Integrated Bioinformatics Analysis to Identify Biomarker Genes and Pathways in Lung Cancer

Lung cancer remains one of the most prevalent causes of cancer-related deaths worldwide, characterized by complex molecular mechanisms and heterogeneous progression. In order to better understand its pathogenesis and improve diagnosis and treatment, applied an integrated bioinformatics approach using transcriptomic single-omics data from TCGA. By using the Kruskal-Wallis test with Bonferroni correction, we identified 26 influential significant genes (IFGs) out of 20,113 linked with lung cancer progression. Among them 10 genes are up-regulated and 16 genes are down-regulated. Functional enrichment and gene ontology analysis highlighted critical pathways alongside disruptions in DNA replication and transcription regulation. Protein-protein interaction (PPI) network analysis identified central hub proteins and the protein-drug interaction (PDI) is analyzed to reveal therapeutic potential in targeting ARPC3 with existing drugs. Furthermore, comorbidity validation using two benchmark databases, dbGaP and OMIM, confirmed the associations for some genes, thereby enhancing the clinical relevance of our findings. Some key biomarkers and pathways are found that could improve treatments of lung cancer and also improve diagnostic accuracy. These findings make a clearer understanding of how the dysregulated genes act in the disease. This study also sets the stage for developing more targeted drugs for people with lung cancer, supporting future research in precision medicine.