PSA and beyond: alternative prostate cancer biomarkers

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

The complex interplay of modifiable risk factors affecting prostate cancer disparities in African American men

Prostate cancer is the second most commonly diagnosed non-skin malignancy and the second leading cause of cancer death among men in the USA. However, the mortality rate of African American men aged 40-60 years is almost 2.5-fold greater than that of European American men. Despite screening and diagnostic and therapeutic advances, disparities in prostate cancer incidence and outcomes remain prevalent. The reasons that lead to this disparity in outcomes are complex and multifactorial. Established non-modifiable risk factors such as age and genetic predisposition contribute to this disparity; however, evidence suggests that modifiable risk factors (including social determinants of health, diet, steroid hormones, environment and lack of diversity in enrolment in clinical trials) are prominent contributing factors to the racial disparities observed. Disparities involved in the diagnosis, treatment and survival of African American men with prostate cancer have also been correlated with low socioeconomic status, education and lack of access to health care. The effects and complex interactions of prostate cancer modifiable risk factors are important considerations for mitigating the incidence and outcomes of this disease in African American men.

An effective urobilin clearance strategy based on paramagnetic beads facilitates microscale proteomic analysis of urine

Urine provides an ideal source for disease biomarker discovery. High-adhesion contaminants such as urobilin, which are difficult to remove from urine, can severely interfere with urinary proteomic analysis. Here, we aimed to establish a strategy based on single-pot, solid-phase-enhanced sample preparation (SP3) technology to prepare samples for urinary proteomics analysis that almost completely eliminates the impact of urobilin. A systematic evaluation of the effects of two urinary protein precipitation methods, two types of protein lysis buffers, and different ratios of magnetic digestion beads on the identification and quantification of the microscale urinary proteome was conducted. Our results indicate that methanol-chloroform precipitation, coupled with efficient lysis facilitated by urea, and subsequent enzymatic digestion using a mix of hydrophilic and hydrophobic magnetic beads offers the best performance. Further applying this strategy to the urine of patients with benign prostatic hyperplasia, prostate cancer and healthy individuals, combined with a narrow window of data-independent acquisition, FGFR4, MYLK, ORM2, GOLM1, SPP1, CD55, CSF1, DLD and TIMP3 were identified as potential biomarkers to discriminate benign prostatic hyperplasia and prostate cancer patients.

Identification and validation of the TRHDE-AS1/hsa-miR-449a/ADAMTS5 axis as a novel prognostic biomarker in prostate cancer

Despite advancements in cancer research, the prognostic implications of competing endogenous RNA (ceRNA) networks in prostate cancer (PCa) remain incompletely understood. This study aimed to elucidate the prognostic relevance of ceRNA networks in PCa, utilizing a comprehensive bioinformatics approach alongside experimental validation. After searching The Cancer Genome Atlas (TCGA) database, RNA sequencing (RNA-Seq) data were extracted to identify differentially expressed RNAs (DERs) between 491 PCa samples and 51 normal prostate tissues, following which a comprehensive bioinformatics strategy was implemented to construct a ceRNA network. An optimal prognostic signature comprising these DERs was then established and validated using TCGA data. In addition, functional validation was performed through RNA pull-down, dual-luciferase reporter assays, quantitative real-time PCR, and western blot analysis conducted in PC-3 and DU145 cell lines, thereby complementing the bioinformatics analysis. A total of 613 DERs, comprising 103 long noncoding RNAs (lncRNAs), 60 microRNAs (miRNAs), and 450 messenger RNAs (mRNAs), were identified and utilized in constructing a ceRNA network, which encompassed 23 lncRNAs, 9 miRNAs, and 52 mRNAs. An optimal prognostic signature was established, including VPS9D1 antisense RNA 1 (VPS9D1-AS1), miR-449a, cyclin-dependent kinase 5 regulatory subunit 1 (CDK5R1), targeting protein for Xklp2 (TPX2), solute carrier family 7 member 11 (SLC7A11), copine7 (CPNE7), and maternal embryonic leucine zipper kinase (MELK), yielding area under the curve (AUC) values exceeding 0.8 across training, validation, and entire datasets. Our experiments results revealed an interaction between lncRNA TRHDE antisense RNA 1 (TRHDE-AS1) and miR-449a and that miR-449a could target the ADAM metallopeptidase with thrombospondin type 1 motif 5 (ADAMTS5) mRNA. Knockdown of miR-449a significantly impeded cell proliferation, G1/S transition, migration and invasion, and promoted apoptosis in PC-3 and DU145 cells. Furthermore, knockdown of miR-449a notably downregulated protein expression of CDK4, cyclin D1, N-cadherin and vimentin, while upregulating protein expression of cleaved caspase-3 and E-cadherin. This study contributes to a deeper understanding of the prognostic-linked ceRNA network in PCa, providing fundamental insights that could improve diagnostic and therapeutic approaches for PCa management.

Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics

Background

Detailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones.

Materials and methods

We applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes.

Results

Results showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16.

Conclusions

CACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals.

Evaluation of miR-148a-3p and miR-106a-5p as Biomarkers for Prostate Cancer: Pilot Study

MicroRNAs (miRNAs) are a class of small non-coding RNAs that may function as tumor suppressors or oncogenes. Alteration of their expression levels has been linked to a range of human malignancies, including cancer. The objective of this investigation is to assess the relative expression levels of certain miRNAs to distinguish between prostate cancer (PCa) from benign prostatic hyperplasia (BPH). Blood plasma was collected from 66 patients diagnosed with BPH and 58 patients with PCa. Real-time PCR technology was used to evaluate the relative expression among the two groups for miR-106a-5p and miR-148a-3p. The significant downregulation of both miRNAs in plasma from PCa versus BPH patients suggests their potential utility as diagnostic biomarkers for distinguishing between these conditions. The concurrent utilization of these two miRNAs slightly enhanced the sensitivity for discrimination among the two analyzed groups, as shown in ROC curve analysis. Further validation of these miRNAs in larger patient cohorts and across different stages of PCa may strengthen their candidacy as clinically relevant biomarkers for diagnosis and prognosis.

Preanalytical stability of molecular forms of prostate-specific antigen in serum samples (PSA, free PSA, [-2]proPSA) and their impact on fPSA/tPSA ratio and PHI

BACKGROUND

Prostate cancer is a common cancer in men. Detection methods include the measurement of biomarkers: prostate-specific antigen (PSA), free PSA, [-2]proPSA, and the calculated indices: fPSA/tPSA ratio and Prostate Health Index (PHI). Proper preanalytical conditions are crucial for precise measurement and failure to adhere to protocols or regulations can influence the diagnostic algorithm. We assessed the stability of the above-mentioned biomarkers, fPSA/tPSA ratio and PHI, under various pre-analytical conditions.

METHODS

Serum samples from 45 males were collected and stored under specific conditions before tPSA, fPSA, and [-2]proPSA were measured. Subsequently, the fPSA/tPSA and PHI were calculated.

