ArticleDiscover oncology2025
Multi-gene panel sequencing reveals the relationship between driver gene mutation and clinical characteristics in lung adenocarcinoma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
- A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma.Discover oncology · 2026Article
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9 authors.
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Abstract
backgroundTesting of multiple cancer related genes using next-generation sequencing (NGS) has been widely used for personalized precision medicine of cancer. Integrated analysis of those NGS data and clinical data has offered new opportunities for investigating the relationship between driver genes' mutations and clinical characteristics in large cohorts. This study aims to explore the mutational landscape and its association with clinical features in a lung adenocarcinoma (LUAD) cohort.
methodsTumor tissues from 132 LUAD patients were subjected to customized 30 genes targeted next-generation sequencing. Somatic mutations of the 30 genes were identified and annotated. Statistical analysis was performed to determine the cooccurrence of mutations of different driver genes and the association relationships between gene mutation and clinical features including gender and age.
resultsA total of 96.97% (128/132) of LUAD patients experienced genetic mutations. EGFR had the highest mutation rate (81, 61.36%) among the 30 genes, followed by TP53 (80, 60.61%), BRAF (30, 22.73%), KRAS (21, 15.91%) and ROS1 (21, 15.91%). The L858R substitution and exon19 deletion were the predominant mutations of EGFR, accounting for 82.71% of EGFR-mutated patients. The 27 mutation sites of EGFR were mainly located in the tyrosine kinase catalytic domain (22/27, 81.48%). Mutations of SDHA (p < 0.01), ERBB2 (p < 0.01), and ESR1 (p < 0.05) were negatively correlated with age, and mutations of NF1 (p < 0.01), KRAS (p < 0.01), and TP53 (p < 0.001) were significantly associated with gender.
conclusionsThis work revealed the mutational landscape and characteristics of 30 core driver genes in a LUAD cohort. Co-mutated genes and genes associated with gender and age indicate their different roles in the corresponding subgroup of the LUAD.
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