Prioritization of Lung Cancer Candidate Genes using Moment of Inertia Tensor Analysis

Abstract

A variety of factors contribute to the complexity of lung cancer progression. To comprehend the disease, candidate genes must be investigated. Present study aimed to employ an alignment-free method to prioritize candidate genes based on the physicochemical properties of the amino acids. It uses the moment of inertia tensor that measures the mass distribution around an axis of rotation, to compute the rotational energy and angular momentum of amino acids in protein sequences. The computed features were compared to those of established lung cancer genes, leading to the identification of 26 candidate genes with a high degree of similarity. These genes participate in critical biological processes that regulate the mitotic cell cycle and cell development. The prognostic significance of these genes was also assessed and four genes (IL1A, CDC25C, IL4R, and TGFBR1) were found to be associated with poor survival. Additionally, the role of prioritized genes and potential drugs that target these genes in other cancer types was also examined. Our method will help to discover new biomarkers and intervention strategies for lung cancer.