scholarly journals Tracing TET1 expression in prostate cancer: discovery of malignant cells with a distinct oncogenic signature

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
U. Schagdarsurengin ◽  
C. Luo ◽  
H. Slanina ◽  
D. Sheridan ◽  
S. Füssel ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Stem Cells ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Significant Proportion ◽  
Dna Demethylation ◽  
Malignant Cells ◽  
Methylation Array ◽  
Normal Prostate ◽  
Tumor Stages

Abstract Background Ten–eleven translocation methylcytosine dioxygenase 1 (TET1) is involved in DNA demethylation and transcriptional regulation, plays a key role in the maintenance of stem cell pluripotency, and is dysregulated in malignant cells. The identification of cancer stem cells (CSCs) driving tumor growth and metastasis is the primary objective of biomarker discovery in aggressive prostate cancer (PCa). In this context, we analyzed TET1 expression in PCa. Methods A large-scale immunohistochemical analysis of TET1 was performed in normal prostate (NOR) and PCa using conventional slides (50 PCa specimens) and tissue microarrays (669 NOR and 1371 PCa tissue cores from 371 PCa specimens). Western blotting, RT-qPCR, and 450 K methylation array analyses were performed on PCa cell lines. Genome-wide correlation, gene regulatory network, and functional genomics studies were performed using publicly available data sources and bioinformatics tools. Results In NOR, TET1 was exclusively expressed in normal cytokeratin 903 (CK903)–positive basal cells. In PCa, TET1 was frequently detected in alpha-methylacyl-CoA racemase (AMACR)–positive tumor cell clusters and was detectable at all tumor stages and Gleason scores. Pearson’s correlation analyses of PCa revealed 626 TET1-coactivated genes (r > 0.5) primarily encoding chromatin remodeling and mitotic factors. Moreover, signaling pathways regulating antiviral processes (62 zinc finger, ZNF, antiviral proteins) and the pluripotency of stem cells were activated. A significant proportion of detected genes exhibited TET1-correlated promoter hypomethylation. There were 161 genes encoding transcription factors (TFs), of which 133 were ZNF-TFs with promoter binding sites in TET1 and in the vast majority of TET1-coactivated genes. Conclusions TET1-expressing cells are an integral part of PCa and may represent CSCs with oncogenic potential.

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.

Androgen regulation of the prostatic tumour suppressor NKX3.1 is mediated by its 3′ untranslated region

2010 ◽  
Vol 425 (3) ◽  
pp. 575-583 ◽  
Author(s):  
Marc A. Thomas ◽  
Darren M. Preece ◽  
Jacqueline M. Bentel
Keyword(s):  
Prostate Cancer ◽  
Untranslated Region ◽  
Significant Proportion ◽  
Cancer Cell Line ◽  
Tumour Suppressor ◽  
Site Directed Mutagenesis ◽  
Deletion Analysis ◽  
Mobility Shift ◽  
Normal Prostate ◽  
Transcriptional Regulatory Mechanisms

The homeodomain transcription factor NKX3.1 is a prostate-specific tumour suppressor, expression of which is reduced or undetectable in the majority of metastatic prostate tumours. In the normal prostate and in prostate cancer cells, NKX3.1 expression is under tight androgenic control that we have shown to be mediated by its ~2.5 kb 3′UTR (3′ untranslated region). Reporter deletion analysis of the NKX3.1 3′UTR identified three regions that were transactivated by DHT (5α-dihydrotestosterone) in the AR (androgen receptor)-expressing prostate cancer cell line LNCaP. Reversal of DHT effects by the anti-androgen bicalutamide supported an AR-mediated mechanism, and bioinformatic analysis of the NKX3.1 3′UTR identified canonical AREs (androgen-response elements) in each of the androgen-responsive regions. EMSAs (electrophoretic mobility-shift assays) indicated binding of the AR DNA-binding domain to two of the AREs, a proximal ARE at +2378–2392 from the transcription start site, and a more distal ARE at +3098–3112. ChIP (chromatin immunoprecipitation) analysis provided further evidence of ligand-dependent recruitment of endogenous AR to sequence encompassing each of the two elements, and site-directed mutagenesis and deletion analysis confirmed the contribution of each of the AREs in reporter assays. The present studies have therefore demonstrated that the NKX3.1 3′UTR functions as an androgen-responsive enhancer, with the proximal ARE contributing the majority and the distal ARE providing a smaller, but significant, proportion of the androgen responsiveness of the NKX3.1 3′UTR. Characterization of androgen-responsive regions of the NKX3.1 gene will assist in the identification of transcriptional regulatory mechanisms that lead to the deregulation of NKX3.1 expression in advanced prostate cancers.

