System Maintenance Notice

Please note that maintenance work is scheduled between 4 AM and 5 AM - CET. During this time, you may experience reduced performance. Thanks for your patience.

Publications

Journal Article Epublish

PubMed: 40546417 DOI: 10.2147/BLCTT.S517354

A Monocyte-Driven Prognostic Model for Multiple Myeloma: Multi-Omics and Machine Learning Insights.

Blood Lymphat Cancer 15:21-37

Authors: Linzhi Xie, Meng Gao, Shiming Tan, Yi Zhou, Jing Liu, Liwen Wang, Xin Li

Affiliations

  • Department of Hematology, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.
  • Department of Blood Transfusion, Third Xiangya Hospital, Central South University, Changsha, Hunan, People's Republic of China.

Abstract

Background

Multiple myeloma (MM) is a haematological malignancy, driven by complex interactions between tumor and immune cells. Nevertheless, the overall pattern of immune cells and MM pathogenesis within the bone marrow tumor microenvironment (BM-TME) remains underexplored.

Methods and Results

Firstly, we performed Mendelian Randomization analysis for 731 immunocyte phenotypes and MM, identifying 21 immune traits significantly associated with increased MM risk (OR>1, P FDR<0.05). Flow cytometry analysis confirmed that the MFI of CD14 (p<0.01) and HLA-DR (p<0.05) on CD14 + monocytes was significantly elevated in early-stage MM. Secondly, we analyzed monocytes gene characteristics in the MM BM-TME via scRNA-seq, identifying 1,447 differentially expressed genes (moDEGs) (p<0.05). Subsequently, based on 482 prognostic moDEGs, we developed and validated an optimal model, termed the Monocyte-related Gene Prognostic Signature (MGPS), by integrating 101 predictive models generated from 10 machine learning algorithms across multiple transcriptome sequencing datasets. MGPS was found to be an independent prognostic factor for MM (HR 2.72, 95% CI: 1.84-4.0, p<0.001), and the MGPS-based nomogram exhibits robust and reliable predictive performances. Next, MM patients with the low MGPS score exhibiting significantly better overall survival (OS) than the high MGPS score (p<0.0001). Finally, we evaluated the predictive value of MGPS for treatment response and explored its molecular mechanisms. Results indicated that low-risk patients are more likely to benefit from immunotherapy, while a high MGPS score reflects cellular functional impairment.

Conclusion

Our findings reveal a complex interplay between immune cells and MM. Through multi-omics analyses and machine learning algorithms, we established a robust monocyte-related prognostic signature. By identifying high-risk patients, MGPS may help refine treatment strategies, such as intensifying immunomodulatory therapies, potentially improving survival and immunotherapy outcomes for MM patients.

MeSH

Cells, Genes, Monocytes, Neoplasms, Multiple Chronic Conditions, Algorithms, Immunotherapy, Machine Learning, Multiple Myeloma, Therapeutics, Learning, Machine Learning Algorithms