Genomic Fabric Remodeling in Prostate Cancer

2021 ◽  
Author(s):  
Sanda Iacobas ◽  
Dumitru A. Iacobas
Keyword(s):  
Prostate Cancer ◽  
Normal Tissue ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mathematical Object ◽  
P53 Signaling ◽  
Large Populations ◽  
Genomic Characterization ◽  
Meta Analyses

Prostate cancer is a leading cause of death among men but its genomic characterization and best therapeutic strategy are still under debate. The Genomic Fabric Paradigm (GFP) considers the transcriptome as a multi-dimensional mathematical object subjected to a dynamic set of expression correlations among the genes. Here, GFP is applied to gene expression profiles of three (one primary, and two secondary) cancer nodules and the surrounding normal tissue from a surgically removed prostate tumor. GFP was used to determine the regulation and rewiring of the P53 signaling, apoptosis, prostate cancer and several other pathways involved in survival and proliferation of the cancer cells. Genes responsible for the block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy and sustained angiogenesis were found as differently regulated in the three cancer nodules with respect to the normal tissue. The analysis indicates that even histo-pathologically equally graded cancer nodules from the same tumor have substantially different transcriptomic organizations, raising legitimate questions about the validity of meta-analyses comparing large populations of healthy and cancer humans. The study suggests that GFP may provide a personalized alternative to the biomarkers’ approach of cancer genomics.

scholarly journals Analyzing cancer gene expression data through the lens of normal tissue-specificity

2021 ◽  
Author(s):  
H. Robert Frost
Keyword(s):  
Gene Expression ◽  
Normal Tissue ◽  
Tissue Specificity ◽  
Cancer Genomics ◽  
Expression Profiles ◽  
Genetic Alterations ◽  
Cancer Type ◽  
Tissue Specific ◽  
Normal Tissues ◽  
Cancer Types

AbstractThe genetic alterations that underlie cancer development are highly tissue-specific with the majority of driving alterations occurring in only a few cancer types and with alterations common to multiple cancer types often showing a tissue-specific functional impact. This tissue-specificity means that the biology of normal tissues carries important information regarding the pathophysiology of the associated cancers, information that can be leveraged to improve the power and accuracy of cancer genomic analyses. Research exploring the use of normal tissue data for the analysis of cancer genomics has primarily focused on the paired analysis of tumor and adjacent normal samples. Efforts to leverage the general characteristics of normal tissue for cancer analysis has received less attention with most investigations focusing on understanding the tissue-specific factors that lead to individual genomic alterations or dysregulated pathways within a single cancer type. To address this gap and support scenarios where adjacent normal tissue samples are not available, we explored the genome-wide association between the transcriptomes of 21 solid human cancers and their associated normal tissues as profiled in healthy individuals. While the average gene expression profiles of normal and cancerous tissue may appear distinct, with normal tissues more similar to other normal tissues than to the associated cancer types, when transformed into relative expression values, i.e., the ratio of expression in one tissue or cancer relative to the mean in other tissues or cancers, the close association between gene activity in normal tissues and related cancers is revealed. As we demonstrate through an analysis of tumor data from The Cancer Genome Atlas and normal tissue data from the Human Protein Atlas, this association between tissue-specific and cancer-specific expression values can be leveraged to improve the prognostic modeling of cancer, the comparative analysis of different cancer types, and the analysis of cancer and normal tissue pairs.

scholarly journals 1100: Gene Expression Profiles of Doxazosin-Treated LNCaP Prostate Cancer Cells

2004 ◽  
Vol 171 (4S) ◽  
pp. 290-290
Author(s):  
José M. Arencibia ◽  
Mónica Del Río ◽  
Ana Bonnin ◽  
Mónica López-Barahona
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Prostate Cancer Cells

scholarly journals Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process

BMC Cancer ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Uma R Chandran ◽  
Changqing Ma ◽  
Rajiv Dhir ◽  
Michelle Bisceglia ◽  
Maureen Lyons-Weiler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Pathways ◽  
Metastatic Process

scholarly journals cDNA macroarray for analysis of gene expression profiles in prostate cancer

2006 ◽  
Vol 119 (7) ◽  
pp. 570-573 ◽  
Author(s):  
Wei-de ZHONG ◽  
Hui-chan HE ◽  
Xue-cheng BI ◽  
Ru-biao OU ◽  
Shao-ai JIANG ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cdna Macroarray

scholarly journals DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Ieva Rauluseviciute ◽  
Finn Drabløs ◽  
Morten Beck Rye
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Dna Methylation ◽  
Epigenetic Regulation ◽  
Normal Tissue ◽  
Expression Profiles ◽  
Expression Data ◽  
Dna Hypermethylation ◽  
Genome Wide ◽  
A Genome

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

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