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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: Mar 15, 2026

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

LPRP: A Gene-Gene Interaction Network Construction Algorithm and Its Application in Breast Cancer Data Analysis.

Lingtao Su^1,2, Xiangyu Meng^3,4, Qingshan Ma⁵

¹College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

Interdisciplinary Sciences, Computational Life Sciences

|September 19, 2016

Summary

Researchers developed a new method, linear and probabilistic relations prediction (LPRP), to build gene-gene interaction (GGI) networks for breast cancer. This analysis revealed significant network changes in tumor samples, highlighting potential drug targets.

Keywords:

Breast cancer Gene–gene interaction Network construction TCGA dataset

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Area of Science:

Oncology
Bioinformatics
Systems Biology

Background:

Understanding gene-gene interactions (GGI) is crucial for deciphering breast cancer complexity.
Previous methods for GGI network construction have limitations in capturing system-level insights.

Purpose of the Study:

To introduce a novel method, linear and probabilistic relations prediction (LPRP), for constructing GGI networks.
To gain system-level insights into breast cancer mechanisms by analyzing tumor and normal breast sample GGI networks.

Main Methods:

Genome-wide GGI networks were constructed for tumor and normal breast samples using the LPRP method.
Gene expression datasets from The Cancer Genome Atlas (TCGA) were utilized.
Network properties (diameter, path length, clustering coefficient, connectivity) were analyzed.

Main Results:

A significant reduction (88.7%) in gene interactions was observed in the tumor GGI network compared to the normal network.
The tumor GGI network exhibited a larger diameter, longer characteristic path length, and reduced clustering coefficient, indicating sparser connections.
Enrichment analysis revealed known cancer pathways, particularly immune response pathways, within the tumor GGI network.

Conclusions:

The LPRP method provides a novel approach to GGI network construction for breast cancer research.
Significant alterations in GGI networks characterize breast tumors, suggesting a shift in biological mechanisms.
The study identified potential drug-targeting genes within the tumor GGI network, offering avenues for therapeutic development.