Histone Modification–Related Gene Signature Shows Prognostic Value in Multiple Myeloma

The histone modification–related (HMR) risk signature demonstrated independent prognostic value in patients with multiple myeloma, according to findings from a multicohort analysis that integrated genomic and transcriptomic data.1

According to investigators, despite the continuous emergence of novel therapeutic agents and the heterogeneity in pathogenesis, the prognosis for patients with multiple myeloma remains poor.2 The development of an HMR prognostic model may help mitigate this unmet need by improving risk stratification and informing individualized therapeutic approaches.1 The model not only improved predictive accuracy when combined with clinical parameters but also showed associations with genomic instability, the tumor immune microenvironment, and drug sensitivity, underscoring its potential relevance to disease biology and personalized therapeutic strategies.

“Our study developed and validated a novel HMR risk signature for multiple myeloma, which exhibited independent prognostic value across multiple cohorts. Integration of the HMR score with clinical parameters improved predictive accuracy and clinical decision benefit in both training and validation cohorts,” explained lead study author Juan Lyu, MD, of Shaoxing People’s Hospital in Zhejiang, China.

Study Overview

Gene expression and clinical data were obtained from GSE24080, GSE136337, GSE136324, and GSE2658 in the Gene Expression Omnibus (GEO) database. RNA sequencing and somatic mutation data were acquired from the MMRF-CoMMpass project via the Genomic Data Commons Data Portal. Patients were eligible if they had complete survival information and overall survival exceeding 1 month.

HMR genes were extracted, and after intersecting this gene set with those detected across the included datasets, 173 genes were selected for further evaluation.

Seven genes were incorporated into a multivariate Cox proportional hazards regression model to generate a prognostic risk score. For each patient, the HMR score was calculated as a linear combination of normalized expression levels weighted by multivariate Cox coefficients. The optimal cutoff for stratifying patients into high- and low-risk groups was determined using the survminer package based on a log-rank test. To ensure cross-cohort comparability, the quantile corresponding to the optimal cutoff in the training cohort was applied to the validation cohorts (GSE136337, GSE2658, and MMRF-CoMMpass).

The prognostic value of the HMR risk score was assessed using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) analysis. GSE136324, which included RNA from whole bone marrow samples, was further analyzed for immune infiltration.

GSEA Enrichment and Association Between HMR Risk Score and Tumor Mutation Burden

Differential expression analysis identified distinct gene expression profiles between high- and low-risk groups defined by HMR risk score. In the GSE24080 dataset, 269 differentially expressed genes (DEGs) were identified, including 216 upregulated and 53 downregulated genes. In the MMRF-CoMMpass dataset, 663 DEGs were identified, comprising 359 upregulated and 304 downregulated genes. Volcano plot visualization demonstrated a clear separation between upregulated and downregulated genes.

Gene Ontology (GO) enrichment revealed that biological processes were predominantly associated with chromosome segregation and nuclear division. In the cellular component category, enrichment was observed for spindle and chromosomal regions, whereas the molecular function category was significantly associated with tubulin binding and microtubule binding. KEGG pathway analysis identified cell cycle as the most significantly enriched pathway. Consistent with these findings, gene set enrichment analysis (GSEA) demonstrated that gene sets related to cell cycle regulation and cellular proliferation were significantly enriched in the high-risk group, supporting the association between the HMR score and increased proliferative activity.

Association Between HMR Risk Score and TMB

Survival analysis demonstrated that patients with higher tumor mutational burden (TMB) experienced significantly worse OS compared with those with lower TMB (P = .032), indicating that TMB serves as an adverse prognostic factor in multiple myeloma. Comparison of TMB between HMR-defined groups showed that high-risk patients exhibited a significantly elevated TMB compared with low-risk patients. Furthermore, a positive correlation was identified between TMB and HMR risk score (P = .00021), suggesting that genomic instability increases with higher HMR scores.

Additionally, analysis of somatic mutations in key multiple myeloma–associated genes revealed higher frequencies of KRAS, NRAS, and TP53 alterations in the high-risk group, with TP53 mutations particularly enriched.

References

1. Lyu J, Lyu S, Qian Y, Feng Y, Zheng Z, Zhang L. Identification and validation of a histone modification–related gene signature to predict the prognosis of multiple myeloma. Front Genet. 2025;16:1613631. doi:10.3389/fgene.2025.1613631
2. Karimi, F., Aghaei, M., Saki, N. et al. Impact of genetic polymorphisms on treatment outcomes of proteasome inhibitors and immunomodulatory drugs in multiple myeloma. Curr Treat Options Oncol. 2025;26:197-212. doi:10.1007/s11864-025-01295-8