scholarly journals Constructing Bayesian networks by integrating gene expression and copy number data identifies NLGN4Y as a novel regulator of prostate cancer progression

Oncotarget ◽  
2016 ◽  
Vol 7 (42) ◽  
pp. 68688-68707 ◽  
Author(s):  
Yixuan Gong ◽  
Li Wang ◽  
Uma Chippada-Venkata ◽  
Xudong Dai ◽  
William K. Oh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Bayesian Networks ◽  
Cancer Progression ◽  
Copy Number ◽  
Copy Number Data ◽  
Prostate Cancer Progression

scholarly journals Modeling human prostate cancer progression in vitro

2018 ◽  
Vol 40 (7) ◽  
pp. 893-902 ◽  
Author(s):  
Teresa T Liu ◽  
Jonathan A Ewald ◽  
Emily A Ricke ◽  
Robert Bell ◽  
Colin Collins ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cell Lines ◽  
Cancer Progression ◽  
Human Prostate ◽  
Molecular Characteristics ◽  
Prostate Cancer Progression ◽  
Altered Gene Expression ◽  
Unique Model ◽  
Altered Gene

Abstract Detailed mechanisms involved in prostate cancer (CaP) development and progression are not well understood. Current experimental models used to study CaP are not well suited to address this issue. Previously, we have described the hormonal progression of non-tumorigenic human prostate epithelial cells (BPH1) into malignant cells via tissue recombination. Here, we describe a method to derive human cell lines from distinct stages of CaP that parallel cellular, genetic and epigenetic changes found in patients with cancers. This BPH1-derived Cancer Progression (BCaP) model represents different stages of cancer. Using diverse analytical strategies, we show that the BCaP model reproduces molecular characteristics of CaP in human patients. Furthermore, we demonstrate that BCaP cells have altered gene expression of shared pathways with human and transgenic mouse CaP data, as well as, increasing genomic instability with TMPRSS2–ERG fusion in advanced tumor cells. Together, these cell lines represent a unique model of human CaP progression providing a novel tool that will allow the discovery and experimental validation of mechanisms regulating human CaP development and progression. This BPH1-derived Cancer Progression (BCaP) model represents different stages of cancer. The BCaP model reproduces molecular characteristics of prostate cancer. The cells have altered gene expression with TMPRSS2-ERG fusion representing a unique model for prostate cancer progression.

scholarly journals Casodex treatment induces hypoxia-related gene expression in the LNCaP prostate cancer progression model

BMC Urology ◽  
2005 ◽  
Vol 5 (1) ◽  
Author(s):  
Christy A Rothermund ◽  
Velliyur K Gopalakrishnan ◽  
James D Eudy ◽  
Jamboor K Vishwanatha
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Progression ◽  
Prostate Cancer Progression ◽  
Related Gene ◽  
Progression Model

Copy number variations in AR-associated and DNA repair genes from plasma cell-free DNA of metastatic CRPC patients.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 281-281 ◽  
Author(s):  
Ratish Gambhira ◽  
Elisa M. Ledet ◽  
Aryeneesh Dotiwala ◽  
Diptasri Mandal ◽  
A. Oliver Sartor
Keyword(s):  
Prostate Cancer ◽  
Dna Repair ◽  
Cancer Progression ◽  
Copy Number ◽  
Genetic Alterations ◽  
Copy Number Variations ◽  
Dna Repair Genes ◽  
Prostate Cancer Progression ◽  
Free Dna ◽  
Repair Genes

281 Background: Cell-free DNA (cfDNA) present in the plasma of advanced cancer patients can reflect tumor related genetic alterations. Recent data suggests copy number variations (CNVs) in AR-associated and DNA repair pathway genes play a potential role in prostate cancer progression. Here, we performed sequencing of cfDNA from 13 mCRPC patients to evaluate its potential in elucidating tumor related genetic variations. The long-term goal of our project is to correlate cfDNA derived genetic alterations with prostate cancer progression and/or therapeutic resistance/responses. Methods: cfDNA was isolated from 13 advanced mCRPC patient plasma samples using the Qiagen circulating nucleic acid kit. 100ng of cfDNA was utilized for library construction; and the libraries were paired-end sequenced on the Illumina HiSeq 2000. The resulting data was analyzed using the GATK best practices bioinformatics pipeline and the visualized using the SNP & Variation Suite v8.x. Results: The bioanalyzer profiles of cfDNA derived from mCRPC patients is highly fragmented with an average fragment size of 306-605bp. Although, several CNVs were found across the genome, we focused analysis on CNVs related to AR associated and DNA repair genes. Our preliminary analysis of cfDNA, despite low sequencing depth, shows full or partial amplifications in AR (13/13), and other genes including FOXA1, NCOR1, NCOR2 and/or PIK3CA (7/13) and NCOR2 (10/13). For DNA repair genes partial/full amplifications were present in BRAC1, BRAC2, ATM, CDK12, MLH1 and/or MSH2 (7/13). Deletions are less reliably detected in the highly fragmented cfDNA. The majority of these CNVs have been reported in the WGS studies from metastatic CRPC tissue derived genomic DNA (cBioPortal). We are currently validating cfDNA genomic alterations by comparing it to germ line DNA derived via qPCR. Conclusions: Our preliminary study indicates that AR and DNA repair related genetic alterations could be found in the cfDNA derived from metastatic CRPC patients. This warrants more detailed examination of these cfDNA genetic alterations for identifying clinically relevant issues in mCRPC patients.