RESULTS

tPSA, fPSA, and [-2]proPSA remained stable during the two freeze-thaw cycles. Storage at 4°C and 22°C resulted in stable tPSA concentrations. However, fPSA levels decreased and [-2]proPSA levels increased over time. The fPSA/tPSA ratio remained stable for 72 h, at which point a decrease was observed in the samples kept at 4°C and 22°C. A gradual increase in PHI was observed in the samples kept at 4°C and 22°C.

CONCLUSIONS

All biomarkers remained stable during two freeze-thaw cycles. tPSA was the most stable analyte when stored at 4°C, as well as at RT. A gradual increase of [-2]proPSA and a slight decrease in fPSA were observed during the storage test. This led to a decrease in the fPSA/tPSA ratio and an elevation in the PHI. We therefore recommend measuring prostate biomarkers promptly following blood collection.

IMPACT

Understanding the pre-analytical stability of prostate biomarkers helps prevent false positive results and improve the accuracy of diagnostics for prostate cancer.

Updated review on analysis of long non-coding RNAs as emerging diagnostic and therapeutic targets in prostate cancers

Despite advancements, prostate cancers (PCa) pose a significant global health challenge due to delayed diagnosis and therapeutic resistance. This review delves into the complex landscape of prostate cancer, with a focus on long-noncoding RNAs (lncRNAs). Also explores the influence of aberrant lncRNAs expression in progressive PCa stages, impacting traits like proliferation, invasion, metastasis and therapeutic resistance. The study elucidates how lncRNAs modulate crucial molecular effectors, including transcription factors and microRNAs, affecting signaling pathways such as androgen receptor signaling. Besides, this manuscript sheds light on novel concepts and mechanisms driving PCa progression through lncRNAs, providing a critical analysis of their impact on the disease's diverse characteristics. Besides, it discusses the potential of lncRNAs as diagnostics and therapeutic targets in PCa. Collectively, this work highlights state of art mechanistic comprehension and rigorous scientific approaches to advance our understanding of PCa and depict innovations in this evolving field of research.

Utility of noninvasive biomarker testing and MRI to predict a prostate cancer diagnosis

PURPOSE

To assess the diagnostic performance and utility of the ExoDx IntelliScore and an OPKO4K score to predict prostate cancer in men presenting with elevated PSA-both as independent predictors and in combination with clinical/MRI characteristics.

METHODS

Patients with elevated PSA were retrospectively reviewed. Abnormal tests were defined as an OPKO4K score ≥ 7.5% and an ExoDx IntelliScore ≥ 15.6. Four regression models and ROC curves were generated based on: (1) age, PSA, and DRE, (2) model 1 + OPKO4K 4Kscore ≥ 7.5%, (3) model 2 + ExoDx IntelliScore ≥ 15.6, and (4) model 3 + MRI PIRADS 4-5.

RESULTS

359 men received an OPKO4K test, 307 had MRI and 113 had ExoDx tests. 163 men proceeded to prostate biopsy and 196 (55%) were saved from biopsy. Mean age was 65.0 ± 8.7 years and mean PSA was 7.1 ± 6.1 ng/mL. Positive biopsies were found in 84 (51.5%) men. The sensitivity and negative predictive value of an OPKO4K score were 86.7% and 72.3%; values for an ExoDx test were 76.5% and 77.1%, respectively. On regression analysis, clinical markers (Age, PSA, DRE) generated an AUC of 0.559. The addition of an OPKO4K score raised the AUC to 0.653. The stepwise addition of an ExoDx score raised the AUC to 0.766. The combined use of both biomarkers, patient characteristics, and MRI yielded an AUC of 0.825.

CONCLUSION

This analysis demonstrates the high negative predictive value of both the OPKO4K score and ExoDX IntelliScore independently while demonstrating that the combination of an OPKO4K score, an ExoDX IntelliScore, and MRI increases predictive capability for biopsy confirmed prostate cancer.

Bio-conjugated carbon dots for the bimodal detection of prostate cancer biomarkers via sandwich fluorescence and electrochemical immunoassays

Bimodal detection facilitates the accurate and reliable detection of cancer biomarkers, which can assist in the early diagnosis of cancer. Herein, S-doped carbon dots (OCDs) with a size of 3 nm and blue emission were synthesized by the hydrothermal treatment of onion extract. The S-doped carbon dots were bioconjugated with an antibody (OCDs@PSAAbHRP) to design a nanoprobe for the detection of prostate specific antigen (PSA), an important serum based prostate cancer biomarker. The detection probe enabled the biomodal assay of PSA via fluorescence immunoassay (FIA) and electrochemical immunoassay (ECIA). In both assays, polyethylenimine stabilized polyaniline nanoparticles (PNPs) were used as the immobilization matrix, which played a major role in widening the linear range of biosensors (0.1 to 100 ng ml-1 for ECIA and 5 to 120 ng ml-1 for FIA). Paper-based and smartphone-integrated fluorescence immuno-array developed using the OCDs@PSAAbHRP detection probe provided cost-effective and rapid detection, while the electrochemical immunoassay provided a high sensitivity (7.8 μA ng-1 ml-1 cm-2) and low detection limit (38 pg ml-1) for PSA detection. The role of OCDs in enhancing the sensor performance was deciphered by carrying out detailed electrochemical studies with HRP enzyme-loaded OCDs. The biosensor was used to detect PSA in human blood serum samples and the results were consistent with conventional techniques. Owing to its analytical properties coupled with simplicity, cost-effectiveness, and portability, the bimodal sensor system has potential for application in clinical analysis.

Targeting tumor and bone microenvironment: Novel therapeutic opportunities for castration-resistant prostate cancer patients with bone metastasis

Despite standard hormonal therapy that targets the androgen receptor (AR) attenuates prostate cancer (PCa) effectively in the initial stage, the tumor ultimately converts to castration-resistant prostate cancer (CRPC), and the acquired resistance is still a great challenge for the management of advanced prostate cancer patients. The tumor microenvironment (TME) consists of multiple cellular and noncellular agents is well known as a vital role during the development and progression of CRPC by establishing communication between TME and tumor cells. Additionally, as primary prostate cancer progresses towards metastasis, and CRPC always experiences bone metastasis, the TME is conducive to the spread of tumors to the distant sits, particularly in bone. In addition, the bone microenvironment (BME) is also closely related to the survival, growth and colonization of metastatic tumor cells. The present review summarized the recent studies which mainly focused on the role of TME or BME in the CRPC patients with bone metastasis, and discussed the underlying mechanisms, as well as the potential therapeutic values of targeting TME and BME in the management of metastatic CRPC patients.

Identification of Plausible Candidates in Prostate Cancer Using Integrated Machine Learning Approaches

Background

Currently, prostate-specific antigen (PSA) is commonly used as a prostate cancer (PCa) biomarker. PSA is linked to some factors that frequently lead to erroneous positive results or even needless biopsies of elderly people.

Objectives

In this pilot study, we undermined the potential genes and mutations from several databases and checked whether or not any putative prognostic biomarkers are central to the annotation. The aim of the study was to develop a risk prediction model that could help in clinical decision-making.

