Wan Lu, Zhang Wenchao, Liu Zhongyue, Yang Zhimin, Tu Chao, Li Zhihong
Key Laboratory of Tumor Models and Individualized Medicine, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, People's Republic of China.
Int J Gen Med. 2022 Feb 1;15:997-1011. doi: 10.2147/IJGM.S352859. eCollection 2022.
Metabolic reprogramming, as one of the hallmarks of cancer, shows promising translational potential for cancer diagnosis, treatment and prognostic prediction. This study aims to construct and validate a prognostic prediction model for osteosarcoma based on glutamine metabolism-related genes.
A group of glutamine metabolism-related genes was identified from a public database and intersected with a list of osteosarcoma survival-related genes, and a risk score model based on sixteen glutamine metabolism-related genes was developed by using LASSO penalized Cox regression analysis.
The prognosis of patients in the high-risk group was significantly worse than that of patients in the low-risk group in the training dataset (high- vs low-risk, 5-year overall survival: 11% vs 88%, p < 0.0001) and in two other external validation cohorts (high- vs low-risk, 5-year overall survival: 39% vs 81%, p = 0.015; 50% vs 94%, p = 0.011).In addition, a novel nomogram was constructed by integrating the risk score and clinical characteristics, including age, sex, metastasis status and chemotherapy response. This nomogram had superior predictive power compared with a nomogram composed of only conventional factors. Gene set enrichment analysis indicated that several well-known malignancy-associated gene sets, including MYC targets V1, DNA repair, and unfolded protein response, were enriched in the high-risk subgroup.
A novel glutamine metabolism-related prognostic prediction model and nomogram for osteosarcoma was developed and validated in the present study, which could predict the survival of patients with osteosarcoma and may facilitate individualized clinical decision-making for patients.
代谢重编程作为癌症的标志之一，在癌症诊断、治疗及预后预测方面显示出良好的转化潜力。本研究旨在构建并验证基于谷氨酰胺代谢相关基因的骨肉瘤预后预测模型。
从公共数据库中鉴定出一组谷氨酰胺代谢相关基因，并与骨肉瘤生存相关基因列表进行交叉分析，采用LASSO惩罚Cox回归分析建立基于16个谷氨酰胺代谢相关基因的风险评分模型。
在训练数据集（高风险组与低风险组，5年总生存率：11%对88%，p<0.0001）以及另外两个外部验证队列中（高风险组与低风险组，5年总生存率：39%对81%，p = 0.015；50%对94%，p = 0.011），高风险组患者的预后明显差于低风险组患者。此外，通过整合风险评分和临床特征（包括年龄、性别、转移状态和化疗反应）构建了一种新型列线图。与仅由传统因素组成的列线图相比，该列线图具有更强的预测能力。基因集富集分析表明，几个著名的恶性肿瘤相关基因集，包括MYC靶点V1、DNA修复和未折叠蛋白反应，在高风险亚组中富集。
本研究开发并验证了一种新型的基于谷氨酰胺代谢的骨肉瘤预后预测模型和列线图，其可预测骨肉瘤患者的生存情况，并可能有助于为患者制定个体化临床决策。
