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.

Integrated-omics of MRI-visible and -invisible prostate cancer identifies molecular correlations with clinical outcome.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17506-e17506
Author(s):  
Eric Kim ◽  
Natalia Miheecheva ◽  
Akshaya Ramachandran ◽  
Yang Lyu ◽  
Ilia Galkin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cell Density ◽  
Malignant Cell ◽  
Prostate Tissue ◽  
Malignant Cells ◽  
Normal Prostate ◽  
Normal Prostate Tissue ◽  
Clinically Significant ◽  
Wasserstein Distances

e17506 Background: The inclusion of multiparametric MRI (mpMRI) to prostate cancer (PCa) diagnostics has increased the detection rate and has improved the prediction rate of clinically significant PCa. Yet, mpMRI has a false negative rate of 12.6%, missing 6-13% of clinically significant PCa. The mechanisms underlying MRI visibility are poorly understood; therefore, to probe the molecular and cellular underpinnings of PCa MRI visibility, we profiled tissue from Gleason score and clinically matched patients with MRI-visible and MRI-invisible PCa who underwent radical prostatectomy. Methods: MRI-invisible (n = 7) and MRI-visible (n = 8) PCa tumors were evaluated with multiplex immunofluorescence (MxIF; 14 markers) and were subjected to gene expression profiling using the HTG EdgeSeq Oncology Biomarker Panel (2,549 genes). Gene expression analysis was also performed using The Cancer Genome Atlas (TCGA), including normal prostate (n = 52) and PCa (n = 387) tissue. Analyses were performed using the BostonGene automated pipeline. Results: MpMRI-visible PCa tumors (62.5%) displayed compact epithelial tumor architecture compared with mpMRI-invisible PCa tumors (28.5%). mpMRI-visible PCa had higher malignant cell density (p = 0.04) and increased neighboring malignant cells (p = 0.07), correlating with MRI-visible PCa complex tumor architecture (r = 0.49 for neighboring malignant cells vs tumor cell density). Tumor stromal organization differences were determined by measuring Wasserstein distances between distributions, and mpMRI-invisible PCa stroma appeared more similar to normal tissue. The visible group exhibited lower expression of stroma-enriched genes such as filamin C (FLNC) (FDR < 0.1) and cellular adhesion-related genes (FDR < 0.4), with gene expression signatures markedly different compared to normal prostate tissue. Higher malignant cell density, neighboring malignant cells, and Wasserstein distances, and low FLNC expression – all mpMRI visibility characteristics – were associated with patient relapse (p = 0.02). Low stroma signature expression in the TCGA cohort correlated with inferior PCa PFS (p = 0.005). Conclusions: This is the first integrated multi-omics analysis of clinically matched mpMRI-visible and -invisible PCa. mpMRI-invisible tumors exhibited molecular, cellular, and structural characteristics more akin to normal prostate tissue, which may render them undetectable by imaging. A stroma-associated gene signature, a mpMRI-invisible tumor feature, correlated with better PCa clinical outcomes.

scholarly journals NGSEA: network-based gene set enrichment analysis for interpreting gene expression phenotypes with functional gene sets

2019 ◽  
Author(s):  
Heonjong Han ◽  
Sangyoung Lee ◽  
Insuk Lee
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Clinical Samples ◽  
Functional Genes ◽  
Biological Processes ◽  
Gene Set Enrichment ◽  
Gene Sets ◽  
Anti Cancer

ABSTRACTGene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets, however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

scholarly journals XGSEA: CROSS-species Gene Set Enrichment Analysis via domain adaptation

2020 ◽  
Author(s):  
Menglan Cai ◽  
Canh Hao Nguyen ◽  
Hiroshi Mamitsuka ◽  
Limin Li
Keyword(s):  
Gene Expression ◽  
Domain Adaptation ◽  
Gene Knockout ◽  
Enrichment Analysis ◽  
Real Data ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

AbstractGene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. However, gene expression are assessed more often for model organisms than minor species. More importantly, gene expression could not be measured under specific conditions for human, due to high healthy risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus predicting enrichment significance (on a phenotype) of a given gene set of a species (target, say human), by using gene expression measured under the same phenotype of the other species (source, say mouse) is a vital and challenging problem, which we call CROSS-species Gene Set Enrichment Problem (XGSEP). For XGSEP, we propose XGSEA (Cross-species Gene Set Enrichment Analysis), with three steps of: 1) running GSEA for a source species to obtain enrichment scores and p-values of source gene sets; 2) representing the relation between source and target gene sets by domain adaptation; and 3) using regression to predict p-values of target gene sets, based on the representation in 2). We extensively validated XGSEA by using four real data sets under various settings, proving that XGSEA significantly outperformed three baseline methods. A case study of identifying important human pathways for T cell dysfunction and reprogramming from mouse ATAC-Seq data further confirmed the reliability of XGSEA. Source code is available through https://github.com/LiminLi-xjtu/XGSEAAuthor summaryGene set enrichment analysis (GSEA) is a powerful tool in the gene sets differential analysis given a ranked gene list. GSEA requires complete data, gene expression with phenotype labels. However, gene expression could not be measured under specific conditions for human, due to high risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus no availability of gene expression leads to more challenging problem, CROSS-species Gene Set Enrichment Problem (XGSEP), in which enrichment significance (on a phenotype) of a given gene set of a species (target, say human) is predicted by using gene expression measured under the same phenotype of the other species (source, say mouse). In this work, we propose XGSEA (Cross-species Gene Set Enrichment Analysis) for XGSEP, with three steps of: 1) GSEA; 2) domain adaptation; and 3) regression. The results of four real data sets and a case study indicate that XGSEA significantly outperformed three baseline methods and confirmed the reliability of XGSEA.

