Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Curr Med Sci. 2021 Oct;41(5):1004-1011. doi: 10.1007/s11596-021-2399-x. Epub 2021 Sep 2.
Various studies have suggested that metabolic genes play a significant role in papillary thyroid cancer (PTC). The current study aimed to identify a metabolic signature related biomarker to predict the prognosis of patients with PTC.
We conducted a comprehensive analysis on the data obtained from the Cancer Genome Atlas (TCGA) database. The correlation between survival result and metabolic genes was evaluated based on the univariate Cox analyses, least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses. The performance of a 7-gene signature was assessed according to Kaplan-Meier and receiver operating characteristic (ROC) analysis. Multivariate Cox regression analysis was adopted to unearth clinical factors related to the recurrence free survival (RFS) of patients with PTC. Finally, a prognostic nomogram was developed based on risk score, cancer status and cancer width to improve the prediction for RFS of PTC patients.
Seven metabolic genes were used to establish the prognostic model. The ROC curve and C-index exhibited high value in training, testing and the whole TCGA datasets. The established nomogram, incorporating the 7-metabolic gene signature and clinical factors, was able to predict the RFS with high effectiveness. The 7-metabolic gene signature-based nomogram had a good performance to predict the RFS of patients with PTC.
Our study identified a 7-metabolic gene signature and established a prognostic nomogram, which were useful in predicting the RFS of PTC.
多项研究表明代谢基因在甲状腺乳头状癌（PTC）中发挥重要作用。本研究旨在鉴定与代谢相关的生物标志物，以预测 PTC 患者的预后。
我们对从癌症基因组图谱（TCGA）数据库中获得的数据进行了全面分析。基于单因素 Cox 分析、最小绝对值收缩和选择算子（LASSO）和多因素 Cox 分析，评估了生存结果与代谢基因之间的相关性。根据 Kaplan-Meier 和接受者操作特征（ROC）分析评估了 7 基因特征的性能。采用多因素 Cox 回归分析揭示与 PTC 患者无复发生存（RFS）相关的临床因素。最后，基于风险评分、癌症状态和癌症宽度，开发了一个预后列线图，以提高对 PTC 患者 RFS 的预测能力。
利用 7 个代谢基因建立了预后模型。ROC 曲线和 C 指数在训练、测试和整个 TCGA 数据集均显示出较高的价值。该列线图纳入了 7 个代谢基因标志物和临床因素，能够有效地预测 RFS。基于 7 个代谢基因的列线图在预测 PTC 患者 RFS 方面具有良好的性能。
本研究鉴定了一个 7 个代谢基因特征，并建立了一个预后列线图，可用于预测 PTC 的 RFS。
