Abstract

Oral cancer is a multifaceted disorder marked by the aberrant activity of several genes and proteins within various cellular networks, which promotes both its emergence and progression. While protein–protein interaction (PPI) network analysis is gaining traction in disease studies, the precise pinpointing of central genes and steadfast biomarkers using sophisticated network topological attributes continues to be a major hurdle. Network edges are essential communication channels for the dissemination of information. Many current techniques for identifying hub genes rely on node characteristics; the majority of them treat the edges identically in unweighted networks. In this paper, computational approaches, particularly PPI network analysis using a new edge-weighting method for identifying hub genes in PPI networks by integrating the H-index, neighbourhood information, and clustering coefficient of connected nodes, are discussed. The proposed approach incorporates neighbourhood characteristics and topological features to improve the identification of hub genes. To evaluate its performance, the top-ranked genes identified by the proposed method were compared with those obtained using CytoHubba. Experimental results demonstrate that the proposed method successfully identifies a set of genes largely consistent with known driver genes associated with oral cancer.

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