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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 3: ANXA7-GTPase as Tumor Suppressor: Mechanisms and Therapeutic Opportunities.
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Chapter title
ANXA7-GTPase as Tumor Suppressor: Mechanisms and Therapeutic Opportunities.
Chapter number 3
Book title
Cancer Gene Networks
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6539-7_3
Pubmed ID
Book ISBNs
978-1-4939-6537-3, 978-1-4939-6539-7
Authors

Ximena Leighton, Ofer Eidelman, Catherine Jozwik, Harvey B. Pollard, Meera Srivastava

Editors

Usha Kasid, Robert Clarke

Abstract

Chromosomal abnormalities, including homozygous deletions and loss of heterozygosity at 10q, are commonly observed in most human tumors, including prostate, breast, and kidney cancers. The ANXA7-GTPase is a tumor suppressor, which is frequently inactivated by genomic alterations at 10q21. In the last few years, considerable amounts of data have accumulated describing inactivation of ANXA7-GTPase in a variety of human malignancies and demonstrating the tumor suppressor potential of ANXA7-GTPase. ANXA7-GTPase contains a calcium binding domain that classifies it as a member of the annexin family. The cancer-specific expression of ANXA7-GTPase, coupled with its importance in regulating cell death, cell motility, and invasion, makes it a useful diagnostic marker of cancer and a potential target for cancer treatment. Recently, emerging evidence suggests that ANXA7-GTPase is a critical factor associated with the metastatic state of several cancers and can be used as a risk biomarker for HER2 negative breast cancer patients. Cross talk between ANXA7, PTEN, and EGFR leads to constitutive activation of PI3K-AKT signaling, a central pathway of tumor cell survival and proliferation. This review focuses on the recent progress in understanding the tumor suppressor functions of ANXA7-GTPase emphasizing the role of this gene in Ca(2+) metabolism, and exploring opportunities for function as an example of a calcium binding GTPase acting as a tumor suppressor and opportunities for ANXA7-GTPase gene cancer therapy.

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 60%
Student > Doctoral Student 1 20%
Student > Ph. D. Student 1 20%
Readers by discipline Count As %
Medicine and Dentistry 3 60%
Agricultural and Biological Sciences 1 20%
Computer Science 1 20%