title: Expression Levels of KMT2C and SLC20A1 Identified by Information-theoretical Analysis Are Powerful Prognostic Biomarkers in Estrogen Receptor-positive Breast Cancer.
abstract: 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.
  
  
  
  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.
  
  
  
  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%.
  
  
  
  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.
authors.affiliation: Department of Information Sciences, Faculty of Science and Technology, Tokyo University of Science, Chiba, Japan. Electronic address: keiko@is.noda.tus.ac.jp.
authors.forename: Keiko
authors.initials: K
authors.lastname: Sato
authors.affiliation: Department of Medicinal and Life Sciences, Faculty of Pharmaceutical Sciences, Tokyo University of Science, Chiba, Japan.
authors.forename: Kazunori
authors.initials: K
authors.lastname: Akimoto
mesh_terms: D014408:Biomarkers, Tumor / Q000378:metabolism*; D001943:Breast Neoplasms / Q000378:metabolism* / Q000473:pathology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008207:Lymphatic Metastasis; D051788:Myeloid-Lymphoid Leukemia Protein / Q000378:metabolism*; D009364:Neoplasm Recurrence, Local / Q000378:metabolism / Q000473:pathology; D011379:Prognosis; D011960:Receptors, Estrogen / Q000378:metabolism*; D050611:Sodium-Phosphate Cotransporter Proteins, Type III / Q000378:metabolism*; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen; C493586:SLC20A1 protein, human; D050611:Sodium-Phosphate Cotransporter Proteins, Type III; D051788:Myeloid-Lymphoid Leukemia Protein
keywords: Classification; ER(+) breast cancer; Gene expression; Information theory; Prognosis