Defining prostate cancer risk before prostate biopsy

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

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

Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer

In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.

Underlying Features of Prostate Cancer-Statistics, Risk Factors, and Emerging Methods for Its Diagnosis

Prostate cancer (PCa) is the most frequently occurring type of malignant tumor and a leading cause of oncological death in men. PCa is very heterogeneous in terms of grade, phenotypes, and genetics, displaying complex features. This tumor often has indolent growth, not compromising the patient's quality of life, while its more aggressive forms can manifest rapid growth with progression to adjacent organs and spread to lymph nodes and bones. Nevertheless, the overtreatment of PCa patients leads to important physical, mental, and economic burdens, which can be avoided with careful monitoring. Early detection, even in the cases of locally advanced and metastatic tumors, provides a higher chance of cure, and patients can thus go through less aggressive treatments with fewer side effects. Furthermore, it is important to offer knowledge about how modifiable risk factors can be an effective method for reducing cancer risk. Innovations in PCa diagnostics and therapy are still required to overcome some of the limitations of the current screening techniques, in terms of specificity and sensitivity. In this context, this review provides a brief overview of PCa statistics, reporting its incidence and mortality rates worldwide, risk factors, and emerging screening strategies.

Immunoarray Measurements of Parathyroid Hormone-Related Peptides Combined with Other Biomarkers to Diagnose Aggressive Prostate Cancer

Parathyroid hormone-related peptide (PTHrP) is related to bone metastasis and hypercalcemia in prostate and breast cancers and should be an excellent biomarker for aggressive forms of these cancers. Current clinical detection protocols for PTHrP are immunoradiometric assay and radioimmunoassay but are not sensitive enough to detect PTHrPs at early stages. We recently evaluated a prostate cancer biomarker panel, including serum monocyte differentiation antigen (CD-14), ETS-related gene protein, pigment epithelial-derived factor, and insulin-like growth factor-1, with promise for identifying aggressive prostate cancers. This panel predicted the need for patient biopsy better than PSA alone. In the present paper, we report an ultrasensitive microfluidic assay for PTHrPs and evaluate their diagnostic value and the value of including them with our prior biomarker panel to diagnose aggressive forms of prostate cancer. The immunoarray features screen-printed carbon sensor electrodes coated with 5 nm glutathione gold nanoparticles with capture antibodies attached. PTHrPs are bound to a secondary antibody attached to a polyhorseradish peroxidase label and delivered to the sensors to provide high sensitivity when activated by H₂O₂ and a mediator. We obtained an unprecedented 0.3 fg mL^-1 limit of detection for PTHrP bioactive moieties PTHrP 1-173 and PTHrP 1-86. We also report the first study of PTHrPs in a large serum pool to identify aggressive malignancies. In assays of 130 human patient serum samples, PTHrP levels distinguished between aggressive and indolent prostate cancers with 83-91% clinical sensitivity and 78-96% specificity. Logistic regression identified the best predictive model as a combination of PTHrP 1-86 and vascular endothelial growth factor-D. PTHrP 1-173 alone also showed a high ability to differentiate aggressive and indolent cancers.

Competitive Endogenous RNA Network Involving miRNA and lncRNA in Non-Hodgkin Lymphoma: Current Advances and Clinical Perspectives

Non-Hodgkin lymphoma (NHL) is a heterogeneous malignancy with variable patient outcomes. There is still a lack of understanding about the different players involved in lymphomagenesis, and the identification of new diagnostic and prognostic biomarkers is urgent. MicroRNAs and long non-coding RNAs emerged as master regulators of B-cell development, and their deregulation has been associated with the initiation and progression of lymphomagenesis. They can function by acting alone or, as recently proposed, by creating competing endogenous RNA (ceRNA) networks. Most studies have focused on individual miRNAs/lncRNAs function in lymphoma, and there is still limited data regarding their interactions in lymphoma progression. The study of miRNAs' and lncRNAs' deregulation in NHL, either alone or as ceRNAs networks, offers new insights into the molecular mechanisms underlying lymphoma pathogenesis and opens a window of opportunity to identify potential diagnostic and prognostic biomarkers. In this review, we summarized the current knowledge regarding the role of miRNAs and lncRNAs in B-cell lymphoma, including their interactions and regulatory networks. Finally, we summarized the studies investigating the potential of miRNAs and lncRNAs as clinical biomarkers, with a special focus on the circulating profiles, to be applied as a non-invasive, easy-to-obtain, and reproducible liquid biopsy for dynamic management of NHL patients.

