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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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Updated: Aug 30, 2025

Author Spotlight: Analyzing Bone Marrow Microenvironment in Murine Hematological Malignancies
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Immune microenvironment characteristics in multiple myeloma progression from transcriptome profiling.

Jin Wang1, Yi Hu1, Habib Hamidi2

  • 1Oncology Biomarker Development, Roche (China) Holding Ltd., Shanghai, China.

Frontiers in Oncology
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

This study reveals immune suppressive cells and TGF-β signatures drive multiple myeloma (MM) progression. Understanding the tumor microenvironment (TME) in newly diagnosed and relapsed/refractory MM offers new therapeutic strategies.

Keywords:
autologous stem cell transplantation (ASCT)immunotherapymicroenvironmentmultiple myelomatranscriptome

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Area of Science:

  • Hematology
  • Immunology
  • Oncology

Background:

  • Multiple myeloma (MM) is a plasma cell malignancy originating in the bone marrow.
  • Current treatments face challenges in overcoming immune dysfunction within the tumor microenvironment (TME) for relapsed/refractory (R/R) MM.
  • Understanding MM TME heterogeneity is crucial for improving patient outcomes.

Purpose of the Study:

  • To analyze transcriptome data from newly diagnosed (ND) and R/R MM patients.
  • To characterize differences in the TME between ND and R/R MM.
  • To identify mechanisms underlying MM progression and immune evasion.

Main Methods:

  • Transcriptome data analysis of ND and R/R MM patient samples.
  • Identification of key gene signatures and cell infiltration patterns within the TME.
  • Correlation of TME characteristics with clinical outcomes and patient phenotypes.

Main Results:

  • Highly expressed cancer testis antigens and immune-suppressive cells (Th2, M2) correlate with MM progression.
  • A TGF-β signature is associated with worse outcomes in R/R MM patients.
  • ND MM patients exhibit distinct immune-high (IFN-γ, MHC-II, CD4+ T-cell response) and immune-low phenotypes.

Conclusions:

  • The MM TME plays a critical role in disease progression and immune evasion.
  • Characterizing TME phenotypes can inform therapeutic strategies for ND MM ineligible for transplant.
  • TME profiling may predict CAR-T therapy response in R/R MM patients.