The Korean Journal of Physiology and Pharmacology

The Korean Society of Pharmacology (대한약리학회)
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Expression of potassium channel genes predicts clinical outcome in lung cancer

  • Ko, Eun-A (Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine) ;
  • Kim, Young-Won (Department of Physiology, College of Medicine, Chung-Ang University) ;
  • Lee, Donghee (Department of Physiology, College of Medicine, Chung-Ang University) ;
  • Choi, Jeongyoon (Department of Physiology, College of Medicine, Chung-Ang University) ;
  • Kim, Seongtae (Department of Physiology, College of Medicine, Chung-Ang University) ;
  • Seo, Yelim (Department of Physiology, College of Medicine, Chung-Ang University) ;
  • Bang, Hyoweon (Department of Physiology, College of Medicine, Chung-Ang University) ;
  • Kim, Jung-Ha (Department of Family Medicine, Chung-Ang University Hospital, College of Medicine, Chung-Ang University) ;
  • Ko, Jae-Hong (Department of Physiology, College of Medicine, Chung-Ang University)
  • Received : 2019.09.06
  • Accepted : 2019.10.02
  • Published : 2019.11.01
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Abstract

Lung cancer is the most common cause of cancer deaths worldwide and several molecular signatures have been developed to predict survival in lung cancer. Increasing evidence suggests that proliferation and migration to promote tumor growth are associated with dysregulated ion channel expression. In this study, by analyzing high-throughput gene expression data, we identify the differentially expressed $K^+$ channel genes in lung cancer. In total, we prioritize ten dysregulated $K^+$ channel genes (5 up-regulated and 5 down-regulated genes, which were designated as K-10) in lung tumor tissue compared with normal tissue. A risk scoring system combined with the K-10 signature accurately predicts clinical outcome in lung cancer, which is independent of standard clinical and pathological prognostic factors including patient age, lymph node involvement, tumor size, and tumor grade. We further indicate that the K-10 potentially predicts clinical outcome in breast and colon cancers. Molecular signature discovered through $K^+$ gene expression profiling may serve as a novel biomarker to assess the risk in lung cancer.

Keywords

References

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