Research Paper Volume 13, Issue 14 pp 18442—18463
An interactive nomogram based on clinical and molecular signatures to predict prognosis in multiple myeloma patients
- 1 Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
- 2 Department of Pharmacy, The Second Xiangya Hospital, Central South University, Institute of Clinical Pharmacy, Central South University, Changsha, China
- 3 Department of Biochemistry and Molecular Biology, School of Life Sciences, Central South University, Changsha, China
- 4 Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China
- 5 Institute for Rational and Safe Medication Practices, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
Received: January 30, 2021 Accepted: June 23, 2021 Published: July 14, 2021https://doi.org/10.18632/aging.203294
How to Cite
Copyright: © 2021 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
MM: multiple myeloma; WGCNA: weighted gene co-expression network analysis; Lasso: least absolute shrinkage and selection operator; ROC: receiver operating characteristic; K-M: Kaplan-Meier; AUC: Areas under the curve; ISS: International Staging System; DSS: Durian Salmon Staging; TOM: topological overlap matrix; dissTOM: dissimilarity TOM; GS: gene significance; C-index: Concordance-index; LDH: lactate dehydrogenase; CREAT: creatinine; Cyto_Abn: cytogenetic abnormalities; EFS: event-free survival; OS: overall survival; UNG2: uracil DNA glycosylase-2.