A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer

Combination of prostate volume and apparent diffusion coefficient can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies

BACKGROUND

Nowadays, there are many patients who undergo unnecessary prostate biopsies after receiving a prostate imaging reporting and data system (PI-RADS) score of 3. Our purpose is to identify cutoff values of the prostate volume (PV) and minimum apparent diffusion coefficient (ADCmin) to stratify those patients to reduce unnecessary prostate biopsies.

METHODS

Data from 224 qualified patients who received prostate biopsies from January 2019 to June 2023 were collected. The Mann-Whitney U test was used to compare non-normal distributed continuous variables, which were recorded as median (interquartile ranges). The correlation coefficients were calculated using Spearman's rank correlation analysis. Categorical variables are recorded by numbers (percentages) and compared by χ2 test. Both univariate and multivariate logistic regression analysis were used to determine the independent predictors. The receiver-operating characteristic curve and the area under the curve (AUC) were used to evaluate the diagnostic performance of clinical variables.

RESULTS

Out of a total of 224 patients, 36 patients (16.07%) were diagnosed with clinically significant prostate cancer (csPCa), whereas 72 patients (32.14%) were diagnosed with any grade prostate cancer. The result of multivariate analysis demonstrated that the PV (p < 0.001, odds ratio [OR]: 0.952, 95% confidence interval [95% CI]: 0.927-0.978) and ADCmin (p < 0.01, OR: 0.993, 95% CI: 0.989-0.998) were the independent factors for predicting csPCa. The AUC values of the PV and ADCmin were 0.779 (95% CI: 0.718-0.831) and 0.799 (95% CI: 0.740-0.849), respectively, for diagnosing csPCa. After stratifying patients by PV and ADCmin, 24 patients (47.06%) with "PV < 55 mL and ADCmin < 685 μm2/s" were diagnosed with csPCa. However, only one patient (1.25%) with PV ≥ 55 mL and ADCmin ≥ 685 μm2/s were diagnosed with csPCa.

CONCLUSIONS

In this study, we found the combination of PV and ADCmin can stratify patients with a PI-RADS score of 3 to reduce unnecessary prostate biopsies. These patients with "PV ≥ 55 mL and ADCmin ≥ 685 μm2/s" may safely avoid prostate biopsies.

Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review

BACKGROUND

Prostate cancer (PCa) is one of the most frequently occurring malignancies. Although most cases are not life-threatening, approximately 20% endure an unfavorable outcome. PSA-based screening reduced mortality but at the cost of an increased overdiagnosis/overtreatment of low-risk (lrPCa) and favorable intermediate-risk (firPCa) PCa. PCa risk-groups are usually identified based on serum Prostate-Specific Antigen (PSA), the Gleason score, and clinical T stage, which have consistent although variable specificity or subjectivity. Thus, more effective and specific tools for risk assessment are needed, ideally making use of minimally invasive methods such as liquid biopsies. In this systematic review we assessed the clinical potential and analytical performance of liquid biopsy-based biomarkers for pre-treatment risk stratification of PCa patients.

METHODS

Studies that assessed PCa pre-treatment risk were retrieved from PubMed, Scopus, and MedLine. PCa risk biomarkers were analyzed, and the studies' quality was assessed using the QUADAS-2 tool.

RESULTS

The final analysis comprised 24 full-text articles, in which case-control studies predominated, mostly reporting urine-based biomarkers (54.2%) and biomarker quantification by qPCR (41.7%). Categorization into risk groups was heterogeneous, predominantly making use of the Gleason score.

CONCLUSION

This systematic review unveils the substantial clinical promise of using circulating biomarkers in assessing the risk for prostate cancer patients. However, the standardization of groups, categories, and biomarker validation are mandatory before this technique can be implemented. Circulating biomarkers might represent a viable alternative to currently available tools, obviating the need for tissue biopsies, and allowing for faster and more cost-effective testing, with superior analytical performance, specificity, and reproducibility.

Fushenmu treatment ameliorates RyR2 with related metabolites in a zebrafish model of barium chloride induced arrhythmia

BACKGROUND

Fushenmu (Pini Radix in Poria, FSM) is a folk parasitic herb that has been mainly used for palpitation and amnesiain in traditional Chinese medicine (TCM). Recently, as an individual herb or a component of formulations, Fushenmu exhibits therapeutic potential for the treatment of cardiac arrhythmias. Yet, how specific targets or pathways of Fushenmu inhibit arrhythmia has not yet been reported.

METHODS

Here, based on clinical functional genomics, metabolomics and molecular biologic technologies, a network construction strategy was adopted to identify FSM therapeutic targets and biomarkers that might explore its functions.

RESULTS

In this study, it was found that FSM recovered arrhythmia-associated heart failure in barium chloride (BaCl2) induced arrhythmic zebrafish embryos, as was evidenced by the shortened cardiac sinus venosus-bulbus arteriosus (SV-BA) distance, smaller cardiovascular bleeding areas, and reduced cardiomyocyte apoptosis. Moreover, analysis via ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-QTOF-ESI-MS/MS) components identification and network pharmacology prediction showed that 11 main active components of FSM acted on 33 candidate therapeutic targets. Metabolomic analysis also suggested that FSM could rescue 242 abnormal metabolites from arrhythmic zebrafish embryos. Further analysis based on the combination of target prediction and metabolomic results illustrated that FSM down-regulated Ryanodine Receptor 2 (RyR2) expressions, inhibited adrenaline and 3',5'-Cyclic AMP (cAMP) levels in a dose-dependent manner, which was confirmed by metabolites quantification and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assay.

