Multi-omics study identifies a four-gene obesity-related prognostic signature in multiple myeloma, indicating potential utility for survival risk stratification.
Key Points
To investigate the genetic interplay between obesity and multiple myeloma and develop an obesity-related multi-omics prognostic model for survival risk stratification.
Screened obesity-related differentially expressed genes in the GSE132604 discovery cohort and prognostic modules in the MMRF-CoMMpass training cohort using weighted gene co-expression network analysis.
Constructed a prognostic signature using univariate Cox regression and four machine-learning algorithms (StepCox, CoxBoost, Lasso, Random Survival Forest), validating performance on the GSE57317 cohort.
Characterized malignant plasma cell subtypes, cellular stemness, trajectories, and in silico virtual gene knockouts (scTenifoldKnk) using single-cell RNA sequencing data from the GSE199359 cohort.
Identified 39 obesity- and prognosis-related genes, refining them into a four-gene prognostic model consisting of MCM4, PHF19, CCT2, and MAGEA1.
The four-gene signature effectively stratified multiple myeloma patients into high- and low-risk categories, with the high-risk cohort demonstrating significantly poorer overall survival.
Single-cell analyses confirmed preferential expression of the four signature genes in malignant plasma cells, with virtual knockouts indicating their role in sustaining malignant transcriptional programs.