J Cancer 2022; 13(6):1796-1807. doi:10.7150/jca.69321 This issue
1. Sun Yat-sen University Cancer Center, Guangzhou, China.
2. State Key Laboratory of Oncology in South China, Guangzhou, China.
3. Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.
Purpose: This study aimed to explore a predictive risk-stratification model combing clinical characteristics and lipid profiles in multiple myeloma (MM) patients.
Methods: The data of 275 patients in Sun Yat-Sen University Cancer Center were retrospectively analyzed and randomly divided into the training (n = 138) and validation (n=137) cohorts. Triglyceride (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), lactate dehydrogenase (LDH), Apolipoprotein B (Apo B) and Apo B/Apolipoprotein A1 (Apo A1) ratio were the prognostic factors identified through univariate and multivariate Cox analysis.
Results: A 6-prognostic factor model was constructed based on Lasso regression. Patients were divided into low- and high-risk groups and the former group showed longer overall survival (OS) time (p<0.05). The area under the curve (AUC) of the risk score model for 5-and 10-year OS were 0.756 [95% CI: 0.661-0.850] and 0.940 [95% CI: 0.883-0.997], which exhibited better accuracy than International Staging System (ISS) and Durie and Salmon (DS) stage.
Conclusion: This study aims to combine the lipid metabolism profile with the clinical characteristics of MM patients to generate a prognostic model. The nomogram integrating ISS stage and risk score increased the prediction accuracy. This model can monitor lipid profile as a simple and effective method, which has certain clinical significance for improving the accuracy of the prognosis and exploring potential therapeutic targets.
Keywords: multiple myeloma, lipid profile, metabolism, prognostic model