Identification of Hub Genes and Therapeutic Targets in Lung Cancer Using Software Tools

Background: Lung cancer remains one of the leading causes of cancer-related mortality globally, necessitating a deeper understanding of its molecular underpinnings. Objectives: This study aimed to identify hub genes associated with lung cancer through a systematic analysis of existing literature and databases. Materials and Methods: We curated a comprehensive Gene Expression Omnibus (GEO) dataset and identified key hub genes linked to lung cancer via PubMed resources. To assess the relevance of these hub genes, we conducted Protein-Protein Interaction (PPI) analysis through the DAVID database, selecting those with high enrichment values. Functional enrichment analysis was performed using DAVID, SHINY GO, and GO NET DICE to elucidate the biological processes and pathways associated with the identified hub genes. Additionally, we employed ChEMBL, Pharos, and Broad tools to assess druggability, integrating chemical, bioactivity, and genomic data. Functional gene partners were grouped to provide a clearer understanding of the interaction networks. Results: The genes were then ranked based on their involvement in various molecular functions, yielding insights into their potential roles in the pathology of lung cancer. Conclusion: This comprehensive analysis underscores critical gene interactions and functional pathways, offering promising targets for future research and therapeutic intervention in the treatment of lung cancer.