Network centrality-driven TOPSIS approach for prioritizing cancer therapeutic targets

Area of Science:

• Oncology and Bioinformatics
• Systems Biology
• Genomics

Background:

• Cancer remains a significant global health challenge, necessitating the discovery of novel therapeutic targets.
• Protein-protein interaction (PPI) networks are crucial for understanding complex biological systems and identifying potential drug targets.

Purpose of the Study:

• To construct a high-confidence cancer PPI network and identify novel, high-priority therapeutic targets.
• To evaluate the prognostic significance of identified candidate genes across various cancer types.

Main Methods:

• Construction of a cancer PPI network with 2564 proteins and 20,747 interactions.
• Application of the TOPSIS multi-criteria decision-making method to rank gene importance.
• Drug-target mapping, Gene Ontology (GO), KEGG pathway enrichment, and survival analysis using TCGA datasets.

Main Results:

• Identified 26 high-priority cancer genes, with 21 linked to existing drugs.
• Five genes (NXF1, CDC5L, MOV10, EP300, CUL7) emerged as unexplored targets.
• Demonstrated significant prognostic roles for CDC5L, EP300, MOV10, CUL7, and NXF1 across multiple cancer types.

Conclusions:

• The integrative TOPSIS-network framework effectively identifies both known and novel cancer therapeutic targets.
• The five unexplored genes warrant further experimental validation for potential therapeutic development in precision oncology.
• This approach provides a robust strategy for expanding the landscape of cancer therapeutics.