scholarly journals Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

2015 ◽  
Vol 6 ◽  
pp. 2438-2448 ◽  
Author(s):  
Andrew Williams ◽  
Sabina Halappanavar
Keyword(s):  
Gene Expression ◽  
Pulmonary Fibrosis ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Driven ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Analysis Methods ◽  
Gene Sets

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

Long-term aspirin use and intratumoral gene expression in prostate cancer.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 106-106
Author(s):  
Konrad Hermann Stopsack ◽  
Ericka M. Ebot ◽  
Mary K Downer ◽  
Jennifer R. Rider ◽  
Lorelei Mucci
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Target Genes ◽  
Gene Set Enrichment Analysis ◽  
Gleason Grade ◽  
Gene Sets ◽  
Regular Aspirin

106 Background: Pharmacoepidemiologic studies suggest prognostic benefits of aspirin in prostate cancer. We hypothesized that prostate cancer tissue from regular aspirin users at cancer diagnosis compared to nonusers is characterized by specific transcriptional changes. Methods: We analyzed tumor whole-transcriptome data from men diagnosed with prostate cancer during prospective follow up of the Physicians Health Study 1 (PHS1, n1 = 150), initially a randomized-controlled trial of aspirin for prevention of cardiovascular disease and cancer. We compared the expression of putative aspirin target genes ( PTGS2, CTNNB1, MYC, AXIN2, PTGER3) between regular aspirin users ( ≥ 3 x 324 mg/week) and non-users at cancer diagnosis. We used Gene Set Enrichment Analysis to identify Gene Ontology biological process gene sets (total, 3609) associated with aspirin use. Leading edge genes of ≥ 3 functionally related gene sets were summarized in a meta-gene score. Results were validated among prostate cancer patients from the Health-Professionals Follow-up Study (HPFS, n2 = 254; aspirin use, ≥ 2 x/week). Results: Regular aspirin use was common (65% in PHS1; 45% in HPFS) and not associated with age, stage, and Gleason grade. In PHS1, none of the predefined genes were associated with aspirin use (all p > 0.05). 26 gene sets were downregulated in aspirin users at a false discovery rate (FDR) < 0.25 (9 at FDR < 0.05). 11 gene sets were clustered functionally around ribosome function and protein translation. A “ribosome” score of 96 genes was higher in aspirin users compared to nonusers (difference, 35.5 standard deviations [SD]; p = 0.0001). There was no dose-dependency for cumulative duration of aspirin use before cancer diagnosis ( ptrend = 0.17) or time since stopping use among non-users at diagnosis ( p = 0.32). In HPFS, neither the pre-defined target genes (all p > 0.18) nor the score were associated with aspirin use (difference, –9.8 SD; p = 0.18). Conclusions: Although RNA acetylation and resulting intratumoral gene expression changes due to aspirin use may be biologically plausible, we were unable to corroborate this association in two-long term prospective studies. Short-term trials might assess if aspirin has direct effects on tumor gene expression.

Comprehensive gene expression profiling of rat lung reveals distinct acute and chronic responses to cigarette smoke inhalation

2007 ◽  
Vol 293 (5) ◽  
pp. L1183-L1193 ◽  
Author(s):  
Christopher S. Stevenson ◽  
Cerys Docx ◽  
Ruth Webster ◽  
Cliff Battram ◽  
Debra Hynx ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stress Response ◽  
Cigarette Smoke ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Smoke Inhalation ◽  
Whole Body ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Chronic obstructive pulmonary disease (COPD) is a smoking-related disease that lacks effective therapies due partly to the poor understanding of disease pathogenesis. The aim of this study was to identify molecular pathways that could be responsible for the damaging consequences of smoking. To do this, we employed Gene Set Enrichment Analysis to analyze differences in global gene expression, which we then related to the pathological changes induced by cigarette smoke (CS). Sprague-Dawley rats were exposed to whole body CS for 1 day and for various periods up to 8 mo. Gene Set Enrichment Analysis of microarray data identified that metabolic processes were most significantly increased early in the response to CS. Gene sets involved in stress response and inflammation were also upregulated. CS exposure increased neutrophil chemokines, cytokines, and proteases (MMP-12) linked to the pathogenesis of COPD. After a transient acute response, the CS-exposed rats developed a distinct molecular signature after 2 wk, which was followed by the chronic phase of the response. During this phase, gene sets related to immunity and defense progressively increased and predominated at the later time points in smoke-exposed rats. Chronic CS inhalation recapitulated many of the phenotypic changes observed in COPD patients including oxidative damage to macrophages, a slowly resolving inflammation, epithelial damage, mucus hypersecretion, airway fibrosis, and emphysema. As such, it appears that metabolic pathways are central to dealing with the stress of CS exposure; however, over time, inflammation and stress response gene sets become the most significantly affected in the chronic response to CS.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

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