Research Paper Advance Articles
Comprehensive genomic characterization of programmed cell death-related genes to predict drug resistance and prognosis for patients with multiple myeloma
- 1 Hematology Department, Handan First Hospital, Handan 056001, China
- 2 Department of Hematology, Key Laboratory of Hematology of Hebei Province, Second Hospital of Hebei Medical University, Shijiazhuang 050000, China
- 3 MDS and MPN Centre, Institute of Haematology and Blood Diseases Hospital, Tianjin 300020, China
Received: October 17, 2024 Accepted: March 3, 2025 Published: April 1, 2025
https://doi.org/10.18632/aging.206234How to Cite
Copyright: © 2025 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Multiple myeloma (MM) is a cancer that is difficult to be diagnosed and treated. This study aimed to identify programmed cell death (PCD)-related molecular subtypes of MM and to assess their impact on patients’ prognosis, immune status, and drug sensitivity.
Methods: We used the ConsensusClusterPlus method to classify molecular subtypes with prognostically relevant PCD genes from the MM patients screened. A prognostic model and a nomogram were established applying one-way COX regression analysis and LASSO Cox regression analysis. MM patients’ sensitivity to chemotherapeutic agents was predicted for at-risk populations.
Results: Six molecular subtypes were classified employing PCD-related genes, notably, three of them had a higher tendency for immune escape and two of them were correlated with a worse prognosis of MM. Furthermore, the C3 subtype had activated pathways such as oxidative phosphorylation and DNA repair, while the C2 and C4 subtypes had activated pathways related to apoptosis. The Risk score showed that the nomogram can correctly predict the OS for MM patients, in particular, patients in the high-risk group had low overall survival (OS). Pharmacovigilance analyses revealed that patients in the high-risk and low-risk groups had greater IC50 values for the drugs SB505124_1194 and AZD7762_1022, respectively.
Conclusions: A 12-gene Risk score model developed with PCD-related genes can accurately predict the survival for MM patients. Our study provided potential targets and strategies for individualized treatment of MM.
Abbreviations
MM: Multiple myeloma; PCD: Programmed cell death; MsigDB: Molecular signatures database; RNA-seq: Transcriptome sequencing; KEGG: Kyoto encyclopedia of genes and genomes; CC: Consensus clustering; CTL: Tumour-infiltrating cytotoxic T lymphocyte; CAF: Tumour-associated fibroblasts; TAM: M2 subtype tumour-associated macrophage; MDSC: Myeloid-derived suppressor cells; KM: Kaplan-Meier; GDSC: Drug Sensitivity in Cancer; IC50: Inhibitory concentration; AUC: Area under ROC curve; DCA: Decision curve analysis; GEO: Gene Expression Omnibus; LASSO: Least Absolute Shrinkage and Selection Operator; OS: Overall Survival; ROC: Receiver Operating Characteristic analysis; GSVA: Gene Set Variant analysis; ssGSEA: single-sample gene set enrichment analysis; TCGA: The Cancer Genome Atlas; TIDE: Tumor Immune Dysfunction and Exclusion; ICIs: Immune Checkpoint Inhibitors.