Scholarlink

Objective <p>Multiple myeloma (MM) exhibits significant heterogeneity, leading to variable treatment responses and poor clinical outcomes. Glutamine metabolism-related genes (GMRGs) represent critical regulators of tumor biology, yet their prognostic and therapeutic significance in MM remains unexplored. This study aims to identify GMRG-driven tumor signatures and establish their clinical utility as prognostic biomarkers, therapeutic targets and enhancers of drug sensitivity.</p> Methods <p>Integrated transcriptomic and single-cell sequencing analyses of public multi-omics cohorts enabled systematic identification of GMRGs in MM through weighted co-expression network analysis coupled with univariate Cox proportional hazards modeling. Clinically prioritized GMRGs showing elevated expression in patient specimens were functionally validated through proliferation assays and pharmacological sensitivity profiling.</p> Results <p>Integrated multi-omics analysis combining single-cell sequencing with bulk transcriptomic profiling and prognostic screening identified 51 prognostic GMRGs, with 10 core signature genes selected for model construction. The risk stratification system demonstrated robust prognostic capacity validated across multiple independent MM cohorts. Pathway enrichment revealed significant involvement in immune system, cell cycle and tumor signaling. MM patient validation identified DLD, SFT2D2, and UBA2 as significantly upregulated genes that promote tumor growth through enhancement of proliferation. Mechanistic investigations via shRNA-mediated knockdown established that DLD and UBA2 silencing significantly enhanced therapeutic efficacy of MM inhibitors.</p> Conclusion <p>Multicohort-validated GMRGs (DLD/UBA2) drive MM progression and MM inhibitor responses. Clinical upregulation and functional silencing confirm dual therapeutic potential as prognostic biomarkers and drug-sensitizing targets.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating single-cell and bulk RNA profiles to uncover glutamine metabolism’s role in prognosis and immune dynamics in multiple myeloma

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月19日

查看全文
  • Fei Zhao,
  • Feifei Che

摘要

Objective

Multiple myeloma (MM) exhibits significant heterogeneity, leading to variable treatment responses and poor clinical outcomes. Glutamine metabolism-related genes (GMRGs) represent critical regulators of tumor biology, yet their prognostic and therapeutic significance in MM remains unexplored. This study aims to identify GMRG-driven tumor signatures and establish their clinical utility as prognostic biomarkers, therapeutic targets and enhancers of drug sensitivity.

Methods

Integrated transcriptomic and single-cell sequencing analyses of public multi-omics cohorts enabled systematic identification of GMRGs in MM through weighted co-expression network analysis coupled with univariate Cox proportional hazards modeling. Clinically prioritized GMRGs showing elevated expression in patient specimens were functionally validated through proliferation assays and pharmacological sensitivity profiling.

Results

Integrated multi-omics analysis combining single-cell sequencing with bulk transcriptomic profiling and prognostic screening identified 51 prognostic GMRGs, with 10 core signature genes selected for model construction. The risk stratification system demonstrated robust prognostic capacity validated across multiple independent MM cohorts. Pathway enrichment revealed significant involvement in immune system, cell cycle and tumor signaling. MM patient validation identified DLD, SFT2D2, and UBA2 as significantly upregulated genes that promote tumor growth through enhancement of proliferation. Mechanistic investigations via shRNA-mediated knockdown established that DLD and UBA2 silencing significantly enhanced therapeutic efficacy of MM inhibitors.

Conclusion

Multicohort-validated GMRGs (DLD/UBA2) drive MM progression and MM inhibitor responses. Clinical upregulation and functional silencing confirm dual therapeutic potential as prognostic biomarkers and drug-sensitizing targets.