Integrative transcriptomic and clinical cohort analysis identifies immunometabolic prognostic signatures in multiple myeloma

Authors

  • Song Jin Author
  • Lijun Song Author
  • Qian Xu Author
  • Tiantian Zhang Author
  • Li Wang Author
  • Yuping Wei Author
  • Haitao Wang Senior Department of Hematology, The Fifth Medical Center of Chinese PLA General Hospital Author https://orcid.org/0000-0002-7102-7521
  • Chao Ma Author
  • Hongqi Yang Department of Health Economics, The Second Medical Center of Chinese PLA General Hospital Author

DOI:

https://doi.org/10.46701/jbbt260502

Keywords:

Multiple myeloma, Immunometabolism, Prognostic biomarkers, β2-microglobulin, LASSO regression

Abstract

Background

Multiple myeloma (MM) exhibits considerable clinical heterogeneity; however, current prognostic models inadequately capture the immunometabolic dysregulation central to its pathogenesis. This study aims to characterize the immunometabolic landscape of MM through integrative transcriptomic and clinical cohort analysis and to identify robust prognostic signatures.

Methods

Transcriptomic data from the GSE24080 dataset (325 MM patients, 234 healthy controls) were analyzed to identify differentially expressed genes (DEGs), perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, estimate immune cell infiltration using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), and develop a molecular classifier via least absolute shrinkage and selection operator (LASSO) regression. A clinical cohort of 101 newly diagnosed MM patients was retrospectively enrolled to validate the clinical relevance of immunometabolic biomarkers. Baseline characteristics were compared across International Staging System (ISS) stages, Spearman correlation analysis was performed to explore inter-relationships among biomarkers, and Cox regression models were constructed to identify independent prognostic factors. Kaplan-Meier survival curves were generated for ISS stages and β2-microglobulin (β2-MG) risk categories.

Results

Transcriptomic analysis identified 342 upregulated and 112 downregulated DEGs in MM, with significant enrichment in the p53 and FOXO signaling pathways. Immune deconvolution revealed decreased naive B cells, memory B cells, and CD8⁺ T cells, alongside increased plasma cells, regulatory T cells (Tregs), monocytes, and macrophages in MM samples. Consensus clustering defined three distinct immunometabolic subtypes, and a 12-gene LASSO classifier demonstrated excellent diagnostic performance (area under the curve [AUC] = 0.979). In the clinical cohort (median age 63.2 years; 60% male), advanced ISS stage was associated with elevated β2-MG levels (p < 0.001), reduced hemoglobin (HGB) concentration (p < 0.001), and an increased bone marrow plasma cell percentage (BMPC) (p = 0.006). Spearman analysis revealed strong negative correlations between β2-MG and HGB (ρ = −0.70, p < 0.001) as well as between BMPC and HGB (ρ = −0.48, p < 0.001). Multivariate Cox regression identified age (HR = 1.041, 95% confidence interval [CI]: 1.004–1.079, p = 0.029), β2-MG (hazard ratio [HR] = 1.065, 95% CI: 1.022–1.110, p = 0.0027), and HGB (HR = 0.978, 95% CI: 0.962–0.994, p = 0.0089) as independent prognostic factors. Kaplan-Meier analysis demonstrated significant survival stratification by both ISS stages and β2-MG risk categories (log-rank p < 0.001 for both).

Conclusions

These findings suggest that β2-MG and HGB, as surrogates of immunometabolic disturbance-driven tumor burden and hematopoietic suppression, offer potential avenues for improved risk stratification and the development of personalized therapeutic strategies in MM.

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2026-05-27

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The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

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Vol. 1 No. 1 (2026): Journal of Blood and Biotherapy

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