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

作者：Lu Sheng, Aotian Guo, Haichuan Hu, Xinxin Ying, Yao Li, Zhengwei Huang, Wangjue Xu, Tao Shen, Xiaotong Hu, Na Yan, Xuan Zhang, Dan Shen, Takaaki Sasaki, Surein Arulananda, Ken Onodera, Zhengfu He · 发表于：Translational Lung Cancer Research · 年份：2024 · DOI：10.21037/tlcr-24-409 · 被引用次数：8 · 研究领域：Lung Cancer Treatments and Mutations、Cancer Genomics and Diagnostics、Bioinformatics and Genomic Networks

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 EGFR, ALK, ROS1, RET, KRAS, MET, BRAF, HER2, PIK3CA and NRAS. At the same time, clinicopathological data were collected and organized for multidimensional correlation analysis. 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 EGFR, KRAS, and MET. Sixty-nine types of EGFR mutations were detected and distributed in the protein tyrosine kinase catalytic domain (56, 81.16%), Furin-like cysteine-rich region (9, 13.04%), receptor binding domain (3, 4.35%), and EGFR transmembrane domain (1, 1.45%). Single gene locus mutation occurred in 343 LUAD patients, but the mutation gene types covered all tested genes. Our findings showed that EGFR mutations were more commonly observed in non-...