Expression Levels of KMT2C and SLC20A1 Identified by Information-theoretical Analysis Are Powerful Prognostic Biomarkers in Estrogen Receptor-positive Breast Cancer

Abstract

Introduction: In general, it has been considered that estrogen receptor-positive (ER⁺) breast cancer has a good prognosis and is responsive to endocrine therapy. However, one third of patients with ER⁺ breast cancer exhibit endocrine therapy resistance, and many patients develop recurrence and die 5 to 10 years after diagnosis. In ER⁺ breast cancer, a major problem is to distinguish those patients most likely to develop recurrence or metastatic disease within 10 years after diagnosis from those with a sufficiently good prognosis.

Materials and methods: We downloaded the messenger RNA expression data and the clinical information for 401 patients with ER⁺ breast cancer from the cBioPortal for Cancer Genomics. An information-theoretical approach was used to identify the prognostic factors for survival in patients with ER⁺ breast cancer and to classify those patients according to the prognostic factors.

Results: The information-theoretical approach contributed to the identification of KMT2C and SLC20A1 as prognostic biomarkers in ER⁺ breast cancer. We found that low KMT2C expression was associated with a poor outcome and high SLC20A1 expression was associated with a poor outcome. Both levels of KMT2C and SLC20A1 expression were significantly and strongly associated with the differentiation of survival. The 10-year survival rate for ER⁺ patients with low KMT2C and high SLC20A1 expression was 0%. In contrast, for ER⁺ patients with high KMT2C and low SLC20A1 expression, the 10-year survival rate was 86.78%.

Conclusion: Our results strongly suggest that clinical examination of the expression of both KMT2C and SLC20A1 in ER⁺ breast cancer will be very useful for the determination of prognosis and therapy.

Keywords: Classification; ER(+) breast cancer; Gene expression; Information theory; Prognosis.