Evidence map›Paper›PMID 40325058›Full record

ArticleScientific reports2025

Bioinformatics analysis of intrinsic drivers of immune dysregulation in multiple myeloma to elucidate immune phenotypes and discover prognostic gene signatures.

Chuan-Feng Fang, Yan Li, Chun Yang, Hua Fang, Chen Li

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Chuan-Feng FangDepartment of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, 150001, People's Republic of China. chuanfengfang@hrbmu.edu.cn.
Yan LiDepartment of Anesthesia, The Fourth Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, 150001, People's Republic of China.
Chun YangDepartment of Clinical Laboratory, The Fourth Affiliated Hospital of Harbin Medical University, Nangang District, Harbin, 150001, People's Republic of China.
Hua FangDepartment of Medical Oncology, Fuxing Hospital of the Capital Medical University, Xicheng District, Beijing, 10001, People's Republic of China.
Chen LiDepartment of Bioengineering, the Hebei Agriculture University, Baoding City, Hebei Provence, 071001, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple myeloma (MM) progression is driven by immune dysregulation within the tumor microenvironment (TME). However, myeloma-intrinsic mechanisms underlying immune dysfunction remain poorly defined, and current immunotherapies show limited efficacy. Using RNA-seq data from 859 MM patients (MMRF-CoMMpass), we integrated xCELL, CIBERSORT, and ESTIMATE algorithms to deconvolute immune-stromal dynamics. Consensus clustering identified immune subtypes, followed by differential gene analysis and LASSO-Cox regression to construct a prognostic model validated in an independent cohort (GSE19784, N = 328). Immune Subtype Classification: Two subgroups emerged: Multiple myeloma-associated immune-related cluster 1 (N = 482): Immune-dysfunctional TME with Th2 cell enrichment, preadipocyte accumulation, and CXCL family suppression, linked to poor survival (P < 0.001). Multiple myeloma-associated immune-related cluster 2 (N = 377): Immune-active TME with cytotoxic CD8 + T/NK cell infiltration and favorable outcomes. Prognostic Gene Signature: Ten immune-related genes (UBE2T, E2F2, EXO1, SH2D2A, DRP2, WNT9A, SHROOM3, TMC8, CDCA7, and GPR132) predicted survival (The One-year AUC = 0.682 and The Over 5-years AUC = 0.714). We define a myeloma-intrinsic immune classification system and a 10-gene prognostic index, offering a framework for risk-stratified immunotherapy. Integration with flow cytometry could optimize precision treatment in MM.

Indexed as

Computational BiologyMultiple MyelomaTranscriptomeBiomarkers, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMalePhenotypePrognosisTumor MicroenvironmentBiomarkers, TumorImmune dysfunctionImmunosuppressMultiple myelomaPrognostic signatureTumor microenvironment

Identifiers

PMID40325058
PMCPMC12053621

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.