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Cancer Systems Biology

Overview of attention for book
Cover of 'Cancer Systems Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Detection of Combinatorial Mutational Patterns in Human Cancer Genomes by Exclusivity Analysis
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    Chapter 2 Discovering Altered Regulation and Signaling Through Network-based Integration of Transcriptomic, Epigenomic, and Proteomic Tumor Data
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    Chapter 3 Analyzing DNA Methylation Patterns During Tumor Evolution
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    Chapter 4 MicroRNA Networks in Breast Cancer Cells
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    Chapter 5 Identifying Genetic Dependencies in Cancer by Analyzing siRNA Screens in Tumor Cell Line Panels
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    Chapter 6 Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells
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    Chapter 7 Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research
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    Chapter 8 Quantitative Analysis of Tyrosine Kinase Signaling Across Differentially Embedded Human Glioblastoma Tumors
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    Chapter 9 Prediction of Clinical Endpoints in Breast Cancer Using NMR Metabolic Profiles
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    Chapter 10 Stochastic and Deterministic Models for the Metastatic Emission Process: Formalisms and Crosslinks
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    Chapter 11 Mechanically Coupled Reaction-Diffusion Model to Predict Glioma Growth: Methodological Details
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    Chapter 12 Profiling Tumor Infiltrating Immune Cells with CIBERSORT
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    Chapter 13 Systems Biology Approaches in Cancer Pathology
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    Chapter 14 Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing
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    Chapter 15 A Robust Optimization Approach to Cancer Treatment under Toxicity Uncertainty
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    Chapter 16 Modeling of Interactions between Cancer Stem Cells and their Microenvironment: Predicting Clinical Response
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    Chapter 17 Methods for High-throughput Drug Combination Screening and Synergy Scoring
Attention for Chapter 4: MicroRNA Networks in Breast Cancer Cells
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Citations

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Chapter title
MicroRNA Networks in Breast Cancer Cells
Chapter number 4
Book title
Cancer Systems Biology
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7493-1_4
Pubmed ID
Book ISBNs
978-1-4939-7492-4, 978-1-4939-7493-1
Authors

Andliena Tahiri, Miriam R. Aure, Vessela N. Kristensen

Abstract

A variety of molecular techniques can be used in order to unravel the molecular composition of cells. In particular, the microarray technology has been used to identify novel biomarkers that may be useful in the diagnosis, prognosis, or treatment of cancer. The microarray technology is ideal for biomarker discovery as it allows for the screening of a large number of molecules at once. In this review, we focus on microRNAs (miRNAs) which are key molecules in cells and regulate gene expression post-transcriptionally. miRNAs are small, single-stranded RNA molecules that bind to complementary mRNAs. Binding of miRNAs to mRNAs leads either to degradation, or translational inhibition of the target mRNA. Roughly one third of all the mRNAs are postulated to be regulated by miRNAs. miRNAs are known to be deregulated in different types of cancer, including breast cancer, and it has been demonstrated that deregulation of several miRNAs can be used as biological markers in cancer. miRNA expression can for example discriminate between normal, benign and malignant breast tissue, and between different breast cancer subtypes.In the post-genomic era, an important task of molecular biology is to understand gene regulation in the context of biological networks. Because miRNAs have such a pronounced role in cells, it is pivotal to understand the mechanisms that underlie their control, and to identify how miRNAs influence cancer development and progression.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 30%
Student > Ph. D. Student 5 22%
Student > Bachelor 3 13%
Student > Doctoral Student 2 9%
Librarian 1 4%
Other 0 0%
Unknown 5 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 35%
Agricultural and Biological Sciences 3 13%
Engineering 2 9%
Medicine and Dentistry 2 9%
Computer Science 1 4%
Other 2 9%
Unknown 5 22%