Methods

An extensive literature review was conducted, and clinical parameters for related comorbidities, such as diabetes, obesity, as well as PCa, were collected. Such parameters were chosen with the understanding that variations in their threshold values could hasten the complicated process of carcinogenesis, more particularly PCa. The gathered data was converted to semi-binary data (-1, -0.5, 0, 0.5, and 1), on which machine learning (ML) methods were applied. First, we cross-checked various publicly available datasets, some published RNA-seq datasets, and our whole-exome sequencing data to find common role players in PCa, diabetes, and obesity. To narrow down their common interacting partners, interactome networks were analysed using GeneMANIA and visualised using Cytoscape, and later cBioportal was used (to compare expression level based on Z scored values) wherein various types of mutation w.r.t their expression and mRNA expression (RNA seq FPKM) plots are available. The GEPIA 2 tool was used to compare the expression of resulting similarities between the normal tissue and TCGA databases of PCa. Later, top-ranking genes were chosen to demonstrate striking clustering coefficients using the Cytoscape-cytoHubba module, and GEPIA 2 was applied again to ascertain survival plots.

Results

Comparing various publicly available datasets, it was found that BLM is a frequent player in all three diseases, whereas comparing publicly available datasets, GWAS datasets, and published sequencing findings, SPFTPC and PPIMB were found to be the most common. With the assistance of GeneMANIA, TMPO and FOXP1 were found as common interacting partners, and they were also seen participating with BLM.

Conclusion

A probabilistic machine learning model was achieved to identify key candidates between diabetes, obesity, and PCa. This, we believe, would herald precision scale modeling for easy prognosis.

Synthetic CRISPR/dCas9-KRAB system driven by specific PSA promoter suppresses malignant biological behavior of prostate cancer cells through negative feedback inhibition of PSA expression

PSA is a type of proto-oncogene that is specifically and highly expressed in embryonic and prostate cancer cells, but not expressed in normal prostate tissue cells. The specific expression of prostate-specific antigen (PSA) is found to be related with the conditional transcriptional regulation of its promoter. Clustered regularly interspaced short palindromic repeats (CRISPR)-dCas9-KRAB is a newly developed transcriptional regulatory system that inhibits gene expression by interupting the DNA transcription process. Induction of CRISPR-dCas9-KRAB expression through the PSA promoter may help feedback inhibition of cellular PSA gene expression via single guide RNA (sgRNA), thereby monitoring and suppressing the malignant state of tumor cells. In this study, we examined the transcriptional activity of the PSA promoter in different prostate cancer cells and normal prostate epithelial cells and determined that it is indeed a prostate cancer cell-specific promoter.Then we constructed the CRISPR-dCas9-KRAB system driven by the PSA promoter, which can inhibit PSA gene expression in the prostate cancer cells at the transcriptional level, and therefore supress the malignant growth and migration of prostate cancer cells and promote their apoptosis in vitro. This study provides a potentially effective anti-cancer strategy for gene therapy of prostate cancer.

Clinically Translatable Hyperpolarized 13C Bicarbonate pH Imaging Method for Use in Prostate Cancer

Solid tumors such as prostate cancer (PCa) commonly develop an acidic microenvironment with pH 6.5-7.2, owing to heterogeneous perfusion, high metabolic activity, and rapid cell proliferation. In preclinical prostate cancer models, disease progression is associated with a decrease in tumor extracellular pH, suggesting that pH imaging may reflect an imaging biomarker to detect aggressive and high-risk disease. Therefore, we developed a hyperpolarized carbon-13 MRI method to image the tumor extracellular pH (pHe) and prepared it for clinical translation for detection and risk stratification of PCa. This method relies on the rapid breakdown of hyperpolarized (HP) 1,2-glycerol carbonate (carbonyl-13C) via base-catalyzed hydrolysis to produce HP 13CO32-, which is neutralized and converted to HP H13CO3-. After injection, HP H13CO3- equilibrates with HP 13CO2 in vivo and enables the imaging of pHe. Using insights gleaned from mechanistic studies performed in the hyperpolarized state, we solved issues of polarization loss during preparation in a clinical polarizer system. We successfully customized a reaction apparatus suitable for clinical application, developed clinical standard operating procedures, and validated the radiofrequency pulse sequence and imaging data acquisition with a wide range of animal models. The results demonstrated that we can routinely produce a highly polarized and safe HP H13CO3- contrast agent suitable for human injection. Preclinical imaging studies validated the reliability and accuracy of measuring acidification in healthy kidney and prostate tumor tissue. These methods were used to support an Investigational New Drug application to the U.S. Food and Drug Administration. This methodology is now ready to be implemented in human trials, with the ultimate goal of improving the management of PCa.

Diagnostic value of total serum/free prostate specific antigen and prostate cancer antigen-3 levels in prostate cancer

BACKGROUND

The purpose of this study was to compare serum total and free prostate specific antigen (PSA) levels and serum prostate cancer antigen-3 (PCA3) levels in patients with prostate cancer in 2018 and 2019.

METHODS

This research was a prospective case-control study. The case group included all patients with suspected prostate cancer, and the control group included individuals without prostate disease who were referred to Ali Asghar and Nour Hospital in Isfahan, Iran, from October 2018 to October 2020. The serum total PSA, free PSA, and PCA3 levels in both groups were measured using the ELISA method with standard kits and compared between the groups.

RESULTS

The two groups were matched in terms of age and body mass index (BMI). The results showed that the mean free PSA level in the control group was significantly higher than that in the case group (P<0.05). Conversely, the mean total PSA level in the case group was significantly higher than that in the control group (P<0.05). However, no significant difference was observed in the mean PCA3 levels between the case and control groups. In addition, the total PSA variable with a cutoff of ≤3.14 exhibited 93% sensitivity and 82% specificity, demonstrating the highest diagnostic accuracy in distinguishing between prostate cancer and healthy individuals. Similarly, the PCA3 value with a cutoff of ≤3.5 had a sensitivity and specificity of 70% and 72%, respectively.

CONCLUSION

Overall, the study results indicated that total PSA and PCA3 levels have higher diagnostic accuracy in distinguishing patients with suspected prostate cancer from healthy individuals.

RAGE as a Novel Biomarker for Prostate Cancer: A Systematic Review and Meta-Analysis

The receptor for advanced glycation end-products (RAGE) has been implicated in driving prostate cancer (PCa) growth, aggression, and metastasis through the fueling of chronic inflammation in the tumor microenvironment. This systematic review and meta-analysis summarizes and analyzes the current clinical and preclinical data to provide insight into the relationships among RAGE levels and PCa, cancer grade, and molecular effects. A multi-database search was used to identify original clinical and preclinical research articles examining RAGE expression in PCa. After screening and review, nine clinical and six preclinical articles were included. The associations of RAGE differentiating benign prostate hyperplasia (BPH) or normal prostate from PCa and between tumor grades were estimated using odds ratios (ORs) and associated 95% confidence intervals (CI). Pooled estimates were calculated using random-effect models due to study heterogeneity. The clinical meta-analysis found that RAGE expression was highly likely to be increased in PCa when compared to BPH or normal prostate (OR: 11.3; 95% CI: 4.4-29.1) and that RAGE was overexpressed in high-grade PCa when compared to low-grade PCa (OR: 2.5; 95% CI: 1.8-3.4). In addition, meta-analysis estimates of preclinical studies performed by albatross plot generation found robustly positive associations among RAGE expression/activation and PCa growth and metastatic potential. This review demonstrates that RAGE expression is strongly tied to PCa progression and can serve as an effective diagnostic target to differentiate between healthy prostate, low-grade PCa, and high-grade PCa, with potential theragnostic applications.

