The profiling, identification, quantification and analysis of differentially expressed genes (DEGs) in response to drug treatment in lung cancer

Abstract

The profiling and identification of genes that are differentially expressed is frequently used to underpin the underlying molecular mechanisms of biological conditions and provides a molecular foothold on biological questions of interest. However, this can be a daunting task since there is a cross talk and overlap of some of the components of the signalling pathways. The deregulation of the cell cycle signalling pathway is a hallmark of cancer, including lung cancer. Proper regulation of the cell cycle results in cellular homeostasis between cell proliferation and cell death. The comprehension of the cell cycle regulation in drug metabolism studies is of significance. This study aimed at elucidating the regulation of cell cycle genes’ in response to LPV/r in lung cells. Thus, this study describes methodology for revealing molecular mechanisms employed by LPV/r to induce stress on genomic DNA. This approach is based on the interrogation of a panel of 84 genes related to the cell cycle pathway, and how the differentially expressed genes’ expression pattern corroborates loss in nuclear integrity (phenotypic observation). MAD2L2, AURKB and CASP3 gene expressions were further confirmed by RT- qPCR. Furthermore, the use of in-silico bioinformatics tools integrates the molecular profiles and phenotypic changes. This approach revealed the activation of the DNA damage response (DDR) pathway in response to LPV/r treatment. The proposed methodology will aid in the comprehension of drug metabolism at genotypic and phenotypic levels. •Gene profiling often reveals the underlying molecular mechanisms. •RT 2 PCR gene arrays have integrated patented quality controls and allow reliable gene expression analysis. •In-silico bioinformatics analysis help reveal pathways affected, that often correspond to phenotypic changes/features.