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A new method for mining information of co-expression network based on multi-cancers integrated data.

Mi-Xiao Hou¹, Ying-Lian Gao², Jin-Xing Liu^3,4

¹School of Information Science and Engineering, Qufu Normal University, Rizhao, China.

BMC Medical Genomics

|January 1, 2020

Summary

This study introduces PMN, a novel method for constructing gene co-expression networks from multi-cancer data. PMN effectively identifies shared genes and pathways across different cancers, aiding disease nature revelation.

Keywords:

Abnormally expressed genes Gene co-expression network Mutual information Pearson correlation coefficient TCGA

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene co-expression networks are crucial for understanding disease mechanisms.
Multi-cancer network construction faces limitations in current node measurement and mining strategies.
Integrating diverse cancer data is essential for comprehensive network analysis.

Purpose of the Study:

To introduce a novel method, PMN, for mining gene co-expression information from integrated multi-cancer data.
To enhance the identification of disease-related genes and pathways across different cancer types.
To address limitations in existing node measurement and mining strategies for complex cancer networks.

Main Methods:

Developed the Prioritized Multi-cancer Network (PMN) method for gene co-expression network construction.
Integrated multi-cancer gene expression data from The Cancer Genome Atlas (TCGA).
Combined linear and nonlinear measures, alongside local and global node characteristics, for robust network analysis.

Main Results:

Uncovered suspicious abnormally expressed genes and shared pathways across different cancers.
Identified both previously validated and novel candidate genes and molecular pathways.
Demonstrated the effectiveness of PMN in identifying biologically relevant connections within multi-cancer datasets.

Conclusions:

The PMN method is highly effective for excavating gene co-expression insights from multi-cancer data.
The approach facilitates the discovery of shared molecular mechanisms underlying different cancers.
Findings provide a foundation for further clinical validation of identified genes and pathways.