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Updated: Oct 10, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Network-based cancer genomic data integration for pattern discovery.

Fangfang Zhu^1,2, Jiang Li³, Juan Liu⁴

¹State Key Laboratory of Nuclear Resources and Environment and School of Water Resources and Environmental Engineering, East China University of Technology, Nanchang, 330013, China.

BMC Genomic Data

|December 10, 2021

Summary

This summary is machine-generated.

We developed a Sparse Network-regularized SVD (SNSVD) method to identify gene functional modules by integrating gene expression data with gene interaction networks. SNSVD improves biological interpretability and identifies relevant cancer-related miRNA-gene modules.

Keywords:

Differentially co-expression analysis Gene co-expression analysis Gene interaction network Sparse SVD Structured sparse learning

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Genes in biological modules often show correlated expression.
Sparse Singular Value Decomposition (SSVD) identifies gene modules but lacks interaction data integration.
Ignoring gene interactions limits biological interpretability of identified modules.

Purpose of the Study:

Develop a Sparse Network-regularized SVD (SNSVD) method.
Integrate gene expression data with prior gene interaction networks.
Identify biologically relevant gene functional modules.

Main Methods:

Developed the Sparse Network-regularized SVD (SNSVD) method.
Integrated protein-protein interaction networks with gene expression data.
Applied SNSVD to simulated and real cancer genomic data, including TCGA miRNA-mRNA data.

Main Results:

SNSVD outperforms traditional SVD-based methods on simulated data.
Identified modules are enriched in GO/KEGG pathways and correspond to dense sub-networks.
Discovered ten differentially co-expressed miRNA-gene modules in breast cancer.

Conclusions:

SNSVD effectively integrates gene interaction networks for improved biological relevance.
Identified modules offer new insights into cancer diagnostics, occurrence, and progression.
SNSVD enhances the identification of functional gene modules compared to SSVD.