Abstract
Two-pore domains potassium channel subunits, encoded by KCNK genes, play vital roles in breast cancer progression. However, the characteristics of most KCNK genes in breast cancer has yet to be clarified. In this study, we comprehensively analyzed the expression, alteration, prognosis, and biological functions of various KCNKs in breast cancer. The expression of KCNK1/4/6/9/10/13 were significantly upregulated, while KCNK2/3/5/7/17 were downregulated in breast cancer tissues compared to normal mammary tissues. Increased expression of KCNK1/3/4/9 was correlated with poor overall survival, while high expression of KCNK2/7/17 predicted better overall survival in breast cancer. Eight KCNK genes were altered in breast cancer patients with a genomic mutation rate ranged from 1.9% to 21%. KCNK1 and KCNK9 were the two most common mutations in breast cancer, occurred in 21% and 18% patients, respectively. Alteration of KCNK genes was associated with the worse clinical characteristics and higher TMB, MSI, and hypoxia score. Using machine learning method, a specific prognostic signature with seven KCNK genes was established, which manifested accuracy in predicting the prognosis of breast cancer in both training and validation cohorts. A nomogram with great predictive performance was afterwards constructed through incorporating KCNK-based risk score with clinical features. Furthermore, KCNKs were correlated with the activation of several tumor microenvironment cells, including T cells, mast cells, macrophages, and platelets. Presentation of antigen, stimulation of G protein signaling and toll-like receptor cascaded were regulated by KCNKs family. Taken together, KCNKs may regulate breast cancer progression via modulating immune response which can serve as ideal prognostic biomarkers for breast cancer patients. Our study provides novel insight for future studies evaluating their usefulness as therapeutic targets.