ArticleGland surgery2021
Development and validation of a novel 14-gene signature for predicting lymph node metastasis in papillary thyroid carcinoma.
Article in Gland surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A nomogram based on the 3-gene signature and clinical characteristics for predicting lymph node metastasis in papillary thyroid cancer.Cancer biomarkers : section A of Disease markers · 2025Article
- Spatial and Single-Cell Transcriptomics Unraveled Spatial Evolution of Papillary Thyroid Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- TIPARP as a prognostic biomarker and potential immunotherapeutic target in male papillary thyroid carcinoma.Cancer cell international · 2024Article
- Deep learning prediction model for central lymph node metastasis in papillary thyroid microcarcinoma based on cytology.Cancer science · 2023Article
- Prediction model of cervical lymph node metastasis based on clinicopathological characteristics of papillary thyroid carcinoma: a dual-center retrospective study.Frontiers in endocrinology · 2023Article
- Development of a joint diagnostic model of thyroid papillary carcinoma with artificial neural network and random forest.Frontiers in genetics · 2022Article
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6 authors.
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Abstract
backgroundThere 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.
methodsPTC 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.
resultsA 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.
conclusionsA novel 14-gene signature was developed for predicting LNM in PTC patients. The risk score also correlated with DFS in PTC patients.
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