Integrated Analysis of a Risk Score System Predicting Prognosis and a ceRNA Network for Differentially Expressed lncRNAs in Multiple Myeloma

Long non-coding RNAs (lncRNAs) are non-protein-coding RNAs longer than 200 nucleotides. Accumulating evidence demonstrates that lncRNA is a potential biomarker for cancer diagnosis and prognosis. However, there are no prognostic biomarkers and lncRNA models for multiple myeloma (MM). Hence, it is necessary to screen novel lncRNA that can potentially participate in the initiation and progression of MM and consequently construct a risk score system for the disease. Raw microarray datasets were obtained from the Gene Expression Omnibus website. Weighted gene co-expression network analysis and principal component analysis identified 12 lncRNAs of interest. Then, univariate, least absolute shrinkage and selection operator Cox regression and multivariate Cox hazard regression analysis identified two lncRNAs (LINC00996 and LINC00525) that were formulated to construct a risk score system to predict survival. Receiver operating characteristic analysis certificated the superior performance in predicting 3-year overall survival (area under the curve = 0.829). The similar prognostic values of the two-lncRNA signature were also observed in the tested The Cancer Genome Atlas dataset. Furthermore, two other lncRNAs (LINC00324 and LINC01128) were differentially expressed between CD138+ plasma cells from normal donors and MM patients and were verified to be associated with cancer stage in the Gene Expression Omnibus dataset. A lncRNA-mediated competing endogenous RNA network, including 2 lncRNAs, 12 mitochondrial RNAs, and 103 target messenger RNAs, was constructed. In conclusion, we developed a two-lncRNA expression signature to predict the prognosis of MM and constructed a key lncRNA-based competing endogenous RNA network in MM. These lncRNAs were associated with survival and are probably involved in the occurrence and progression of MM.