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Cancer Epigenetics for Precision Medicine

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Cover of 'Cancer Epigenetics for Precision Medicine'

Table of Contents

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    Book Overview
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    Chapter 1 Early Epigenetic Markers for Precision Medicine
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    Chapter 2 Interplay Between Genetic and Epigenetic Changes in Breast Cancer Subtypes
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    Chapter 3 Role of Microbiome in Carcinogenesis Process and Epigenetic Regulation of Colorectal Cancer
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    Chapter 4 Epigenome-Based Precision Medicine in Lung Cancer
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    Chapter 4 Review on Current Trends of Deep Learning
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    Chapter 5 Epigenetics in Hematological Malignancies
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    Chapter 6 MicroRNAs Role in Prostate Cancer
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    Chapter 7 Effects of Dietary Nutrients on Epigenetic Changes in Cancer
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    Chapter 8 Diet, Microbiome, and Epigenetics in the Era of Precision Medicine
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    Chapter 9 Alcohol-Induced Epigenetic Changes in Cancer
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    Chapter 10 Epigenetic Basis of Circadian Rhythm Disruption in Cancer
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    Chapter 11 Epigenetic Changes of the Immune System with Role in Tumor Development
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    Chapter 12 DNA Methylation as a Biomarker of Aging in Epidemiologic Studies
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    Chapter 13 Challenges and Opportunities in Social Epigenomics and Cancer
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    Chapter 14 Epigenetic and Genetic Regulation of PDCD1 Gene in Cancer Immunology
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    Chapter 15 Methylation and MicroRNA Profiling to Understand Racial Disparities of Prostate Cancer
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    Chapter 16 Analysis of DNA Hypermethylation in Pancreatic Cancer Using Methylation-Specific PCR and Bisulfite Sequencing
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    Chapter 17 Pyrosequencing Methylation Analysis
Attention for Chapter 6: MicroRNAs Role in Prostate Cancer
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Chapter title
MicroRNAs Role in Prostate Cancer
Chapter number 6
Book title
Cancer Epigenetics for Precision Medicine
Published in
Methods in molecular biology, September 2018
DOI 10.1007/978-1-4939-8751-1_6
Pubmed ID
Book ISBNs
978-1-4939-8750-4, 978-1-4939-8751-1
Authors

Ovidiu Balacescu, Ramona G. Dumitrescu, Catalin Marian

Abstract

Prostate cancer still represents a major health problem for men worldwide. Due to the specific limitation of the currently used clinical biomarkers for prostate cancer, there is a need to identify new and more accurate prostate-specific biomarkers, both for diagnosis and prediction. Small noncoding species of RNAs called microRNAs (miRNAs) have emerged as possible biomarkers in cancer tissues as well as biological fluids, including for prostate cancer. Moreover, it has been shown that miRNAs could be used as therapeutic targets in different cancer types, including prostate cancer, playing an important role in improving diagnosis and prognosis; and miRNAs have the potential to be clinically useful as predictors of response to personalized cancer therapy and as predictors of prognosis. The analysis of miRNAs in prostate tissue is rather straightforward and has been routinely done on fresh tissue. In addition, due to the more stable nature of miRNAs, they are amenable to be analyzed in archived formalin fixed paraffin embedded tissue as well, and also in serum, plasma and urine, using various analytical platforms including microarrays, next generation sequencing and real time PCR. Moreover, although the existence or prostasomes (microvesicles secreted by prostate cells including prostate cancer cells) has been known for years and they were studied as a source of biomarkers for prostate cancer, only recently it has been described that these vesicles also contain miRNAs that could be used as biomarkers in prostate cancer. This chapter underscores the feasibility of current technologies for miRNA analysis and their importance in prostate cancer biology. Moreover, elucidating the specific alteration of miRNA expression and how to modulate it in prostate tissue will open new avenues for developing therapeutic strategies for prostate cancer treatment.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 6 17%
Student > Ph. D. Student 6 17%
Researcher 4 11%
Student > Master 3 9%
Professor 1 3%
Other 3 9%
Unknown 12 34%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 23%
Medicine and Dentistry 4 11%
Immunology and Microbiology 4 11%
Agricultural and Biological Sciences 1 3%
Unspecified 1 3%
Other 2 6%
Unknown 15 43%