Metabolomics of prostate cancer: Knock-in versus knock-out prostate

Several metabolomics-derived biomarkers of prostate cancer (PC) have been reported with pre-radical prostatectomy (RP) (knock-in PC) conditions; however, uncontested PC biomarkers panel appraisal and investigation of correlative evidence of these measures is lacking through post-RP (knock-out PC). We sought to explore patients' filtered serum-based metabolomics derived signature measures in knock-in PC (n = 90) using nuclear magnetic resonance spectroscopy and multiple rigorous statistical analyses, and to develop the correlative evidence of these measures through knock-out PC (n = 90) follow-up on the 15th and 30th days. The glutamate, citrate and glycine were observed as hallmarks of PC. Observed trends revealed; augmented glutamate level in knock-in PC following a sudden drop and subsequently upside of glutamate at 15th and 30th days of knock-out PC, reduction of citrate in knock-in PC subsequently gradual increase of citrate in knock-out PC, and glycine lessening in knock-in PC following augmentation on 30th day of knock-out PC. This study-based evidence clears the doubts regarding the discovery of metabolomics-derived PC biomarkers.

Prostate Cancer Liquid Biopsy Biomarkers' Clinical Utility in Diagnosis and Prognosis

Prostate cancer (PCa) is the most common cancer in men worldwide. The current gold standard for diagnosing PCa relies on a transrectal ultrasound-guided systematic core needle biopsy indicated after detection changes in a digital rectal examination (DRE) and elevated prostate-specific antigen (PSA) level in the blood serum. PSA is a marker produced by prostate cells, not just cancer cells. Therefore, an elevated PSA level may be associated with other symptoms such as benign prostatic hyperplasia or inflammation of the prostate gland. Due to this marker's low specificity, a common problem is overdiagnosis, which leads to unnecessary biopsies and overtreatment. This is associated with various treatment complications (such as bleeding or infection) and generates unnecessary costs. Therefore, there is no doubt that the improvement of the current procedure by applying effective, sensitive and specific markers is an urgent need. Several non-invasive, cost-effective, high-accuracy liquid biopsy diagnostic biomarkers such as Progensa PCA3, MyProstateScore ExoDx, SelectMDx, PHI, 4K, Stockholm3 and ConfirmMDx have been developed in recent years. This article compares current knowledge about them and their potential application in clinical practice.

Biosensors Designed for Clinical Applications

Emerging and validated biomarkers promise to revolutionize clinical practice, shifting the emphasis away from the management of chronic disease towards prevention, early diagnosis and early intervention. The challenge of detecting these low abundance protein and nucleic acid biomarkers within the clinical context demands the development of highly sensitive, even single molecule, assays that are also capable of selectively measuring a small number of defined analytes in complex samples such as whole blood, interstitial fluid, saliva or urine. Success relies on significant innovations in nanomaterials, bioreceptor engineering, transduction strategies and microfluidics. Primarily using examples from our work, this article discusses some recent advance in the selective and sensitive detection of disease biomarkers, highlights key innovations in sensor materials and identifies issues and challenges that need to be carefully considered especially for researchers entering the field.

MicroRNA Regulation of Breast Cancer Stemness

Recent advances in our understanding of breast cancer have demonstrated that cancer stem-like cells (CSCs, also known as tumor-initiating cell (TICs)) are central for progression and recurrence. CSCs are a small subpopulation of cells present in breast tumors that contribute to growth, metastasis, therapy resistance, and recurrence, leading to poor clinical outcome. Data have shown that cancer cells can gain characteristics of CSCs, or stemness, through alterations in key signaling pathways. The dysregulation of miRNA expression and signaling have been well-documented in cancer, and recent studies have shown that miRNAs are associated with breast cancer initiation, progression, and recurrence through regulating CSC characteristics. More specifically, miRNAs directly target central signaling nodes within pathways that can drive the formation, maintenance, and even inhibition of the CSC population. This review aims to summarize these research findings specifically in the context of breast cancer. This review also discusses miRNAs as biomarkers and promising clinical therapeutics, and presents a comprehensive summary of currently validated targets involved in CSC-specific signaling pathways in breast cancer.

