Expression profiling of ion channel genes predicts clinical outcome in breast cancer.
BACKGROUND
Ion channels play a critical role in a wide variety of biological processes, including the development of human cancer. However, the overall impact of ion channels on tumorigenicity in breast cancer remains controversial.


METHODS
We conduct microarray meta-analysis on 280 ion channel genes. We identify candidate ion channels that are implicated in breast cancer based on gene expression profiling. We test the relationship between the expression of ion channel genes and p53 mutation status, ER status, and histological tumor grade in the discovery cohort. A molecular signature consisting of ion channel genes (IC30) is identified by Spearman's rank correlation test conducted between tumor grade and gene expression. A risk scoring system is developed based on IC30. We test the prognostic power of IC30 in the discovery and seven validation cohorts by both Cox proportional hazard regression and log-rank test.


RESULTS
22, 24, and 30 ion channel genes are found to be differentially expressed with a change in p53 mutation status, ER status, and tumor histological grade in the discovery cohort. We assign the 30 tumor grade associated ion channel genes as the IC30 gene signature. We find that IC30 risk score predicts clinical outcome (P < 0.05) in the discovery cohort and 6 out of 7 validation cohorts. Multivariate and univariate tests conducted in two validation cohorts indicate that IC30 is a robust prognostic biomarker, which is independent of standard clinical and pathological prognostic factors including patient age, lymph node status, tumor size, tumor grade, estrogen and progesterone receptor status, and p53 mutation status.


CONCLUSIONS
We identified a molecular gene signature IC30, which represents a promising diagnostic and prognostic biomarker in breast cancer. Our results indicate that information regarding the expression of ion channels in tumor pathology could provide new targets for therapy in human cancers.
Department of Physiology, College of Medicine, Chung-Ang University, Seoul 156-756, South Korea. haena@cau.ac.kr.
Jae-Hong
JH
Ko
Eun A
EA
Ko
Wanjun
W
Gu
Inja
I
Lim
Hyoweon
H
Bang
Tong
T
Zhou
D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D001943:Breast Neoplasms / Q000378:metabolism* / Q000401:mortality; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D007473:Ion Channels / Q000235:genetics / Q000378:metabolism*; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D015999:Multivariate Analysis; D009154:Mutation; D060787:Neoplasm Grading; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone; D018709:Statistics, Nonparametric; D059467:Transcriptome; D016159:Tumor Suppressor Protein p53 / Q000235:genetics
D014408:Biomarkers, Tumor; D007473:Ion Channels; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53