Identification of a five-lncRNA signature for predicting the risk of tumor recurrence in patients with breast cancer

Abstract

Long non-coding RNAs (lncRNAs) are a major class of non-coding RNAs, and the functional deregulations of lncRNAs have been shown to be associated with the development and progression of BC. In this work, we conduct an integrative analysis on five re-annotated lncRNA expression datasets from the Gene Expression Omnibus (GEO) which included a total of 891 BC samples. We identified a five-lncRNA signature that was significantly associated with DFS in the training cohort of 327 patients. We found the five-lncRNA signature could effectively stratify patients in the training dataset into high- and low-risk groups with significantly different DFS (p = 3.29 × 10−5, log-rank test). The five-lncRNA signature was effectively validated in four independent cohorts, and prognostic analysis results showed that the five-lncRNA signature was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments. Furthermore, GSEA suggested that the five-lncRNA signature was involved in BC metastasis-related pathways. Our findings indicate that these five lncRNAs may be implicated in BC pathogenesis, and further, these lncRNAs may potentially serve as novel candidate biomarkers for the identification of BC patients at high risk for tumor recurrence.