Discovering Disease Genes in PPI Networks: A Bridge from Centrality to Communities

Targeted therapies have become pivotal in modern clinical oncology, driven by a molecularlevel understanding of cancer’s intricacies, its progression, and innovative research and technology. Personalized and targeted treatments hinge on identifying key genes, hub genes, or biomarkers. Protein-protein interaction (PPI) networks are instrumental in understanding the molecular basis of diseases. While existing literature has identified significant genes based on network centrality, investigations based on community-aware centrality have been notably absent. This omission matters because disease networks frequently display modular structures, necessitating a new perspective. This study bridges the gap between network centrality and community-based investigations. Indeed, in modular networks, node influence can be categorized into two types: local impact within its community, determined by intra-community connections, and global effect on nodes in other communities, established through inter-community links. This concept extends conventional centrality measures to networks with a community structure. Initially, we performed a comparative analysis of seven PPI networks related to cancer and noncancerous conditions. We explore the correlation between classical network centralities and their equivalents at the global (inter-community) and local (intra-community) levels. Notably, we consistently observed a high correlation between network degree and local degree centrality in all PPIs, leading us to select local degree centrality for further investigation. Pronounced modularity characterizes prostate and cervical disease networks. Consequently, we investigate these networks to identify key genes at the local community level and validate them by examining their expression levels. Variations in gene expression between cancerous and non-cancerous tissues bolster our findings. We identify a novel set of genes as potential key players in prostate and cervical cancer. Specifically, in cervical cancer, the top genes at the mesoscopic level include AKT1, CDK2, BRCA1, VEGFA, SRC, PSMD14, MRPL3, TP53, and NUP37. Meanwhile, the top genes identified in prostate cancer are FOS, TP53, UBA52, HLA-B, TSPO, and CD19. Although we focus on cancer data, our methodology’s versatility makes it applicable to other disease networks, opening avenues to identify key genes as potential drug targets. ### Competing Interest Statement The authors have declared no competing interest.