scholarly journals Human Prostate Tissue MicroRNAs and Their Predicted Target Pathways Linked to Prostate Cancer Risk Factors

Cancers ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 3537
Author(s):  
Max Enwald ◽  
Terho Lehtimäki ◽  
Pashupati P. Mishra ◽  
Nina Mononen ◽  
Teemu J. Murtola ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Target Genes ◽  
Expression Profiles ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Hdl Cholesterol ◽  
Microrna Expression ◽  
Prostate Tissue ◽  
Multiple Testing Correction

MicroRNAs are important in prostate cancer development, progression and metastasis. The aim of this study was to test microRNA expression profile in prostate tissue obtained from prostate cancer patients for associations with various prostate cancer related factors and to pinpoint the predicted target pathways for these microRNAs. Prostate tissue samples were obtained at prostatectomy from patients participating in a trial evaluating impact of pre-operative atorvastatin on serum prostate specific antigen (PSA) and Ki-67 expression in prostate tissue. Prostate tissue microRNA expression profiles were analyzed using OpenArray® MicroRNA Panel. Pathway enrichment analyses were conducted for predicted target genes of microRNAs that correlated significantly with studied factors. Eight microRNAs correlated significantly with studied factors of patients after Bonferroni multiple testing correction. MiR-485-3p correlated with serum HDL-cholesterol levels. In atorvastatin-treated subjects, miR-34c-5p correlated with a change in serum PSA and miR-138-3p with a change in total cholesterol. In the placebo arm, both miR-576-3p and miR-550-3p correlated with HDL-cholesterol and miR-627 with PSA. In pathway analysis, these eight microRNAs related significantly to several pathways relevant to prostate cancer. This study brings new evidence from the expression of prostate tissue microRNAs and related pathways that may link risk factors to prostate cancer and pinpoint new therapeutic possibilities.

scholarly journals DETECTION OF PROSTATE CANCER: A REVIEW

2018 ◽  
Vol 11 (6) ◽  
pp. 25
Author(s):  
Pratibha Rani ◽  
Kamaldeep Singh ◽  
Anania Arjuna ◽  
Savita Devi
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Chemical Exposure ◽  
Dietary Factors ◽  
Early Screening ◽  
Cancer Risk Factors ◽  
Worldwide Population ◽  
Male Patients

Prostate cancer is the most commonly diagnosed non-cutaneous cancer in men. Prostate-specific antigen (PSA) is the biomarker used for the screening of prostate cancer and other prostate-related problems. Not only the genetic factors are involved dietary factors, environmental factors but also responsible for the development of prostate cancer. Risk factors such as family history, age, chemical exposure, infection, and smoking are at the peak point for the development of prostate disease. Advanced age is one of the main risk factors. Radical prostatectomy is the most common therapy for small group of patients with high-grade tumors. Early screening of PSA reduces the incidence rate of prostate cancer. Mostly prostate abnormalities are seen in among male patients above the age of 50 years or older. In worldwide population, the epidemiology of prostate cancer is high in western countries and less in Asian countries.

Prostate Cancer

2018 ◽  
Author(s):  
Kathryn M. Wilson ◽  
Lorelei Mucci
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Family History ◽  
Prostate Specific Antigen ◽  
Advanced Prostate Cancer ◽  
Advanced Disease ◽  
African Ancestry ◽  
Specific Antigen ◽  
Dietary Factors ◽  
Increased Risk

Prostate cancer is among the most commonly diagnosed cancers among men, ranking second in cancer globally and first in Western countries. There are marked variations in incidence globally, and its incidence must be interpreted in the context of diagnostic intensity and screening. The uptake of prostate-specific antigen screening since the 1990s has led to dramatic increases in incidence in many countries, resulting in an increased proportion of indolent cancers that would never have come to light clinically in the absence of screening. Risk factors differ when studying prostate cancer overall versus advanced disease. Older age, African ancestry, and family history are established risk factors for prostate cancer. Obesity and smoking are not associated with risk overall, but are associated with increased risk of advanced prostate cancer. Several additional lifestyle factors, medications, and dietary factors are now emerging as risk factors for advanced disease.

scholarly journals Identification of Tumorigenic and Prognostic Biomarkers in Colorectal Cancer Based on microRNA Expression Profiles

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuntao Shi ◽  
Yingying Zhuang ◽  
Jialing Zhang ◽  
Mengxue Chen ◽  
Shangnong Wu
Keyword(s):  
Target Genes ◽  
Cox Regression ◽  
Expression Profiles ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Risk Groups ◽  
Normal Mucosa ◽  
Microrna Expression ◽  
Functional Enrichment

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.

