98. A bioinformatics analysis of differentially expressed genes in non-small cell lung cancer subtypes

Aly Abdelkareem

Patrick Shi

Patrick Shi is an intern at the Washington Institute for Health Sciences. His research interests include biology, medicine, and environmental science. He is particularly interested in the application of genomics research using bioinformatics.


Patrick Shi, Wenqiang Chen

Washington Institute for Health Sciences, Arlington, VA, United States

Lung cancer is the most commonly diagnosed cancer type and leading cause of cancer death worldwide. Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) are major subtypes of Non-Small Cell Lung Cancer (NSCLC), accounting for 82% of all lung cancer. Understanding the differences in genes causing the proliferation of LUAD and LUSC is key to advance diagnosis and targeted treatment development. The aim of this study is to identify candidate genes and potential tumorigenesis mechanisms distinguishing LAUD and LUSC. Three pooled transcriptomic datasets (GSE10245, GSE37745, and GSE43580) were analyzed from the Gene Expression Omnibus (GEO) database, with each dataset statistically tested for differentially expressed genes (DEGs). DEGs between lung LUAD and LUSC of the three datasets were functionally analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. A protein-protein interaction (PPI) network was constructed to screen out candidate genes. This study identified 138 shared DEGs among three patient-level gene expression datasets, containing 39 upregulated genes and 99 downregulated genes. The GO and KEGG enrichment analysis results showed that the functions of DEGs were mainly involved in epidermis development, cornified envelope, structural constituent of epidermis, and estrogen signaling pathway. Finally, through the PPI network, eight core genes were identified: KRT14, KRT5, KRT6A, KRT16, SPRR1A, SPRR1B, SPRR3 and KRT6B. In conclusion, we elucidate key genes and molecular mechanisms linked to NSCLC subtypes. These findings have the potential to facilitate improved diagnostic and therapeutic targets for LUAD and LUSC biomarkers.