Zhang Bingxin, Wang Quanqiang, Lin Zhili, Zheng Ziwei, Zhou Shujuan, Zhang Tianyu, Zheng Dong, Chen Zixing, Zheng Sisi, Zhang Yu, Lin Xuanru, Dong Rujiao, Chen Jingjing, Qian Honglan, Hu Xudong, Zhuang Yan, Zhang Qianying, Jin Zhouxiang, Jiang Songfu, Ma Yongyong
Department of Hematology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Department of Hepatobiliary Surgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Front Cell Dev Biol. 2023 Jun 2;11:1198949. doi: 10.3389/fcell.2023.1198949. eCollection 2023.
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. 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. 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 experiments with cell lines and clinical subjects further provided convincing evidence for our study. 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.
代谢重编程是癌症的一个重要特征。糖酵解为多发性骨髓瘤（MM）的发展提供了条件。由于MM具有高度异质性且难以治愈，风险评估和治疗选择仍然困难。我们通过最小绝对收缩和选择算子（LASSO）Cox回归分析构建了一个糖酵解相关的预后模型。该模型在两个独立的外部队列、细胞系和我们的临床标本中得到了验证。还对该模型的生物学特性、免疫微环境和治疗反应（包括免疫治疗）进行了探索。最后，综合多个指标构建了一个列线图，以协助对生存结果进行个性化预测。在MM中观察到了广泛的糖酵解相关基因变异和异质性表达谱。该预后模型在区分不同预后人群方面表现良好，并被证明是一个独立的预后因素。这种预后特征与多种恶性特征密切相关，如高风险临床特征、免疫功能障碍、干细胞样特征、癌症相关通路，这与MM的生存结果相关。在治疗方面，高危组对硼替佐米、阿霉素等传统药物以及免疫治疗表现出耐药性。列线图生成的联合评分显示出比其他临床指标更高的临床获益。细胞系和临床受试者实验进一步为我们的研究提供了令人信服的证据。我们开发并验证了MM糖酵解相关预后模型的效用，该模型为MM患者的预后评估和治疗选择提供了新方向。
