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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

Published on: October 3, 2025

A pathways-based prediction model for classifying breast cancer subtypes.

Tong Wu¹, Yunfeng Wang², Ronghui Jiang³

¹Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China.

|September 24, 2017

Summary

This summary is machine-generated.

This study identifies 136 core breast cancer genes that accurately classify subtypes. These genes highlight key pathways, improving subtype classification and risk assessment for breast cancer patients.

Keywords:

breast cancer classification prediction model co-expression network pathway enrichment subtype-specific gene

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Breast cancer exhibits significant heterogeneity, classified into four subtypes with distinct biological characteristics, treatment responses, and prognoses.
Accurate subtyping is crucial for effective treatment strategies and patient outcomes.

Purpose of the Study:

To identify core genes and pathways associated with breast cancer subtypes.
To develop improved classification models for breast cancer subtypes.

Main Methods:

Systematic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas (TCGA) database.
Unsupervised clustering to identify differentially expressed genes.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.
Development of Support Vector Machine (SVM) classification models.

Main Results:

Identified 136 core breast cancer genes that effectively categorize patients into their respective subtypes.
Discovered six key functional pathways regulated by these genes, including JAK-STAT signaling and inflammatory mediator regulation.
SVM models based on these pathways achieved effective classification of different breast cancer subtypes.

Conclusions:

The identified 136 core genes provide valuable insights into breast cancer subtype-specific mechanisms.
Pathway-based classification models show potential for improving the accuracy of breast cancer risk assessment and subtype identification.
This research may enhance diagnostic and prognostic accuracy for breast cancer.