Zeng Furong, Su Juan, Peng Cong, Liao Mengting, Zhao Shuang, Guo Ying, Chen Xiang, Deng Guangtong
Hunan Key Laboratory of Skin Cancer and Psoriasis, Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Front Oncol. 2020 Sep 9;10:1710. doi: 10.3389/fonc.2020.01710. eCollection 2020.
Metabolic reprogramming is closely related to melanoma. However, the prognostic role of metabolism-related genes (MRGs) remains to be elucidated. We aimed to establish a nomogram by combining MRGs signature and clinicopathological factors to predict melanoma prognosis. Eighteen prognostic MRGs between melanoma and normal samples were identified using The Cancer Genome Atlas (TCGA) and GSE15605. (HR = 0.881, 95% CI = 0.788-0.984, = 0.025) and (HR = 1.124, 95% CI = 1.007-1.255, = 0.037) were ultimately identified as independent prognostic MRGs with LASSO regression and multivariate Cox regression. The MRGs signature was established according to these two genes and externally validated in the Gene Expression Omnibus (GEO) dataset. Kaplan-Meier survival analysis indicated that patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group. Furthermore, the MRGs signature was identified as an independent prognostic factor for melanoma survival. An MRGs nomogram based on the MRGs signature and clinicopathological factors was developed in TCGA cohort and validated in the GEO dataset. Calibration plots showed good consistency between the prediction of nomogram and actual observation. The receiver operating characteristic curve and decision curve analysis indicated that MRGs nomogram had better OS prediction and clinical net benefit than the stage system. To our knowledge, we are the first to develop a prognostic nomogram based on MRGs signature with better predictive power than the current staging system, which could assist individualized prognosis prediction and improve treatment.
代谢重编程与黑色素瘤密切相关。然而，代谢相关基因（MRGs）的预后作用仍有待阐明。我们旨在通过结合MRGs特征和临床病理因素建立一个列线图，以预测黑色素瘤的预后。利用癌症基因组图谱（TCGA）和GSE15605鉴定了黑色素瘤和正常样本之间的18个预后MRGs。最终，通过LASSO回归和多变量Cox回归确定（HR = 0.881，95%CI = 0.788 - 0.984， = 0.025）和（HR = 1.124，95%CI = 1.007 - 1.255， = 0.037）为独立的预后MRGs。根据这两个基因建立了MRGs特征，并在基因表达综合数据库（GEO）数据集中进行了外部验证。Kaplan-Meier生存分析表明，高危组患者的总生存期（OS）明显低于低危组。此外，MRGs特征被确定为黑色素瘤生存的独立预后因素。在TCGA队列中开发了基于MRGs特征和临床病理因素的MRGs列线图，并在GEO数据集中进行了验证。校准图显示列线图预测与实际观察之间具有良好的一致性。受试者工作特征曲线和决策曲线分析表明，MRGs列线图在OS预测和临床净效益方面优于分期系统。据我们所知，我们是第一个基于MRGs特征开发预后列线图的，其预测能力优于当前的分期系统，这有助于个体化预后预测并改善治疗。
