Previous studies have revealed that upregulation of interleukin 15 receptor α (IL15RA) contributes to improved prognosis of breast cancer. The present study aimed to elucidate the molecular mechanisms underlying the antitumor effect induced by IL15RA upregulation, and to identify a gene signature capable of predicting the survival of patients with breast cancer. Using paired gene expression and methylation data of breast cancer samples from The Cancer Genome Atlas data portal, differentially expressed genes (DEGs) were identified in hypermethylated and hypomethylated IL15RA breast cancer samples. Furthermore, a gene signature-based risk-scoring model was developed according to the Cox regression coefficients of survival-associated DEGS. The gene signature was applied to classify patients with breast cancer and hypermethylated IL15RA into two risk groups via Kaplan-Meier survival analysis of overall survival (OS) time. Functional enrichment analysis was conducted to decipher the biological roles of the DEGs between the two risk groups. A total of 326 DEGs were present in the hypomethylation and hypermethylation samples compared with in the normal samples. A four-gene signature [SH3 and cysteine rich domain 2 (STAC2), proline rich 11 (PRR11), homeobox C11 (HOXC11) and nucleolar and spindle associated protein 1 (NUSAP1)] was identified as able to successfully separate patients with breast cancer and hypermethylated IL15RA into two risk groups with significantly different OS time. The signature revealed similar predictive performance in an independent set. Significant enrichment of the 'receptor interaction' and 'cell adhesion molecules (CAM)' pathways, which involved the DEGs, occurred between the two risk groups. These findings suggested that IL15RA may participate in the regulation of STAC2, PRR11, HOXC11, NUSAP1, and 'ECM-receptor interaction' and 'cell adhesion molecules' pathways, and therefore in the suppression of breast cancer development and progression. The four-gene signature may have potential prognostic value for breast cancer.
Keywords: Gene Ontology; hypermethylation; pathway; risk score; survival.