Screening and identification of gene expression in large cohorts of clinical tissue samples unveils the major involvement of EZH2 and SOX2 in lung cancer.

Lung adenocarcinoma (LUAD), the primary subtype of Non-Small Cell Lung Cancer (NSCLC), accounts for 80 % to 85 % of cases. Due to suboptimal screening method, LUAD is often detected in late stage, leading to aggressive progression and poor outcomes. Therefore, early disease prognosis for the LUAD is high priority. In order to identify early detection biomarkers, we conducted a meta-analysis of mRNA expression TCGA and GTEx datasets from LUAD patients. A total of 795 differentially expressed genes (DEGs) were identified by exploring the Network-Analyst tool and utilizing combined effect size methods. DEGs refer to genes whose expression levels are significantly different (either higher or lower) compared to their normal baseline expression levels. KEGG pathway enrichment analysis highlighted the TNF signaling pathway as being prominently associated with these DEGs. Subsequently, using the MCODE and CytoHubba plugins in Cytoscape software, we filtered out the top 10 genes. Among these, SOX2 was the only gene exhibiting higher expression, while the others were downregulated. Consequently, our subsequent research focused on SOX2. Further transcription factor-gene network analysis revealed that enhancer of zeste homolog 2 (EZH2) is a significant partner of SOX2, potentially playing a crucial role in euchromatin-heterochromatin dynamics. Structure of SOX2 protein suggest that it is a non-druggable transcription factor, literature survey suggests the same. SOX2 is considered challenging to target directly, or "non-druggable," because of several intrinsic properties that make it difficult to design effective therapeutic agents against it. The primary function of SOX2 is to bind DNA and regulates gene expression. Unlike enzymes or receptors with defined active sites or binding pockets, transcription factors typically have relatively flat or diffuse surfaces that do not offer obvious "pockets" for small molecules to bind effectively. Hence, we drove our focus to investigate on potential drug(s) targeting EZH2. Molecular docking analyses predicted most probable inhibitors of EZH2. We employed several predictive analysis tools and identified GSK343, as a promising inhibitor of EZH2.