diagnostic tests.

Evaluation of alternative serum biomarkers to monitor the progression of chronic HBV and HCV infection

Chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections are among the most serious health conditions affecting about 600 million people worldwide leading to a number of severe liver diseases. Due to the lack of warning signs or mild symptoms during the early stage of the infection, a molecular signature associated with disease progression would be useful. Based on our recent paper where candidate biomarkers were determined through topological and modularity analysis of protein interaction networks (PINs), this study was focused on the evaluation of MIF, TNFRSF1A, FAS and TMSB4X as diagnostic biomarkers in chronic HBV and HCV infections. The aim was to establish a molecular profile, by combining those markers, that would discriminate the different stages during the progression of chronic hepatitis. One hundred and fifteen patients infected with HBV or HCV categorized into three groups: non-cirrhotic, cirrhotic and with HCC, and 20 healthy subjects were enrolled in this study. Serum levels of the aforementioned factors were measured by ELISA. TNFRSF1A serum levels appeared statistically significantly increased in all patient groups compared to control group with a p-value of <0.05. Furthermore, the combination of TNFRSF1A and TMSB4X serum levels successfully classified 63, 47% of patients indicating an association with HBV and HCV infections. Thus, variations of serum levels of TNFRSF1A and TMSB4X could be associated with the different stages of the disease and may be utilized for further research. On the other hand, we found no contribution of MIF and FAS serum levels for successful classification of patients.

Molecular analyses of prostate tumors for diagnosis of malignancy on fine-needle aspiration biopsies

Prostate cancer (PCa) is a common cancer and remains the second-leading cause of cancer-associated mortality in men, but diagnosis of PCa remains a main clinical challenge. To investigate the involvement of differentially expressing genes in PCa with deregulated pathways to allow earlier diagnosis of the disease, transcriptomic analyses of differential expression genes in fine-needle aspiration (FNA) biopsies helped to discriminate PCa from benign prostatic hyperplasia (BPH). We identified 255 genes that were deregulated in prostate tumors compared with BPH tissues. qRT-PCR was conducted to examine the expression levels of the four genes in FNA biopsies and confirmed that ITGBL1 was significantly up-regulated and HOXA7, KRT15 and TGM4 were down-regulated in the PCa compared to the BPH, with a sensitivity of 87.1% and a specificity of 87.8%; the area under the receiver operating characteristic curve was estimated at 0.94, which was significantly improved compared with PSA alone (AUC = 0.82). Moreover, the increased expression of ITGBL1 correlated with total cholesterol, triglyceride and PSA. Our results demonstrated that transcriptomic analyses in FNA biopsies could facilitate rapid identification of potential targets for therapy and diagnosis of PCa.

Identification of Transcription Factor YY1 as a Regulator of a Prostate Cancer-Specific Pathway Using Proteomic Analysis

Prostate-specific antigen, a biomarker used to diagnose prostate cancer, exhibits poor sensitivity. Although previous studies have focused on identifying a new diagnostic biomarker, the molecules or networks identified in these studies are also present in other cancers, making it difficult to detect prostate cancer specifically. A unique characteristic of the prostate gland is the increased mitochondrial energy metabolism when normal prostate cells progress to cancer cells. Thus, we attempted to find a prostate cancer-specific signature present in this unique environment. Proteins that were differentially expressed between a prostate cell line and three prostate cancer cell lines were identified using proteomic analysis. Not surprisingly, the most prevalent proteins detected by network analysis of proteins that were up-regulated at least 1.2-fold in cancer cells, compared to that in normal prostate cells, were those involved in mitochondrial energy metabolism. In addition, we showed that Yin Yang 1 (YY1) was a major transcription factor involved in regulating energy metabolism. To determine whether YY1 regulates genes associated with mitochondrial energy metabolism in prostate cells, cells were subjected to quantitative polymerase chain reaction analysis in the presence or absence of the YY1 inhibitor NP-001. Notably, inhibition of YY1 resulted in reduced expression of genes related to the Krebs cycle and electron transport chain in prostate cancer cell lines. Based on this finding, we suggest that there is a tumor-specific signature that regulates mitochondrial energy metabolism in prostate cancer cells. This work provides a foundation for further work on identifying a means for the specific diagnosis of prostate cancer.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Overexpression and purification of folded domain of prostate cancer related proteins MSMB and PSA

