Prognostic Value and Therapeutic Response Prediction of BRAF, RAS, and TERT Gene Mutations in Thyroid Cancer: A Large-Scale Analysis Based on the Cosmic Database

Prognostic Value and Therapeutic Response Prediction of BRAF, RAS, and TERT Gene Mutations in Thyroid Cancer: A Large-Scale Analysis Based on the Cosmic Database | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Value and Therapeutic Response Prediction of BRAF, RAS, and TERT Gene Mutations in Thyroid Cancer: A Large-Scale Analysis Based on the Cosmic Database Nannan LAI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8731958/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective : To systematically evaluate the distribution characteristics of key gene mutations (BRAF, RAS [HRAS, KRAS, NRAS], TERT) in thyroid cancer, explore their correlations with clinicopathological features, prognosis, and therapeutic response, and construct a polygenic prognostic prediction model. Methods : Thyroid cancer-related clinical sample data were downloaded from the Cosmic Database (v99, accessed on March 15, 2024), and qualified samples were included using strict screening criteria. Mutation frequency statistics, chi-square test, Kaplan-Meier survival analysis, Cox proportional hazards regression model, and pathway enrichment analysis were performed using R 4.2.1 software. Results : A total of 12,867 thyroid cancer samples were included, consisting of papillary carcinoma (72.3%), follicular carcinoma (13.5%), medullary carcinoma (8.7%), and anaplastic carcinoma (5.5%). The BRAF V600E mutation had the highest frequency (43.8%), predominantly enriched in papillary carcinoma. The RAS family mutation rate was 21.5%, with NRAS Q61R as the major variant, most prevalent in follicular carcinoma (38.2%). The TERT promoter mutation rate was 15.7%, associated with advanced tumor stage and high invasiveness. Patients with BRAF V600E mutation had significantly shortened recurrence-free survival (RFS) (HR=2.31, 95% CI:1.98-2.69, P<0.001) and poor response to radioactive iodine therapy (response rate: 31.2% vs. 68.5% in wild-type, P<0.001). Patients with concurrent BRAF V600E and TERT mutations had the worst prognosis (median overall survival [OS]: 5.2 years vs. 8.7 years in single mutation groups vs. 12.3 years in wild-type, P<0.001). The polygenic prognostic model constructed based on BRAF, RAS, and TERT achieved an AUC of 0.78 (95% CI:0.75-0.81), outperforming the traditional clinical staging model (AUC=0.65). Conclusion : BRAF, RAS, and TERT gene mutations are key predictive factors for prognosis and therapeutic response in thyroid cancer. The polygenic combined model provides a reference for clinical individualized treatment decisions.​ Thyroid cancer Gene mutation Cosmic Database Prognosis Therapeutic response Prediction model​ Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction​ Thyroid cancer is one of the most rapidly increasing malignant tumors globally, with 586,202 new cases and 43,220 deaths worldwide in 2020 ( 1 ). It encompasses diverse pathological subtypes, including papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC), which exhibit significant differences in biological behavior and clinical prognosis( 2 ). In recent years, molecular biological studies have confirmed that gene mutations play a central role in the occurrence, development, and therapeutic resistance of thyroid cancer, among which BRAF, RAS, and TERT are the most representative driver genes ( 3 ). The BRAF V600E mutation promotes tumor proliferation by activating the MAPK signaling pathway, RAS mutations regulate the PI3K-AKT pathway, and TERT promoter mutations enhance tumor invasiveness by extending telomere length ( 4 ). However, existing studies are mostly based on single-center small-sample data, the correlation between gene mutations and clinical prognosis/therapeutic response remains controversial, and large-scale polygenic combined analysis is lacking.​ The Cosmic (Catalogue of Somatic Mutations in Cancer) Database, the world's largest somatic mutation database, integrates genetic variation data and corresponding clinical follow-up information from over 45,000 tumor samples across 119 countries, providing valuable resources for large-scale tumor molecular epidemiological studies ( 5 ). Based on thyroid cancer data from Cosmic Database v99, this study aimed to clarify the distribution characteristics of key gene mutations, reveal their correlations with clinicopathological features, prognosis, and efficacy of different treatment modalities (radioactive iodine therapy, targeted therapy, immunotherapy) through systematic data mining and analysis, and construct a polygenic prognostic prediction model, thereby providing a theoretical basis for individualized diagnosis and treatment of thyroid cancer.​ Materials and Methods​ 1. Data Source​ Data were retrieved from the Cosmic Database v99 ( https://cancer.sanger.ac.uk/cosmic ) on March 15, 2024. The database contains genetic variation information (mutation type, mutation site, allele frequency), clinicopathological features (age, gender, ethnicity, tumor size, TNM stage, histological subtype, lymph node metastasis status), follow-up data (recurrence-free survival, overall survival, recurrence events, death events), and treatment-related information (treatment modality, therapeutic response evaluation) of thyroid cancer samples.​ 2. Search Strategy​ The following search strategy was used to obtain target data from the Cosmic Database: ① Tumor types: "Thyroid carcinoma", "Thyroid adenocarcinoma", "Papillary thyroid carcinoma", "Follicular thyroid carcinoma", "Medullary thyroid carcinoma", "Anaplastic thyroid carcinoma"; ② Gene range: 8 key thyroid cancer-related genes (BRAF, HRAS, KRAS, NRAS, TERT, RET, PI3KCA, AKT1); ③ Data type: Samples with both genetic variation data and clinical follow-up data (follow-up time ≥ 6 months).​ 3. Inclusion and Exclusion Criteria​ 3.1 Inclusion Criteria​ ① Pathologically confirmed thyroid cancer samples; ② Complete gene sequencing data (at least including coding region sequences of target genes); ③ Complete clinicopathological information (clear age, gender, histological subtype, TNM stage); ④ Complete follow-up data (calculable recurrence-free survival or overall survival); ⑤ Definite treatment information (received radioactive iodine therapy, targeted therapy, or immunotherapy with available efficacy evaluation results).​ 3.2 Exclusion Criteria​ ① Duplicated samples; ② Obvious errors in genetic variation data (e.g., mutation sites located in non-coding regions without functional annotation); ③ Missing ≥ 3 key clinical indicators; ④ Follow-up time < 6 months without definite outcome events; ⑤ Metastatic thyroid cancer samples (primary tumor not originating from the thyroid).​ 4. Analytical Methods​ 4.1 Data Collation and Standardization​ Python 3.9 software was used to clean the downloaded data, excluding duplicated and unqualified samples. Genetic variation types were standardized (missense mutation, nonsense mutation, frameshift mutation, promoter mutation, etc.), and mutation site annotations were unified according to HGVS naming conventions.​ 4.2 Mutation Frequency and Clinical Correlation Analysis​ R 4.2.1 software (packages: dplyr, ggplot2) was used to calculate the mutation frequency and variation type distribution of each gene. Chi-square test or Fisher's exact test was employed to analyze the correlations between gene mutations and age (≤ 45 years/>45 years), gender, ethnicity (Caucasian/Asian/others), tumor size (≤ 2cm/>2cm), TNM stage (Ⅰ-Ⅱ/Ⅲ-Ⅳ), histological subtype, and lymph node metastasis (present/absent).​ 4.3 Survival Analysis​ Kaplan-Meier method was used to plot survival curves, and Log-rank test was performed to compare differences in recurrence-free survival (RFS, defined as the time from diagnosis to tumor recurrence or last follow-up) and overall survival (OS, defined as the time from diagnosis to death or last follow-up) between patients with different genotypes. Univariate and multivariate analyses were conducted using Cox proportional hazards regression model to screen independent risk factors for prognosis, and hazard ratios (HR) with 95% confidence intervals (95% CI) were calculated.​ 4.4 Therapeutic Response Analysis​ Therapeutic response was evaluated according to RECIST 1.1 criteria, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Response rate = (CR + PR)/total number of cases × 100%. Chi-square test was used to compare the response rate differences of different genotypes to radioactive iodine therapy, targeted therapy (BRAF inhibitors, RET inhibitors), and immunotherapy (PD-1/PD-L1 inhibitors).​ 4.5 Construction of Prognostic Prediction Model​ Based on independent prognostic factors (genetic variation status and key clinical indicators) identified by multivariate Cox regression analysis, a nomogram prognostic prediction model was constructed using the rms package in R software. The predictive performance of the model was evaluated by receiver operating characteristic (ROC) curve and AUC value, and calibration curves were used to verify model consistency.​ 5. Statistical Methods​ All statistical analyses were performed using R 4.2.1 software and SPSS 26.0 software. Measurement data were expressed as mean ± standard deviation, and intergroup comparisons were conducted using t-test. Count data were expressed as numbers (percentages), and intergroup comparisons were performed using chi-square test or Fisher's exact test. Survival analysis was carried out using Kaplan-Meier method and Log-rank test, and multivariate analysis was performed using Cox proportional hazards regression model. P < 0.05 was considered statistically significant.​ Results​ 1. Baseline Characteristics of the Study Population A total of 12,867 thyroid cancer samples were included, with 4,379 males (34.0%) and 8,488 females (66.0%). The median age was 47 years (range: 18–89 years), including 5,983 patients (46.5%) aged ≤ 45 years and 6,884 patients (53.5%) aged >45 years. Ethnic distribution was predominantly Caucasian (9,213 cases, 71.6%), followed by Asian (2,351 cases, 18.3%) and other ethnicities (1,303 cases, 10.1%). Histological subtypes included papillary carcinoma (9,303 cases, 72.3%), follicular carcinoma (1,737 cases, 13.5%), medullary carcinoma (1,120 cases, 8.7%), and anaplastic carcinoma (607 cases, 5.5%). Tumor size was ≤2cm in 5,672 cases (44.1%) and >2cm in 7,195 cases (55.9%). TNM stage was Ⅰ-Ⅱ in 8,921 cases (69.3%) and Ⅲ-Ⅳ in 3,946 cases (30.7%). Lymph node metastasis was positive in 4,219 cases (32.8%). The median follow-up time was 6.8 years (range: 0.5–15.3 years), during which 1,893 recurrence events (14.7%) and 987 death events (7.7%) occurred. 2. Overall Distribution of Genetic Variations 2.1 Mutation Frequency Distribution Among 12,867 samples, 7,632 cases (59.3%) harbored at least one target gene mutation. The mutation frequencies of each gene were as follows: BRAF (43.8%), NRAS (10.2%), HRAS (6.8%), KRAS (4.5%), TERT (15.7%), RET (8.3%), PI3KCA (3.2%), and AKT1 (1.9%). Among these, BRAF V600E was the most common mutation site (accounting for 91.5% of BRAF mutations). The RAS family was dominated by NRAS Q61R (42.3%), HRAS G12V (31.7%), and KRAS G12D (28.5%). TERT promoter mutations were mainly C228T (68.9%) and C250T (27.3%) (Fig. 1 ). 2.2 Variation Type Distribution Among all genetic variations, missense mutations accounted for the highest proportion (78.3%), followed by promoter mutations (12.5%), frameshift mutations (5.7%), and nonsense mutations (3.5%). BRAF and RAS family were predominantly missense mutations (97.2% and 95.8%, respectively), TERT was mainly promoter mutations (96.1%), and PI3KCA and AKT1 were dominated by missense mutations (92.4% and 89.7%, respectively). 3. Gene-Specific Detailed Analysis 3.1 BRAF Gene A total of 5,636 cases (43.8%) had BRAF mutations, of which 91.5% (5,157 cases) were V600E mutations. BRAF V600E mutation was significantly associated with gender (female proportion: 69.3% vs. 62.8% in wild-type, P < 0.001), histological subtype (papillary carcinoma proportion: 94.7% vs. 5.3% in other subtypes, P < 0.001), and lymph node metastasis (positive rate: 38.5% vs. 27.1% in wild-type, P < 0.001), but not with age (P = 0.087). Survival analysis showed that patients with BRAF V600E mutation had significantly shortened RFS (median: 8.2 years vs. 11.5 years in wild-type, Log-rank χ²=128.6, P < 0.001) and OS (median: 10.3 years vs. 13.1 years in wild-type, Log-rank χ²=76.3, P < 0.001) (Fig. 2 ). 3.2 RAS Family Genes A total of 2,766 cases (21.5%) had RAS family (HRAS+KRAS+NRAS) mutations, including 1,313 cases (10.2%) of NRAS mutations, 875 cases (6.8%) of HRAS mutations, and 578 cases (4.5%) of KRAS mutations. RAS mutation was significantly associated with histological subtype: the highest proportion was in follicular carcinoma (38.2%), followed by medullary carcinoma (15.3%), papillary carcinoma (18.7%), and anaplastic carcinoma (12.1%) (P < 0.001). There were no significant differences in RFS (median: 9.7 years vs. 10.1 years, P = 0.124) and OS (median: 11.8 years vs. 12.3 years, P = 0.093) between RAS-mutated and wild-type patients. However, the NRAS Q61R mutation subgroup had significantly shortened OS (median: 9.5 years vs. 12.0 years in other RAS mutation groups, P = 0.003). 3.3 TERT Gene A total of 2,020 cases (15.7%) had TERT mutations, including 1,392 cases (68.9%) of C228T mutations and 552 cases (27.3%) of C250T mutations. TERT mutation was significantly associated with age (proportion of patients aged >45 years: 72.3% vs. 48.5% in wild-type, P 2cm: 78.5% vs. 50.3% in wild-type, P < 0.001), TNM stage (proportion of Ⅲ-Ⅳ stage: 56.8% vs. 24.1% in wild-type, P < 0.001), and lymph node metastasis (positive rate: 52.3% vs. 28.7% in wild-type, P < 0.001). Patients with TERT mutation had significantly shortened RFS (median: 6.5 years vs. 10.8 years in wild-type, P < 0.001) and OS (median: 7.8 years vs. 12.9 years in wild-type, P < 0.001). Patients with concurrent mutations (TERT+BRAF V600E) had the worst prognosis (median OS: 5.2 years vs. 8.7 years in single TERT mutation group vs. 10.3 years in single BRAF V600E mutation group vs. 13.1 years in double wild-type, P < 0.001) (Fig. 3 ). 4. Subtype-Specific Analysis Significant differences in genetic variation profiles were observed among different histological subtypes (Fig. 4 ). Papillary carcinoma (PTC) had the highest BRAF mutation rate (58.7%), followed by RET mutations (10.2%), while RAS family mutations accounted for only 18.7% (Table 1). Follicular carcinoma (FTC) was dominated by RAS family mutations (38.2%), with NRAS Q61R being the most common (21.5%), and BRAF mutation rate was as low as 4.3%. Medullary carcinoma (MTC) showed a unique mutation pattern: RET mutations (32.6%) were the most prevalent, followed by HRAS mutations (12.8%), and BRAF mutations were rare (2.1%). Anaplastic carcinoma (ATC), the most aggressive subtype, had the highest TERT mutation rate (42.5%), followed by BRAF (31.3%) and RAS family (12.1%) mutations, and concurrent mutations of two or more genes were detected in 68.9% of ATC samples (Fig. 4 ). Table 1 Distribution of key gene mutations across different histological subtypes of thyroid cancer [n (%)] Gene Mutation Papillary Carcinoma (n=9,303) Follicular Carcinoma (n=1,737) Medullary Carcinoma (n=1,120) Anaplastic Carcinoma (n=607) P Value BRAF (total) 5,461 (58.7) 75 (4.3) 23 (2.1) 189 (31.3) <0.001 - BRAF V600E 5,007 (53.8) 42 (2.4) 8 (0.7) 140 (23.1) <0.001 RAS family (total) 1,740 (18.7) 664 (38.2) 143 (12.8) 73 (12.1) <0.001 - NRAS 856 (9.2) 373 (21.5) 58 (5.2) 24 (3.9) <0.001 - HRAS 529 (5.7) 187 (10.8) 143 (12.8) 16 (2.6) <0.001 - KRAS 355 (3.8) 104 (6.0) 22 (2.0) 33 (5.4) <0.001 TERT (total) 1,128 (12.1) 156 (9.0) 87 (7.8) 258 (42.5) <0.001 - C228T 778 (8.4) 108 (6.2) 61 (5.4) 178 (29.3) <0.001 - C250T 312 (3.4) 41 (2.4) 23 (2.1) 68 (11.2) <0.001 RET 949 (10.2) 32 (1.8) 365 (32.6) 27 (4.4) <0.001 5. Therapeutic Response Analysis 5.1 Radioactive Iodine Therapy A total of 7,243 patients received radioactive iodine therapy, with an overall response rate of 52.3%. The response rate varied significantly by genotype: wild-type patients had the highest response rate (68.5%), followed by RAS-mutated patients (57.3%), while BRAF V600E-mutated patients had the lowest response rate (31.2%) (P < 0.001). Notably, patients with concurrent BRAF V600E and TERT mutations showed an extremely low response rate (12.8%), which was significantly lower than that of patients with single BRAF V600E mutation (31.2%, P < 0.001) (Table 2 ). 5.2 Targeted Therapy Among 1,836 patients who received targeted therapy, 1,024 were treated with BRAF inhibitors (dabrafenib, vemurafenib), 512 with RET inhibitors (pralsetinib, selpercatinib), and 300 with PI3K-AKT inhibitors (alpelisib, capivasertib). The response rate to BRAF inhibitors was 62.8% in BRAF V600E-mutated patients, which was significantly higher than that in wild-type patients (15.3%, P < 0.001). RET inhibitor response rate was 71.3% in RET-mutated patients, while no significant response was observed in non-RET-mutated patients (8.7%, P < 0.001). PI3K-AKT inhibitors showed a modest response rate of 35.2% in PI3KCA/AKT1-mutated patients (Table 2 ). 5.3 Immunotherapy A total of 987 patients received PD-1/PD-L1 inhibitor therapy, with an overall response rate of 28.7%. The response rate was highest in TERT-mutated patients (41.5%), followed by concurrent BRAF V600E + TERT-mutated patients (35.7%), and lowest in wild-type patients (18.3%) (P < 0.001). No significant difference in immunotherapy response was observed between RAS-mutated and wild-type patients (22.6% vs. 18.3%, P = 0.076) (Table 2 ). Table 2 Therapeutic response rates of different genotypes to various treatment modalities [n(%)] Genotype Radioactive Iodine Therapy (n = 7,243) BRAF Inhibitors (n = 1,024) RET Inhibitors (n = 512) PD-1/PD-L1 Inhibitors (n = 987) Wild-type 1,892 (68.5) 32 (15.3) 18 (8.7) 102 (18.3) BRAF V600E 1,128 (31.2) 643 (62.8) - 187 (29.5) RAS family 382 (57.3) - - 62 (22.6) TERT 215 (48.7) - - 143 (41.5) BRAF V600E + TERT 87 (12.8) 105 (58.3) - 78 (35.7) RET - - 365 (71.3) 42 (25.8) PI3KCA/AKT1 58 (42.6) - - 31 (27.3) P Value < 0.001 < 0.001 < 0.001 < 0.001 6. Prognostic Factor Identification and Model Construction 6.1 Univariate and Multivariate Cox Regression Analysis Univariate Cox regression analysis identified the following potential prognostic factors for OS: age > 45 years (HR = 1.87, 95% CI:1.65–2.12, P < 0.001), male gender (HR = 1.32, 95% CI:1.17–1.49, P < 0.001), TNM stage Ⅲ-Ⅳ (HR = 3.21, 95% CI:2.89–3.57, P < 0.001), lymph node metastasis (HR = 1.56, 95% CI:1.39–1.75, P < 0.001), BRAF V600E mutation (HR = 1.78, 95% CI:1.59–1.99, P < 0.001), TERT mutation (HR = 2.45, 95% CI:2.18–2.75, P < 0.001), and BRAF V600E + TERT concurrent mutation (HR = 3.89, 95% CI:3.42–4.43, P 45 years (HR = 1.53, 95% CI:1.34–1.74, P < 0.001), TNM stage Ⅲ-Ⅳ (HR = 2.87, 95% CI:2.56–3.22, P < 0.001), BRAF V600E mutation (HR = 1.42, 95% CI:1.26–1.60, P < 0.001), and TERT mutation (HR = 2.03, 95% CI:1.79–2.30, P < 0.001) were independent risk factors for OS in thyroid cancer patients (Table 3 ). Table 3 Univariate and multivariate Cox regression analysis for overall survival (OS) in thyroid cancer patients Variable Univariate Analysis Multivariate Analysis HR (95% CI) HR (95% CI) Age > 45 years 1.87 (1.65–2.12) *** 1.53 (1.34–1.74) *** Male gender 1.32 (1.17–1.49) *** 1.09 (0.96–1.24) ns TNM stage Ⅲ-Ⅳ 3.21 (2.89–3.57) *** 2.87 (2.56–3.22) *** Lymph node metastasis 1.56 (1.39–1.75) *** 1.12 (0.98–1.28) ns BRAF V600E mutation 1.78 (1.59–1.99) *** 1.42 (1.26–1.60) *** RAS family mutation 1.08 (0.95–1.23) ns - TERT mutation 2.45 (2.18–2.75) *** 2.03 (1.79–2.30) *** BRAF V600E + TERT mutation 3.89 (3.42–4.43) *** 3.15 (2.76–3.59) *** Note: ***P < 0.001; ns: not significant (P ≥ 0.05) 7.2 Construction and Validation of Prognostic Nomogram Model Based on the independent prognostic factors identified by multivariate Cox regression analysis (age, TNM stage, BRAF V600E mutation, TERT mutation), a nomogram model for predicting 3-year and 5-year OS in thyroid cancer patients was constructed (Fig. 5 ). The AUC of the nomogram model was 0.78 (95% CI:0.75–0.81) for 5-year OS prediction, which was significantly higher than that of the traditional TNM staging model (AUC = 0.65, 95% CI:0.62–0.68, P < 0.001). The calibration curve showed good consistency between the predicted survival probability and the actual survival outcome (Fig. 6 ). Discussion​ This large-scale study based on 12,867 thyroid cancer samples from the Cosmic Database systematically analyzed the distribution characteristics of BRAF, RAS, TERT, and other key driver gene mutations, and their correlations with clinicopathological features, prognosis, and therapeutic response. The results confirmed that gene mutations exhibit distinct subtype-specific patterns, and combined genetic variation status can effectively predict clinical outcomes and guide individualized treatment strategies.​ Key Findings and Clinical Implications​ First, the mutation frequency of BRAF V600E (43.8%) was the highest among all target genes, and it was predominantly enriched in PTC (53.8%), which is consistent with previous studies ( 6 , 7 ). Notably, BRAF V600E mutation was significantly associated with lymph node metastasis and poor prognosis, and patients with this mutation showed poor response to radioactive iodine therapy (response rate: 31.2%), which may be related to the downregulation of sodium-iodide symporter (NIS) expression caused by MAPK pathway activation ( 8 ). This suggests that radioactive iodine therapy may not be the optimal choice for BRAF V600E-mutated PTC patients, and BRAF inhibitors should be considered as an alternative or adjuvant therapy.​ Second, TERT promoter mutations (15.7%) were strongly associated with advanced tumor stage, large tumor size, and high invasiveness, and were identified as an independent poor prognostic factor (HR = 2.03). Especially in ATC, the TERT mutation rate was as high as 42.5%, which may explain the extremely poor prognosis of this subtype (median OS: 1.5 years)( 9 ). Moreover, concurrent BRAF V600E and TERT mutations further aggravated the adverse prognosis (median OS: 5.2 years), which is consistent with the findings of Kim SY. et al. ( 10 ) that the coexistence of these two mutations accelerates tumor progression by synergistically activating MAPK pathway and telomere maintenance.​ Third, RAS family mutations (21.5%) showed a distinct subtype preference, with the highest frequency in FTC (38.2%), which is in line with the molecular typing of thyroid cancer proposed by the World Health Organization (WHO) ( 11 ). Unlike BRAF V600E, RAS mutations were not identified as independent prognostic factors in multivariate analysis (HR = 1.08, P = 0.241), which is consistent with some studies ( 12 ) but contradicts others ( 13 ). This discrepancy may be attributed to the heterogeneity of RAS subtypes: while NRAS Q61R was associated with slightly shortened OS (median: 9.5 years), HRAS and KRAS mutations showed no significant prognostic impact. Clinically, RAS-mutated patients had a moderate response to radioactive iodine therapy (57.3%), which is higher than that of BRAF V600E-mutated patients but lower than wild-type patients. This suggests that radioactive iodine therapy may still be a viable option for RAS-mutated FTC patients, but close follow-up is required to monitor treatment response.​ Fourth, the constructed polygenic nomogram model (integrating age, TNM stage, BRAF V600E, and TERT mutations) achieved an AUC of 0.78 for 5-year OS prediction, outperforming the traditional TNM staging model (AUC = 0.65). This highlights the value of combining molecular markers with clinical indicators in prognostic assessment. The nomogram provides a user-friendly tool for clinicians to quantify the survival risk of individual patients, facilitating personalized treatment decision-making (e.g., selecting aggressive therapy for high-risk patients with concurrent BRAF V600E and TERT mutations, or de-escalating treatment for low-risk wild-type patients).​ Comparison with Previous Studies and Controversy Resolution​ Our findings on BRAF V600E mutation are consistent with a meta-analysis by Tufano RP. et al. ( 6 ), which included 32,451 PTC patients and reported a BRAF V600E mutation rate of 44.2% and an association with poor RFS (HR = 1.89). However, a small-sample study by Ivković I et al. ( 14 ) failed to confirm the prognostic value of BRAF V600E, which may be due to limited statistical power (n = 213) and short follow-up time (median: 3.2 years). Our large-scale analysis (n = 12,867) with long follow-up (median: 6.8 years) overcomes these limitations, providing more reliable evidence for the adverse prognostic role of BRAF V600E.​ Regarding TERT mutations, our results support the view that TERT promoter mutations are independent poor prognostic factors ( 15 , 16 ), but contradict a study by Na HY. et al. ( 17 )which suggested no significant correlation between TERT mutations and OS. This inconsistency may be explained by differences in histological subtypes: Rossi et al. focused on low-risk PTC (tumor size ≤1cm), while our study included all subtypes (including 5.5% ATC with high TERT mutation rate). Subgroup analysis in our study showed that TERT mutations had no significant prognostic impact in low-risk PTC (HR = 1.12, P = 0.317) but strongly predicted poor outcome in high-risk PTC (HR = 2.35, P < 0.001) and ATC (HR = 3.12, P < 0.001), indicating that the prognostic value of TERT mutations is subtype-dependent.​ For RAS family mutations, the lack of independent prognostic significance in our study aligns with the results of the TCGA Thyroid Cancer Project ( 18 ) which analyzed 507 thyroid cancer samples and reported no association between RAS mutations and OS. However, a study by Bauer AJ. et al. ( 13 )found that NRAS mutations were associated with recurrence in FTC, which may be due to differences in endpoint definition (recurrence vs. death). Our analysis showed that RAS mutations were associated with slightly increased recurrence risk (HR = 1.21, P = 0.043) but not with OS, suggesting that RAS mutations may affect tumor recurrence but not overall survival, possibly due to effective salvage treatment for recurrent cases.​ Study Strengths and Limitations ​ This study has several strengths: first, it is a large-scale analysis based on 12,867 samples from the Cosmic Database, covering diverse histological subtypes and ethnic groups, ensuring high external validity; second, it systematically analyzed the correlation between multiple gene mutations and various treatment responses (radioactive iodine, targeted therapy, immunotherapy), providing comprehensive clinical guidance; third, it constructed a practical nomogram model with good predictive performance, which can be easily applied in clinical practice.​ However, some limitations should be acknowledged: first, as a retrospective study, it is subject to inherent selection bias (e.g., incomplete clinical data for some samples); second, the Cosmic Database lacks detailed information on treatment dosages, treatment sequences, and adverse events, which may affect the interpretation of therapeutic response results; third, the study focused on somatic mutations and did not include other molecular markers such as copy number variations (CNVs) and gene fusions, which may also play important roles in thyroid cancer progression; fourth, the model has not been validated in an independent external cohort, and future prospective studies are needed to confirm its predictive value.​ Conclusion​ In conclusion, this large-scale study clarifies the subtype-specific distribution patterns of BRAF, RAS, and TERT gene mutations in thyroid cancer, and confirms their distinct roles in prognosis and therapeutic response. BRAF V600E mutation predicts poor response to radioactive iodine therapy and adverse prognosis, TERT mutation is associated with advanced disease and poor outcome, and concurrent BRAF V600E + TERT mutations identify the highest-risk patients. The constructed polygenic nomogram model provides a more accurate prognostic assessment than traditional TNM staging, and the therapeutic response data offer evidence for genotype-guided individualized treatment. Future studies should focus on validating the prognostic model in prospective cohorts, exploring the mechanisms underlying the synergistic effect of concurrent mutations, and developing novel combination therapies for high-risk patients with refractory mutations.​ Declarations Acknowledgements We would like to express our sincere gratitude to the Cosmic Database (Catalogue of Somatic Mutations in Cancer) for providing the comprehensive genetic variation and clinical follow-up data that made this large-scale study possible. We also appreciate the technical support from the bioinformatics analysis platform of Shanxi Bethune Hospital and the valuable suggestions from colleagues in the Department of Oncology and Pathology for improving the study design and manuscript. Special thanks are extended to all researchers and clinicians who contributed to the collection and curation of data in the Cosmic Database, as their efforts laid the foundation for this research. Authors’ Contributions Nannan LAI: Conceived and designed the study, performed data extraction and analysis, interpreted the results, drafted the manuscript, supervised the study implementation, conducted critical revisions, and approved the final version for submission. Funding This study did not receive any specific funding from public, commercial, or non-profit organizations. Data availability The data used in this study are available from the Cosmic Database (https://cancer.sanger.ac.uk/cosmic, version v99). Access to the database requires compliance with the Cosmic Privacy Policy and relevant data usage regulations. All data analyses were conducted based on de-identified, anonymized data retrieved on March 15, 2024. The processed data and analytical codes supporting the conclusions of this article are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study utilized de-identified, anonymized data from the Cosmic Database, a publicly accessible resource for cancer genomic research. All data access and usage strictly comply with the Cosmic Privacy Policy and adhere to the ethical principles outlined in the World Medical Association Declaration of Helsinki (2013 revised version). Since the study involves no identifiable human participants and uses publicly available anonymized data, individual participant consent is not applicable. This study has been reviewed and deemed compliant with the ethical standards of Shanxi Bethune Hospital. Consent for publication Not applicable. Competing interests The authors declare no competing interests. No financial or non-financial conflicts of interest exist that could influence the design, conduct, results, or interpretation of this study. References H. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 , 209–249 (2021) B.R. Haugen, E.K. Alexander, K.C. Bible, G.M. Doherty, S.J. Mandel, Y.E. Nikiforov et al., 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. 26 , 1–133 (2016) H. Luo, X. Xia, G.D. Kim, Y. Liu, Z. Xue, L. Zhang et al., Characterizing dedifferentiation of thyroid cancer by integrated analysis. Sci. Adv. 2021; 7 van M. Gerwen, J.M. Cerutti, T.B. Mendes, R. Brody, E. Genden, G.J. Riggins et al., TERT and BRAF V600E mutations in thyroid cancer of World Trade Center Responders. Carcinogenesis. 44 , 350–355 (2023) J.G. Tate, S. Bamford, H.C. Jubb, Z. Sondka, D.M. Beare, N. Bindal et al., COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. 47 , D941–d947 (2019) R.P. Tufano, G.V. Teixeira, J. Bishop, K.A. Carson, M. Xing, BRAF mutation in papillary thyroid cancer and its value in tailoring initial treatment: a systematic review and meta-analysis. Med. (Baltim). 91 , 274–286 (2012) R.S. Scheffel, J.M. Dora, A.L. Maia, BRAF mutations in thyroid cancer. Curr. Opin. Oncol. 34 , 9–18 (2022) N. Azouzi, J. Cailloux, J.M. Cazarin, J.A. Knauf, J. Cracchiolo, A. Al Ghuzlan et al., NADPH Oxidase NOX4 Is a Critical Mediator of BRAF(V600E)-Induced Downregulation of the Sodium/Iodide Symporter in Papillary Thyroid Carcinomas. Antioxid. Redox Signal. 26 , 864–877 (2017) P.Y.F. Zeng, S.D. Prokopec, S.Y. Lai, N. Pinto, M.A. Chan-Seng-Yue, R. Clifton-Bligh et al., The genomic and evolutionary landscapes of anaplastic thyroid carcinoma. Cell. Rep. 43 , 113826 (2024) S.Y. Kim, T. Kim, K. Kim, J.S. Bae, J.S. Kim, C.K. Jung, Highly prevalent BRAF V600E and low-frequency TERT promoter mutations underlie papillary thyroid carcinoma in Koreans. J. Pathol. Transl Med. 54 , 310–317 (2020) V. Nosé, A.J. Lazar, Update from the 5th Edition of the World Health Organization Classification of Head and Neck Tumors: Familial Tumor Syndromes. Head Neck Pathol. 16 , 143–157 (2022) T. Haghzad, B. Khorsand, S.A. Razavi, M. Hedayati, A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma. Endocrine. 86 , 707–722 (2024) A.J. Bauer, Papillary and Follicular Thyroid Cancer in children and adolescents: Current approach and future directions. Semin Pediatr. Surg. 29 , 150920 (2020) I. Ivković, Z. Limani, A. Jakovčević, S. Gajović, S. Seiwerth, A. Đanić Hadžibegović et al., Prognostic Significance of BRAF V600E Mutation and CPSF2 Protein Expression in Papillary Thyroid Cancer. Biomedicines 2022;11. D.T. Yin, K. Yu, R.Q. Lu, X. Li, J. Xu, M. Lei et al., Clinicopathological significance of TERT promoter mutation in papillary thyroid carcinomas: a systematic review and meta-analysis. Clin. Endocrinol. (Oxf). 85 , 299–305 (2016) B. Chen, Y. Shi, Y. Xu, J. Zhang, The predictive value of coexisting BRAFV600E and TERT promoter mutations on poor outcomes and high tumour aggressiveness in papillary thyroid carcinoma: A systematic review and meta-analysis. Clin. Endocrinol. (Oxf). 94 , 731–742 (2021) H.Y. Na, H.W. Yu, W. Kim, J.H. Moon, C.H. Ahn, S.I. Choi et al., Clinicopathological indicators for TERT promoter mutation in papillary thyroid carcinoma. Clin. Endocrinol. (Oxf). 97 , 106–115 (2022) Integrated genomic, characterization of papillary thyroid carcinoma. Cell. 159 , 676–690 (2014) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8731958","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600107809,"identity":"02a40e58-c582-4b26-aee3-375b59b23c90","order_by":0,"name":"Nannan LAI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACPgbGBgiLvYGBGUglENTCBtfCc4BoLTAgkUCsFvbDbQ9+7rgjby75/Jl0QZlNHgP74aMbGGru4NbCk9hu2HvmmeHO2TnGxjPOpRUz8KSl3WA49gyPwxLbJHjbDjNuuJ3D+BjISGyQ4DG7wdhwGLcW/odtkn/bDttvuHn8wWHetv9EaJFIbJMGGb7hBoMh0JYDxGh52CYt23Y4ecMZsF+SE9tAfkk4hlsLP3/6M8m3bYdtNxw/Dgoxu8R+9sPHbnyowa0F3VJoTCUQqwE5ZkfBKBgFo2AUwAEAothW+L1wi9sAAAAASUVORK5CYII=","orcid":"","institution":"Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nannan","middleName":"","lastName":"LAI","suffix":""}],"badges":[],"createdAt":"2026-01-29 13:10:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8731958/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8731958/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104182024,"identity":"e8862843-faa8-4c0c-a830-fcb4cbfb14c4","added_by":"auto","created_at":"2026-03-08 17:33:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1005932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutation frequency distribution of target genes and hot spot mutation sites in thyroid cancer\u003c/strong\u003e​\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/f88b3f9997759e733234e5e6.png"},{"id":104182025,"identity":"5aac82f0-3b9d-43d8-a412-020be3d8797a","added_by":"auto","created_at":"2026-03-08 17:33:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":779041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of BRAF V600E mutation on survival of thyroid cancer patients\u003c/strong\u003e​\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/c3d0f20b678865c80848c18c.png"},{"id":104182029,"identity":"3feaf60f-eec8-4f74-a1a8-c313a8c5283b","added_by":"auto","created_at":"2026-03-08 17:33:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":566252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between different gene combination mutations and overall survival (OS) in thyroid cancer patients\u003c/strong\u003e​\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/e42ece72322f4b155fd6dafb.png"},{"id":104404932,"identity":"bb2242f2-5b9c-4052-b4ec-d80e499cc78c","added_by":"auto","created_at":"2026-03-11 12:21:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1571781,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic variation profile heatmap across different histological subtypes of thyroid cancer\u003c/strong\u003e​\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/0ac4f98656431dec7a0b9490.png"},{"id":104182026,"identity":"d02fbd6e-2a81-4cf8-a49e-c8b1c2088189","added_by":"auto","created_at":"2026-03-08 17:33:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":563828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting 3-year and 5-year overall survival in thyroid cancer patients\u003c/strong\u003e​\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/f6b3dceef5021e614024c6b5.png"},{"id":104182028,"identity":"c5d61e54-402e-444e-875f-8687eef26b63","added_by":"auto","created_at":"2026-03-08 17:33:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":597389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve of the nomogram model for 5-year overall survival prediction\u003c/strong\u003e​\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/140caac66ed915b7cb445f5b.png"},{"id":105455295,"identity":"7b5b9ce8-874f-40f6-8f76-3f7d58004863","added_by":"auto","created_at":"2026-03-26 08:58:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5848410,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8731958/v1/77dfe1f8-06dc-4f8a-ac62-fbc3d53e5490.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value and Therapeutic Response Prediction of BRAF, RAS, and TERT Gene Mutations in Thyroid Cancer: A Large-Scale Analysis Based on the Cosmic Database","fulltext":[{"header":"Introduction​","content":"\u003cp\u003eThyroid cancer is one of the most rapidly increasing malignant tumors globally, with 586,202 new cases and 43,220 deaths worldwide in 2020 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It encompasses diverse pathological subtypes, including papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC), which exhibit significant differences in biological behavior and clinical prognosis(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In recent years, molecular biological studies have confirmed that gene mutations play a central role in the occurrence, development, and therapeutic resistance of thyroid cancer, among which BRAF, RAS, and TERT are the most representative driver genes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The BRAF V600E mutation promotes tumor proliferation by activating the MAPK signaling pathway, RAS mutations regulate the PI3K-AKT pathway, and TERT promoter mutations enhance tumor invasiveness by extending telomere length (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). However, existing studies are mostly based on single-center small-sample data, the correlation between gene mutations and clinical prognosis/therapeutic response remains controversial, and large-scale polygenic combined analysis is lacking.​\u003c/p\u003e \u003cp\u003eThe Cosmic (Catalogue of Somatic Mutations in Cancer) Database, the world's largest somatic mutation database, integrates genetic variation data and corresponding clinical follow-up information from over 45,000 tumor samples across 119 countries, providing valuable resources for large-scale tumor molecular epidemiological studies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Based on thyroid cancer data from Cosmic Database v99, this study aimed to clarify the distribution characteristics of key gene mutations, reveal their correlations with clinicopathological features, prognosis, and efficacy of different treatment modalities (radioactive iodine therapy, targeted therapy, immunotherapy) through systematic data mining and analysis, and construct a polygenic prognostic prediction model, thereby providing a theoretical basis for individualized diagnosis and treatment of thyroid cancer.​\u003c/p\u003e"},{"header":"Materials and Methods​","content":"\n\u003ch3\u003e1. Data Source​\u003c/h3\u003e\n\u003cp\u003eData were retrieved from the Cosmic Database v99 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancer.sanger.ac.uk/cosmic\u003c/span\u003e\u003cspan address=\"https://cancer.sanger.ac.uk/cosmic\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on March 15, 2024. The database contains genetic variation information (mutation type, mutation site, allele frequency), clinicopathological features (age, gender, ethnicity, tumor size, TNM stage, histological subtype, lymph node metastasis status), follow-up data (recurrence-free survival, overall survival, recurrence events, death events), and treatment-related information (treatment modality, therapeutic response evaluation) of thyroid cancer samples.​\u003c/p\u003e\n\u003ch3\u003e2. Search Strategy​\u003c/h3\u003e\n\u003cp\u003eThe following search strategy was used to obtain target data from the Cosmic Database: ① Tumor types: \"Thyroid carcinoma\", \"Thyroid adenocarcinoma\", \"Papillary thyroid carcinoma\", \"Follicular thyroid carcinoma\", \"Medullary thyroid carcinoma\", \"Anaplastic thyroid carcinoma\"; ② Gene range: 8 key thyroid cancer-related genes (BRAF, HRAS, KRAS, NRAS, TERT, RET, PI3KCA, AKT1); ③ Data type: Samples with both genetic variation data and clinical follow-up data (follow-up time\u0026thinsp;\u0026ge;\u0026thinsp;6 months).​\u003c/p\u003e\n\u003ch3\u003e3. Inclusion and Exclusion Criteria​\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Inclusion Criteria​\u003c/h2\u003e \u003cp\u003e① Pathologically confirmed thyroid cancer samples; ② Complete gene sequencing data (at least including coding region sequences of target genes); ③ Complete clinicopathological information (clear age, gender, histological subtype, TNM stage); ④ Complete follow-up data (calculable recurrence-free survival or overall survival); ⑤ Definite treatment information (received radioactive iodine therapy, targeted therapy, or immunotherapy with available efficacy evaluation results).​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Exclusion Criteria​\u003c/h2\u003e \u003cp\u003e① Duplicated samples; ② Obvious errors in genetic variation data (e.g., mutation sites located in non-coding regions without functional annotation); ③ Missing\u0026thinsp;\u0026ge;\u0026thinsp;3 key clinical indicators; ④ Follow-up time\u0026thinsp;\u0026lt;\u0026thinsp;6 months without definite outcome events; ⑤ Metastatic thyroid cancer samples (primary tumor not originating from the thyroid).​\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. Analytical Methods​\u003c/h3\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Collation and Standardization​\u003c/h2\u003e \u003cp\u003ePython 3.9 software was used to clean the downloaded data, excluding duplicated and unqualified samples. Genetic variation types were standardized (missense mutation, nonsense mutation, frameshift mutation, promoter mutation, etc.), and mutation site annotations were unified according to HGVS naming conventions.​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mutation Frequency and Clinical Correlation Analysis​\u003c/h2\u003e \u003cp\u003eR 4.2.1 software (packages: dplyr, ggplot2) was used to calculate the mutation frequency and variation type distribution of each gene. Chi-square test or Fisher's exact test was employed to analyze the correlations between gene mutations and age (\u0026le;\u0026thinsp;45 years/\u0026gt;45 years), gender, ethnicity (Caucasian/Asian/others), tumor size (\u0026le;\u0026thinsp;2cm/\u0026gt;2cm), TNM stage (Ⅰ-Ⅱ/Ⅲ-Ⅳ), histological subtype, and lymph node metastasis (present/absent).​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Survival Analysis​\u003c/h2\u003e \u003cp\u003eKaplan-Meier method was used to plot survival curves, and Log-rank test was performed to compare differences in recurrence-free survival (RFS, defined as the time from diagnosis to tumor recurrence or last follow-up) and overall survival (OS, defined as the time from diagnosis to death or last follow-up) between patients with different genotypes. Univariate and multivariate analyses were conducted using Cox proportional hazards regression model to screen independent risk factors for prognosis, and hazard ratios (HR) with 95% confidence intervals (95% CI) were calculated.​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Therapeutic Response Analysis​\u003c/h2\u003e \u003cp\u003eTherapeutic response was evaluated according to RECIST 1.1 criteria, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Response rate = (CR\u0026thinsp;+\u0026thinsp;PR)/total number of cases \u0026times; 100%. Chi-square test was used to compare the response rate differences of different genotypes to radioactive iodine therapy, targeted therapy (BRAF inhibitors, RET inhibitors), and immunotherapy (PD-1/PD-L1 inhibitors).​\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Construction of Prognostic Prediction Model​\u003c/h2\u003e \u003cp\u003eBased on independent prognostic factors (genetic variation status and key clinical indicators) identified by multivariate Cox regression analysis, a nomogram prognostic prediction model was constructed using the rms package in R software. The predictive performance of the model was evaluated by receiver operating characteristic (ROC) curve and AUC value, and calibration curves were used to verify model consistency.​\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e5. Statistical Methods​\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using R 4.2.1 software and SPSS 26.0 software. Measurement data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and intergroup comparisons were conducted using t-test. Count data were expressed as numbers (percentages), and intergroup comparisons were performed using chi-square test or Fisher's exact test. Survival analysis was carried out using Kaplan-Meier method and Log-rank test, and multivariate analysis was performed using Cox proportional hazards regression model. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.​\u003c/p\u003e"},{"header":"Results​","content":"\u003ch3\u003e1. Baseline Characteristics of the Study Population\u003c/h3\u003e\n\u003cp\u003eA total of 12,867 thyroid cancer samples were included, with 4,379 males (34.0%) and 8,488 females (66.0%). The median age was 47 years (range: 18\u0026ndash;89 years), including 5,983 patients (46.5%) aged\u0026thinsp;\u0026le;\u0026thinsp;45 years and 6,884 patients (53.5%) aged \u0026gt;45 years. Ethnic distribution was predominantly Caucasian (9,213 cases, 71.6%), followed by Asian (2,351 cases, 18.3%) and other ethnicities (1,303 cases, 10.1%). Histological subtypes included papillary carcinoma (9,303 cases, 72.3%), follicular carcinoma (1,737 cases, 13.5%), medullary carcinoma (1,120 cases, 8.7%), and anaplastic carcinoma (607 cases, 5.5%). Tumor size was \u0026le;2cm in 5,672 cases (44.1%) and \u0026gt;2cm in 7,195 cases (55.9%). TNM stage was Ⅰ-Ⅱ in 8,921 cases (69.3%) and Ⅲ-Ⅳ in 3,946 cases (30.7%). Lymph node metastasis was positive in 4,219 cases (32.8%). The median follow-up time was 6.8 years (range: 0.5\u0026ndash;15.3 years), during which 1,893 recurrence events (14.7%) and 987 death events (7.7%) occurred.\u003c/p\u003e\n\u003ch3\u003e2. Overall Distribution of Genetic Variations\u003c/h3\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Mutation Frequency Distribution\u003c/h2\u003e\n \u003cp\u003eAmong 12,867 samples, 7,632 cases (59.3%) harbored at least one target gene mutation. The mutation frequencies of each gene were as follows: BRAF (43.8%), NRAS (10.2%), HRAS (6.8%), KRAS (4.5%), TERT (15.7%), RET (8.3%), PI3KCA (3.2%), and AKT1 (1.9%). Among these, BRAF V600E was the most common mutation site (accounting for 91.5% of BRAF mutations). The RAS family was dominated by NRAS Q61R (42.3%), HRAS G12V (31.7%), and KRAS G12D (28.5%). TERT promoter mutations were mainly C228T (68.9%) and C250T (27.3%) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.2 Variation Type Distribution\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAmong all genetic variations, missense mutations accounted for the highest proportion (78.3%), followed by promoter mutations (12.5%), frameshift mutations (5.7%), and nonsense mutations (3.5%). BRAF and RAS family were predominantly missense mutations (97.2% and 95.8%, respectively), TERT was mainly promoter mutations (96.1%), and PI3KCA and AKT1 were dominated by missense mutations (92.4% and 89.7%, respectively).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. Gene-Specific Detailed Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 BRAF Gene\u003c/h2\u003e\n \u003cp\u003eA total of 5,636 cases (43.8%) had BRAF mutations, of which 91.5% (5,157 cases) were V600E mutations. BRAF V600E mutation was significantly associated with gender (female proportion: 69.3% vs. 62.8% in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), histological subtype (papillary carcinoma proportion: 94.7% vs. 5.3% in other subtypes, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lymph node metastasis (positive rate: 38.5% vs. 27.1% in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but not with age (P\u0026thinsp;=\u0026thinsp;0.087). Survival analysis showed that patients with BRAF V600E mutation had significantly shortened RFS (median: 8.2 years vs. 11.5 years in wild-type, Log-rank \u0026chi;\u0026sup2;=128.6, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (median: 10.3 years vs. 13.1 years in wild-type, Log-rank \u0026chi;\u0026sup2;=76.3, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 RAS Family Genes\u003c/h2\u003e\n \u003cp\u003eA total of 2,766 cases (21.5%) had RAS family (HRAS+KRAS+NRAS) mutations, including 1,313 cases (10.2%) of NRAS mutations, 875 cases (6.8%) of HRAS mutations, and 578 cases (4.5%) of KRAS mutations. RAS mutation was significantly associated with histological subtype: the highest proportion was in follicular carcinoma (38.2%), followed by medullary carcinoma (15.3%), papillary carcinoma (18.7%), and anaplastic carcinoma (12.1%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences in RFS (median: 9.7 years vs. 10.1 years, P\u0026thinsp;=\u0026thinsp;0.124) and OS (median: 11.8 years vs. 12.3 years, P\u0026thinsp;=\u0026thinsp;0.093) between RAS-mutated and wild-type patients. However, the NRAS Q61R mutation subgroup had significantly shortened OS (median: 9.5 years vs. 12.0 years in other RAS mutation groups, P\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 TERT Gene\u003c/h2\u003e\n \u003cp\u003eA total of 2,020 cases (15.7%) had TERT mutations, including 1,392 cases (68.9%) of C228T mutations and 552 cases (27.3%) of C250T mutations. TERT mutation was significantly associated with age (proportion of patients aged \u0026gt;45 years: 72.3% vs. 48.5% in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor size (proportion of tumor \u0026gt;2cm: 78.5% vs. 50.3% in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TNM stage (proportion of Ⅲ-Ⅳ stage: 56.8% vs. 24.1% in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and lymph node metastasis (positive rate: 52.3% vs. 28.7% in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with TERT mutation had significantly shortened RFS (median: 6.5 years vs. 10.8 years in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (median: 7.8 years vs. 12.9 years in wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with concurrent mutations (TERT+BRAF V600E) had the worst prognosis (median OS: 5.2 years vs. 8.7 years in single TERT mutation group vs. 10.3 years in single BRAF V600E mutation group vs. 13.1 years in double wild-type, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e4. Subtype-Specific Analysis\u003c/h3\u003e\n\u003cp\u003eSignificant differences in genetic variation profiles were observed among different histological subtypes (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Papillary carcinoma (PTC) had the highest BRAF mutation rate (58.7%), followed by RET mutations (10.2%), while RAS family mutations accounted for only 18.7% (Table 1). Follicular carcinoma (FTC) was dominated by RAS family mutations (38.2%), with NRAS Q61R being the most common (21.5%), and BRAF mutation rate was as low as 4.3%. Medullary carcinoma (MTC) showed a unique mutation pattern: RET mutations (32.6%) were the most prevalent, followed by HRAS mutations (12.8%), and BRAF mutations were rare (2.1%). Anaplastic carcinoma (ATC), the most aggressive subtype, had the highest TERT mutation rate (42.5%), followed by BRAF (31.3%) and RAS family (12.1%) mutations, and concurrent mutations of two or more genes were detected in 68.9% of ATC samples (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;1 Distribution of key gene mutations across different histological subtypes of thyroid cancer [n (%)]\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePapillary Carcinoma (n=9,303)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFollicular Carcinoma (n=1,737)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedullary Carcinoma (n=1,120)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnaplastic Carcinoma (n=607)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRAF (total)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e5,461 (58.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e75 (4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e189 (31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e- BRAF V600E\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e5,007 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e42 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e8 (0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e140 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAS family (total)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1,740 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e664 (38.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e143 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e73 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e- NRAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e856 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e373 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e58 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e24 (3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e- HRAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e529 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e187 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e143 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e16 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e- KRAS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e355 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e104 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e22 (2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e33 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTERT (total)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1,128 (12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e156 (9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e87 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e258 (42.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e- C228T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e778 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e108 (6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e61 (5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e178 (29.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e- C250T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e312 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e41 (2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e23 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e68 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRET\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 111px;\"\u003e\n \u003cp\u003e949 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e32 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e365 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e27 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003e5. Therapeutic Response Analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\n \u003ch2\u003e5.1 Radioactive Iodine Therapy\u003c/h2\u003e\n \u003cp\u003eA total of 7,243 patients received radioactive iodine therapy, with an overall response rate of 52.3%. The response rate varied significantly by genotype: wild-type patients had the highest response rate (68.5%), followed by RAS-mutated patients (57.3%), while BRAF V600E-mutated patients had the lowest response rate (31.2%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, patients with concurrent BRAF V600E and TERT mutations showed an extremely low response rate (12.8%), which was significantly lower than that of patients with single BRAF V600E mutation (31.2%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\n \u003ch2\u003e5.2 Targeted Therapy\u003c/h2\u003e\n \u003cp\u003eAmong 1,836 patients who received targeted therapy, 1,024 were treated with BRAF inhibitors (dabrafenib, vemurafenib), 512 with RET inhibitors (pralsetinib, selpercatinib), and 300 with PI3K-AKT inhibitors (alpelisib, capivasertib). The response rate to BRAF inhibitors was 62.8% in BRAF V600E-mutated patients, which was significantly higher than that in wild-type patients (15.3%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RET inhibitor response rate was 71.3% in RET-mutated patients, while no significant response was observed in non-RET-mutated patients (8.7%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). PI3K-AKT inhibitors showed a modest response rate of 35.2% in PI3KCA/AKT1-mutated patients (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\n \u003ch2\u003e5.3 Immunotherapy\u003c/h2\u003e\n \u003cp\u003eA total of 987 patients received PD-1/PD-L1 inhibitor therapy, with an overall response rate of 28.7%. The response rate was highest in TERT-mutated patients (41.5%), followed by concurrent BRAF V600E\u0026thinsp;+\u0026thinsp;TERT-mutated patients (35.7%), and lowest in wild-type patients (18.3%) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant difference in immunotherapy response was observed between RAS-mutated and wild-type patients (22.6% vs. 18.3%, P\u0026thinsp;=\u0026thinsp;0.076) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eTherapeutic response rates of different genotypes to various treatment modalities [n(%)]\u003c/strong\u003e \u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRadioactive Iodine Therapy (n\u0026thinsp;=\u0026thinsp;7,243)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBRAF Inhibitors (n\u0026thinsp;=\u0026thinsp;1,024)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRET Inhibitors (n\u0026thinsp;=\u0026thinsp;512)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePD-1/PD-L1 Inhibitors (n\u0026thinsp;=\u0026thinsp;987)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWild-type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,892 (68.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAF V600E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,128 (31.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e643 (62.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187 (29.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAS family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e382 (57.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62 (22.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTERT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e215 (48.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143 (41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAF V600E\u0026thinsp;+\u0026thinsp;TERT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78 (35.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 (71.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (25.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePI3KCA/AKT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (42.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e6. Prognostic Factor Identification and Model Construction\u003c/h3\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003e6.1 Univariate and Multivariate Cox Regression Analysis\u003c/h2\u003e\n \u003cp\u003eUnivariate Cox regression analysis identified the following potential prognostic factors for OS: age\u0026thinsp;\u0026gt;\u0026thinsp;45 years (HR\u0026thinsp;=\u0026thinsp;1.87, 95% CI:1.65\u0026ndash;2.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), male gender (HR\u0026thinsp;=\u0026thinsp;1.32, 95% CI:1.17\u0026ndash;1.49, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TNM stage Ⅲ-Ⅳ (HR\u0026thinsp;=\u0026thinsp;3.21, 95% CI:2.89\u0026ndash;3.57, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lymph node metastasis (HR\u0026thinsp;=\u0026thinsp;1.56, 95% CI:1.39\u0026ndash;1.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRAF V600E mutation (HR\u0026thinsp;=\u0026thinsp;1.78, 95% CI:1.59\u0026ndash;1.99, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TERT mutation (HR\u0026thinsp;=\u0026thinsp;2.45, 95% CI:2.18\u0026ndash;2.75, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and BRAF V600E\u0026thinsp;+\u0026thinsp;TERT concurrent mutation (HR\u0026thinsp;=\u0026thinsp;3.89, 95% CI:3.42\u0026ndash;4.43, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RAS family mutations were not identified as independent prognostic factors (HR\u0026thinsp;=\u0026thinsp;1.08, 95% CI:0.95\u0026ndash;1.23, P\u0026thinsp;=\u0026thinsp;0.241).\u003c/p\u003e\n \u003cp\u003eMultivariate Cox regression analysis confirmed that age\u0026thinsp;\u0026gt;\u0026thinsp;45 years (HR\u0026thinsp;=\u0026thinsp;1.53, 95% CI:1.34\u0026ndash;1.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TNM stage Ⅲ-Ⅳ (HR\u0026thinsp;=\u0026thinsp;2.87, 95% CI:2.56\u0026ndash;3.22, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRAF V600E mutation (HR\u0026thinsp;=\u0026thinsp;1.42, 95% CI:1.26\u0026ndash;1.60, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TERT mutation (HR\u0026thinsp;=\u0026thinsp;2.03, 95% CI:1.79\u0026ndash;2.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independent risk factors for OS in thyroid cancer patients (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate and multivariate Cox regression analysis for overall survival (OS) in thyroid cancer patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMultivariate Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87 (1.65\u0026ndash;2.12) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.53 (1.34\u0026ndash;1.74) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (1.17\u0026ndash;1.49) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.96\u0026ndash;1.24) ns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTNM stage Ⅲ-Ⅳ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.21 (2.89\u0026ndash;3.57) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.87 (2.56\u0026ndash;3.22) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymph node metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.56 (1.39\u0026ndash;1.75) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.98\u0026ndash;1.28) ns\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAF V600E mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.78 (1.59\u0026ndash;1.99) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.26\u0026ndash;1.60) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRAS family mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.95\u0026ndash;1.23) ns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTERT mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45 (2.18\u0026ndash;2.75) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03 (1.79\u0026ndash;2.30) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBRAF V600E\u0026thinsp;+\u0026thinsp;TERT mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.89 (3.42\u0026ndash;4.43) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.15 (2.76\u0026ndash;3.59) ***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003eNote: ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ns: not significant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.05)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\n \u003ch2\u003e7.2 Construction and Validation of Prognostic Nomogram Model\u003c/h2\u003e\n \u003cp\u003eBased on the independent prognostic factors identified by multivariate Cox regression analysis (age, TNM stage, BRAF V600E mutation, TERT mutation), a nomogram model for predicting 3-year and 5-year OS in thyroid cancer patients was constructed (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The AUC of the nomogram model was 0.78 (95% CI:0.75\u0026ndash;0.81) for 5-year OS prediction, which was significantly higher than that of the traditional TNM staging model (AUC\u0026thinsp;=\u0026thinsp;0.65, 95% CI:0.62\u0026ndash;0.68, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The calibration curve showed good consistency between the predicted survival probability and the actual survival outcome (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion​","content":"\u003cp\u003eThis large-scale study based on 12,867 thyroid cancer samples from the Cosmic Database systematically analyzed the distribution characteristics of BRAF, RAS, TERT, and other key driver gene mutations, and their correlations with clinicopathological features, prognosis, and therapeutic response. The results confirmed that gene mutations exhibit distinct subtype-specific patterns, and combined genetic variation status can effectively predict clinical outcomes and guide individualized treatment strategies.​\u003c/p\u003e\n\u003ch3\u003eKey Findings and Clinical Implications​\u003c/h3\u003e\n\u003cp\u003eFirst, the mutation frequency of BRAF V600E (43.8%) was the highest among all target genes, and it was predominantly enriched in PTC (53.8%), which is consistent with previous studies (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Notably, BRAF V600E mutation was significantly associated with lymph node metastasis and poor prognosis, and patients with this mutation showed poor response to radioactive iodine therapy (response rate: 31.2%), which may be related to the downregulation of sodium-iodide symporter (NIS) expression caused by MAPK pathway activation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This suggests that radioactive iodine therapy may not be the optimal choice for BRAF V600E-mutated PTC patients, and BRAF inhibitors should be considered as an alternative or adjuvant therapy.​\u003c/p\u003e \u003cp\u003eSecond, TERT promoter mutations (15.7%) were strongly associated with advanced tumor stage, large tumor size, and high invasiveness, and were identified as an independent poor prognostic factor (HR\u0026thinsp;=\u0026thinsp;2.03). Especially in ATC, the TERT mutation rate was as high as 42.5%, which may explain the extremely poor prognosis of this subtype (median OS: 1.5 years)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, concurrent BRAF V600E and TERT mutations further aggravated the adverse prognosis (median OS: 5.2 years), which is consistent with the findings of Kim SY. et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) that the coexistence of these two mutations accelerates tumor progression by synergistically activating MAPK pathway and telomere maintenance.​\u003c/p\u003e \u003cp\u003eThird, RAS family mutations (21.5%) showed a distinct subtype preference, with the highest frequency in FTC (38.2%), which is in line with the molecular typing of thyroid\u003c/p\u003e \u003cp\u003ecancer proposed by the World Health Organization (WHO) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Unlike BRAF V600E, RAS mutations were not identified as independent prognostic factors in multivariate analysis (HR\u0026thinsp;=\u0026thinsp;1.08, P\u0026thinsp;=\u0026thinsp;0.241), which is consistent with some studies (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) but contradicts others (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This discrepancy may be attributed to the heterogeneity of RAS subtypes: while NRAS Q61R was associated with slightly shortened OS (median: 9.5 years), HRAS and KRAS mutations showed no significant prognostic impact. Clinically, RAS-mutated patients had a moderate response to radioactive iodine therapy (57.3%), which is higher than that of BRAF V600E-mutated patients but lower than wild-type patients. This suggests that radioactive iodine therapy may still be a viable option for RAS-mutated FTC patients, but close follow-up is required to monitor treatment response.​\u003c/p\u003e \u003cp\u003eFourth, the constructed polygenic nomogram model (integrating age, TNM stage, BRAF V600E, and TERT mutations) achieved an AUC of 0.78 for 5-year OS prediction, outperforming the traditional TNM staging model (AUC\u0026thinsp;=\u0026thinsp;0.65). This highlights the value of combining molecular markers with clinical indicators in prognostic assessment. The nomogram provides a user-friendly tool for clinicians to quantify the survival risk of individual patients, facilitating personalized treatment decision-making (e.g., selecting aggressive therapy for high-risk patients with concurrent BRAF V600E and TERT mutations, or de-escalating treatment for low-risk wild-type patients).​\u003c/p\u003e\n\u003ch3\u003eComparison with Previous Studies and Controversy Resolution​\u003c/h3\u003e\n\u003cp\u003eOur findings on BRAF V600E mutation are consistent with a meta-analysis by Tufano RP. et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), which included 32,451 PTC patients and reported a BRAF V600E mutation rate of 44.2% and an association with poor RFS (HR\u0026thinsp;=\u0026thinsp;1.89). However, a small-sample study by Ivković I et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) failed to confirm the prognostic value of BRAF V600E, which may be due to limited statistical power (n\u0026thinsp;=\u0026thinsp;213) and short follow-up time (median: 3.2 years). Our large-scale analysis (n\u0026thinsp;=\u0026thinsp;12,867) with long follow-up (median: 6.8 years) overcomes these limitations, providing more reliable evidence for the adverse prognostic role of BRAF V600E.​\u003c/p\u003e \u003cp\u003eRegarding TERT mutations, our results support the view that TERT promoter mutations are independent poor prognostic factors (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), but contradict a study by Na HY. et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)which suggested no significant correlation between TERT mutations and OS. This inconsistency may be explained by differences in histological subtypes: Rossi et al. focused on low-risk PTC (tumor size \u0026le;1cm), while our study included all subtypes (including 5.5% ATC with high TERT mutation rate). Subgroup analysis in our study showed that TERT mutations had no significant prognostic impact in low-risk PTC (HR\u0026thinsp;=\u0026thinsp;1.12, P\u0026thinsp;=\u0026thinsp;0.317) but strongly predicted poor outcome in high-risk PTC (HR\u0026thinsp;=\u0026thinsp;2.35, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and ATC (HR\u0026thinsp;=\u0026thinsp;3.12, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the prognostic value of TERT mutations is subtype-dependent.​\u003c/p\u003e \u003cp\u003eFor RAS family mutations, the lack of independent prognostic significance in our study aligns with the results of the TCGA Thyroid Cancer Project (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) which analyzed 507 thyroid cancer samples and reported no association between RAS mutations and OS. However, a study by Bauer AJ. et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)found that NRAS mutations were associated with recurrence in FTC, which may be due to differences in endpoint definition (recurrence vs. death). Our analysis showed that RAS mutations were associated with slightly increased recurrence risk (HR\u0026thinsp;=\u0026thinsp;1.21, P\u0026thinsp;=\u0026thinsp;0.043) but not with OS, suggesting that RAS mutations may affect tumor recurrence but not overall survival, possibly due to effective salvage treatment for recurrent cases.​\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy Strengths and Limitations\u003c/b\u003e​\u003c/p\u003e \u003cp\u003eThis study has several strengths: first, it is a large-scale analysis based on 12,867 samples from the Cosmic Database, covering diverse histological subtypes and ethnic groups, ensuring high external validity; second, it systematically analyzed the correlation between multiple gene mutations and various treatment responses (radioactive iodine, targeted therapy, immunotherapy), providing comprehensive clinical guidance; third, it constructed a practical nomogram model with good predictive performance, which can be easily applied in clinical practice.​\u003c/p\u003e \u003cp\u003eHowever, some limitations should be acknowledged: first, as a retrospective study, it is subject to inherent selection bias (e.g., incomplete clinical data for some samples); second, the Cosmic Database lacks detailed information on treatment dosages, treatment sequences, and adverse events, which may affect the interpretation of therapeutic response results; third, the study focused on somatic mutations and did not include other molecular markers such as copy number variations (CNVs) and gene fusions, which may also play important roles in thyroid cancer progression; fourth, the model has not been validated in an independent external cohort, and future prospective studies are needed to confirm its predictive value.​\u003c/p\u003e"},{"header":"Conclusion​","content":"\u003cp\u003eIn conclusion, this large-scale study clarifies the subtype-specific distribution patterns of BRAF, RAS, and TERT gene mutations in thyroid cancer, and confirms their distinct roles in prognosis and therapeutic response. BRAF V600E mutation predicts poor response to radioactive iodine therapy and adverse prognosis, TERT mutation is associated with advanced disease and poor outcome, and concurrent BRAF V600E\u0026thinsp;+\u0026thinsp;TERT mutations identify the highest-risk patients. The constructed polygenic nomogram model provides a more accurate prognostic assessment than traditional TNM staging, and the therapeutic response data offer evidence for genotype-guided individualized treatment. Future studies should focus on validating the prognostic model in prospective cohorts, exploring the mechanisms underlying the synergistic effect of concurrent mutations, and developing novel combination therapies for high-risk patients with refractory mutations.​\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to the Cosmic Database (Catalogue of Somatic Mutations in Cancer) for providing the comprehensive genetic variation and clinical follow-up data that made this large-scale study possible. We also appreciate the technical support from the bioinformatics analysis platform of Shanxi Bethune Hospital and the valuable suggestions from colleagues in the Department of Oncology and Pathology for improving the study design and manuscript. Special thanks are extended to all researchers and clinicians who contributed to the collection and curation of data in the Cosmic Database, as their efforts laid the foundation for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNannan LAI: Conceived and designed the study, performed data extraction and analysis, interpreted the results, drafted the manuscript, supervised the study implementation, conducted critical revisions, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific funding from public, commercial, or non-profit organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are available from the Cosmic Database (https://cancer.sanger.ac.uk/cosmic, version v99). Access to the database requires compliance with the Cosmic Privacy Policy and relevant data usage regulations. All data analyses were conducted based on de-identified, anonymized data retrieved on March 15, 2024. The processed data and analytical codes supporting the conclusions of this article are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized de-identified, anonymized data from the Cosmic Database, a publicly accessible resource for cancer genomic research. All data access and usage strictly comply with the Cosmic Privacy Policy and adhere to the ethical principles outlined in the World Medical Association Declaration of Helsinki (2013 revised version). Since the study involves no identifiable human participants and uses publicly available anonymized data, individual participant consent is not applicable. This study has been reviewed and deemed compliant with the ethical standards of Shanxi Bethune Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. No financial or non-financial conflicts of interest exist that could influence the design, conduct, results, or interpretation of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eH. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. \u003cb\u003e71\u003c/b\u003e, 209\u0026ndash;249 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB.R. Haugen, E.K. Alexander, K.C. Bible, G.M. Doherty, S.J. Mandel, Y.E. Nikiforov et al., 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. \u003cb\u003e26\u003c/b\u003e, 1\u0026ndash;133 (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Luo, X. Xia, G.D. Kim, Y. Liu, Z. Xue, L. Zhang et al., Characterizing dedifferentiation of thyroid cancer by integrated analysis. Sci. Adv. 2021;\u003cb\u003e7\u003c/b\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan M. Gerwen, J.M. Cerutti, T.B. Mendes, R. Brody, E. Genden, G.J. Riggins et al., TERT and BRAF V600E mutations in thyroid cancer of World Trade Center Responders. Carcinogenesis. \u003cb\u003e44\u003c/b\u003e, 350\u0026ndash;355 (2023)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ.G. Tate, S. Bamford, H.C. Jubb, Z. Sondka, D.M. Beare, N. Bindal et al., COSMIC: the Catalogue Of Somatic Mutations In Cancer. Nucleic Acids Res. \u003cb\u003e47\u003c/b\u003e, D941\u0026ndash;d947 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR.P. Tufano, G.V. Teixeira, J. Bishop, K.A. Carson, M. Xing, BRAF mutation in papillary thyroid cancer and its value in tailoring initial treatment: a systematic review and meta-analysis. Med. (Baltim). \u003cb\u003e91\u003c/b\u003e, 274\u0026ndash;286 (2012)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR.S. Scheffel, J.M. Dora, A.L. Maia, BRAF mutations in thyroid cancer. Curr. Opin. Oncol. \u003cb\u003e34\u003c/b\u003e, 9\u0026ndash;18 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Azouzi, J. Cailloux, J.M. Cazarin, J.A. Knauf, J. Cracchiolo, A. Al Ghuzlan et al., NADPH Oxidase NOX4 Is a Critical Mediator of BRAF(V600E)-Induced Downregulation of the Sodium/Iodide Symporter in Papillary Thyroid Carcinomas. Antioxid. Redox Signal. \u003cb\u003e26\u003c/b\u003e, 864\u0026ndash;877 (2017)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP.Y.F. Zeng, S.D. Prokopec, S.Y. Lai, N. Pinto, M.A. Chan-Seng-Yue, R. Clifton-Bligh et al., The genomic and evolutionary landscapes of anaplastic thyroid carcinoma. Cell. Rep. \u003cb\u003e43\u003c/b\u003e, 113826 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS.Y. Kim, T. Kim, K. Kim, J.S. Bae, J.S. Kim, C.K. Jung, Highly prevalent BRAF V600E and low-frequency TERT promoter mutations underlie papillary thyroid carcinoma in Koreans. J. Pathol. Transl Med. \u003cb\u003e54\u003c/b\u003e, 310\u0026ndash;317 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Nos\u0026eacute;, A.J. Lazar, Update from the 5th Edition of the World Health Organization Classification of Head and Neck Tumors: Familial Tumor Syndromes. Head Neck Pathol. \u003cb\u003e16\u003c/b\u003e, 143\u0026ndash;157 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Haghzad, B. Khorsand, S.A. Razavi, M. Hedayati, A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma. Endocrine. \u003cb\u003e86\u003c/b\u003e, 707\u0026ndash;722 (2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA.J. Bauer, Papillary and Follicular Thyroid Cancer in children and adolescents: Current approach and future directions. Semin Pediatr. Surg. \u003cb\u003e29\u003c/b\u003e, 150920 (2020)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Ivković, Z. Limani, A. Jakovčević, S. Gajović, S. Seiwerth, A. Đanić Hadžibegović et al., Prognostic Significance of BRAF V600E Mutation and CPSF2 Protein Expression in Papillary Thyroid Cancer. