scholarly journals Small RNAs as biomarkers to differentiate benign and malign prostate diseases: An alternative for transrectal punch biopsy of the prostate?

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247930
Author(s):  
Lukas Markert ◽  
Jonas Holdmann ◽  
Claudia Klinger ◽  
Michael Kaufmann ◽  
Karin Schork ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Small Rnas ◽  
Punch Biopsy ◽  
Machine Learning Algorithms ◽  
P Value ◽  
Prostate Hyperplasia ◽  
Prostate Diseases ◽  
Exosomal Mirna ◽  
Benign Prostate

Prostate cancer (PCa) is the most common cancer and the third most frequent cause of male cancer death in Germany. MicroRNAs (miRNA) appear to be involved in the development and progression of PCa. A diagnostic differentiation from benign prostate hyperplasia (BPH) is often only possible through transrectal punch biopsy. This procedure is described as painful and carries risks. It was investigated whether urinary miRNAs can be used as biomarkers to differentiate the prostate diseases above. Therefore urine samples from urological patients with BPH (25) or PCa (28) were analysed using Next-Generation Sequencing to detect the expression profile of total and exosomal miRNA/piRNA. 79 miRNAs and 5 piwi-interacting RNAs (piRNAs) were significantly differentially expressed (adjusted p-value < 0.05 and log2-Fc > 1 or < -1). Of these, 6 miRNAs and 2 piRNAs could be statistically validated (AUC on test cohort > = 0.7). In addition, machine-learning algorithms were used to identify a panel of 22 additional miRNAs, whose interaction makes it possible to differentiate the groups as well. There are promising individual candidates for potential use as biomarkers in prostate cancer. The innovative approach of applying machine learning methods to this kind of data could lead to further small RNAs coming into scientific focus, which have so far been neglected.

scholarly journals Prostate Stem Cells in the Development of Benign Prostate Hyperplasia and Prostate Cancer: Emerging Role and Concepts

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Akhilesh Prajapati ◽  
Sharad Gupta ◽  
Bhavesh Mistry ◽  
Sarita Gupta
Keyword(s):  
Prostate Cancer ◽  
Stem Cells ◽  
Benign Prostate Hyperplasia ◽  
Normal Prostate ◽  
Prostate Hyperplasia ◽  
Regulatory Factors ◽  
Prostate Cells ◽  
Hormonal Imbalance ◽  
Benign Prostate ◽  
Insight Into

Benign Prostate hyperplasia (BPH) and prostate cancer (PCa) are the most common prostatic disorders affecting elderly men. Multiple factors including hormonal imbalance, disruption of cell proliferation, apoptosis, chronic inflammation, and aging are thought to be responsible for the pathophysiology of these diseases. Both BPH and PCa are considered to be arisen from aberrant proliferation of prostate stem cells. Recent studies on BPH and PCa have provided significant evidence for the origin of these diseases from stem cells that share characteristics with normal prostate stem cells. Aberrant changes in prostate stem cell regulatory factors may contribute to the development of BPH or PCa. Understanding these regulatory factors may provide insight into the mechanisms that convert quiescent adult prostate cells into proliferating compartments and lead to BPH or carcinoma. Ultimately, the knowledge of the unique prostate stem or stem-like cells in the pathogenesis and development of hyperplasia will facilitate the development of new therapeutic targets for BPH and PCa. In this review, we address recent progress towards understanding the putative role and complexities of stem cells in the development of BPH and PCa.

scholarly journals Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance

2021 ◽  
Author(s):  
Nikita Sushentsev ◽  
Leonardo Rundo ◽  
Oleg Blyuss ◽  
Tatiana Nazarenko ◽  
Aleksandr Suvorov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Cancer Progression ◽  
Predictive Value ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Control Group ◽  
P Value ◽  
Lasso Regression

Abstract Objectives To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). Methods The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5–16 years’ experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong’s test. Results The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34–0.77). Conclusions PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. Key Points • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

scholarly journals The Association between Trichomonas Vaginalis Infection and The Risk of Benign Prostate Hyperplasia, Prostate Cancer, and Bladder Cancer in Patients: A Nationwide Population-Based Case-Control Study

2021 ◽  
Author(s):  
Hung Yi Yang ◽  
Ruei-Yu Su ◽  
Chi-Hsiang Chung ◽  
Kuo-Yang Huang ◽  
Wu-Chien Chien ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Bladder Cancer ◽  
Trichomonas Vaginalis ◽  
Case Control Study ◽  
Benign Prostate Hyperplasia ◽  
Case Control ◽  
Control Group ◽  
Prostate Hyperplasia ◽  
Benign Prostate ◽  
Control Study

