Bioinformatics analysis of gene expression profiles to identify key genes associated with Nonsmall cell lung cancer (NSCLC)

Background: Lung cancer is one of the most common malignancies in the world, as well as the leading causeof cancer death, with over one million people dying from it each year. Non-small cell lung cancer (NSCLC)accounts for roughly ۸۵ percent of all lung malignancies and has a five-year survival rate of fewer than ۲۰%.An accurate understanding of the molecular mechanisms underpinning lung cancer progression, as well asthe genes (proteins) implicated in the disease's pathogenesis, is critical for the development of diagnostic andtreatment techniques.Materials and Methods: In this study, four microbial gene expression datasets GSE۲۷۲۶۲, GSE۳۱۲۱۰,GSE۱۹۱۸۸, and GSE۱۹۸۰۴ from Gene Expression Omnibus (GEO) obtained and different genes expressed)(DEGs) were analyzed between SCLC samples and healthy control samples using GEO۲R analytical toolkit.In addition to the STRING database and Cytoscape software to create and analyze the protein-proteininteraction network (PPI) genes of expression and reduction commonly expressed between these Four datasets and determination of hub and key genes were used. Confirmation of key gene expression and associatedsurvival was also confirmed using the GEPIA and HPA databases. Functional enrichment analysis andfunctional pathway enrichment analysis for DEGs were performed by the Enrichr web server.Results and conclusion: After gene integration, a total of ۸۵۰ DEGs (including ۲۶۴ overexpressed genesand ۵۸۶ overexpressed genes) were identified. Findings related to the pathogenesis of NSCLC were identifiedand after checking the susceptibility of genes (proteins) (CCNB۲, TTK, PBK, RRM۲, CCNB۱, CCNA۲,HMMR, KIF۱۱, and TYMS as potential targets in the pathogenesis of NSCLC disease and design of targettargets. Drugs were identified against the disease, and functional enrichment showed that the cell cycle is themost important pathway involved in the pathogenesis of NSCLC with the highest significant value.

Keywords:

Lung cancer , NSCLC , GEO , Cytoscape , DEGs