Overexpression of domains of a human protein using recombinant DNA technology has been challenging because individual domains intend to accumulate as non-soluble aggregate when expressed separately. Studies on identifying right sequences for a domain to be able to fold independently may help understand the folding pattern and underlying protein-engineering events to isolate the functional domains of a protein. In this report, individual domains of prostate cancer related biomarkers; MSMB and PSA were overexpressed in bacterial system and purified in their folded forms using affinity chromatography. The western blotting experiment using domain specific antibodies further confirmed these proteins. The designed nucleotide sequences domains were truncated using fold index software and folding were predicted by phyre2 and I-TASSER software. Other parameters were optimized for their overexpression and purification using Co-NTA affinity chromatography. Purified domains of each protein showed secondary structures such as α + β type for PSA, α/β and β type for the each domains of PSA and MSMB respectively. This is the first report on producing PSA and MSMB individual domains in functional folded forms. This study may help produce the folded domain of many such proteins to be used for better diagnostic purpose.

Diagnostic and prognostic epigenetic biomarkers in cancer

Growing cancer incidence and mortality worldwide demands development of accurate biomarkers to perfect detection, diagnosis, prognostication and monitoring. Urologic (prostate, bladder, kidney), lung, breast and colorectal cancers are the most common and despite major advances in their characterization, this has seldom translated into biomarkers amenable for clinical practice. Epigenetic alterations are innovative cancer biomarkers owing to stability, frequency, reversibility and accessibility in body fluids, entailing great potential of assay development to assist in patient management. Several studies identified putative epigenetic cancer biomarkers, some of which have been commercialized. However, large multicenter validation studies are required to foster translation to the clinics. Herein we review the most promising epigenetic detection, diagnostic, prognostic and predictive biomarkers for the most common cancers.

The αvβ6 integrin is transferred intercellularly via exosomes

Exosomes, cell-derived vesicles of endosomal origin, are continuously released in the extracellular environment and play a key role in intercellular crosstalk. In this study, we have investigated whether transfer of integrins through exosomes between prostate cancer (PrCa) cells occurs and whether transferred integrins promote cell adhesion and migration. Among others, we have focused on the αvβ6 integrin, which is not detectable in normal human prostate but is highly expressed in human primary PrCa as well as murine PrCa in Pten(pc-/-) mice. After confirming the fidelity of the exosome preparations by electron microscopy, density gradient, and immunoblotting, we determined that the αvβ6 integrin is actively packaged into exosomes isolated from PC3 and RWPE PrCa cell lines. We also demonstrate that αvβ6 is efficiently transferred via exosomes from a donor cell to an αvβ6-negative recipient cell and localizes to the cell surface. De novo αvβ6 expression in an αvβ6-negative recipient cell is not a result of a change in mRNA levels but is a consequence of exosome-mediated transfer of this integrin between different PrCa cells. Recipient cells incubated with exosomes containing αvβ6 migrate on an αvβ6 specific substrate, latency-associated peptide-TGFβ, to a greater extent than cells treated with exosomes in which αvβ6 is stably or transiently down-regulated by shRNA or siRNA, respectively. Overall, this study shows that exosomes from PrCa cells may contribute to a horizontal propagation of integrin-associated phenotypes, which would promote cell migration, and consequently, metastasis in a paracrine fashion.

Serum fucosylated prostate-specific antigen (PSA) improves the differentiation of aggressive from non-aggressive prostate cancers

BACKGROUND

Clinically, it is still challenging to differentiate aggressive from non-aggressive prostate cancers (Pca) by non-invasive approaches. Our recent studies showed that overexpression of alpha (1-6) fucosyltransferase played an important role in Pca cells. In this study, we have investigated levels of glycoproteins and their fucosylated glycoforms in sera of Pca patients, as well as the potential utility of fucosylated glycoproteins in the identification of aggressive Pca.

MATERIAL AND METHODS

Serum samples from histomorphology-proven Pca cases were included. Prostate-specific antigen (PSA), tissue inhibitor of metallopeptidase 1 (TIMP1) and tissue plasminogen activator (tPA), and their fucosylated glycoforms were captured by Aleuria Aurantia Lectin (AAL), followed by the multiplex magnetic bead-based immunoassay. The level of fucosylated glycoproteins was correlated with patients' Gleason score of the tumor.

