| Literature DB >> 28935015 |
Jing Zhao1, Jie Gao2, Liping Guo3, Xiaoxu Hu4, Qi Liu3, Jinyin Zhao3, Licheng Liu4, Jun Jiang4, Mengzhao Wang1, Zhiyong Liang2, Yan Xu1, Minjiang Chen1, Li Zhang1, Longyun Li1, Wei Zhong1.
Abstract
BACKGROUND: Epidermal growth factor receptor (EGFR) and KRAS gene are important driver genes of non-small cell lung cancer (NSCLC). The studies are mainly focused on detection of EGFR gene for advanced NSCLC, and the mutation feature of EGFR and KRAS gene in early NSCLC tissue is unknown. This study aims to investigate the mutations of EGFR and KRAS gene in NSCLC, and the relationship between the genotype and clinicopathologic features.Entities:
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Year: 2017 PMID: 28935015 PMCID: PMC5973370 DOI: 10.3779/j.issn.1009-3419.2017.09.05
Source DB: PubMed Journal: Zhongguo Fei Ai Za Zhi ISSN: 1009-3419
13例EGFR-KRAS双突变样本的DNA测序分析
DNA sequencing data of 3 specimens with EGFR-KRAS double mutations. EGFR: epidermal growth factor receptor.
EGFR及KRAS基因突变情况与患者临床病理特征之间的关系
The relationship between EGFR and KRAS mutations and clinical variables of patients
| Clinical variables | ||||||||
| Mutant | Wild-type | Mutant | Wild-type | |||||
| *:Others include large cell carcinoma and sarcomatoid carcinoma. | ||||||||
| Gender | 0.076 | 0.048 | ||||||
| Male | 374 | 110 (29.4) | 264 (70.6) | 175 | 29 (16.6) | 146 (83.4) | ||
| Female | 380 | 150 (39.5) | 230 (60.5) | 145 | 13 (9.0) | 132 (91.0) | ||
| Age (years) | 0.357 | 0.762 | ||||||
| < 60 | 350 | 127 (36.3) | 223 (63.7) | 164 | 28 (17.1) | 136 (82.9) | ||
| ≥60 | 404 | 133 (32.9) | 271 (67.1) | 156 | 24 (15.4) | 132 (84.6) | ||
| Histology | < 0.01 | 0.268 | ||||||
| Adenocarcinoma | 623 | 241 (38.7) | 382 (61.3) | 255 | 36 (14.1) | 219 (85.9) | ||
| Squamous | 91 | 9 (10.0) | 82 (90.0) | 26 | 2 (7.7) | 24 (92.3) | ||
| Adenosquamous | 1 | 0 (0.0) | 1 (100.0) | 1 | 0 (0.0) | 1 (100.0) | ||
| Others* | 39 | 10 (25.6) | 29 (74.4) | 38 | 4 (10.5) | 34 (89.5) | ||
2基于EGFR和KRAS基因分型的单因素分析。A:基于EGFR和KRAS分型的年龄分布情况;B:基于EGFR和KRAS分型的性别分布情况;C:基于EGFR和KRAS分型的病理类型分布情况。
Single factor analysis based on EGFR and KRAS genotype. A: Age distribution based on EGFR and KRAS genotype; B: Gender distribution based on EGFR and KRAS genotype; C: Pathological type based on EGFR and KRAS genotype.