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Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
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Published on: March 17, 2016

Gene expression profiling of human breast tissue samples using SAGE-Seq.

Zhenhua Jeremy Wu1, Clifford A Meyer, Sibgat Choudhury

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts 02115, USA.

Genome Research
|November 4, 2010
PubMed
Summary

Ultra high-throughput sequencing, SAGE-Seq, accurately quantifies mammary cell transcriptomes. This method enhances breast cancer research by identifying novel biomarkers and therapeutic targets, revealing greater cancer transcriptome diversity.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Cancer Research

Background:

  • Accurate transcriptome quantification is crucial for understanding normal and neoplastic mammary epithelial cells.
  • Traditional methods like Serial Analysis of Gene Expression (SAGE) have limitations in sensitivity and gene discovery.
  • Ultra high-throughput sequencing offers potential for improved transcriptomic analysis.

Purpose of the Study:

  • To present SAGE-Seq as a powerful application for accurate quantification of normal and neoplastic mammary epithelial cell transcriptomes.
  • To develop and validate data analysis pipelines for SAGE-Seq data.
  • To compare SAGE-Seq with traditional SAGE for gene expression profiling in breast cancer.

Main Methods:

  • Development of data analysis pipelines for mapping sense/antisense strands, library normalization, and differential gene expression analysis.
  • Application of SAGE-Seq to normal and neoplastic mammary epithelial cells.
  • Comparison of SAGE-Seq with traditional SAGE using normal and cancerous breast tissues.

Main Results:

  • SAGE-Seq enables accurate transcriptome quantification and identification of differentially expressed genes.
  • Cancer transcriptomes exhibit significantly higher diversity compared to normal cells.
  • Transcript discovery plateaus at approximately 10 million reads, suggesting a minimum desired sequencing depth of five million reads.
  • SAGE-Seq demonstrates higher sensitivity than traditional SAGE in detecting low-abundance genes, including breast cancer-related transcription factors and G protein-coupled receptors (GPCRs).
  • SAGE-Seq identified genes and pathways aberrantly activated in breast cancer missed by traditional SAGE.

Conclusions:

  • SAGE-Seq is a powerful and sensitive method for transcriptome quantification in mammary epithelial cells.
  • The method facilitates the discovery of novel biomarkers and therapeutic targets for human diseases, particularly breast cancer.
  • SAGE-Seq provides deeper insights into cancer biology by revealing higher transcriptome diversity and identifying previously undetected molecular alterations.