Abstract
Background: Non-small cell lung cancer is defined at the molecular level by mutations and alterations to oncogenes, including AKT1, ALK, BRAF, EGFR, HER2, KRAS, MEK1, MET, NRAS, PIK3CA, RET, and ROS1. A better understanding of non-small cell lung cancer requires a thorough consideration of these oncogenes. However, the complexity of the problem arises from high-dimensional gene vector space, which complicates the identification of cluster boundaries, and hence gene expression cluster membership. This paper aims to analyze potential biological biomarkers for tumorigenesis in lung cancer based on different treatment solutions. Results: Genes BRAF, RET, and ROS1 show an overexpression transition by one cluster from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. Genes MET, ALK, and PIK3CA show an overexpression transition by two clusters from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. SME1 shows an under-expression transition by two clusters from non-treatment to the treatment states, a stabilization in the 3 treatment states at the same cluster. Conclusions: We present a novel fusion-based approach for gene expression profiling of non-small cell lung cancer under non-thermal plasma treatment. The main contribution of the proposed approach is to exploit Dempster-Shafer evidence theory-based data fusion to combine information from different samples in the considered dataset. This minimizes uncertainty and enhances the reliability and validity of decisions, leading to a better description of genes related to non-small cell lung cancer. We also propose use of fuzzy c-means-with-range clustering to track changes of genes' states under different non-thermal plasma treatments.