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Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised

Shikha Roy¹, Rakesh Kumar¹, Vaibhav Mittal¹

¹International Centre for Genetic Engineering and Biotechnology, New Delhi, India.

Scientific Reports

|March 7, 2020

Summary

This summary is machine-generated.

This study introduces machine learning models for Invasive Ductal Carcinoma (IDC) staging using gene expression data. The developed Duct-BRCA-CSP web server aids in distinguishing early from late IDC stages for better cancer treatment.

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Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Accurate staging of Invasive Ductal Carcinoma (IDC) is crucial for breast cancer prognosis and personalized treatment.
Identifying genomic factors associated with IDC progression remains a challenge for precise stage determination.

Purpose of the Study:

To develop machine learning models for differentiating early and late stages of Invasive Ductal Carcinoma (IDC).
To create a publicly accessible web server (Duct-BRCA-CSP) for IDC stage prediction using gene expression profiles.

Main Methods:

Utilized RNA-seq gene expression data from 610 breast cancer patients via The Cancer Genome Atlas (TCGA).
Developed and evaluated two-class machine learning classification models using supervised learning algorithms and feature selection.
Trained a classifier on reduced datasets focusing on IDC driver genes.

Main Results:

Successfully developed machine learning classifiers capable of distinguishing between early and late IDC stages.
The Duct-BRCA-CSP web server provides a tool for predicting IDC stage based on RNA-seq data.
Gained insights into molecular events associated with IDC progression.

Conclusions:

Machine learning models can effectively predict IDC stages, aiding clinical decision-making.
The Duct-BRCA-CSP server offers a valuable resource for researchers and clinicians in breast cancer staging.
Genomic profiling holds significant potential for advancing breast cancer diagnostics.