Potential diagnostic value of multiple indicators combined with total prostate-specific antigen in prostate cancer

OBJECTIVE

We aimed to investigate the diagnostic value of different laboratory indicators in combination with total prostate-specific antigen (TPSA) for prostate cancer (PCa).

METHODS

In this retrospective study, we selected 291 patients who underwent prostate biopsy. Patients were divided into the benign prostatic hyperplasia group and the PCa group. In both groups, patients were again divided into a group with TPSA 4.0-10.0 ng/mL and a group with TPSA >10.0 ng/mL. Clinical data including age, pre-puncture TPSA, free prostate-specific antigen (FPSA), and prostate volume (PV) were collected from all patients. We calculated the metrics PSA/PV (prostate-specific antigen density, PSAD), age/PV (AVR), age × PV/TPSA (PSA-AV), and (FPSA/TPSA)/PSAD [(F/T)/PSAD]). We plotted receiver operating characteristic (ROC) curves and calculated the area under the ROC curve (AUC).

RESULTS

We found statistically significant differences in PV, PSAD, AVR, PSA-AV, and (F/T) PSAD for patients with TPSA 4.0-10.0 ng/mL and TPSA >10 ng/mL. We further plotted the ROC of individual or combined indices in different subgroups and calculated the AUC. We found that the diagnostic efficacy of the combined indices was higher with TPSA >10 ng/mL.

CONCLUSION

The combination of TPSA with multiple indicators may improve diagnostic accuracy for PCa.

Recent advances and future perspectives in the therapeutics of prostate cancer

Prostate cancer (PC) is one of the most common cancers in males and the fifth leading reason of death. Age, ethnicity, family history, and genetic defects are major factors that determine the aggressiveness and lethality of PC. The African population is at the highest risk of developing high-grade PC. It can be challenging to distinguish between low-risk and high-risk patients due to the slow progression of PC. Prostate-specific antigen (PSA) is a revolutionary discovery for the identification of PC. However, it has led to an increase in over diagnosis and over treatment of PC in the past few decades. Even if modifications are made to the standard PSA testing, the specificity has not been found to be significant. Our understanding of PC genetics and proteomics has improved due to advances in different fields. New serum, urine, and tissue biomarkers, such as PC antigen 3 (PCA3), have led to various new diagnostic tests, such as the prostate health index, 4K score, and PCA3. These tests significantly reduce the number of unnecessary and repeat biopsies performed. Chemotherapy, radiotherapy, and prostatectomy are standard treatment options. However, newer novel hormone therapy drugs with a better response have been identified. Androgen deprivation and hormonal therapy are evolving as new and better options for managing hormone-sensitive and castration-resistant PC. This review aimed to highlight and discuss epidemiology, various risk factors, and developments in PC diagnosis and treatment regimens.

Biomolecule-functionalized nanoformulations for prostate cancer theranostics

BACKGROUND

Even with the advancement in the areas of cancer nanotechnology, prostate cancer still poses a major threat to men's health. Nanomaterials and nanomaterial-derived theranostic systems have been explored for diagnosis, imaging, and therapy for different types of cancer still, for prostate cancer they have not delivered at full potential because of the limitations like in vivo biocompatibility, immune responses, precise targetability, and therapeutic outcome associated with the nanostructured system.

AIM OF REVIEW

Functionalizing nanomaterials with different biomolecules and bioactive agents provides advantages like specificity towards cancerous tumors, improved circulation time, and modulation of the immune response leading to early diagnosis and targeted delivery of cargo at the site of action.

KEY SCIENTIFIC CONCEPTS OF REVIEW

In this review, we have emphasized the classification and comparison of various nanomaterials based on biofunctionalization strategy and source of biomolecules such that it can be used for possible translation in clinical settings and future developments. This review highlighted the opportunities for embedding highly specific biological targeting moieties (antibody, aptamer, oligonucleotides, biopolymer, peptides, etc.) on nanoparticles which can improve the detection of prostate cancer-associated biomarkers at a very low limit of detection, direct visualization of prostate tumors and lastly for its therapy. Lastly, special emphasis was given to biomimetic nanomaterials which include functionalization with extracellular vesicles, exosomes and viral particles and their application for prostate cancer early detection and drug delivery. The present review paves a new pathway for next-generation biofunctionalized nanomaterials for prostate cancer theranostic application and their possibility in clinical translation.

Tumor-derived extracellular vesicle nucleic acids as promising diagnostic biomarkers for prostate cancer

Liquid biopsy as a non-invasive method has a bright future in cancer diagnosis. Tumor-related extracellular vesicles (EVs) and their components (nucleic acids, proteins, and lipids) in biofluids may exert multiple functions in tumor growth, metastasis, immune escape, and angiogenesis. Among all the components, nucleic acids have attracted the most interest due to their simplicity of extraction and detection. In this review, the biological functions of EVs in prostate cancer (PCa) genesis and progression were summarized. Moreover, the diagnostic value of EV RNA markers found in clinical body fluid samples was reviewed, including their trends, challenging isolation methods, and diagnostic efficacy. Lastly, because relatively much progress has been made in PCa, studies on EV DNA markers are also discussed.

Plasmatic MicroRNAs and Treatment Outcomes of Patients with Metastatic Castration-Resistant Prostate Cancer: A Hospital-Based Cohort Study and In Silico Analysis

Prostate cancer (PCa) is one of the most common malignancies among men worldwide. Inevitably, all advanced PCa patients develop metastatic castration-resistant prostate cancer (mCRPC), an aggressive phase of the disease. Treating mCRPC is challenging, and prognostic tools are needed for disease management. MicroRNA (miRNA) deregulation has been reported in PCa, constituting potential non-invasive prognostic biomarkers. As such, this study aimed to evaluate the prognostic potential of nine miRNAs in the liquid biopsies (plasma) of mCRPC patients treated with second-generation androgen receptor axis-targeted (ARAT) agents, abiraterone acetate (AbA) and enzalutamide (ENZ). Low expression levels of miR-16-5p and miR-145-5p in mCRPC patients treated with AbA were significantly associated with lower progression-free survival (PFS). The two miRNAs were the only predictors of the risk of disease progression in AbA-stratified analyses. Low miR-20a-5p levels in mCRPC patients with Gleason scores of <8 were associated with worse overall survival (OS). The transcript seems to predict the risk of death regardless of the ARAT agent. According to the in silico analyses, miR-16-5p, miR-145-5p, and miR-20a-5p seem to be implicated in several processes, namely, cell cycle, proliferation, migration, survival, metabolism, and angiogenesis, suggesting an epigenetic mechanism related to treatment outcome. These miRNAs may represent attractive prognostic tools to be used in mCRPC management, as well as a step further in the identification of new potential therapeutic targets, to use in combination with ARAT for an improved treatment outcome. Despite the promising results, real-world validation is necessary.

Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data

Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of post-surgery progression-free survival with a granularity within a year is attained, improving accuracy with respect to the current state of the art. The development of novel ML techniques tailored to the problem of combining multi-omics and clinical prognostic biomarkers is a promising new line of attack for sharpening the capability to diversify and personalize cancer patient treatments. The proposed approach allows a finer post-surgery stratification of patients within the clinical high-risk category, with a potential impact on the surveillance regime and the timing of treatment decisions, complementing existing prognostic methods.

Targeting Sphingosine 1-Phosphate Metabolism as a Therapeutic Avenue for Prostate Cancer

Prostate cancer (PC) is the second most common cancer in men worldwide. More than 65% of men diagnosed with PC are above 65. Patients with localized PC show high long-term survival, however with the disease progression into a metastatic form, it becomes incurable, even after strong radio- and/or chemotherapy. Sphingosine 1-phosphate (S1P) is a bioactive lipid that participates in all the steps of oncogenesis including tumor cell proliferation, survival, migration, invasion, and metastatic spread. The S1P-producing enzymes sphingosine kinases 1 and 2 (SK1 and SK2), and the S1P degrading enzyme S1P lyase (SPL), have been shown to be highly implicated in the onset, development, and therapy resistance of PC during the last 20 years. In this review, the most important studies demonstrating the role of S1P and S1P metabolic partners in PC are discussed. The different in vitro, ex vivo, and in vivo models of PC that were used to demonstrate the implication of S1P metabolism are especially highlighted. Furthermore, the most efficient molecules targeting S1P metabolism that are under preclinical and clinical development for curing PC are summarized. Finally, the possibility of targeting S1P metabolism alone or combined with other therapies in the foreseeable future as an alternative option for PC patients is discussed. Research Strategy: PubMed from INSB was used for article research. First, key words "prostate & sphingosine" were used and 144 articles were found. We also realized other combinations of key words as "prostate cancer bone metastasis" and "prostate cancer treatment". We used the most recent reviews to illustrate prostate cancer topic and sphingolipid metabolism overview topic.

Feasibility study of expressing epcam + /vimentin + CTC in prostate cancer diagnosis

PURPOSE

Prostate cancer (PCa) is one of the most common malignancies in men and one of the leading causes of cancer-related deaths; circulating tumor cells (CTC) are malignant cells that have broken off from original tumor or metastatic sites and extravasated into the blood vessels either naturally or maybe as a consequence of surgical procedures. This study aims to explore the feasibility of liquid biopsy technique to diagnose prostate cancer.

METHOD

We constructed an assay platform integrating magnetic separation and fluorescence in situ hybridization (FISH) to effectively capture prostate cancer CTCs and evaluate the distribution between healthy volunteers and prostate cancer patients, respectively.

RESULTS

There was a significant difference in the number of CTCs between the healthy population and prostate cancer patients (P < 0.001). The results of the study showed that the CTCs capture identification system has good sensitivity and specificity in identifying prostate cancer patients.

CONCLUSION

The CTCs test allows us to accurately identify patients who are at high risk for prostate cancer, allowing for early intervention and treating patients effectively.

Global and regional quality of care index for prostate cancer: an analysis from the Global Burden of Disease study 1990-2019

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers worldwide, with a significant burden on societies and healthcare providers. We aimed to develop a metric for PCa quality of care that could demonstrate the disease's status in different countries and regions (e.g., socio-demographic index (SDI) quintiles) and assist in improving healthcare policies.

METHODS

Basic burden of disease indicators for various regions and age-groups were retrieved from Global Burden of Disease Study 1990-2019, which then were used to calculate four secondary indices: mortality to incidence ratio, DALYs to prevalence ratio, prevalence to incidence ratio, and YLLs to YLDs ratio. These four indices were combined through a principal component analysis (PCA), producing the quality of care index (QCI).

RESULTS

PCa's age-standardized incidence rate increased from 34.1 in 1990 to 38.6 in 2019, while the age-standardized death rate decreased in the same period (18.1 to 15.3). From 1990 to 2019, global QCI increased from 74 to 84. Developed regions (high SDI) had the highest PCa QCIs in 2019 (95.99), while the lowest QCIs belonged to low SDI countries (28.67), mainly from Africa. QCI peaked in age groups 50 to 54, 55 to 59, or 65 to 69, depending on the socio-demographic index.

CONCLUSIONS

Global PCa QCI stands at a relatively high value (84 in 2019). Low SDI countries are affected the most by PCa, mainly due to the lack of effective preventive and treatment methods in those regions. In many developed countries, QCI decreased or stopped rising after recommendations against routine PCa screening in the 2010-2012 period, highlighting the role of screening in reducing PCa burden.

Development and Validation of a Urinary microRNA Biomarker Panel as a Tool for Early Detection of Prostate Cancer in a Chinese Population

Introduction: Urinary microRNAs (miRNAs) may serve as promising biomarkers for non-invasive early detection of prostate cancer (PCa). We aimed to identify multi-miRNA urinary biomarker panel for early detection of PCa. Methods: Urine samples from 83 PCa patients and 88 healthy control subjects in a Chinese population were collected for miRNA profiling. The absolute expression of 360 unique miRNAs were measured in each sample using a highly sensitive and robust RT-qPCR workflow. Candidate urinary miRNA biomarkers were identified based on differential expression between PCa patients and healthy controls. Multi-miRNA biomarker panels were optimized for detection of PCa using three regression algorithms (Lasso, Stepwise, Exhaustive) to identify an optimal biomarker panel with best detection performance and least number of miRNAs. Results: A total of 312 miRNAs were detected in urine samples, 10 candidate urinary miRNA biomarkers differentially expressed between PCa and healthy samples were identified. A panel comprising these 10 miRNAs detected PCa with an area under the curve (AUC) of 0.738. Optimization of multi-miRNA panels resulted in a 6-miRNA biomarker panel (hsa-miR-375, hsa-miR-520d-5p, hsa-miR-199b-5p, hsa-miR-518e-5p, hsa-miR-31-3p and hsa-miR-4306) that had an AUC of 0.750. Conclusion: We identified a urinary miRNA biomarker panel for early detection of PCa in a Chinese population.

