A novel multi-stage fusion based approach for gene expression profiling in non-small cell lung cancer
Résumé
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 con-
sideration 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.