Enhancing multiple myeloma staging: a novel cell death risk model approach

Zeyu Deng ^1,2,3, Hongkai Zhu ^1,2,3, Zhaoshun Yuan ⁴

¹Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
²Institute of Hematology, Central South University, Changsha, Hunan, People's Republic of China.
³Hunan Engineering Research Center of Cell Immunotherapy for Hematopoietic Malignancies, Changsha, Hunan, People's Republic of China.
⁴Department of Cardiovascular Surgery, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
⁵National Cancer Center Exploratory Oncology Research & Clinical Trial Center, Kashiwa, Japan.
⁶Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China. chengzhao@csu.edu.cn.
⁷Institute of Hematology, Central South University, Changsha, Hunan, People's Republic of China. chengzhao@csu.edu.cn.
⁸Hunan Engineering Research Center of Cell Immunotherapy for Hematopoietic Malignancies, Changsha, Hunan, People's Republic of China. chengzhao@csu.edu.cn.
⁹Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China. ruijuanli@csu.edu.cn.
¹⁰Institute of Hematology, Central South University, Changsha, Hunan, People's Republic of China. ruijuanli@csu.edu.cn.
¹¹Hunan Engineering Research Center of Cell Immunotherapy for Hematopoietic Malignancies, Changsha, Hunan, People's Republic of China. ruijuanli@csu.edu.cn.
¹²Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China. penghongling@csu.edu.cn.
¹³Institute of Hematology, Central South University, Changsha, Hunan, People's Republic of China. penghongling@csu.edu.cn.
¹⁴Hunan Engineering Research Center of Cell Immunotherapy for Hematopoietic Malignancies, Changsha, Hunan, People's Republic of China. penghongling@csu.edu.cn.
¹⁵Hunan Key Laboratory of Tumor Models and Individualized Medicine, Changsha, Hunan, People's Republic of China. penghongling@csu.edu.cn.

⁰Department of Hematology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.

Clinical and Experimental Medicine +

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May 8, 2024

View abstract on PubMed

Summary

A new gene signature predicting survival in multiple myeloma (MM) patients was developed. This cell death pathway model, combined with the International Staging System (ISS), improves risk stratification for better patient outcomes.

Area Of Science

Oncology
Genomics
Bioinformatics

Background

Prognosticating survival in multiple myeloma (MM) is clinically challenging.
Existing models may lack precision in predicting patient outcomes.

Purpose Of The Study

To develop a novel prognostic model for multiple myeloma (MM) using gene expression data.
To improve risk stratification for MM patients by integrating a new gene signature with the International Staging System (ISS).

Main Methods

Utilized transcriptomic and clinical data from 2,088 MM patients (GEO, TCGA).
Applied nested lasso regression to identify 28 gene pairings in cell death pathways.
Validated the model using TIME ROC and multivariate COX regression analyses.
Integrated the gene signature with the ISS to create a refined staging system.

Main Results

A 28-gene pairing signature effectively stratified MM patients into high-risk and low-risk groups.
The high-risk group showed significantly shorter survival across training and validation datasets.
The novel tripartite classification system demonstrated superior predictive accuracy (higher C-index) compared to existing models.

Conclusions

A clinically viable gene pairing model based on cellular mortality has been developed.
Synthesizing this model with the ISS creates an enhanced prognostic tool for multiple myeloma.
This approach offers improved predictive power for patient survival trajectories in MM.

Keywords:

Cell-death Gene expression signature Multiple myeloma