Identification of Candidate mRNA Genes and Their Potential MicroRNA Targets in Lung Cancer Induced by Smoking Tobacco

Background: Smoking is considered the single highest risk factor for lung cancer and has been suggested to be associated with accelerated somatic mutations in respiratory mucosa that lead to the development of lung cancer. MicroRNAs serve as modulators in smoking-induced mRNA gene expression changes in the human airway epithelium and are linked to the development of lung cancer. The thermodynamics in the microRNA (miRNA)–mRNA interactions may be affected in tobacco smokers, consequently, leading to phenotypic variations in lung cancer patients. Therefore, this study aimed to investigate the impact of smoking tobacco on somatic mutations in mRNA genes and assess their potential impact on miRNA–mRNA interactions in lung cancers. Methods: The clinically significant pathogenic variants in mRNA genes in the dataset in lung cancer cases linked to smoking tobacco (n = 330) were obtained from the Cancer Atlas database (TCGA, http://cancergenome.nih.gov/) and used to assess the potential role of tobacco consumption in driving the genetic alterations in proto-oncogenes associated with lung cancer. The analysis of the miRNA interaction with the top five altered mRNA proto-oncogenes in lung cancer cases due to tobacco consumption was performed using the target prediction function in the miRDP program (Database version 5.2.3.1, https://mirdb.org/). Results: We identified the top five mRNA proto-oncogenes enriched with simple somatic mutations (SSM) in lung cancer were TP53, EGFR, KRAS, FAT4, and KMT2D. Interestingly, we observed the highest incidence of SSM in the Tumor Protein p53 (TP53) gene at 63.64%. Similarly, the SSM incidence was 23.94% in the Epidermal Growth Factor Receptor (EGFR), 22.12% in the Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), 18.48% in the FAT Atypical Cadherin 4 (FAT4), and 14.24% in the Lysine (K)-Specific Methyltransferase 2D (KMT2D) genes. Subsequently, we used a bioinformatics approach to assess the effect of miRNA–mRNA interactions in lung cancer among the top five SSM-enriched mRNA ...