Prostate Cancer Diagnosis in the Clinic Using an 8-Protein Biomarker Panel

The inability to distinguish aggressive from indolent prostate cancer is a longstanding clinical problem. Prostate specific antigen (PSA) tests and digital rectal exams cannot differentiate these forms. Because only ∼10% of diagnosed prostate cancer cases are aggressive, existing practice often results in overtreatment including unnecessary surgeries that degrade patients' quality of life. Here, we describe a fast microfluidic immunoarray optimized to determine 8-proteins simultaneously in 5 μL of blood serum for prostate cancer diagnostics. Using polymeric horseradish peroxidase (poly-HRP, 400 HRPs) labels to provide large signal amplification and limits of detection in the sub-fg mL^-1 range, a protocol was devised for the optimization of the fast, accurate assays of 100-fold diluted serum samples. Analysis of 130 prostate cancer patient serum samples revealed that some members of the protein panel can distinguish aggressive from indolent cancers. Logistic regression was used to identify a subset of the panel, combining biomarker proteins ETS-related gene protein (ERG), insulin-like growth factor-1 (IGF-1), pigment epithelial-derived factor (PEDF), and serum monocyte differentiation antigen (CD-14) to predict whether a given patient should be referred for biopsy, which gave a much better predictive accuracy than PSA alone. This represents the first prostate cancer blood test that can predict which patients will have a high biopsy Gleason score, a standard pathology score used to grade tumors.

MicroRNAs (miRNAs) and Long Non-Coding RNAs (lncRNAs) as New Tools for Cancer Therapy: First Steps from Bench to Bedside

Non-coding RNAs represent a significant proportion of the human genome. After having been considered as 'junk' for a long time, non-coding RNAs are now well established as playing important roles in maintaining cellular homeostasis and functions. Some non-coding RNAs show cell- and tissue-specific expression patterns and are specifically deregulated under pathological conditions (e.g. cancer). Therefore, non-coding RNAs have been extensively studied as potential biomarkers in the context of different diseases with a focus on microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) for several years. Since their discovery, miRNAs have attracted more attention than lncRNAs in research studies; however, both families of non-coding RNAs have been established to play an important role in gene expression control, either as transcriptional or post-transcriptional regulators. Both miRNAs and lncRNAs can regulate key genes involved in the development of cancer, thus influencing tumour growth, invasion, and metastasis by increasing the activation of oncogenic pathways and limiting the expression of tumour suppressors. Furthermore, miRNAs and lncRNAs are also emerging as important mediators in drug-sensitivity and drug-resistance mechanisms. In the light of these premises, a number of pre-clinical and early clinical studies are exploring the potential of non-coding RNAs as new therapeutics. The aim of this review is to summarise the latest knowledge of the use of miRNAs and lncRNAs as therapeutic tools for cancer treatment.

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.

Multiparametric (mp) MRI of prostate cancer

Prostate cancer (PCa) is one of the most prevalent cancers in men. A large number of men are detected with PCa; however, the clinical behavior ranges from low-grade indolent tumors that never develop into a clinically significant disease to aggressive, invasive tumors that may rapidly progress to metastatic disease. The challenges in clinical management of PCa are at levels of screening, diagnosis, treatment, and follow-up after treatment. Magnetic resonance imaging (MRI) methods have shown a potential role in detection, localization, staging, assessment of aggressiveness, targeting biopsies, etc. in PCa patients. Multiparametric MRI (mpMRI) is emerging as a better option compared to the individual imaging methods used in the evaluation of PCa. There are attempts to improve the reproducibility and reliability of mpMRI by using an objective scoring system proposed in the prostate imaging reporting and data system (PIRADS) for standardized reporting. Prebiopsy mpMRI may be used to detect PCa in men with elevated prostate-specific antigen or abnormal digital rectal examination and to enable targeted biopsies. mpMRI can also be used to decide on clinical management of patients, for example active surveillance, and may help in detecting only the pathology that requires detection. It can potentially not only guide patient selection for initial and repeat biopsy but also reduce false-negative biopsies. This review presents a description of the MR methods most commonly applied for investigations of prostate. The anatomical, functional and metabolic parameters obtained from these MR methods are discussed with regard to their physical basis and their contribution to mpMRI investigations of PCa.

