Detection of Stage-wise Biomarkers in Lung Adenocarcinoma Using Multiplex Analysis

Abstract

Introduction: Lung cancer is the leading cancer in terms of morbidity and mortality rate. Its prevalence has been steadily increasing over the world in recent years. An integrated study is unavoidable to analyse the cascading interrelationships between molecular cell components at multiple levels resulting in hidden biological events in cancer. Methods: Multiplex network modeling is a unique methodology that could be used as an integrative method for dealing with diverse interactions. Here, we have employed a multiplex framework to model the lung adenocarcinoma (LUAD) network by incorporating co-expression correlations, methylation relations, and protein physical binding interactions as network layers. Hub nodes identified from the multiplex network utilizing centrality measures, including degree, eigenvector, and random walk with a random jump technique, are considered as biomarker genes. These stage-wise biomarker genes identified for LUAD are investigated using GO enrichment analysis, pathway analysis, and literature evidence to determine their significance in tumor progression. Results: The study has identified a set of stage-specific biomarkers in LUAD. The 31 genes identified from the results of multiple centrality analysis can be targeted as novel diagnostic biomarkers in LUAD. Multiple signaling pathways identified here may be considered as potential targets of interest. Conclusion: Based on the analysis results, patients may be identified by their stage of cancer progression, which can aid in treatment decision-making.