AURKA Identified as Potential Lung Cancer Marker through Comprehensive Bioinformatic Analysis and Experimental Verification.

Abstract

Non-small-cell lung cancer (NSCLC) is a malignancy with high overall morbidity and mortality due to a lack of reliable methods for early diagnosis and successful treatment of the condition. We identified genes that would be valuable for the diagnosis and prognosis of lung cancer. Common DEGs (DEGs) in three GEO datasets were selected for KEGG and GO enrichment analysis. A protein-protein interaction (PPI) network was constructed using the STRING database, and molecular complex detection (MCODE) identified hub genes. Gene expression profiling interactive analysis (GEPIA) and the Kaplan-Meier method analyzed hub genes expression and prognostic value. Quantitative PCR and western blotting were used to test for differences in hub gene expression in multiple cell lines. The CCK-8 assay was used to determine the IC50 of the AURKA inhibitor CCT137690 in H1993 cells. Transwell and clonogenic assays validated the function of AURKA in lung cancer, and cell cycle experiments explored its possible mechanism of action. Overall, 239 DEGs were identified from three datasets. AURKA, BIRC5, CCNB1, DLGAP5, KIF11, and KIF15 had shown great potential for lung cancer diagnosis and prognosis. In vitro experiments suggested that AURKA significantly influenced the proliferation and migration of lung cancer cells and activities related to the dysregulation of the cell cycle. AURKA, BIRC5, CCNB1, DLGAP5, KIF11, and KIF15 may be critical genes that influence the occurrence, development, and prognosis of NSCLC. AURKA significantly affects the proliferation and migration of lung cancer cells by disrupting the cell cycle.