Construction of a prognosis‑associated long noncoding RNA‑mRNA network for multiple myeloma based on microarray and bioinformatics analysis.

Abstract

At present, the association between prognosis-associated long noncoding RNAs (lncRNAs) and mRNAs is yet to be reported in multiple myeloma (MM). The aim of the present study was to construct prognostic models with lncRNAs and mRNAs, and to map the interactions between these lncRNAs and mRNAs in MM. LncRNA and mRNA data from 559 patients with MM were acquired from the Genome Expression Omnibus (dataset GSE24080), and their prognostic values were calculated using the survival package in R. Multivariate Cox analysis was used on the top 20 most significant prognosis-associated mRNAs and lncRNAs to develop prognostic signatures. The performances of these prognostic signatures were tested using the survivalROC package in R, which allows for time-dependent receiver operator characteristic (ROC) curve estimation. Weighted correlation network analysis (WGCNA) was conducted to investigate the associations between lncRNAs and mRNAs, and a lncRNA-mRNA network was constructed using Cytoscape software. Univariate Cox regression analysis identified 39 lncRNAs and 1,445 mRNAs that were significantly associated with event-free survival of MM patients. The top 20 most significant survival-associated lncRNAs and mRNAs were selected as candidates for analyzing independent MM prognostic factors. Both signatures could be used to separate patients into two groups with distinct outcomes. The areas under the ROC curves were 0.739 for the lncRNA signature and 0.732 for the mRNA signature. In the lncRNA-mRNA network, a total of 143 mRNAs were positively or negatively associated with 23 prognosis-associated lncRNAs. NCRNA00201, LOC115110 and RP5-968J1.1 were the most dominant drivers. The present study constructed a model that predicted prognosis in MM and formed a network with the corresponding prognosis-associated mRNAs, providing a novel perspective for the clinical diagnosis and treatment of MM, and suggesting novel directions for interpreting the mechanisms underlying the development of MM.