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Original Article

Korean J Physiol Pharmacol 2019; 23(6): 529-537

Published online November 1, 2019 https://doi.org/10.4196/kjpp.2019.23.6.529

Copyright © Korean J Physiol Pharmacol.

Expression of potassium channel genes predicts clinical outcome in lung cancer

Eun-A Ko1, Young-Won Kim2, Donghee Lee2, Jeongyoon Choi2, Seongtae Kim2, Yelim Seo2, Hyoweon Bang2, Jung-Ha Kim3, and Jae-Hong Ko2,*

1Department of Physiology and Cell Biology, University of Nevada, Reno School of Medicine, Reno, Nevada 89557, USA, 2Department of Physiology, College of Medicine, Chung-Ang University, Seoul 06974, 3Department of Family Medicine, Chung-Ang University Hospital, College of Medicine, Chung-Ang University, Seoul 06973, Korea

Correspondence to:Jae-Hong Ko
E-mail: akdongyi01@cau.ac.kr

Received: September 6, 2019; Revised: October 2, 2019; Accepted: October 2, 2019

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


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: Biomarker, Gene expression, K+ channel, Lung cancer, Molecular signature