Extracellular Vesicles as Biomarkers in Head and Neck Squamous Cell Carcinoma: From Diagnosis to Disease-Free Survival

Head and neck squamous cell carcinomas (HNSCCs) arising from different anatomical sites present with different incidences and characteristics, which requires a personalized treatment strategy. Despite the extensive research that has conducted on this malignancy, HNSCC still has a poor overall survival rate. Many attempts have been made to improve the outcomes, but one of the bottlenecks is thought to be the lack of an effective biomarker with high sensitivity and specificity. Extracellular vesicles (EVs) are secreted by various cells and participate in a great number of intercellular communications. Based on liquid biopsy, EV detection in several biofluids, such as blood, saliva, and urine, has been applied to identify the existence and progression of a variety of cancers. In HNSCC, tumor-derived EVs exhibit many functionalities by transporting diverse cargoes, which highlights their importance in tumor screening, the determination of multidisciplinary therapy, prediction of prognosis, and evaluation of therapeutic effects. This review illustrates the classification and formation of EV subtypes, the cargoes conveyed by these vesicles, and their respective functions in HNSCC cancer biology, and discloses their potential as biomarkers during the whole process of tumor diagnosis, treatment, and follow-up.

Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

Emerging RNA-Based Therapeutic and Diagnostic Options: Recent Advances and Future Challenges in Genitourinary Cancers

Renal cell carcinoma, bladder cancer, and prostate cancer are the most widespread genitourinary tumors. Their treatment and diagnosis have significantly evolved over recent years, due to an increasing understanding of oncogenic factors and the molecular mechanisms involved. Using sophisticated genome sequencing technologies, the non-coding RNAs, such as microRNAs, long non-coding RNAs, and circular RNAs, have all been implicated in the occurrence and progression of genitourinary cancers. Interestingly, DNA, protein, and RNA interactions with lncRNAs and other biological macromolecules drive some of these cancer phenotypes. Studies on the molecular mechanisms of lncRNAs have identified new functional markers that could be potentially useful as biomarkers for effective diagnosis and/or as targets for therapeutic intervention. This review focuses on the mechanisms underlying abnormal lncRNA expression in genitourinary tumors and discusses their role in diagnostics, prognosis, and treatment.

Nurturing the marriages of urinary liquid biopsies and nano-diagnostics for precision urinalysis of prostate cancer

Prostate cancer remains the second-most common cancer diagnosed in men, despite the increasingly widespread use of serum prostate-specific antigen (PSA) screening. The controversial clinical implications and cost benefits of PSA screening have been highlighted due to its poor specificity, resulting in a high rate of overdiagnosis and underdiagnosis. Thus, the development of novel biomarkers for prostate cancer detection remains an intriguing challenge. Urine is emerging as a source for prostate cancer biomarker discovery. Currently, new urine biomarkers already outperform serum PSA in clinical diagnosis. Meanwhile, the advances in nanotechnology have provided a suite of diagnostic tools to study prostate cancer in more detail, sparking a new era of biomarker discoveries. In this review, we envision that future prostate cancer diagnosis will probably integrate multiplex nano-diagnostic approaches to detect novel urinary biomarkers. However, challenges remain in differentiating indolent from aggressive cancers to better inform treatment decisions, and clinical translation still needs to be overcome.

Alterations of plasma exosomal proteins and motabolies are associated with the progression of castration-resistant prostate cancer

BACKGROUND

Current diagnosis tools for prostate cancer (PCa) such as serum PSA detection and prostate biopsy cannot distinguish dormant tumors from invasive malignancies, either be used as prognosis marker for castration resistant prostate cancer (CRPC), the lethal stage of PCa patients. Exosomes have been widely investigated as promising biomarkers for various diseases. We aim to characterize the proteomic and metabolomic profile of exosomes and to evaluate their potential value for the diagnosis of PCa, especially CRPC. We also investigate the functions of some specific exosome biomarkers in the progression of CRPC.

METHODS

Integrated proteomics and metabolomics analysis were performed for plasma-derived exosomes collected from tumor-free controls (TFC), PCa and CRPC patients. Expression of specific exosomal proteins were further validated by targeted 4D-parallel reaction monitoring (PRM) mass spectrometry among the three cohorts. Tissue distribution and functional role of exosomal protein LRG1 was studied in clinical PCa tissue samples and cell line models.

RESULTS

Three potential exosomal protein markers were identified. The apolipoprotein E level in PCa samples was 1.7-fold higher than that in TFC (receiver operating characteristic value, 0.74). Similarly, the levels of exosome-derived leucine-rich alpha2-glycoprotein 1 (LRG1) and inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) in the CRPC group were 1.7 and 2.04 times, respectively, higher than those in the PCa group (ROC values, 0.84 and 0.85, respectively), indicating that LRG1 and ITIH3 could serve as predictive markers for CRPC. For metabolomic evaluation of exosomes, a series of differentially expressed metabolites were identified, and a combined metabolite panel showed ROC value of 0.94 for distinguishing PCa from TFC and 0.97 for distinguishing CRPC from PCa. Immunohistochemistry of tissue microarray showed that LRG1 protein was significantly upregulated in advanced prostate cancer and functional assay revealed that ectopic expression of LRG1 can significantly enhance the malignant phenotype of prostate cancer cells. More importantly, PCa cell derived LRG1-overexpressed exosomes remarkably promoted angiogenesis.

CONCLUSION

Integration of proteomics and metabolomics data generated proteomic and metabolic signatures of plasma exosomes that may facilitate discrimination of CRPC from PCa and TFC patients, suggesting the potential of exosomal proteins and metabolites as CRPC markers. The study also confirmed the important role of exosomal protein LRG1 in PCa malignant progression.

The role of LncRNA LBX2-AS1 in cancers: functions, mechanisms and potential clinical utility

Increasingly advanced biology technique has revealed that long non-coding RNAs (lncRNA) as critical factors that exert significant regulatory effects on biological functions by modulating gene transcription, epigenetic modifications and protein translation. A newly emerging lncRNA, ladybird homeobox 2 (LBX2)-antisense RNA 1 (LBX2-AS1), was found to be highly expressed in various tumors. Moreover, it is functionally linked to the regulation of essential tumor-related biological processes, such as cell proliferation and apoptosis, through interactions with multiple signaling molecules/pathways. The important roles played by LBX2-AS1 in cancer initiation and progression suggest that this lncRNA has enormous clinical potential for use as a novel biomarker or therapeutic target. In this article, we retrospectively review the latest advances in research exploring the roles of the lncRNA LBX2-AS1 in oncology field, highlighting its involvement in a comprehensive network of molecular mechanisms underlying diverse cancers and examining its potential applications in clinical practice.

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

Current strategies for the clinical management of prostate cancer are inadequate for a precise risk stratification between indolent and aggressive tumors. Recently developed tissue-based molecular biomarkers have refined the risk assessment of the disease. The characterization of tissue biopsy components and subsequent identification of relevant tissue-based molecular alterations have the potential to improve the clinical decision making and patient outcomes. However, tissue biopsies are invasive and spatially restricted due to tumor heterogeneity. Therefore, there is an urgent need for complementary diagnostic and prognostic options. Liquid biopsy approaches are minimally invasive with potential utility for the early detection, risk stratification, and monitoring of tumors. In this review, we focus on tissue and liquid biopsy biomarkers for early diagnosis and risk stratification of prostate cancer, including modifications on the genomic, epigenomic, transcriptomic, and proteomic levels. High-risk molecular alterations combined with orthogonal clinical parameters can improve the identification of aggressive tumors and increase patient survival.

