| Title |
Effects of CYP3A5 genetic polymorphism and smoking on the prognosis of non-small-cell lung cancer
|
|---|---|
| Published in |
OncoTargets and therapy, March 2016
|
| DOI | 10.2147/ott.s94144 |
| Pubmed ID | |
| Authors |
Li-Peng Jiang, Zhi-Tu Zhu, Chun-Yan He |
| Abstract |
We aimed to explore the impacts of the rs776746 polymorphism in the CYP3A5 gene and smoking on the prognosis of non-small-cell lung cancer (NSCLC). Our study enrolled 104 early NSCLC patients undergoing surgery and 107 advanced NSCLC patients undergoing chemotherapy, hospitalized between December 2009 and December 2012 at the First Affiliated Hospital of Liaoning Medical University. All subjects with complete follow-up data were pathologically diagnosed. The rs776746 polymorphism and different genotypes (*1/*1, *1/*3, and *3/*3) were identified by polymerase chain-reaction restriction fragment-length polymorphism. Clinical response to chemotherapy in NSCLC patients with *1/*1 + *1/*3 genotypes were significantly worse than in those with the *3/*3 genotype (17.78% vs 56.45%, P<0.001), and after Bonferroni adjustment, the differences still showed significance (P c<0.01). The mortality risk of NSCLC patients undergoing chemotherapy with the *3/*3 genotype was 0.617 times those with *1/*1 + *1/*3 genotypes (relative risk [RR] 0.617, 95% confidence interval [CI] 0.402-0.948; P=0.028), while the mortality risk of smoking patients was 1.743 times greater than that of nonsmoker patients (RR 1.743, 95% CI 1.133-2.679; P=0.042). Furthermore, a 3.087-fold mortality risk was found in NSCLC patients undergoing surgery with the *3/*3 genotype compared with those with *1/*1 + *1/*3 genotypes (RR 3.087, 95% CI 1.197-7.961; P=0.020). In NSCLC patients undergoing surgery, the mortality risk of smokers was 1.896 times greater than nonsmokers (RR 1.896, 95% CI 1.040-3.455; P=0.037). Our study demonstrated that the CYP3A5 rs776746 polymorphism and smoking may influence the prognosis of NSCLC patients undergoing chemotherapy and surgery. |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 2 | 20% |
| Student > Doctoral Student | 1 | 10% |
| Student > Ph. D. Student | 1 | 10% |
| Student > Master | 1 | 10% |
| Researcher | 1 | 10% |
| Other | 1 | 10% |
| Unknown | 3 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 30% |
| Biochemistry, Genetics and Molecular Biology | 2 | 20% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 10% |
| Medicine and Dentistry | 1 | 10% |
| Unknown | 3 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2016.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from OncoTargets and therapy
#2,078
of 3,016 outputs
Outputs of similar age
#269,141
of 312,601 outputs
Outputs of similar age from OncoTargets and therapy
#75
of 108 outputs
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