Cumulative Prostate Cancer Risk Assessment with the Aid of the Free-to-Total Prostate Specific Antigen Ratio

2004 ◽  
Vol 45 (2) ◽  
pp. 160-165 ◽  
Author(s):  
Gunnar Aus ◽  
Charlotte Becker ◽  
Stefan Franzén ◽  
Hans Lilja ◽  
Pär Lodding ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Assessment ◽  
Cancer Risk ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Cancer Risk Assessment ◽  
Total Prostate Specific Antigen

scholarly journals Risk Factors for Prostate Cancer Detection After a Negative Biopsy: A Novel Multivariable Longitudinal Approach

2010 ◽  
Vol 28 (10) ◽  
pp. 1714-1720 ◽  
Author(s):  
Peter H. Gann ◽  
Angela Fought ◽  
Ryan Deaton ◽  
William J. Catalona ◽  
Edward Vonesh
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Specific Antigen ◽  
Time Dependent ◽  
Negative Biopsy ◽  
Gland Volume ◽  
Piecewise Exponential ◽  
Over Time ◽  
Negative Biopsies

Purpose To introduce a novel approach for the time-dependent quantification of risk factors for prostate cancer (PCa) detection after an initial negative biopsy. Patients and Methods Data for 1,871 men with initial negative biopsies and at least one follow-up biopsy were available. Piecewise exponential regression models were developed to quantify hazard ratios (HRs) and define cumulative incidence curves for PCa detection for subgroups with specific patterns of risk factors over time. Factors evaluated included age, race, serum prostate-specific antigen (PSA) concentration, PSA slope, digital rectal examination, dysplastic glands or prostatitis on biopsy, ultrasound gland volume, urinary symptoms, and number of negative biopsies. Results Four hundred sixty-five men had PCa detected, after a mean follow-up time of 2.8 years. All of the factors were independent predictors of PCa detection except for PSA slope, as a result of its correlation with time-dependent PSA level, and race. PSA (HR = 3.90 for > 10 v 2.5 to 3.9 ng/mL), high-grade prostatic intraepithelial neoplasia/atypical glands (HR = 2.97), gland volume (HR = 0.39 for > 50 v < 25 mL), and number of repeat biopsies (HR = 0.36 for two v zero repeat biopsies) were the strongest predictors. Men with high-risk versus low-risk event histories had a 20-fold difference in PCa detection over 5 years. Conclusion Piecewise exponential models provide an approach to longitudinal analysis of PCa risk that allows clinicians to see the interplay of risk factors as they unfold over time for individual patients. With these models, it is possible to identify distinct subpopulations with dramatically different needs for monitoring and repeat biopsy.

scholarly journals Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Johanna Tolksdorf ◽  
Michael W. Kattan ◽  
Stephen A. Boorjian ◽  
Stephen J. Freedland ◽  
Karim Saba ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Factors ◽  
Family History ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prostate Biopsy ◽  
Prostate Cancer Risk ◽  
High Grade ◽  
Prediction Tools ◽  
History Of

Abstract Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). Conclusions We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

scholarly journals Pre-treatment risk stratification of prostate cancer patients:A critical review

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
George Rodrigues ◽  
Padraig Warde ◽  
Tom Pickles ◽  
Juanita Crook ◽  
Michael Brundage ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Gleason Score ◽  
Critical Review ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Original Research ◽  
Pre Treatment

Introduction:  The use of accepted prostate cancer risk stratification groups based on prostate-specific antigen, T stage and Gleason score assists in therapeutic treatment decision-making, clinical trial design and outcome reporting. The utility of integrating novel prognostic factors into an updated risk stratification schema is an area of current debate. The purpose of this work is to critically review the available literature on novel pre-treatment prognostic factors and alternative prostate cancer risk stratification schema to assess the feasibility and need for changes to existing risk stratification systems. Methods:  A systematic literature search was conducted to identify original research publications and review articles on prognostic factors and risk stratification in prostate cancer. Search terms included risk stratification, risk assessment, prostate cancer or neoplasms, and prognostic factors. Abstracted information was assessed to draw conclusions regarding the potential utility of changes to existing risk stratification schema. Results:  The critical review identified three specific clinically relevant potential changes to the most commonly used three-group risk stratification system: (1) the creation of a very-low risk category; (2) the splitting of intermediate-risk into a low- and highintermediate risk groups; and (3) the clarification of the interface between intermediate- and high-risk disease. Novel pathological factors regarding high-grade cancer, subtypes of Gleason score 7 and percentage biopsy cores positive were also identified as potentially important risk-stratification factors. Conclusions:  Multiple studies of prognostic factors have been performed to create currently utilized prostate cancer risk stratification systems. We propose potential changes to existing systems.

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