A computational approach to generate highly conserved gene co-expression networks with RNA-seq data

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Gene co-expression networks are crucial for understanding cellular functions.
• Identifying dynamic changes in these networks, especially in cancer, is challenging.
• Existing methods may lack robustness or introduce bias in differential network analysis.

Purpose of the Study:

• To develop a robust and unbiased pipeline for constructing and analyzing differential gene co-expression networks.
• To identify genes and interactions that are lost, conserved, or newly acquired in cancer.
• To enable functional interpretation of network alterations in disease.

Main Methods:

• A consensus approach using random downsampling of data subsets to build gene-gene interaction networks.
• Differential network analysis to compare cancer and healthy tissue networks.
• Leveraging RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases.

Main Results:

• The protocol successfully identified network nodes (genes) that were lost, conserved, and acquired in cancer.
• Demonstrated the ability to interpret the functional significance of these observed network changes.
• Provided a proof-of-concept using real-world tumor and healthy tissue RNA-seq data.

Conclusions:

• The described consensus network construction approach offers an unbiased method for differential gene co-expression network analysis.
• This pipeline is valuable for uncovering cancer-specific gene interaction patterns and their functional implications.
• The protocol facilitates a deeper understanding of cancer biology through network-based approaches.