RESULT

Among three fucosylated glycoproteins, the fucosylated PSA was significantly increased and correlated with the tumor Gleason score (p<0.05). The ratio of fucosylated PSA showed a marked increase in aggressive tumors in comparison to non-aggressive tumors. ROC analysis also showed an improved predictive power of fucosylated PSA in the identification of aggressive Pca.

CONCLUSIONS

Our data demonstrated that fucosylated PSA has a better predictive power to differentiate aggressive tumors from non-aggressive tumors, than that of native PSA and two other glycoproteins. The fucosylated PSA has the potential to be used as a surrogate biomarker.

Biomarkers and personalized risk stratification for patients with clinically localized prostate cancer

Prostate cancer (PCa) is the most common neoplasia among men in developed countries and a leading cause of cancer-related morbidity and mortality. PCa is a very heterogeneous disease, both clinically and biologically. Currently, it is difficult to stratify patients into risk groups that entail different disease management. Therefore, a personalized view of this disease is mandatory, through the development of new and more accurate biomarkers that may help clinicians to stratify patients according to threat that PCa poses for each patient. Hence, this review focuses on recent developments of molecular and immunohistochemical biomarkers for PCa risk stratification that might enable a personalized approach to PCa patients. However, despite the increasing amount of available data, there is also an urgent need to translate the most promising biomarkers for clinical use through large multicenter validation trials. Ultimately, these will contribute for an improved clinical management of PCa patients.

Effects of USPSTF guidelines on patterns of screening and treatment outcomes for prostate cancer

The updated United States Preventive Services Task Force (USPSTF) for prostate cancer in 2012 recommends against prostate-specific antigen (PSA) based screening for men of all ages. Prostate cancer is the second most common and second most deadly cancer in American men. PSA screening for prostate cancer has been present since 1994 leading to an over diagnosis and over treatment of low volume disease. There is an overall agreement of men towards the guidelines but even with the understanding of the USPSTF, these men tend to follow more personal beliefs that have been influenced by their knowledge of the disease process and physician influence. Physicians also followed the directions of the patients and opted not to change their current practice of PSA screening despite the new guidelines. Time, legal, and ethical issues were some of the barriers that physicians faced in tailoring their practice towards screening. The importance of informed consent is highlighted by both the patients and the physicians and clearly more effective when the patient was pre-informed of the disease process and prompted the physicians to initiate conversation of informed screening. Younger patients were inclined towards aggressive treatment and older patients opted towards watchful waiting both with emphasis on the importance of evidence-based information provided by the physician. Decision aids were useful in making informed decisions and could be used to educate patients on screening purposes and treatment options. However, even with well-created decision aids and physician influence, patients’ own belief system played a major part in healthcare decision making in either screening or treatment for prostate cancer.

Lectin approaches for glycoproteomics in FDA-approved cancer biomarkers

The nine FDA-approved protein biomarkers for the diagnosis and management of cancer are approaching maturity, but their different glycosylation compositions relevant to early diagnosis still remain practically unexplored at the sub-glycoproteome scale. Lectins generally exhibit strong binding to specific sub-glycoproteome components and this property has been quite poorly addressed as the basis for the early diagnosis methods. Here, we discuss some glycoproteome issues that make tackling the glycoproteome particularly challenging in the cancer biomarkers field and include a brief view for next generation technologies.

Evaluation of 12-lipoxygenase (12-LOX) and plasminogen activator inhibitor 1 (PAI-1) as prognostic markers in prostate cancer

In carcinoma of prostate, a causative role of platelet 12-lipoxygenase (12-LOX) and plasminogen activator inhibitor 1 (PAI-1) for tumor progression has been firmly established in tumor and/or adjacent tissue. Our goal was to investigate if 12-LOX and/or PAI-1 in patient's plasma could be used to predict outcome of the disease. The study comprised 149 patients (age 70±9) divided into two groups: a study group with carcinoma confirmed by positive biopsy of prostate (n=116) and a reference group (n=33) with benign prostatic hyperplasia (BPH). The following parameters were determined by the laboratory test in plasma or platelet-rich plasma: protein level of 12-LOX, PAI-1, thromboglobulin (TGB), prostate specific antigen (PSA), C-reactive protein (CRP), hemoglobin (HGB, and hematocrit (HCT), as well as red (RBC) and white blood cells (WBC), number of platelets (PLT), international normalized ratio of blood clotting (INR), and activated partial thromboplastin time (APTT). The only difference of significance was noticed in the concentration of 12-LOX in platelet rich plasma, which was lower in cancer than in BPH group. Standardization to TGB and platelet count increases the sensitivity of the test that might be used as a biomarker to assess risk for prostate cancer in periodically monitored patients.

