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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.

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Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Published on: February 21, 2014

A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status.

Meysam Bastani¹, Larissa Vos, Nasimeh Asgarian

¹Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada.

|December 7, 2013

Summary

A new three-gene classifier accurately predicts estrogen receptor (ER) status in breast cancer using RNA expression. This RNA-based method offers a more reproducible and objective alternative to traditional immunohistochemical analysis for ER-status determination.

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Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

Area of Science:

Oncology
Genomics
Biomarker Discovery

Background:

Accurate estrogen receptor (ER) status determination is crucial for breast cancer treatment selection.
Current immunohistochemical (IHC) methods for ER-status assessment face technical and reproducibility challenges.
RNA expression analysis offers a more objective and quantitative approach to ER-status determination.

Purpose of the Study:

To develop a parsimonious RNA-based classifier for predicting hormone receptor status in breast cancer.
To create a machine learning tool for analyzing gene expression data to identify key genes for ER-status prediction.

Main Methods:

Applied a machine learning tool to a training dataset of 176 frozen breast tumors with established ER-status.
Utilized gene expression microarray data for classifier development.
Validated the classifier on an independent dataset and four public databases.

Main Results:

Developed a three-gene classifier with a cross-validation accuracy of 93.17±2.44% for predicting ER-status.
Achieved over 90% accuracy when the classifier was applied to independent validation sets and public databases across different platforms.
The RNA-based classifier demonstrated superior separation of recurrence-free survival curves compared to IHC-based ER-status analysis.

Conclusions:

The developed classifier is efficient, parsimonious, and suitable for high-throughput, accurate, and low-cost clinical use.
This RNA-based method serves as a proof-of-principle for developing other RNA-based biomarker tests.
The findings support the clinical utility of RNA expression analysis for objective biomarker assessment in oncology.