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Cancer Gene Networks

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Cover of 'Cancer Gene Networks'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction: Cancer Gene Networks.
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    Chapter 2 Emerging Methods in Chemoproteomics with Relevance to Drug Discovery.
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    Chapter 3 ANXA7-GTPase as Tumor Suppressor: Mechanisms and Therapeutic Opportunities.
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    Chapter 4 Experimental and Study Design Considerations for Uncovering Oncometabolites.
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    Chapter 5 Targeting Deubiquitinating Enzymes and Autophagy in Cancer.
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    Chapter 6 Quantitative Clinical Imaging Methods for Monitoring Intratumoral Evolution.
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    Chapter 7 Transcriptome and Proteome Analyses of TNFAIP8 Knockdown Cancer Cells Reveal New Insights into Molecular Determinants of Cell Survival and Tumor Progression.
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    Chapter 8 Network-Oriented Approaches to Anticancer Drug Response.
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    Chapter 9 CRISPR/Cas-Mediated Knockin in Human Pluripotent Stem Cells.
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    Chapter 10 Complete Transcriptome RNA-Seq.
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    Chapter 11 Computational Methods and Correlation of Exon-skipping Events with Splicing, Transcription, and Epigenetic Factors.
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    Chapter 12 Tissue Engineering Platforms to Replicate the Tumor Microenvironment of Multiple Myeloma.
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    Chapter 13 microRNA Target Prediction.
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    Chapter 14 Evaluating the Delivery of Proteins to the Cytosol of Mammalian Cells.
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    Chapter 15 Validation of Biomarker Proteins Using Reverse Capture Protein Microarrays.
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    Chapter 16 Chemical Synthesis of Activity-Based Diubiquitin Probes.
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    Chapter 17 Profiling the Dual Enzymatic Activities of the Serine/Threonine Kinase IRE1α.
Attention for Chapter 4: Experimental and Study Design Considerations for Uncovering Oncometabolites.
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Chapter title
Experimental and Study Design Considerations for Uncovering Oncometabolites.
Chapter number 4
Book title
Cancer Gene Networks
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6539-7_4
Pubmed ID
Book ISBNs
978-1-4939-6537-3, 978-1-4939-6539-7
Authors

Majda Haznadar, Ewy A. Mathé

Editors

Usha Kasid, Robert Clarke

Abstract

Metabolomics as a field has gained attention due to its potential for biomarker discovery, namely because it directly reflects disease phenotype and is the downstream effect of posttranslational modifications. The field provides a "top-down," integrated view of biochemistry in complex organisms, as opposed to the traditional "bottom-up" approach that aims to analyze networks of interactions between genes, proteins and metabolites. It also allows for the detection of thousands of endogenous metabolites in various clinical biospecimens in a high-throughput manner, including tissue and biofluids such as blood and urine. Of note, because biological fluid samples can be collected relatively easily, the time-dependent fluctuations of metabolites can be readily studied in detail.In this chapter, we aim to provide an overview of (1) analytical methods that are currently employed in the field, and (2) study design concepts that should be considered prior to conducting high-throughput metabolomics studies. While widely applicable, the concepts presented here are namely applicable to high-throughput untargeted studies that aim to search for metabolite biomarkers that are associated with a particular human disease.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 17%
Unknown 5 83%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Researcher 1 17%
Unspecified 1 17%
Other 1 17%
Student > Doctoral Student 1 17%
Other 0 0%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 67%
Unspecified 1 17%
Medicine and Dentistry 1 17%