Exploring glyoxalase 1 expression in prostate cancer tissues: targeting the enzyme by ethyl pyruvate defangs some malignancy-associated properties

BACKGROUND

The glyoxalase (GLO)1 is part of a ubiquitous detoxification system in the glycolytic pathway of normal and tumor cells. It protects against cellular damage caused by cytotoxic metabolites.

METHODS

Aiming at exploring the role of GLO1 in prostate cancer, we evaluated and targeted the expression of GLO1 in prostate cancer tissues and cell lines and analyzed its correlation with grading systems and tumor growth indices.

RESULTS

Immunohistochemical studies on 37 prostate cancer specimens revealed a positive correlation between Helpap-grading and the cytoplasmic (P = 0.002)/nuclear (P = 0.006) GLO1 level. A positive correlation between Ki-67 proliferation marker and the cytoplasmic GLO1 (P = 0.006) was evident. Furthermore, the highest GLO1 level was detected in the androgen-sensitive LNCaP compared to the androgen-independent Du-145 and PC-3 prostate cell lines and the breast cancer cell MCF-7, both at protein and mRNA level. Treating cancer cells with ethyl pyruvate was found to defang some malignancy-associated properties of cancer cells including proliferation, invasion and anchorage-independent growth. In vitro results revealed that the potency of ethyl pyruvate is increased when cells are metabolically activated by growth stimulators, for example, by fetal calf serum, dihydrotestosterone, tumor growth factor-β1 and leptin.

CONCLUSIONS

The positive correlation of GLO1 expression level in prostate cancer tissues with the pathological grade and proliferation rate may assign GLO1 as a risk factor for prostate cancer development and progression. Furthermore, our data indicate that inhibitors of GLO1 might be useful to decelerate the cancer cell growth by a novel therapeutic approach that we may call "induced metabolic catastrophe."

Evaluation of prediction models for the staging of prostate cancer

Background

There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease.

Methods

A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study. Statistical models based on routinely used clinical variables were built using logistic regression, random forests and k nearest neighbours to predict prostate cancer stage. The predictive ability of the models was examined using discrimination metrics, calibration curves and clinical relevance, explored using decision curve analysis. The N = 603 patients were then applied to the 2007 Partin table to compare the predictions from the current gold standard in staging prediction to the models developed in this study.

Results

30% of the study cohort had non organ-confined disease. The model built using logistic regression illustrated the highest discrimination metrics (AUC = 0.622, Sens = 0.647, Spec = 0.601), best calibration and the most clinical relevance based on decision curve analysis. This model also achieved higher discrimination than the 2007 Partin table (ECE AUC = 0.572 & 0.509 for T1c and T2a respectively). However, even the best statistical model does not accurately predict prostate cancer stage.

Conclusions

This study has illustrated the inability of the current clinical variables and the 2007 Partin table to accurately predict prostate cancer stage. New biomarker features are urgently required to address the problem clinician’s face in identifying the most appropriate treatment for their patients. This paper also demonstrated a concise methodological approach to evaluate novel features or prediction models.

Molecular signatures in urologic tumors

Urologic tumors continue to represent a huge fraction of cancer cases in the United States, with over 376,310 estimated new diagnoses in 2013. As with many types of tumors, urologic tumors vary greatly in their phenotype, ranging from minimally invasive to malignancies possessing great metastatic potential. The increasing need for more efficient and less invasive methods of cancer detection, as well as the ability to predict severity of the disease phenotype is readily evident--yet reliable methods remain elusive in a clinical setting today. Comprehensive panels of gene clusters are being developed toward the generation of molecular signatures in order to better diagnose urologic malignancies, and identify effective treatment strategies in the emerging era of personalized medicine. In this review, we discuss the current literature on the credibility and biomarker value of such molecular signatures in the context of clinical significance relating to the pathological aggressiveness of urologic tumors (prostate, bladder and renal cancer)--also exploiting their predictive potential in the response to treatment.