Evidence map›Paper›PMID 38916672›Full record

ArticleClinical and experimental medicine2024

Identification of lipid metabolism-related gene signature in the bone marrow microenvironment of multiple myelomas through deep analysis of transcriptomic data.

Dan Feng, Zhen Wang, Shengji Cao, Hui Xu, Shijun Li

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Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Dan FengDepartment of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Liaoning, Dalian, 116011, China.
Zhen WangDepartment of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Liaoning, Dalian, 116011, China.
Shengji CaoDepartment of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Liaoning, Dalian, 116011, China.
Hui XuDepartment of Clinical Laboratory, Harbin Medical University Cancer Hospital, Harbin, 150000, Heilongjiang, China.
Shijun LiDepartment of Clinical Laboratory, The First Affiliated Hospital of Dalian Medical University, Liaoning, Dalian, 116011, China. lishijun@dmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Dysregulated lipid metabolism in the bone marrow microenvironment (BMM) plays a vital role in multiple myeloma (MM) development, progression, and drug resistance. However, the exact mechanism by which lipid metabolism impacts the BMM, promotes tumorigenesis, and triggers drug resistance remains to be fully elucidated.By analyzing the bulk sequencing and single-cell sequencing data of MM patients, we identified lipid metabolism-related genes differential expression significantly associated with MM prognosis, referred to as LMRPgenes. Using a cohort of ten machine learning algorithms and 117 combinations, LMRPgenes predictive models were constructed. Further exploration of the effects of the model risk score (RS) on the survival status, immune status of patients with BMM, and response to immunotherapy was conducted. The study also facilitated the identification of personalized therapeutic strategies targeting specified risk categories within patient cohorts.Analysis of the scRNA-seq data revealed increased lipid metabolism-related gene enrichment scores (LMESs) in erythroblasts and progenitor, malignant, and Tprolif cells but decreased LMESs in lymphocytes. LMESs were also strongly correlated with most of the 50 hallmark pathways within these cell populations. An elevated malignant cell ratio and reduced lymphocytes were observed in the high LMES group. Moreover, the LMRPgenes predictive model, consisting of 14 genes, showed great predictive power. The risk score emerged as an independent indicator of poor outcomes. Inverse relationships between the RS and immune status were noted, and a high RS was associated with impaired immunotherapy responses. Drug sensitivity assays indicated the effectiveness of bortezomib, buparlisib, dinaciclib, staurosporine, rapamycin, and MST-312 in the high-RS group, suggesting their potential for treating patients with high-RS values and poor response to immunotherapy. Ultimately, upon verification via qRT-PCR, we observed a significant upregulation of ACBD6 in NDMM group compared to the control group.Our research enhances the knowledge base regarding the association between lipid metabolism-related genes (LMRGs) and the BMM in MM patients, offering substantive insights into the mechanistic effects of the BMM mediated by LMRGs.

Indexed as

Lipid MetabolismMultiple MyelomaTumor MicroenvironmentBone MarrowGene Expression ProfilingGene Expression Regulation, NeoplasticHumansPrognosisTranscriptomeACBD6Bone marrow microenvironmentImmune infiltrationLipid metabolism-related genesMachine learningMultiple myelomaPrognosis

Identifiers

PMID38916672
PMCPMC11199273

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