Differential Gene Expression Analysis of Lung Cancer Using Public data

Abstract

Lung adenocarcinoma (LUAD) could demonstrate distinct molecular alterations that appear to depend on the significant smoking status, though the critical transcriptomic differences might provide valuable opportunities for identifying new biomarkers. Moreover, lung cancer may account for 2.21 million cancer cases each year and appears to show the top cause of cancer- related deaths worldwide. Thus, tobacco smoking might indicate the biggest risk factor for lung cancer. However, cases also happen in non-smokers, especially women. Given that molecular processes separate lung adenocarcinomas in smokers from those in non-smokers, these mech- anisms are not well understood. Comparative transcriptomic and bioinformatics analyses can systematically identify changes in gene expression and pathways that smoking disrupts. Nev- ertheless, identifying genes and signaling pathways that smoking affects may help understand disease progress and find treatment targets. Additionally, combining gene expression data with protein–protein interaction (PPI) networks and drug–target mapping can reveal chances for re-purposing drugs for targeted lung cancer treatment. Notwithstanding previous approaches, the study involves identifying differentially expressed genes (DEGs) between smoker and non- smoker lung adenocarcinoma samples using GEO2R. Moreover, the analysis might construct a protein–protein interaction (PPI) network and identify hub genes using STRING. Furthermore, pathway and functional enrichment analysis uses Enrichr. Thus, findings may give insight into molecular targets and repurpose a drug candidate, supporting development of diagnostic biomarkers and targeted therapies. However, bioinformatics integrative analysis might provide insights into smoking-associated molecular mechanisms in LUAD and adds to advancement of precision medicine strategies in lung cancer treatment.