Comprehensive genomic characterization of programmed cell death-related genes to predict drug resistance and prognosis for patients with multiple myeloma.

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.