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 DDX52 knockdown inhibits the growth of prostate cancer cells by regulating c-Myc signaling

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wandong Yu ◽  
Hangbin Ma ◽  
Junhong Li ◽  
Jinchao Ge ◽  
Pengyu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Large Scale ◽  
Bioinformatics Analysis ◽  
Genetic Locus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Normal Prostate ◽  
Signaling Mechanism

Abstract Background DDX52 is a type of DEAD/H box RNA helicase that was identified as a novel prostate cancer (PCa) genetic locus and possible causal gene in a European large-scale transcriptome-wide association study. However, the functions of DDX52 in PCa remain undetermined. The c-Myc oncogene plays a crucial role in the development of PCa, but the factors that regulate the activity of c-Myc in PCa are still unknown. Methods We determined DDX52 protein levels in PCa tissues using immunohistochemistry (IHC). DDX52 expression and survival outcomes in other PCa cohorts were examined using bioinformatics analysis. The inhibition of DDX52 via RNA interference with shRNA was used to clarify the effects of DDX52 on PCa cell growth in vitro and in vivo. Gene set enrichment analysis and RNA sequencing were used to explore the signaling regulated by DDX52 in PCa. Western blotting and IHC were used to determine the possible DDX52 signaling mechanism in PCa. Results DDX52 expression was upregulated in PCa tissues. Bioinformatics analysis showed that the level of DDX52 further increased in advanced PCa, with a high DDX52 level indicating a poor outcome. In vitro and in vivo experiments showed that downregulating DDX52 impeded the growth of PCa cells. High DDX52 levels contributed to activating c-Myc signaling in PCa patients and PCa cells. Furthermore, DDX52 expression was regulated by c-Myc and positively correlated with c-Myc expression in PCa. Conclusion DDX52 was overexpressed in PCa tissues in contrast to normal prostate tissues. DDX52 knockdown repressed the growth of PCa cells in vitro and in vivo. Deleting c-Myc inhibited DDX52 expression, which affected the activation of c-Myc signaling.

scholarly journals Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes in prostate cancer

2018 ◽  
Author(s):  
Su-Liang Li ◽  
Yun Ye ◽  
Sheng-Yu Wang
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Scale Effects ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Regulation Of Transcription ◽  
Normal Prostate ◽  
Ppi Networks

AbstractPurpose: Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa remain poorly understood. This study aims to investigate the underlying molecular mechanisms of PCa with bioinformatics.Methods: Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the R software limma package. The interactions between the differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes. Co-expression of the miRNAs, lncRNAs and mRNAs were selected to construct the mRNA-miRNA and-lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. The protein-protein interaction (PPI) networks were constructed, and the transcription factors were annotated. The expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis.Results: The results demonstrated that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue.Conclusion: Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs were revealed and a model for predicting the mechanism of PCa was provided. This study provides new insight for the exploration of the molecular mechanisms of PCa and valuable clues for further research.

scholarly journals Development and Verification of a Prostate Cancer Prognostic Signature Based on an Immunogenomic Landscape Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Hong Cheng ◽  
Yi Wang ◽  
Chunhui Liu ◽  
Tiange Wu ◽  
Shuqiu Chen ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Normal Prostate ◽  
Tissue Samples ◽  
Tumor Stages ◽  
Normal Prostate Tissue ◽  
Immune Related Genes

