ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1549742

This article is part of the Research TopicHarnessing Big Data for Precision Medicine: Revolutionizing Diagnosis and Treatment StrategiesView all 39 articles

Comprehensive Analysis of Plasma Cell Heterogeneity and Immune Interactions in multiple Myeloma

Provisionally accepted
Shuang  QuShuang Qu1Zhihai  ZhengZhihai Zheng1Xiaoling  GuoXiaoling Guo2Jiaqi  MeiJiaqi Mei3Sicong  JiangSicong Jiang3Biyun  ChenBiyun Chen1*
  • 1Department of Hematology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China
  • 2Translational Medicine Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, Guangzhou, China
  • 3Department of Hematology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi Province, 330006, China, Nanchang, China

The final, formatted version of the article will be published soon.

Objective: This study focused on the role of plasma cells in multiple myeloma (MM) and the associated potential mechanisms. Transcriptomic data of MM and various gene sets from several public databases were downloaded for subsequent analyses. Through single-cell sequencing, 10 major cell types were identified and annotated. The differential gene expression and pathway enrichment between different plasma cell subtypes as well as cell communication analysis, transcriptional regulation analysis, and enrichment analysis in conjunction with the malignant subpopulation were performed. Next, the samples were clustered into two groups by applying nonnegative matrix factorization (NMF). Additional analysis revealed notable disparities in survival between the two clusters, correlation with genes involved in classical metabolic pathways and pathway dysregulation, thus confirming the stability and validity of the clustering. Subsequently, Weighted Gene Co-expression Network Analysis was performed and hub genes from the modules most strongly associated with the clustering groups were extracted. We then constructed a prognostic prediction model using Least Absolute Shrinkage and Selection Operator and multiCox regression 2 analysis. The predictive accuracy of the model was evaluated and robustness were confirmed in a separate validation cohort. The gene and pathway dysregulation for the two risk groups was analyzed. Ultimately, an investigation was conducted into the association between the risk model and various immunological features, in terms of antitumor immunotherapy, the tumor microenvironment, and immune checkpoints. This study provides an in-depth investigation into the potential mechanisms underlying MM development and offers new directions to improve therapeutic approaches and enhance patient outcomes.

Keywords: Multiple Myeloma, Plasma Cells, single-cell sequencing, Weighted gene coexpression network analysis, Tumor Microenvironment

Received: 21 Dec 2024; Accepted: 31 Mar 2025.

Copyright: © 2025 Qu, Zheng, Guo, Mei, Jiang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Biyun Chen, Department of Hematology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China

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