Identification of methylation sites and signature genes with prognostic value for luminal breast cancer.

BACKGROUND: Robust and precise molecular prognostic predictors for luminal breast cancer are required. This study aimed to identify key methylation sites in luminal breast cancer, as well as precise molecular tools for predicting prognosis. METHODS: We compared methylation levels of normal and luminal breast cancer samples from The Cancer Genome Atlas dataset. The relationships among differentially methylated sites, corresponding mRNA expression levels and prognosis were further analysed. Differentially expressed genes in normal and cancerous samples were analysed, followed by the identification of prognostic signature genes. Samples were divided into low- and high-risk groups based on the signature genes. Prognoses of low- and high-risk groups were compared. The Gene Expression Omnibus dataset were used to validate signature genes for prognosis prediction. Prognosis of low- and high-risk groups in Luminal A and Luminal B samples from the TCGA and the Metabric cohort dataset were analyzed. We also analysed the correlation between clinical features of low- and high- risk groups as well as their differences in gene expression. RESULTS: Fourteen methylation sites were considered to be related to luminal breast cancer prognosis because their methylation levels, mRNA expression and prognoses were closely related to each other. The methylation level of SOSTDC1 was used to divide samples into hypo- and hyper-methylation groups. We also identified an mRNA signature, comprising eight transcripts, ESCO2, PACSIN1, CDCA2, PIGR, PTN, RGMA, KLK4 and CENPA, which was used to divide samples into low- and high-risk groups. The low-risk group showed significantly better prognosis than the high-risk group. A correlation analysis revealed that the risk score was an independent prognostic factor. Low- and high- risk groups significantly correlated with the survival ratio in Luminal A samples, but not in Luminal B samples on the basis of the TCGA and the Metabric cohort dataset. Further functional annotation demonstrated that the differentially expressed genes were mainly involved in cell cycle and cancer progression. CONCLUSIONS: We identified several key methylation sites and an mRNA signature for predicting luminal breast cancer prognosis. The signature exhibited effective and precise prediction of prognosis and may serve as a prognostic and diagnostic marker for luminal breast cancer.