PurposeProstate cancer (PCa) has a high incidence among older men. Until now, there are no immunological markers available to predict PCa patients’ survival. Therefore, it is necessary to explore the immunological characteristics of PCa.MethodsFirst, we retrieved RNA-seq and clinical data of 499 PCa and 52 normal prostate tissue samples from the Cancer Genome Atlas (TCGA). We identified 193 differentially expressed immune-related genes (IRGs) between PCa and normal prostate tissues. Functional enrichment analyses showed that the immune system can participate in PCa initiation. Then, we constructed a correlation network between transcription factors (TFs) and IRGs. We performed univariate and multivariate Cox regression analyses and identified five key prognostic IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1). Finally, a predictive nomogram was established and verified by the C-index.ResultsWe successfully constructed and validated an immune-related PCa prediction model. The signature could independently predict PCa patients’ survival. Results showed that high-immune-risk patients were correlated with advanced stage. We also validated the S100A2 expression in vitro using PCa and normal prostate tissues. We found that higher S100A2 expressions were related to lower biochemical recurrences. Additionally, higher AMH expressions were related to higher Gleason score, lymph node metastasis and positive rate, and tumor stages, and higher ATGR1 expressions were related to lower PSA value.ConclusionOverall, we detected five IRGs (S100A2, NOX1, IGHV7-81, AMH, and AGTR1) that can be used as independent PCa prognostic factors.

EXPRESSION PATTERN OF GENES ASSOCIATED WITH TUMOR MICROENVIRONMENT IN PROSTATE CANCER

2018 ◽  
Vol 40 (4) ◽  
pp. 315-322 ◽  
Author(s):  
G V Gerashchenko ◽  
O V Grygoruk ◽  
O A Kononenko ◽  
O P Gryzodub ◽  
E O Stakhovsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Pattern ◽  
Expression Patterns ◽  
Immune Defense ◽  
Pten Expression ◽  
Molecular Characteristics ◽  
Prostate Tumors ◽  
Normal Prostate ◽  
Tumor Stages

Aim: To assess relative expression (RE) levels of CAF-, TAM-specific, immune defense-associated genes in prostate tumors and to show correlation of RE with clinical, pathological and molecular characteristics, with the aim to define clinically significant specific alterations in a gene expression pattern. Methods: RE of 23 genes was analyzed by a quantitative polymerase chain reaction in 37 freshly frozen samples of prostate cancer tissues of a different Gleason score (GS) and at various tumor stages, compared with RE in 37 paired conventionally normal prostate tissue (CNT) samples and 20 samples of prostate adenomas. Results: Differences in RE were shown for 11 genes out of 23 studied, when tumor samples were compared with corresponding CNTs. 7 genes, namely ACTA2, CXCL14, CTGF, THY1, FAP, CD163, CCL17 were upregulated in tumors. 4 genes, namely CCR4, NOS2A, MSMB, IL1R1 were downregulated in tumors. 14 genes demonstrated different RE in TNA at different stages: CXCL12, CXCL14, CTGF, FAP, HIF1A, THY1, CCL17, CCL22, CCR4, CD68, CD163, NOS2A, CTLA4, IL1R1. RE changes of 9 genes — CXCL12, CXCL14, HIF1A, CCR4, CCL17, NOS2A, CTLA4, IL1R1, IL2RA — were found in tumors with different GS. Moreover, 9 genes showed differences in RE in TNA, dependently on the presence or absence of the TMPRSS2/ERG fusion and 7 genes showed differences in RE of groups with differential PTEN expression. Significant correlations were calculated between RE of 9 genes in adenocarcinomas and the stage, and GS; also, between RE of 2 genes and the fusion presence; and between RE of 4 genes and PTEN expression. Conclusions: Several gene expression patterns were identified that correlated with the GS, stage and molecular characteristics of tumors, i.e. presence of the TMPRSS2/ERG fusion and alterations in PTEN expression. These expression patterns can be used for molecular profiling of prostate tumors, with the aim to develop personalized medicine approaches. However, the proposed profiling requires a more detailed analysis and a larger cohort of patients with prostate tumor.

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