scholarly journals Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
S. N. Thibodeau ◽  
A. J. French ◽  
S. K. McDonnell ◽  
J. Cheville ◽  
S. Middha ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Candidate Genes ◽  
Prostate Cancer Risk ◽  
Prostate Tissue ◽  
Data Set ◽  
Normal Prostate ◽  
Risk Snps ◽  
Normal Prostate Tissue ◽  
Eqtl Data

scholarly journals An expanded variant list and assembly annotation identifies multiple novel coding and noncoding genes for prostate cancer risk using a normal prostate tissue eQTL data set

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0214588
Author(s):  
Melissa S. DeRycke ◽  
Melissa C. Larson ◽  
Asha A. Nair ◽  
Shannon K. McDonnell ◽  
Amy J. French ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Prostate Cancer Risk ◽  
Prostate Tissue ◽  
Data Set ◽  
Normal Prostate ◽  
Normal Prostate Tissue ◽  
Eqtl Data

Transcriptional regulation and prostate cancer risk loci.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 1554-1554
Author(s):  
Robert J. Klein ◽  
Xing Xu ◽  
Wasay Hussain ◽  
James Farber ◽  
Riina-Minna Vaananen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Cancer Risk ◽  
Prostate Cancer Risk ◽  
Regulatory Elements ◽  
Prostate Tissue ◽  
Expression Levels ◽  
Hypersensitive Sites ◽  
Risk Snps

1554 Background: While genome-wide association studies (GWAS) have identified numerous loci at which common single nucleotide polymorphisms (SNPs) are clearly associated with the risk of developing prostate cancer, the mechanism by which these variants influence disease is not clear. Based on the observation that regions of the genome with marks of transcriptional regulatory elements harbor SNPs that are more likely to be under negative selective pressure, we hypothesized that many prostate cancer associated SNPs, or their correlated proxies in linkage disequilibrium, function by altering regulation of nearby genes through alteration of transcription factor binding sites. Two predictions of this hypothesis are that there will be an enrichment of prostate cancer risk SNPs or their proxies in regulatory elements in prostate tissue and that some of these SNPs will correlate with the expression levels of nearby genes. Methods: To test the first prediction, we compared the number of prostate cancer risk SNPs (or their proxies) that overlap with DNase hypersensitive sites in the LNCaP prostate cancer cell line with randomly selected SNPs matched for allele frequency, distance from nearby genes, and local gene density. To test the second prediction, we asked if any of the known prostate cancer risk SNPs are associated with mRNA expression levels of nearby genes. Results: We found a four-fold enrichment of real prostate cancer risk SNPs overlapping with DNase hypersensitive sites versus the random set (p=0.03) at an r2 threshold of 0.95. In both tumor and benign prostate tissue, we found that a recently identified prostate cancer risk SNP is associated with expression levels of IRX4, a transcription factor previously implicated only in cardiac development. Conclusions: These data support the hypothesis that many GWAS-identified risk SNPs alter transcriptional regulation of nearby genes.

Differential gene expression in prostate tissue according to vasectomy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 298-298
Author(s):  
Kathryn M Wilson ◽  
Travis Gerke ◽  
Ericka Ebot ◽  
Jennifer A Sinnott ◽  
Jennifer R. Rider ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prostate Tissue ◽  
Gene Set Enrichment ◽  
Normal Prostate ◽  
Gene Sets ◽  
Normal Prostate Tissue

298 Background: We previously found that vasectomy was associated with an increased risk of prostate cancer, and particularly, risk of lethal prostate cancer in the Health Professionals Follow-up Study (HPFS). However, the possible biological basis for this finding is unclear. In this study, we explored possible biological mechanisms by assessing differences in gene expression in the prostate tissue of men with and without a history of vasectomy prostate cancer diagnosis. Methods: Within the HPFS, vasectomy data and gene expression data (20,254 genes) was available from archival tumor tissue from 263 cases, 124 of whom also had data for adjacent normal tissue. To relate expression of individual genes to vasectomy we used linear regression adjusting for age and year at diagnosis. We ran gene set enrichment analysis to identify pathways of genes associated with vasectomy. Results: Among 263 cases, 67 (25%) reported a vasectomy prior to cancer diagnosis. Mean age at diagnosis was 66 years among men without and 65 years among men with vasectomy. Median time between vasectomy and prostate cancer diagnosis was 25 years. Gene expression in tumor tissue was not associated with vasectomy status. In adjacent normal tissue, three individual genes were associated with vasectomy with Bonferroni-corrected p-values of < 0.10: RAPGEF6, OR4C3, and SLC35F4. Gene set enrichment analysis found five pathways upregulated and seven pathways downregulated in men with vasectomy compared to those without in normal prostate tissue with a FDR < 0.05. Upregulated pathways included several immune-related gene sets and G-protein-coupled receptor gene sets. Conclusions: We identified significant differences in gene expression profiles in normal prostate tissue according to vasectomy status among men treated for prostate cancer. The fact that such differences existed several decades after vasectomy provides support for the idea that vasectomy may play a role in the etiology of prostate cancer.