An overview of prostate cancer (PCa) diagnosis: Potential role of miRNAs

Prostate cancer is the second most frequently diagnosed cancer among men worldwide, with the estimated sixth leading cause of cancer death. Despite major advancements in clinical biology and imaging, digital rectal examination (DRE), prostate-specific antigen (PSA), and biopsies indication remain the keystone for screening. Several kits are used to detect genomic changes and non-coding RNAs in the sample. However, its indication remains controversial for screening purposes. There is an urged need for non-invasive biomarkers to implement precision medicine. Recent research shows that miRNAs have an important role in the diagnostic, prognostic, and therapeutic agents as non-invasive biomarkers. Though prostate cancer data remains controversial in other cancer types, such as breast cancer, miR-21 expression is upregulated. Here, we reported a prolonged revision of miRNAs as prostate cancer prognostic, diagnostic, and predictive tools, including data on androgen receptor (AR) signaling, epithelial-mesenchymal transition (EMT) process, and cancer stem cells (CSCs) regulation. The combined utilization of miRNAs with other tests will help patients and clinicians to select the most appropriate personalized treatment and to avoid overdiagnosis and unnecessary biopsies. Future clinical applications of our reported novel miRNAs have a substantial role in the primary diagnosis of prostate cancer to help treatment decisions.

Recent advances in machine learning methods for predicting LncRNA and disease associations

Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.

Urinary PCA3 a Superior Diagnostic Biomarker for Prostate Cancer among Ghanaian Men

Introduction. Prostate cancer is one of the most commonly diagnosed cancers in men. Prostate-specific antigen (PSA) has been the biomarker of choice for screening and diagnosis of prostate cancer. However, inefficiencies exist with its diagnostic capabilities. This study thus evaluated the diagnostic and prognostic potential of urinary PCA3 as an alternative biomarker for prostate cancer in the Ghanaian population. Methods. A hospital-based cross-sectional study was conducted at the Urology Department of the 37 Military Hospital, Accra, Ghana. A total of 237 participants aged 40 years and above with any form of suspected prostate disorder were recruited into the study after written informed consent was obtained. Total serum PSA levels was measured using the electrochemiluminescence method and transrectal ultrasound-guided systematic core needle biopsies were obtained from each study participant. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic accuracies of serum PSA, DRE, and PCA3 as diagnostic tools for prostate cancer. These three diagnostic tools were also evaluated in various combinations to ascertain the combinations with the best diagnostic accuracy. Results. Prostate cancer was diagnosed in 26.6% of the participants. Benign prostate hyperplasia and prostatitis were diagnosed in 48.5% and 24.9% participants, respectively. DRE had a sensitivity of 93.7% and a specificity of 12.1%. PSA had a sensitivity of 92.1% and a specificity of 16.1%. PCA3 had a sensitivity of 57.1% and a specificity of 85.6% and showed a better accuracy ( $\text{AUC}=83.0$ ) compared to PSA ( $\text{AUC}=60.0$ ) and DRE ( $\text{AUC}=65.0$ ) as individual diagnostic tools. The combination of DRE+PCA3 score had the best diagnostic accuracy ( $\text{AUC}=0.80$ ) with a sensitivity and specificity of 60.3% and 80.5%, respectively. Conclusion. The urinary PCA3 assay showed a better diagnostic performance compared to serum PSA and DRE. PCA3 as a stand-alone and in combination with DRE could be a suitable complimentary marker in diagnosis and management of prostate cancer.

Bioplatforms in liquid biopsy: advances in the techniques for isolation, characterization and clinical applications

Tissue biopsy analysis has conventionally been the gold standard for cancer prognosis, diagnosis and prediction of responses/resistances to treatments. The existing biopsy procedures used in clinical practice are, however, invasive, painful and often associated with pitfalls like poor recovery of tumor cells and infeasibility for repetition in single patients. To circumvent these limitations, alternative non-invasive, rapid and economical, yet sturdy, consistent and dependable, biopsy techniques are required. Liquid biopsy is an emerging technology that fulfills these criteria and potentially much more in terms of subject-specific real-time monitoring of cancer progression, determination of tumor heterogeneity and treatment responses, and specific identification of the type and stages of cancers. The present review first briefly revisits the state-of-the-art technique of liquid biopsy and then proceeds to address in detail, the advances in the potential clinical applications of four major biological agencies present in liquid biopsy samples (circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes and tumor-educated platelets (TEPs)). Finally, the authors conclude with the limitations that need to be addressed in order for liquid biopsy to effectively replace the conventional invasive biopsy methods in the clinical settings.

HSP27/Menin Expression as New Prognostic Serum Biomarkers of Prostate Cancer Aggressiveness Independent of PSA

The screening of PCa is based on two tests, the total PSA test and the rectal examination. However, PSA is not specific for PCa stage confirmation, leading in false positive result and involving PCa over-diagnosis and over-treatment. HSP27 and Menin have been found to be overexpressed in a wide range of human cancers. Recent studies showed how HSP27 interacts with and stabilizes Menin to lead PCa progression and treatment resistance. The purpose of our study was to evaluate the correlation of HSP27 and Menin molecular expression, and their prognosis value in PCa with respect to clinicopathological features. Elisa was employed to measure serum HSP27 and Menin concentrations in 73 PCa patients and 80 healthy individuals. Immunohistochemistry (IHC) was used to determine HSP27 and Menin tissue expression in 57 tumors and 4 Benign Prostatic Hyperplasia (BPH) tissues. Serum HSP27 expression correlated with its tissue expression in all PCa patients, whereas serum Menin expression correlated only with tissue expression in aggressive PCa patients. Moreover, the results showed a positive correlation between HSP27 and Menin either in serum (r = 0.269; p = 0.021) or in tissue (r = 0.561; p < 0.0001). In aggressive PCa, serum expression of HSP27 and Menin was positively correlated (r = 0.664; R = 0.441; p = 0.001). The correlation between HSP27 and Menin expression in tissue was found only in patients with aggressive PCa (r = 0.606; R = 0.367; p = 0.004). Statistical analysis showed that the expression of both biomarkers was positively correlated with the hormone resistance or sensitivity, tumor aggressiveness, metastasis, Gleason Score, death and did not significantly correlate with age and PSA. Survival was illustrated by Kaplan−Meier curves; increased HSP27 and Menin expression correlated with shorter survival of PCa patients (p = 0.001 and p < 0.0001, respectively). Accuracy in predicting aggressiveness was quantified by the Area Under the Curve (AUC) of Receiver Operating Characteristic (ROC). We demonstrated that the combination of HSP27/Menin was statistically greater than PSA; it achieved an AUC of 0.824 (95% CI, 0.730−0.918; p < 0.0001). However, HSP27/Menin/PSA combination decreased the diagnostic value with an AUC of 0.569 (95% CI, 0.428−0.710; p = 0.645). Our work suggests the potential role of HSP27/Menin as diagnostic and prognostic biomarkers.