Relevance of MIC-1 in the Era of PSA as a Serum Based Predictor of Prostate Cancer: A Critical Evaluation

To reduce the ambiguity of contradictory observations in different studies regarding the expression level of Macrophage Inhibitory Cytokine-1 (MIC-1) in serum in prostate cancer (PC), benign prostatic hyperplasia (BPH) and healthy controls (HC), we designed this double-blind study. The study comprises 240 sera from PC, BPH and HC subjects. The expression level of MIC-1 in PC, BPH and HC were appraised using Western blot (WB) and ELISA based approach. WB and ELISA appraisal reveals that the expression level of MIC-1 is significantly higher in PC than in HC or BPH subjects. Regression analysis revealed a significant correlation between MIC-1 vs. PSA (r = 0.09; p < 0.001) and MIC-1 vs. GS (r = 0.7; p < 0.001). ROC analysis using discriminant predicted probability revealed that the MIC-1 was better than PSA. Moreover, the combination of MIC-1 and PSA was allowing 99.1% AUC for the differentiation of BPH + PC from HC, 97.9% AUC for differentiation of BPH from HC, 98.6% AUC for differentiation of PC from HC, and 96.7% AUC for the differentiation of PC from BPH. The augmented expression of MIC-1 in PC compared to BPH and HC subjects is in concurrent of the over-expression of MIC-1 in PC reports and confiscates the contradictory findings of other studies.

NMR spectroscopy of filtered serum of prostate cancer: A new frontier in metabolomics

BACKGROUND

To address the shortcomings of digital rectal examinations (DRE), serum prostate-specific antigen (PSA), and trans-rectal ultrasound (TRUS) for precise determination of prostate cancer (PC) and differentiation from benign prostatic hyperplasia (BPH), we applied (1) H-nuclear magnetic resonance (NMR) spectroscopy as a surrogate tactic for probing and prediction of PC and BPH.

METHODS

The study comprises 210 filtered sera from suspected PC, BPH, and a healthy subjects' cohort (HC). The filtered serum approach delineates to identify and quantify 52 metabolites using (1) H NMR spectroscopy. All subjects had undergone clinical evaluations (DRE, PSA, and TRUS) followed by biopsy for Gleason score, if needed. NMR-measured metabolites and clinical evaluation data were examined separately using linear multivariate discriminant function analysis (DFA) to probe the signature descriptors for each cohort.

RESULTS

DFA indicated that glycine, sarcosine, alanine, creatine, xanthine, and hypoxanthine were able to determine abnormal prostate (BPH + PC). DFA-based classification presented high precision (86.2% by NMR and 68.1% by clinical laboratory method) in discriminating HC from BPH + PC. DFA reveals that alanine, sarcosine, creatinine, glycine, and citrate were able to discriminate PC from BPH. DFA-based categorization exhibited high accuracy (88.3% by NMR and 75.2% by clinical laboratory method) to differentiate PC from BPH.

CONCLUSIONS

(1) H NMR-based metabolic profiling of filtered-serum sample appears to be assuring, swift, and least-invasive for probing and prediction of PC and BPH with its signature metabolic profile. This novel technique is not only on a par with histopathological evaluation of PC determination but is also comparable to liquid chromatography-based mass spectrometry to identify the metabolites. Prostate 76:1106-1119, 2016. © 2016 Wiley Periodicals, Inc.

NMR-based metabolomics of prostate cancer: a protagonist in clinical diagnostics

Advances in the application of NMR spectroscopy-based metabolomic profiling of prostate cancer comprises a potential tactic for understanding the impaired biochemical pathways arising due to a disease evolvement and progression. This technique involves qualitative and quantitative estimation of plethora of small molecular weight metabolites of body fluids or tissues using state-of-the-art chemometric methods delivering an important platform for translational research from basic to clinical, to reveal the pathophysiological snapshot in a single step. This review summarizes the present arrays and recent advancements in NMR-based metabolomics and a glimpse of currently used medical imaging tactics, with their role in clinical diagnosis of prostate cancer.

Metabolomic profiling of hormone-dependent cancers: a bird's eye view

Hormone-dependent cancers present a significant public health challenge, because they are among the most common cancers in the world. One factor associated with cancer development and progression is metabolic reprogramming. By understanding these alterations, we can identify potential markers and novel biochemical therapeutic targets. Metabolic profiling is an advanced technology that allows investigators to assess low-molecular-weight compounds that reflect physiological alterations. Current research in metabolomics on prostate (PCa) and breast cancer (BCa) have made great strides in uncovering specific metabolic pathways that are associated with cancer development, progression, and resistance. In this review, we highlight some of the major findings and potential therapeutic advances that have been reported utilizing this technology.

