A prognostic model for multiple myeloma based on lipid metabolism related genes.
10.11817/j.issn.1672-7347.2025.240592
- Author:
Zhengjiang LI
1
,
2
;
Liang ZHAO
3
;
Fangming SHI
1
;
Jiaojiao GUO
4
,
5
;
Wen ZHOU
1
,
6
Author Information
1. Cancer Research Institute, Xiangya School of Basic Medical Sciences, Central South University, Changsha
2. lzj0330@csu.edu.cn.
3. Department of Hematology, Xiangya Hospital, Central South University, Changsha 410008, China.
4. Department of Hematology, Xiangya Hospital, Central South University, Changsha 410008, China. jiaojiao_zhongnan@
5. com.
6. wenzhou@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
lipid metabolism;
machine learning;
multiple myeloma;
prognostic model;
risk stratification
- MeSH:
Humans;
Multiple Myeloma/mortality*;
Prognosis;
Lipid Metabolism/genetics*;
Transcriptome;
Machine Learning;
Male;
Female;
Gene Expression Profiling;
Algorithms
- From:
Journal of Central South University(Medical Sciences)
2025;50(4):517-530
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy, with disease progression driven by cytogenetic abnormalities and a complex bone marrow microenvironment. This study aims to construct a prognostic model for MM based on transcriptomic data and lipid metabolism related genes (LRGs), and to identify potential drug targets for high-risk patients to support clinical decision-making.
METHODS:In this study, 2 transcriptomic datasets covering 985 newly diagnosed MM patients were retrieved from the Gene Expression Omnibus (GEO) database. Univariate Cox regression and 101 machine learning algorithms were used for gene selection. An LRG-based prognostic model was constructed using Stepwise Cox (both directions) and random survival forest (RSF) algorithms. The association between the prognostic score and clinical events was evaluated, and model performance was assessed using time-dependent receiver operating characteristic (ROC) curves and the C-index. The added predictive value of combining prognostic scores with clinical variables and staging systems was also analyzed. Differentially expressed genes between high- and low-risk groups were identified using limma and clusterProfiler and subjected to pathway enrichment analysis. Drug sensitivity analysis was conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database and oncoPredict to identify potential therapeutic targets for high-risk patients. The functional role of key LRGs in the model was validated via in vitro cell experiments.
RESULTS:An LRG-based prognostic model (LRG17) was successfully developed using transcriptomic data and machine learning. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.962, 0.912, and 0.842 for 3-, 5-, and 7-year survival, respectively. Patients were stratified into high- and low-risk groups, with high-risk patients showing significantly shorter overall survival (OS) and event-free survival (EFS) (both P<0.001) and worse clinical profiles (e.g., lower albumin, higher β2-microglobulin and lactate dehydrogenase levels). Enrichment analysis revealed that high-risk patients were significantly enriched for pathways related to chromosome segregation and mitosis, whereas low-risk patients were enriched for immune response and immune cell activation pathways. Drug screening suggested that AURKA inhibitor BMS-754807 and FGFR3 inhibitor I-BET-762 may be more effective in high-risk patients. Functional assays demonstrated that silencing of key LRG PLA2G4A significantly inhibited cell viability and induced apoptosis.
CONCLUSIONS:LRGs serve as promising biomarkers for prognosis prediction and risk stratification in MM. The overexpression of chromosomal instability-related and high-risk genetic event-associated genes in high-risk patients may explain their poorer outcomes. Given the observed resistance to bortezomib and lenalidomide in high-risk patients, combination therapies involving BMS-754807 or I-BET-762 may represent effective alternatives.