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.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Artificially Intelligent Nanoarray Detects Various Cancers by Liquid Biopsy of Volatile Markers

Cancer is usually not symptomatic in its early stages. However, early detection can vastly improve prognosis. Liquid biopsy holds great promise for early detection, although it still suffers from many disadvantages, mainly searching for specific cancer biomarkers. Here, a new approach for liquid biopsies is proposed, based on volatile organic compound (VOC) patterns in the blood headspace. An artificial intelligence nanoarray based on a varied set of chemi-sensitive nano-based structured films is developed and used to detect and stage cancer. As a proof-of-concept, three cancer models are tested showing high incidence and mortality rates in the population: breast cancer, ovarian cancer, and pancreatic cancer. The nanoarray has >84% accuracy, >81% sensitivity, and >80% specificity for early detection and >97% accuracy, 100% sensitivity, and >88% specificity for metastasis detection. Complementary mass spectrometry analysis validates these results. The ability to analyze such a complex biological fluid as blood, while considering data of many VOCs at a time using the artificially intelligent nanoarray, increases the sensitivity of predictive models and leads to a potential efficient early diagnosis and disease-monitoring tool for cancer.

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer-A Modeling Study

Simple Summary

Hormonal therapy for prostate cancer is often applied past the point of resistance, hence losing any future clinical value to the evolution of resistant strains. If the undesirable outcome of the treatment is forewarned, then clinicians can have an opportunity to adjust the treatment, which can result in better management of the cancer. Using a mechanistic mathematical model, we introduce two methods to enhance the accuracy of classical biomarkers for hormonal therapy failure. Our results show the value in measuring both prostate-specific antigen and androgen during hormonal treatment, which can potentially allow for better management of prostate cancer.

Abstract

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression

Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Pten^f/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.

Small extracellular vesicle-associated miR-6068 promotes aggressive phenotypes of prostate cancer through miR-6068/HIC2/SIRT1 axis

Early diagnosis and treatment of patients with aggressive prostate cancer (PCa) remains a clinically unmet need. We aimed to determine the levels of small extracellular vesicle (sEV)-associated microRNAs (miRs); miR-4737, miR-6068, and miR-6076 in a large panel of PCa cells and delineate the biological significance of miR-6068 in promoting PCa cells. sEVs were isolated from the conditioned medium of PCa cells, followed by RNA extraction and quantitative Real-Time PCR analysis. Functional assays were performed, and the protein expression of hypermethylated in cancer 2 (HIC2), as a potential miR-6068 target gene, was evaluated in PCa tissues by immunohistochemistry. sEV-associated miR-6068, miR-4737, and miR-6076 levels displayed large and significant differences compared to normal cells. miR-6068 was explicitly upregulated in sEV of PC-3 and CWR-R1ca cells (P<0.010). Suppression of miR-6068 in CWR-R1ca cells decreased cell proliferation, colony formation, and cell migration. In contrast, upregulation of miR-6068 in RC77T/E cells decreased HIC2 levels and increased cell aggressive phenotypes. The overexpression of HIC2 in PCa tissues was primarily observed in the cytoplasm compared to benign prostatic hyperplasia (BPH) and normal tissues (P<0.0001). This study confirms the differential packaging of miR-4737, miR-6068, and miR-6076 in sEVs of PCa cells. MiR-6068 promotes PCa cells to acquire aggressive phenotypes by inhibiting the HIC2/Sirtuin 1 (SIRT1) axis.

Recent advances in nanomaterials-based optical sensors for detection of various biomarkers (inorganic species, organic and biomolecules)

This review briefly emphasizes the different detection approaches (electrochemical sensors, chemiluminescence, surface-enhanced Raman scattering), functional nanostructure materials (quantum dots, metal nanoparticles, metal nanoclusters, magnetic nanomaterials, metal oxide nanoparticles, polymer-based nanomaterials, and carbonaceous nanomaterials) and detection mechanisms. Further, this review emphasis on the integration of functional nanomaterials with optical spectroscopic techniques for the identification of various biomarkers (nucleic acids, glucose, uric acid, oxytocin, dopamine, ascorbic acid, bilirubin, spermine, serotonin, thiocyanate, Pb²⁺ , Cu²⁺ , Hg²⁺ , F^- , peptides, and cancer biomarkers (mucin 1, prostate specific antigen, carcinoembryonic antigen, CA15-3, human epidermal growth factor receptor 2, C-reactive protein, and interleukin-6). Analytical characteristics of nanomaterials-based optical sensors are summarized in Tables, providing the insights of nanomaterials-based optical sensors for biomarkers detection. Finally, the opportunities and challenges of nanomaterials-based optical analytical approaches for the detection of various biomarkers (inorganic, organic, biomolecules, peptides and proteins) are discussed.

miRNAs as molecular biomarkers for prostate cancer

MicroRNAs (miRNAs) are short noncoding RNA able to regulate specific mRNA stability, thus influencing target gene expression. Disrupted levels of several miRNA have been associated with prostate cancer, the leading cause of cancer death among men and the fifth leading cause of death worldwide. Here, we investigated whether miR-145, miR-148, and miR-185 circulating levels in plasma could be used as molecular biomarkers, to allow distinguishing between individuals with benign prostatic hyperplasia, precancerous lesion, and prostate cancer. In this study, we recruited 170 urological clinic patients with suspected prostate cancer who underwent prostate biopsy. Total RNA was isolated from plasma, and TaqMan MicroRNA assays were used to analyze miR-145, miR-185, and miR-148 expression. First, differential miRNA expression among patient groups was evaluated. Then, miRNA levels were combined with clinical assessment outcomes, including results from invasive tests, using multivariate analysis to examine their ability in discriminating among the three patient groups. Our results suggest that miRNA is a promising molecular tool for clinical management of at-risk patients.

LMX1B Activated Circular RNA GFRA1 Modulates the Tumorigenic Properties and Immune Escape of Prostate Cancer

Prostate cancer (PCa) is the most common cancer affecting men, with increasing global mortality and morbidity rates. Despite the progress in the diagnosis and treatment of PCa, patient outcomes remain poor, and novel therapeutic targets for PCa are urgently needed. Recently, circular RNAs (circRNAs) have been studied in-depth as potential biomarkers for many diseases. In this study, circRNA microarrays using four pairs of PCa tissues were utilized to show that circGFRA1 was upregulated in PCa tumor tissues. CircGFRA1 is suggested to play an oncogene role in PCa progression as the silencing of circGFRA1 inhibited the proliferation, migration, and immune escape activity of PCa cells. Furthermore, by utilizing bioinformatics analysis, RIP, RNA pull-down, and luciferase reporter assays, our results showed that LMX1B could bind to the GFRA1 promoter and regulate circGFRA1 expression in PCa cells and circGFRA1 upregulated HECTD1 expression through sponging miR-3064-5p. This novel LMX1B/circGFRA1/miR-3064-5p/HECTD1 axis identified in PCa provides new insights for developing novel therapeutic strategies for PCa.