Novel non-invasive biomarkers that distinguish between benign prostate hyperplasia and prostate cancer

BACKGROUND

The objective of this study was to discover and to validate novel noninvasive biomarkers that distinguish between benign prostate hyperplasia (BPH) and localized prostate cancer (PCa), thereby helping to solve the diagnostic dilemma confronting clinicians who treat these patients.

METHODS

Quantitative iTRAQ LC/LC/MS/MS analysis was used to identify proteins that are differentially expressed in the urine of men with BPH compared with those who have localized PCa. These proteins were validated in 173 urine samples from patients diagnosed with BPH (N = 83) and PCa (N = 90). Multivariate logistic regression analysis was used to identify the predictive biomarkers.

RESULTS

Three proteins, β2M, PGA3, and MUC3 were identified by iTRAQ and validated by immunoblot analyses. Univariate analysis demonstrated significant elevations in urinary β2M (P < 0.001), PGA3 (P = 0.006), and MUC3 (P = 0.018) levels found in the urine of PCa patients. Multivariate logistic regression analysis revealed AUC values ranging from 0.618 for MUC3 (P = 0.009), 0.625 for PGA3 (P < 0.008), and 0.668 for β2M (P < 0.001). The combination of all three demonstrated an AUC of 0.710 (95% CI: 0.631 - 0.788, P < 0.001); diagnostic accuracy improved even more when these data were combined with PSA categories (AUC = 0.812, (95% CI: 0.740 - 0.885, P < 0.001).

CONCLUSIONS

Urinary β2M, PGA3, and MUC3, when analyzed alone or when multiplexed with clinically defined categories of PSA, may be clinically useful in noninvasively resolving the dilemma of effectively discriminating between BPH and localized PCa.

Metabolomics-derived prostate cancer biomarkers: fact or fiction?

Despite continuing research for precise probing and grading of prostate cancer (PC) biomarkers, the indexes lack sensitivity and specificity. To search for PC biomarkers, we used proton nuclear magnetic resonance ((1)H NMR)-derived serum metabolomics. The study comprises 102 serum samples obtained from low-grade (LG, n = 40) and high-grade (HG, n = 30) PC cases and healthy controls (HC, n = 32). (1)H NMR-derived serum data were examined using principal component analysis and orthogonal partial least-squares discriminant analysis. The strength of the model was verified by internal cross-validation using the same samples divided into 70% as training and 30% as test data sets. Receiver operating characteristic (ROC) curve examination was also achieved. Serum metabolomics reveals that four biomarkers (alanine, pyruvate, glycine, and sarcosine) were able to accurately (ROC 0.966) differentiate 90.2% of PC cases with 84.4% sensitivity and 92.9% specificity compared with HC. Similarly, three biomarkers, alanine, pyruvate, and glycine, were able to precisely (ROC 0.978) discriminate 92.9% of LG from HG PC with 92.5% sensitivity and 93.3% specificity. The robustness of these biomarkers was confirmed by prediction of the test data set with >99% diagnostic precision for PC determination. These findings demonstrate that (1)H NMR-based serum metabolomics is a promising approach for probing and grading PC.

Dietary lutein modulates growth and survival genes in prostate cancer cells

Lutein is a carotenoid pigment present in fruits and vegetables that has anti-inflammatory and antitumor properties. In this study, we examined the effect of lutein on proliferation and survival-associated genes in prostate cancer (PC-3) cells. We found that in vitro culture of PC-3 cells with lutein induced mild decrease in proliferation that improved in combination treatment with peroxisome proliferator-activated receptor gamma (PPARγ) agonists and other chemotherapeutic agents. Flow cytometry analyses showed that lutein improved drug-induced cell cycle arrest and apoptosis in prostate cancer. Gene array and quantitative reverse transcription-polymerase chain reaction analyses showed that lutein altered the expression of growth and apoptosis-associated biomarker genes in PC-3 cells. These findings highlight that lutein modulates the expression of growth and survival-associated genes in prostate cancer cells.