Computational Analysis of Drug Resistance Network in Lung Adenocarcinoma

Background: Lung cancer is a significant health problem and accounts for one-third of the deaths worldwide. A great majority of these deaths are caused by Non-Small Cell Lung Cancer (NSCLC). Chemotherapy is the leading treatment method for NSCLC, but resistance to chemotherapeutics is an important limiting factor that reduces the treatment success of patients with NSCLC. Objective: In this study, the relationship between differentially expressed genes affecting the survival of the patients, according to the bioinformatics analyses, and the mechanism of drug resistance is investigated for nonsmall cell lung adenocarcinoma patients. Methods: Five hundred thirteen patient samples were compared with fifty-nine control samples. The employed dataset was downloaded from The Cancer Genome Atlas (TCGA) database. The information on how the drug activity altered against the expressional diversification of the genes was extracted from the NCI-60 database. Four hundred thirty-three drugs with known Mechanism of Action (MoA) were analyzed. Diversifications of the activity of these drugs related to genes were considered based on nine lung cancer cell lines virtually. The analyses were performed using R programming language, GDCRNATools, rcellminer, and Cytoscape. Results: This work analyzed the common signaling pathways and expressional alterations of the proteins in these pathways associated with survival and drug resistance in lung adenocarcinoma. Deduced computational data demonstrated that proteins of EGFR, JNK/MAPK, NF-ΚB, PI3K /AKT/mTOR, JAK/STAT, and Wnt signaling pathways were associated with the molecular mechanism of resistance to anticancer drugs in NSCLC cells. Conclusion: To understand the relationships between resistance to anticancer drugs and EGFR, JNK/MAPK, NF-ΚB, PI3K /AKT/mTOR, JAK/STAT, and Wnt signaling pathways is an important approach to design effective therapeutics for individuals with NSCLC adenocarcinoma.