Correlation analysis between driver gene mutation and clinicopathological features in lung adenocarcinoma based on real-world cumulative clinical data.

Background: Driver genes are essential predictors of targeted therapeutic efficacy. Detecting driver gene mutations in lung adenocarcinoma (LUAD) patients can help to screen for targeted drugs and improve patient survival benefits. This study aims to investigate the mutation characterization of driver genes and their correlation with clinicopathological features in LUAD. Methods: A total of 440 LUAD patients were selected from Sir Run Run Shaw Hospital between July 2019 and September 2022. Postoperative tissue specimens were analyzed for gene mutations using next-generation sequencing technology, focusing, including epidermal growth factor receptor Results: Of 440 LUAD patients, driver gene mutations were not detected in 48 patients. The proportion of patients with driver gene mutations was as high as 89.09%. The top three driver genetic mutations were Conclusions: LUAD patients exhibit diverse genetic mutations, which may co-occur simultaneously. Integrated analysis of multiple mutations is essential for accurate diagnosis and effective treatment of the disease. The use of NGS can significantly expand our understanding of gene mutations and facilitate integrated analysis of multiple gene mutations, providing critical evidence for targeted treatment methods.