BACKGROUND: There is still no reasonably accurate method of preoperatively predicting central lymph node metastasis (LNM), and it is essential to develop an effective evaluation model for predicting LNM in papillary thyroid carcinoma (PTC) patients. METHODS: PTC samples were collected from The Cancer Genome Atlas database. Candidate genes were identified as continuously upregulated or downregulated genes in the process of N0 to N1a and N1a to N1b. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct the predictive model for LNM. Multivariate logistic regression analysis was performed to screen the potential factors related to LNM, and a nomogram was established. The risk score of the gene signature model for predicting disease-free survival (DFS) was evaluated by Kaplan-Meier analysis. RESULTS: A 14-gene signature was developed by LASSO regression for predicting LNM based on 69 differential expression genes (DEGs) that were continuously upregulated or downregulated in the progress of PTC. The receiver operating characteristic (ROC) curves of the 14-gene signature predicting LNM, central LNM and lateral LNM were generated. The area under the ROC (AUC) values were 0.806 [95% confidence interval (CI): 0.7608-0.8815], 0.755 (95% CI: 0.6839-0.8263) and 0.821 (95% CI: 0.7608-0.8815). The nomogram's C-index value, including the 14-gene signature and other potential risk factors, was 0.786 (95% CI: 0.7296-0.8425), and the calibration exhibited fairly good consistency with the perfect prediction. Based on the 14-gene risk score, high-risk PTC patients had a worse DFS. CONCLUSIONS: A novel 14-gene signature was developed for predicting LNM in PTC patients. The risk score also correlated with DFS in PTC patients. 2021 Gland Surgery. All rights reserved.
BACKGROUND: There is still no reasonably accurate method of preoperatively predicting central lymph node metastasis (LNM), and it is essential to develop an effective evaluation model for predicting LNM in papillary thyroid carcinoma (PTC) patients. METHODS: PTC samples were collected from The Cancer Genome Atlas database. Candidate genes were identified as continuously upregulated or downregulated genes in the process of N0 to N1a and N1a to N1b. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct the predictive model for LNM. Multivariate logistic regression analysis was performed to screen the potential factors related to LNM, and a nomogram was established. The risk score of the gene signature model for predicting disease-free survival (DFS) was evaluated by Kaplan-Meier analysis. RESULTS: A 14-gene signature was developed by LASSO regression for predicting LNM based on 69 differential expression genes (DEGs) that were continuously upregulated or downregulated in the progress of PTC. The receiver operating characteristic (ROC) curves of the 14-gene signature predicting LNM, central LNM and lateral LNM were generated. The area under the ROC (AUC) values were 0.806 [95% confidence interval (CI): 0.7608-0.8815], 0.755 (95% CI: 0.6839-0.8263) and 0.821 (95% CI: 0.7608-0.8815). The nomogram's C-index value, including the 14-gene signature and other potential risk factors, was 0.786 (95% CI: 0.7296-0.8425), and the calibration exhibited fairly good consistency with the perfect prediction. Based on the 14-gene risk score, high-risk PTC patients had a worse DFS. CONCLUSIONS: A novel 14-gene signature was developed for predicting LNM in PTC patients. The risk score also correlated with DFS in PTC patients. 2021 Gland Surgery. All rights reserved.
Authors: Emmanuelle M L Ruiz; Tianhua Niu; Mourad Zerfaoui; Muthusamy Kunnimalaiyaan; Paul L Friedlander; Asim B Abdel-Mageed; Emad Kandil Journal: Surgery Date: 2019-11-09 Impact factor: 3.982
Authors: M Brassard; I Borget; A Edet-Sanson; A-L Giraudet; O Mundler; M Toubeau; F Bonichon; F Borson-Chazot; L Leenhardt; C Schvartz; C Dejax; I Brenot-Rossi; M-E Toubert; M Torlontano; E Benhamou; M Schlumberger Journal: J Clin Endocrinol Metab Date: 2011-03-09 Impact factor: 5.958
Authors: Carlo La Vecchia; Matteo Malvezzi; Cristina Bosetti; Werner Garavello; Paola Bertuccio; Fabio Levi; Eva Negri Journal: Int J Cancer Date: 2014-10-13 Impact factor: 7.396