Zi-Han Xi1, Xian-Xiong Ma2, Heng-Yu Chen3,4, Yuan-Hang Yu1, Lei Li5, Tao Huang6. 1. Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. 2. Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. 3. Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. 4. NHC Key Laboratory of Hormones and Development, Tianjin Institute of Endocrinology, Tianjin Medical University Chu Hsien-I Memorial Hospital, Tianjin, 300134, China. 5. Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. leili2008@hust.edu. 6. Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China. huangtaowh@163.com.
Abstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Our study identified a 7-metabolic gene signature and established a prognostic nomogram, which were useful in predicting the RFS of PTC.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Our study identified a 7-metabolic gene signature and established a prognostic nomogram, which were useful in predicting the RFS of PTC.