TopControl: A Tool to Prioritize Candidate Disease-associated Genes based on Topological Network Features

Area of Science:

• Bioinformatics
• Genomics
• Computational Biology

Background:

• Identifying disease-associated genes is crucial for understanding disease mechanisms.
• Differential gene expression analysis often yields numerous candidate genes, necessitating prioritization.
• Existing gene prioritization methods require enhancement for improved accuracy and efficiency.

Purpose of the Study:

• To develop and validate a novel gene prioritization system.
• To leverage Transcription Factor (TF)-microRNA (miRNA) co-regulatory networks for gene ranking.
• To identify promising disease-associated genes in cancer datasets.

Main Methods:

• Construction of a TF-miRNA co-regulatory network for candidate genes.
• Prioritization of genes based on topological and biological network features.
• Application of the system to breast invasive carcinoma and liver hepatocellular carcinoma datasets.

Main Results:

• The novel prioritization technique successfully identified a significant proportion of known disease-associated genes.
• The system highlighted novel candidate genes for further investigation in cancer.
• Topological and biological network properties effectively ranked gene candidates.

Conclusions:

• The developed TF-miRNA co-regulatory network system offers a powerful approach for gene prioritization.
• This method enhances the identification of disease-associated genes, including novel candidates.
• The findings have implications for advancing cancer gene discovery and therapeutic strategies.