A long non-coding RNA signature to improve prognosis prediction of gastric cancer. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Zhu, Xiaoqiang; Tian, Xianglong; Yu, Chenyang; Shen, Chaoqin; Yan, Tingting; Hong, Jie; Wang, Zheng; Fang, Jing-Yuan; Chen, Haoyan
• Is Part Of: Molecular cancer. Volume 15:Issue 1(2016)
• Page Start: 1
• Page End: 16
• Publication Date: 2016-12
• Subjects: Gastric cancer -- LncRNAs -- LNR -- GSEA -- Survival; Cancer -- Molecular aspects -- Periodicals; 616.994
• DOI: 10.1186/s12943-016-0544-0
• Languages: English
• ISSNs: 1476-4598

Abstract

Abstract Background Increasing evidence suggests long non-coding RNAs (lncRNAs) are frequently aberrantly expressed in cancers, however, few related lncRNA signatures have been established for prediction of cancer prognosis. We aimed at developing alncRNA signature to improve prognosis prediction of gastric cancer (GC). Methods Using a lncRNA-mining approach, we performed lncRNA expression profiling in large GC cohorts from Gene Expression Ominus (GEO), including GSE62254 data set (N = 300) and GSE15459 data set (N = 192). We established a set of 24-lncRNAs that were significantly associated with the disease free survival (DFS) in the test series. Results Based on this 24-lncRNA signature, the test series patients could be classified into high-risk or low-risk subgroup with significantly different DFS (HR = 1.19, 95 % CI = 1.13–1.25, P < 0.0001). The prognostic value of this 24-lncRNA signature was confirmed in the internal validation series and another external validation series, respectively. Further analysis revealed that the prognostic value of this signature was independent of lymph node ratio (LNR) and postoperative chemotherapy. Gene set enrichment analysis (GSEA) indicated that high risk score group was associated with several cancer recurrence and metastasis associated pathways. Conclusions The identification of the prognostic lncRNAs indicates the potential roles of lncRNAs in GC biogenesis. Our results may provide an efficient classification tool for clinical prognosis evaluation of GC.