Gene Regulatory Network Based Biomarker Transition from Normalcy to Malignancy: A study of Lung Cancer Using Differential Gene Expression Data

Lung cancer is a global threat, and understanding its pathology through gene regulatory network (GRN)-based biomarkers could offer significant insights. In this study, GRN-based biomarkers have been constructed to demonstrate the topology shift of GRN from a normal condition to a lung cancer condition. This study employed three distinct processes to identify significant genes from differential gene expression data related to lung cancer: 1) simple variance-based screening, 2) a recent gene selection algorithm incorporating simulated annealing and a biologically inspired objective function, and 3) a volcano plot comparing log2 fold changes against -log10 P values. These comprehensive methods identified 21 common genes as significant genes, including KRT5, SFTPC, KRT16, and AKR1B10, which have strong associations with lung cancer pathology according to previous literature. To gain deeper insights into the underlying biological mechanisms, co-expression-modelled GRNs have been constructed for both normal and lung cancer conditions based on these significant genes. These networks provide a detailed visual and mathematical representation of gene interactions within the cellular environment, illustrating how these interactions differ between healthy and diseased states. The shift in network biomarker topologies from normal to diseased conditions have been closely analysed using centrality measures, which quantify the importance of genes based on their connectivity and position within the network. Notably, the pronounced change in the degree of centrality for genes such as GJB6 between normal and diseased states suggests their pivotal roles in the transition to lung cancer. Rigorous statistical and machine learning methods validated the significance of the selected genes and their interconnections, ensuring the robustness of the findings.