Use of a tissue biomarker panel to predict which men with a rising PSA post-definitive prostate cancer therapy will have systemic progression

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 5017-5017
Author(s):  
R. B. Jenkins ◽  
T. Nakagawa ◽  
T. Kollmeyer ◽  
B. Morlan ◽  
E. Bergstrahl ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Expression Pattern ◽  
Cancer Progression ◽  
Gene Expression Pattern ◽  
Prostate Cancer Progression ◽  
Definitive Therapy ◽  
Psa Recurrence ◽  
Additional Therapy

5017 Background: The majority of men with prostate cancer are diagnosed with cancers with low mortality. Such men are treated with radical prostatectomy, external beam radiotherapy, or brachytherapy and followed by serum PSA evaluations. Some men with a rising PSA therapy will have local recurrence or metastasis, but many will have no other evidence of recurrent disease other than a rising PSA. The PSA doubling time has been used to determine which of these men deserve adjuvant hormonal ablation, radiation therapy, or observation. We hypothesize that additional biomarkers will predict which men with a rising PSA post-definitive therapy would benefit from additional therapy. Methods: We designed a custom array containing 526 RNA targets whose expression has been reported to be altered in association with prostate cancer progression. We included targets from Mayo Clinic prostate cancer research. Together with a second commercial array, 530 genes implicated in prostate cancer progression and 420 other cancer-related genes were evaluated. A case-control design was used to test the association of the expression results with outcome. Cases were men post-radical prostatectomy who developed systemic progression within 5 years after PSA recurrence (N=213). Controls were matched men post-radical prostatectomy with PSA recurrence but no evidence of clinical progression within 5 years (N=213). Results: Of 426 eligible patients, paraffin blocks were available on 418 (98.1%). RNA was prepared from all 418 blocks, and both arrays were both successful on 405 (96.9%) RNAs. Upon univariate analysis, 40 genes were highly significantly over- or under-expressed in the cases versus controls (1x10-22 < p < 1x10-7). Recursive partitioning (RP) selected 4 genes (TPX2, FAM13C1, TOPO2A and TSP2) that distinguished cases from controls. Random Forest analysis selected 24 genes (including 3 of the RP 4). A multivariable ROC analysis using these 24 genes generated an AUC of 0.80 (95% CI: 0.75–0.84). Conclusions: A specific gene expression pattern was significantly associated with systemic progression after PSA recurrence. The measurement of gene expression pattern may be useful for determining which men may benefit from additional therapy after PSA recurrence. No significant financial relationships to disclose.

scholarly journals Identification of markers of prostate cancer progression using candidate gene expression

2011 ◽  
Vol 106 (1) ◽  
pp. 157-165 ◽  
Author(s):  
S E T Larkin ◽  
S Holmes ◽  
I A Cree ◽  
T Walker ◽  
V Basketter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Candidate Gene ◽  
Cancer Progression ◽  
Prostate Cancer Progression

scholarly journals Chromatin accessibility analysis uncovers regulatory element landscape in prostate cancer progression

2020 ◽  
Author(s):  
Joonas Uusi-Mäkelä ◽  
Ebrahim Afyounian ◽  
Francesco Tabaro ◽  
Tomi Häkkinen ◽  
Alessandro Lussana ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Progression ◽  
Regulatory Element ◽  
Regulatory Region ◽  
Regulatory Elements ◽  
Chromatin Accessibility ◽  
Prostate Cancer Progression ◽  
Accessibility Analysis ◽  
A Genome

AbstractAberrant oncogene functions and structural variation alter the chromatin structure in cancer cells. While gene regulation by chromatin states has been studied extensively, chromatin accessibility and its relevance in aberrant gene expression during prostate cancer progression is not well understood. Here, we report a genome-wide chromatin accessibility analysis of clinical tissue samples of benign prostatic hyperplasia (BPH), untreated primary prostate cancer (PC) and castration-resistant prostate cancer (CRPC) and integrative analysis with transcriptome, methylome, and proteome profiles of the same samples to uncover disease-relevant regulatory elements and their association to altered gene expression during prostate cancer progression. While promoter accessibility is consistent during disease initiation and progression, at distal sites chromatin accessibility is variable enabling transcription factors (TFs) binding patterns that are differently activated in different patients and disease stages. We identify consistent progression-related chromatin alterations during the progression to CRPC. By studying the TF binding patterns, we demonstrate the activation and suppression of androgen receptor-driven regulatory programs during PC progression and identify complementary TF regulatory modules characterized by e.g. MYC and glucocorticoid receptor. By correlation analysis we assign at least one putative regulatory region for 62% of genes and 85% of proteins differentially expressed during prostate cancer progression. Taken together, our analysis of the chromatin landscape in PC identifies putative regulatory elements for the majority of cancer-associated genes and characterizes their impact on the cancer phenotype.

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