A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Frontiers in Cell and Developmental Biology

Cell and Developmental Biology

Ziwei Zheng¹  Yan Zhuang¹  Honglan Qian¹  Bingxin Zhang¹  Quanqiang Wang¹  Xudong Hu¹  Shujuan Zhou¹  Qianying Zhang¹  Yu Zhang¹  Dong Zheng¹  Sisi Zheng¹  Xuanru Lin¹  Tianyu Zhang¹  Songfu Jiang¹  Rujiao Dong¹  Zhili Lin¹  Jingjing Chen¹  Yongyong Ma²  Zhouxiang Jin³  Zixing Chen³ 

[1] Department of Hematology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;Department of Hematology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;Key Laboratory of Intelligent Treatment and Life Support for Critical Diseases of Zhejiang Province, Wenzhou, Zhejiang, China;Zhejiang Engineering Research Center for Hospital Emergency and Process Digitization, Wenzhou, Zhejiang, China;Department of Hepatobiliary Surgery, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China;

关键词: multiple myeloma;    glycolysis;    prognostic signature;    tumor microenvironment;    risk stratification;    therapeutic targets;   

DOI  :  10.3389/fcell.2023.1198949

 received in 2023-04-02, accepted in 2023-05-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM’s great heterogeneity and incurability, risk assessment and treatment choices are still difficult.Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes.Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study.Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.

【 授权许可】

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Copyright © 2023 Zhang, Wang, Lin, Zheng, Zhou, Zhang, Zheng, Chen, Zheng, Zhang, Lin, Dong, Chen, Qian, Hu, Zhuang, Zhang, Jin, Jiang and Ma.