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MicroRNA and Cancer

Overview of attention for book
Cover of 'MicroRNA and Cancer'

Table of Contents

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    Book Overview
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    Chapter 1 Noncoding RNAs in DNA Damage Response: Opportunities for Cancer Therapeutics
  3. Altmetric Badge
    Chapter 2 MicroRNAs in Breast Cancer: Diagnostic and Therapeutic Potential
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    Chapter 3 Involvement of miRNAs and Pseudogenes in Cancer
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    Chapter 4 MicroRNAs Reprogram Tumor Immune Response
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    Chapter 5 Apolipoprotein B mRNA Editing Enzyme, Catalytic Polypeptide-Like Gene Expression, RNA Editing, and MicroRNAs Regulation
  7. Altmetric Badge
    Chapter 6 MicroRNAs Change the Landscape of Cancer Resistance
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    Chapter 7 MicroRNA, Noise, and Gene Expression Regulation
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    Chapter 8 Deep Sequencing Reveals a MicroRNA Expression Signature in Triple-Negative Breast Cancer
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    Chapter 9 Detection of Plasma MicroRNA Signature in Osteosarcoma Patients
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    Chapter 10 Identification of E6/E7-Dependent MicroRNAs in HPV-Positive Cancer Cells
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    Chapter 11 Combination of Anti-miRNAs Oligonucleotides with Low Amounts of Chemotherapeutic Agents for Pancreatic Cancer Therapy
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    Chapter 12 Evaluation of MicroRNA Delivery In Vivo
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    Chapter 13 Angiogenesis Analysis by In Vitro Coculture Assays in Transwell Chambers in Ovarian Cancer
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    Chapter 14 Application of Individual qPCR Performance Parameters for Quality Control of Circulating MicroRNA Data
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    Chapter 15 Construction of Multi-Potent MicroRNA Sponge and Its Functional Evaluation
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    Chapter 16 MicroRNA Sequencing Data Analysis Toolkits
Attention for Chapter 8: Deep Sequencing Reveals a MicroRNA Expression Signature in Triple-Negative Breast Cancer
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Chapter title
Deep Sequencing Reveals a MicroRNA Expression Signature in Triple-Negative Breast Cancer
Chapter number 8
Book title
MicroRNA and Cancer
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7435-1_8
Pubmed ID
Book ISBNs
978-1-4939-7433-7, 978-1-4939-7435-1
Authors

Yao-Yin Chang, Liang-Chuan Lai, Mong-Hsun Tsai, Eric Y. Chuang, Chang, Yao-Yin, Lai, Liang-Chuan, Tsai, Mong-Hsun, Chuang, Eric Y.

Abstract

Deep sequencing is an advanced technology in genomic biology to detect the precise order of nucleotides in a strand of DNA/RNA molecule. The analysis of deep sequencing data also requires sophisticated knowledge in both computational software and bioinformatics. In this chapter, the procedures of deep sequencing analysis of microRNA (miRNA) transcriptome in triple-negative breast cancer and adjacent normal tissue are described in detail. As miRNAs are critical regulators of gene expression and many of them were previously reported to be associated with the malignant progression of human cancer, the analytical method that accurately identifies deregulated miRNAs in a specific type of cancer is thus important for the understanding of its tumor behavior. We obtained raw sequence reads of miRNA expression from 24 triple-negative breast cancers and 14 adjacent normal tissues using deep sequencing technology in this work. Expression data of miRNA reads were normalized with the quantile-quantile scaling method and were analyzed statistically. A miRNA expression signature composed of 25 differentially expressed miRNAs showed to be an effective classifier between triple-negative breast cancers and adjacent normal tissues in a hierarchical clustering analysis.

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X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 36%
Professor > Associate Professor 2 18%
Librarian 1 9%
Student > Doctoral Student 1 9%
Other 1 9%
Other 2 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 27%
Medicine and Dentistry 2 18%
Pharmacology, Toxicology and Pharmaceutical Science 1 9%
Computer Science 1 9%
Agricultural and Biological Sciences 1 9%
Other 2 18%
Unknown 1 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 November 2017.
All research outputs
#14,957,976
of 23,007,053 outputs
Outputs from Methods in molecular biology
#4,728
of 13,159 outputs
Outputs of similar age
#255,656
of 442,275 outputs
Outputs of similar age from Methods in molecular biology
#508
of 1,498 outputs
Altmetric has tracked 23,007,053 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,159 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,275 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.