Key genes associated with the progression of non-small cell lung adenocarcinoma in smokers and non-smokers

Backgrounds: Non-small cell lung cancer is the most prevalent type of lung cancer, accountingfor ۸۵ percent of all cases. It is frequently diagnosed at an advanced stage and has a poorprognosis. Understanding the key genes involved in adenocarcinoma tumor progression insmokers and non-smokers can lead to early identification and multimodal treatments.Materials and Methods: GSE۵۰۰۸۱ CEL files were normalized using robust multi-arrayaverage (RMA) via the R programming language, and differentially expressed genes (DEGs) ofadenocarcinoma stages ۱ and ۲ were detected in two separate groups of smokers and nonsmokersusing the following criteria: |𝑙𝑜𝑔۲𝐹𝐶| > ۱ and P-value <۰.۰۵. The STRING databasewas used to create a PPI network, and then examined using Cytoscape to track hub genes.Enrichment study was carried using “Enrichr”.Results: In non-smokers and smokers, ۳۵۸ DEGs (۵۷ up-regulated and ۳۰۱ down-regulated) and۲۴۵ DEGs (۴۳ up-regulated and ۲۰۲ down-regulated) were identified, respectively. KEGGpathway analysis was enriched in protein digestion and absorption for smokers andphosphatidylinositol signaling system for non-smokers. According to Gene Ontology, Smokers’DEGs enriched significantly in alveolar lamellar body and non-smokers in bone morphogenesis.In smokers, three hub genes were found: CF transmembrane conductance regulator (CFTR),microtubule-associated protein ۲ (MAP۲), and NEDD۴ like E۳ ubiquitin-protein ligase(NEDD۴L). Non-smokers were found to have erb-b۲ receptor tyrosine kinase ۴ (ERBB۴) andheat shock protein ۹۰ alpha family class B member ۱ (HSP۹۰AB۱) as hub genes, which could beemployed as therapeutic targets.Conclusion: In summary, identifying discrete hub genes for both smokers and non-smokers andunderstanding the pathways and ontologies involved can help us better understand the diseaseprogression and provide novel biomarkers for future therapeutics.

کلیدواژه ها:

Non-small cell lung cancer ، Lung adenocarcinoma ، Gene expression analysis ، Bioinformatics