CONCLUSION

In summary, this study revealed that FSM mitigated BaCl2 induced cardiac damage caused by arrhythmia by suppressing RyR2 expressions, decreasing adrenaline and cAMP through the adrenergic signalling pathway.

Urinary extracellular vesicles miRNA-A new era of prostate cancer biomarkers

Prostate cancer is the second most common male cancer worldwide showing the highest rates of incidence in Western Europe. Although the measurement of serum prostate-specific antigen levels is the current gold standard in PCa diagnosis, PSA-based screening is not considered a reliable diagnosis and prognosis tool due to its lower sensitivity and poor predictive score which lead to a 22%-43% overdiagnosis, unnecessary biopsies, and over-treatment. These major limitations along with the heterogeneous nature of the disease have made PCa a very unappreciative subject for diagnostics, resulting in poor patient management; thus, it urges to identify and validate new reliable PCa biomarkers that can provide accurate information in regard to disease diagnosis and prognosis. Researchers have explored the analysis of microRNAs (miRNAs), messenger RNAs (mRNAs), small proteins, genomic rearrangements, and gene expression in body fluids and non-solid tissues in search of lesser invasive yet efficient PCa biomarkers. Although the presence of miRNAs in body fluids like blood, urine, and saliva initially sparked great interest among the scientific community; their potential use as liquid biopsy biomarkers in PCa is still at a very nascent stage with respect to other well-established diagnostics and prognosis tools. Up to date, numerous studies have been conducted in search of PCa miRNA-based biomarkers in whole blood or blood serum; however, only a few studies have investigated their presence in urine samples of which less than two tens involve the detection of miRNAs in extracellular vesicles isolated from urine. In addition, there exists some discrepancy around the identification of miRNAs in PCa urine samples due to the diversity of the urine fractions that can be targeted for analysis such as urine circulating cells, cell-free fractions, and exosomes. In this review, we aim to discuss research output from the most recent studies involving the analysis of urinary EVs for the identification of miRNA-based PCa-specific biomarkers.

States of quinolinic acid excess in urine: A systematic review of human studies

Introduction

Quinolinic acid is an intermediate compound derived from the metabolism of dietary tryptophan. Its accumulation has been reported in patients suffering a broad spectrum of diseases and conditions. In this manuscript, we present the results of a systematic review of research studies assessing urinary quinolinic acid in health and disease.

Methods

We performed a literature review using PubMed, Cochrane, and Scopus databases of all studies reporting data on urinary quinolinic acid in human subjects from December 1949 to January 2022.

Results

Fifty-seven articles met the inclusion criteria. In most of the reported studies, compared to the control group, quinolinic acid was shown to be at increased concentration in urine of patients suffering from different diseases and conditions. This metabolite was also demonstrated to correlate with the severity of certain diseases including juvenile idiopathic inflammatory myopathies, graft vs. host disease, autism spectrum disorder, and prostate cancer. In critically ill patients, elevated quinolinic acid in urine predicted a spectrum of adverse outcomes including hospital mortality.

Conclusion

Quinolinic acid has been implicated in the pathophysiology of multiple conditions. Its urinary accumulation appears to be a feature of acute physiological stress and several chronic diseases. The exact significance of these findings is still under investigation, and further studies are needed to reveal the subsequent implications of this accumulation.

From Omics to Multi-Omics Approaches for In-Depth Analysis of the Molecular Mechanisms of Prostate Cancer

Cancer arises following alterations at different cellular levels, including genetic and epigenetic modifications, transcription and translation dysregulation, as well as metabolic variations. High-throughput omics technologies that allow one to identify and quantify processes involved in these changes are now available and have been instrumental in generating a wealth of steadily increasing data from patient tumors, liquid biopsies, and from tumor models. Extensive investigation and integration of these data have led to new biological insights into the origin and development of multiple cancer types and helped to unravel the molecular networks underlying this complex pathology. The comprehensive and quantitative analysis of a molecule class in a biological sample is named omics and large-scale omics studies addressing different prostate cancer stages have been performed in recent years. Prostate tumors represent the second leading cancer type and a prevalent cause of cancer death in men worldwide. It is a very heterogenous disease so that evaluating inter- and intra-tumor differences will be essential for a precise insight into disease development and plasticity, but also for the development of personalized therapies. There is ample evidence for the key role of the androgen receptor, a steroid hormone-activated transcription factor, in driving early and late stages of the disease, and this led to the development and approval of drugs addressing diverse targets along this pathway. Early genomic and transcriptomic studies have allowed one to determine the genes involved in prostate cancer and regulated by androgen signaling or other tumor-relevant signaling pathways. More recently, they have been supplemented by epigenomic, cistromic, proteomic and metabolomic analyses, thus, increasing our knowledge on the intricate mechanisms involved, the various levels of regulation and their interplay. The comprehensive investigation of these omics approaches and their integration into multi-omics analyses have led to a much deeper understanding of the molecular pathways involved in prostate cancer progression, and in response and resistance to therapies. This brings the hope that novel vulnerabilities will be identified, that existing therapies will be more beneficial by targeting the patient population likely to respond best, and that bespoke treatments with increased efficacy will be available soon.