scholarly journals Multispectral Photoacoustic Imaging of Prostate Cancer: Preliminary Ex-vivo Results

2013 ◽  
Vol 3 ◽  
pp. 41 ◽  
Author(s):  
Vikram S. Dogra ◽  
Bhargava K. Chinni ◽  
Keerthi S. Valluru ◽  
Jean V. Joseph ◽  
Ahmed Ghazi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Predictive Value ◽  
Ex Vivo ◽  
Prostate Tissue ◽  
Human Prostate ◽  
Normal Prostate ◽  
Mean Intensity ◽  
Preliminary Results ◽  
Normal Prostate Tissue ◽  
Malignant Prostate

Objective: The objective of this study is to validate if ex-vivo multispectral photoacoustic (PA) imaging can differentiate between malignant prostate tissue, benign prostatic hyperplasia (BPH), and normal human prostate tissue. Materials and Methods: Institutional Review Board's approval was obtained for this study. A total of 30 patients undergoing prostatectomy for biopsy-confirmed prostate cancer were included in this study with informed consent. Multispectral PA imaging was performed on surgically excised prostate tissue and chromophore images that represent optical absorption of deoxyhemoglobin (dHb), oxyhemoglobin (HbO2), lipid, and water were reconstructed. After the imaging procedure is completed, malignant prostate, BPH and normal prostate regions were marked by the genitourinary pathologist on histopathology slides and digital images of marked histopathology slides were obtained. The histopathology images were co-registered with chromophore images. Region of interest (ROI) corresponding to malignant prostate, BPH and normal prostate were defined on the chromophore images. Pixel values within each ROI were then averaged to determine mean intensities of dHb, HbO2, lipid, and water. Results: Our preliminary results show that there is statistically significant difference in mean intensity of dHb (P < 0.0001) and lipid (P = 0.0251) between malignant prostate and normal prostate tissue. There was difference in mean intensity of dHb (P < 0.0001) between malignant prostate and BPH. Sensitivity, specificity, positive predictive value, and negative predictive value of our imaging system were found to be 81.3%, 96.2%, 92.9% and 89.3% respectively. Conclusion: Our preliminary results of ex-vivo human prostate study suggest that multispectral PA imaging can differentiate between malignant prostate, BPH and normal prostate tissue.

scholarly journals Development and Validation of a Multiparameterized Artificial Neural Network for Prostate Cancer Risk Prediction and Stratification

2018 ◽  
pp. 1-10 ◽  
Author(s):  
David A. Roffman ◽  
Gregory R. Hart ◽  
Michael S. Leapman ◽  
James B. Yu ◽  
Fangliang L. Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Prediction ◽  
Prostate Cancer Risk ◽  
Area Under The Curve ◽  
Personal Health Information ◽  
Personal Health ◽  
Training Set ◽  
Data Set

Purpose To develop and validate a multiparameterized artificial neural network (ANN) on the basis of personal health information for prostate cancer risk prediction and stratification. Methods The 1997 to 2015 National Health Interview Survey adult survey data were used to train and validate a multiparameterized ANN, with parameters including age, body mass index, diabetes status, smoking status, emphysema, asthma, race, ethnicity, hypertension, heart disease, exercise habits, and history of stroke. We developed a training set of patients ≥ 45 years of age with a first primary prostate cancer diagnosed within 4 years of the survey. After training, the sensitivity and specificity were obtained as functions of the cutoff values of the continuous output of the ANN. We also evaluated the ANN with the 2016 data set for cancer risk stratification. Results We identified 1,672 patients with prostate cancer and 100,033 respondents without cancer in the 1997 to 2015 data sets. The training set had a sensitivity of 21.5% (95% CI, 19.2% to 23.9%), specificity of 91% (95% CI, 90.8% to 91.2%), area under the curve of 0.73 (95% CI, 0.71 to 0.75), and positive predictive value of 28.5% (95% CI, 25.5% to 31.5%). The validation set had a sensitivity of 23.2% (95% CI, 19.5% to 26.9%), specificity of 89.4% (95% CI, 89% to 89.7%), area under the curve of 0.72 (95% CI, 0.70 to 0.75), and positive predictive value of 26.5% (95% CI, 22.4% to 30.6%). For the 2016 data set, the ANN classified all 13,031 patients into low-, medium-, and high-risk subgroups and identified 5% of the cancer population as high risk. Conclusion A multiparameterized ANN that is based on personal health information could be used for prostate cancer risk prediction with high specificity and low sensitivity. The ANN can further stratify the population into three subgroups that may be helpful in refining prescreening estimates of cancer risk.

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