An Interactive Nomogram Based on Clinical and Molecular Signatures to Predict Prognosis in Multiple Myeloma Patients

Although novel drugs and treatments have been developed and improved, multiple myeloma (MM) is still recurrent and difficult to cure. In the present study, the magenta module containing 400 hub genes was determined from the training dataset of GSE24080 through weighted gene co-expression network analysis (WGCNA). Then, using the least absolute shrinkage and selection operator (Lasso) analysis, a fifteen-gene signature was firstly selected and the predictive performance for overall survival (OS) was favorable, which was identified by Receiver Operating Characteristic (ROC) curves. The risk score model was constructed based on survival-associated fifteen genes from the Lasso model, which classified MM patients into high-risk and low-risk groups. Areas under the curve (AUC) of ROC curve and log-rank test showed that the high-risk group was correlated to the dismal survival outcome of MM patients, which was also identified in testing dataset of GSE9782. The calibration plot, the AUC value of the ROC curve and Concordance-index showed that the interactive nomogram with risk score could favorably predict the probability of multi-year OS of MM patients. Therefore, it may help clinicians make a precise therapeutic decision based on the easy-to-use tool of the nomogram.