Abstract Introduction: Trichomonas vaginalis infection is one of the most widespread sexually transmitted infections in the world. There are approximately 276 million cases worldwide. Most men remain undiagnosed and untreated because they are asymptomatic. The chronic inflammation induced by persistent infection may increase the risk of developing genitourinary cancers. In this study, we aimed to investigate the association between trichomoniasis and benign prostate hyperplasia (BPH), prostate cancer (PCa), and bladder cancer (BC) in Taiwan.Material and method: We designed a case-control study by using the database of the National Health Insurance program in Taiwan. We used the International Classification of Diseases, 9th Revision classifications to classify all the medical conditions in the case and control groups. All odds ratios (ORs) and 95% confidence intervals (CIs) were analyzed using multivariable logistic regression to adjust for all comorbidities and variables.Result: From 2000 to 2015, we enrolled a total of 62,544 individuals as the case group and 187,632 as the control group. Trichomoniasis exposure had a significant association with BPH and PCa (adjusted OR: BPH = 2.685, 95% CI = 1.233–4.286, P = 0.013; PCa = 5.801, 95% CI = 1.296–26.035, P = 0.016). The relative risk was much higher if patients had both trichomoniasis and depression (adjusted OR = 7.682, 95% CI = 5.730–9.451, P < 0.001).Conclusion: Men with trichomoniasis had a significantly higher risk of developing BPH and PCa than those without. Healthcare professionals should not only pay more attention to disease treatment, but also to public health education.

scholarly journals Identification of hub genes and their clinical value for predicting the development of prostate cancer from benign prostate hyperplasia by bioinformatic analysis

2021 ◽  
Author(s):  
Xi Chen ◽  
Junjie Ma ◽  
Chengdang Xu ◽  
Licheng Wang ◽  
Yicong Yao ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Survival Data ◽  
Benign Prostate Hyperplasia ◽  
Disease Free Survival ◽  
Ppi Network ◽  
Hub Genes ◽  
Prostate Hyperplasia ◽  
Hub Gene ◽  
Benign Prostate ◽  
Geo Database

Abstract BackgroundProstate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in elderly males. The two diseases have some commonalities: both are growth depend on hormone and respond to antiandrogen therapy. Some studies have shown that genetic factors are responsible for the occurrences of both diseases. There may be a correlation between BPH and PCa. MethodsThe GEO database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were utilized to find pathways in which the DEGs were enriched. The STRING database can provide a protein–protein interaction (PPI) network, and Cytoscape software can find hub genes in PPI network. GEPIA can be used to analyze expression and survival data for hub genes. R software was used to progress regression analysis, decision curve analysis and built nomograph. UALCAN and The Human Protein Atlas was utilized to test the results. Finally, we made clinical and cell experiments to verify the results.ResultsSixty DEGs, consisting of 15 up-regulated and 45 down-regulated genes, were found based on the GEO database. Using Cytoscape, we found 7 hub gene in the PPI network. The hub gene expression was tested on TCGA database. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Meanwhile, all hub genes exclude CXCR4 has diagnostic value in predicting PCa and their mutations are risk factors leading to PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect the disease-free survival (DFS). The same results reflected in different database. In addition, we also chose three hub gene, MYC, MYL9, and SNAI2, to validate their functions in clinical specimens and cells.ConclusionThese identified hub genes can help us to understand the process and mechanism by which BPH develops into PCa and provide achievable targets for predicting which BPH patients may later develop PCa.

scholarly journals Analysis of Urinary Prostate-Specific Antigen Glycoforms in Samples of Prostate Cancer and Benign Prostate Hyperplasia

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Chun-Jen Hsiao ◽  
Tzong-Shin Tzai ◽  
Chein-Hung Chen ◽  
Wen-Horng Yang ◽  
Chung-Hsuan Chen
Keyword(s):  
Prostate Cancer ◽  
Prostate Specific Antigen ◽  
Benign Prostate Hyperplasia ◽  
Clinical Sample ◽  
Specific Antigen ◽  
Ion Accumulation ◽  
Detection Sensitivity ◽  
Prostate Hyperplasia ◽  
Liquid Chromatography Tandem Mass ◽  
Benign Prostate

Glycans of prostate-specific antigen (PSA) in prostate cancer were found to be different from that in benign disease. It is difficult to analyze heterogeneous PSA glycoforms in each individual specimen because of low protein abundance and the limitation of detection sensitivity. We developed a method for prostate cancer diagnosis based on PSA glycoforms. Specific glycoforms were screened in each clinical sample based on liquid chromatography-tandem mass spectrometry with ion accumulation. To look for potential biomarkers, normalized abundance of each glycoform in benign prostate hyperplasia (BPH) and in prostate cancer was evaluated. The PSA glycoform, Hex5HexNAc4NeuAc1dHex1, and monosialylated, sialylated, and unfucosylated glycoforms differed significantly between the prostate cancer and BPH samples. The detection sensitivity (87.5%) and specificity (60%) for prostate cancer identification are higher than those of the serum PSA marker. As low as 100 amol PSA could be detected with the ion accumulation method which has not been reported before. The improved detection specificity can help reduce unnecessary examinations.

scholarly journals The Panel of 12 Cell-Free MicroRNAs as Potential Biomarkers in Prostate Neoplasms

Diagnostics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 38 ◽  
Author(s):  
Maria Yu. Konoshenko ◽  
Evgeniy A. Lekchnov ◽  
Olga E. Bryzgunova ◽  
Ivan A. Zaporozhchenko ◽  
Sergey V. Yarmoschuk ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Large Scale ◽  
Benign Prostate Hyperplasia ◽  
Scale Validation ◽  
Biological Fluids ◽  
Prostate Neoplasms ◽  
Great Promise ◽  
Prostate Hyperplasia ◽  
Diagnostic Potential ◽  
Benign Prostate

Prostate cancer is a global biological, medical, and social issue aggravated by the lack of reliable, highly specific, and sensitive non-invasive tests for diagnosis and staging of prostate cancer. One prospective source of biomarkers are the cell-free miRNAs present in various biological fluids. In the present study, we validated the diagnostic potential of cell-free miRNAs: miR-19b, miR-22, miR-92a, miR-378, miR-425, miR-30e, miR-31, miR-125b, miR-200b, miR-205, miR-375, and miR-660; we estimated the required sample size and the minimal miRNA set for a subsequent large-scale validation study. Relative expression of 12 miRNA combined in 31 ratios was investigated in three fractions of biological fluids (urine extracellular vesicles, clarified urine, and plasma) obtained from patients with prostate cancer (n = 10), benign prostate hyperplasia (n = 8), and healthy volunteers (n = 11). Eight of the miRNAs found in urine vesicles (miR-19b, miR-30e, miR-31, miR-92a, miR-125, miR-200, miR-205, and miR-660) showed great promise and when combined into six ratios (miR-125b/miR-30e, miR-200/miR-30e, miR-205/miR-30e, miR-31/miR-30e, miR-660/miR-30e, and miR-19b/miR-92a) could classify patients with prostate cancer, benign prostate hyperplasia, and healthy donors with 100% specificity, 100% sensitivity, and with a high degree of reliability for most donors.

Investigation of GSTP1 and epigenetic regulators expression pattern in a population of Iranian patients with prostate cancer

2020 ◽  
Vol 28 (4) ◽  
pp. 327-334
Author(s):  
Mahan Mohammadi ◽  
Shiva Irani ◽  
Iman Salahshourifar ◽  
Jalil Hosseini ◽  
Afshin Moradi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
High Efficiency ◽  
Rna Extraction ◽  
P Value ◽  
Prostate Hyperplasia ◽  
Expression Of Genes ◽  
Elevated Expression ◽  
Epigenetic Regulators ◽  
The Impact ◽  
Iranian Patients

BACKGROUND AND AIM: Prostate cancer is the leading cause of death in many countries. It is important to diagnose the disease in the early stages. Current methods detect the disease with low specificity. Examining the expression of genes responsible for disease and their epigenetic regulators are good tools in this regard. MATERIAL AND METHODS: In this prospective case-control study, 40 Iranian patients with cancer, 40 Iranian patients with prostate hyperplasia, and 40 control samples were examined. After blood sampling from each individual, RNA extraction and cDNA synthesis, GSTP1, HDAC, DNMT3A, and DNMT3B expressions were measured in three understudy groups using specific primers and Real-Time PCR method. RESULTS: A reverse correlation was identified between loss of GSTP1 expression and overexpression of HDAC, DNMT3A, and DNMT3B (P value < 0.0001) with a beneficial pattern of cancer development with high efficiency. The significant decrease of GSTP1 expression in patients in comparison to the healthy controls and the elevated expression levels of the studied epigenetic regulators in PCA and BPH samples indicate the impact of the regulators on GSTP1 expression activity. CONCLUSION: This study showed that the measurement of combined GSTP1 and its epigenetic regulators’ expression could be used as suitable genetic markers for the detection and separation of healthy individuals from prostatic patient groups in the Iranian population. However, a similar study in a larger population of case and control could help us to distinguish between normal, benign, and malignant conditions.

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