Relationship between the BRAF-V600E variant allele frequency and clinicopathological characteristics of papillary thyroid carcinoma and its clinical application

Objective To investigate the relationship between B-Raf Proto-Oncogene, Serine/Threonine Kinase (BRAF) V600E variant allele frequency (VAF) and clinicopathological characteristics in papillary thyroid carcinoma (PTC) patients. To investigate the risk factors for central lymph node metastasis (cLNM) in PTC and establish a predictive model. Method Tissue specimens of 143 PTC patients who underwent thyroidectomy at the Department of Thyroid Surgery, the Third Affiliated Hospital of Nanjing Medical University from January 2018 to December 2023 were collected, and BRAF-V600E VAF was detected. According to the median VAF, patients were divided into low and high-frequency mutation group. The relationship between BRAF-V600E VAF and clinicopathological characteristics of the two groups was analyzed by using χ2 test, T test and Mann-Whitney U test. Patients were divided into two groups according to the presence or absence of cLNM. The clinicopathological characteristics of the two groups were compared. The multivariate logistic regression analysis was used to identify the independent risk factors for cLNM and a predictive nomogram model was established. The predictive efficacy was evaluated by the area under the curve (AUC). Results BRAF-V600E VAF was associated with T stage, N stage, primary lesion size, number of metastatic lymph nodes, invasion, and risk of intermediate-to-high risk recurrence in PTC (P < 0.05), but not with age, sex, TNM stage, multifocus, unilateral or bilateral (P > 0.05). Multivariate analysis showed that sex, multifocus and BRAF-V600E VAF were independent risk factors for cLNM in PTC (P < 0.05). The independent risk factors were used to establish a nomogram predictive model. The AUC value of this model was 0.778 (P < 0.05). Conclusion BRAF-V600E VAF is associated with high invasive characteristics of PTC. The predictive model developed in this study can effectively predict cLNM in PTC.