↓ Skip to main content

Oncotarget

Article Metrics

Driver genes in non-small cell lung cancer: Characteristics, detection methods, and targeted therapies

Overview of attention for article published in Oncotarget, April 2017
Altmetric Badge

Citations

dimensions_citation
15 Dimensions

Readers on

mendeley
102 Mendeley
Title
Driver genes in non-small cell lung cancer: Characteristics, detection methods, and targeted therapies
Published in
Oncotarget, April 2017
DOI 10.18632/oncotarget.17016
Pubmed ID
Authors

Qing-Ge Zhu, Shi-Ming Zhang, Xiao-Xiao Ding, Bing He, Hu-Qin Zhang

Abstract

Lung cancer is one of the most common causes of cancer-related death in the world. The large number of lung cancer cases is non-small cell lung cancer (NSCLC), which approximately accounting for 75% of lung cancer. Over the past years, our comprehensive knowledge about the molecular biology of NSCLC has been rapidly enriching, which has promoted the discovery of driver genes in NSCLC and directed FDA-approved targeted therapies. Of course, the targeted therapies based on driver genes provide a more exact option for advanced non-small cell lung cancer, improving the survival rate of patients. Now, we will review the landscape of driver genes in NSCLC including the characteristics, detection methods, the application of target therapy and challenges.

Mendeley readers

The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 102 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 17%
Student > Master 16 16%
Student > Ph. D. Student 15 15%
Student > Bachelor 11 11%
Student > Doctoral Student 8 8%
Other 17 17%
Unknown 18 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 38 37%
Medicine and Dentistry 21 21%
Agricultural and Biological Sciences 10 10%
Engineering 4 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 2%
Other 5 5%
Unknown 22 22%