Identification and validation of potential prognostic lncRNA biomarkers for predicting survival in patients with multiple myeloma

Meng Zhou, Hengqiang Zhao, Zhenzhen Wang, Liang Cheng, Lei Yang, Hongbo Shi, Haixiu Yang, Jie Sun

Dysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated. LncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis. Four lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR = 2.718, 95 % CI = 1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (Sβ2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM. Our results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.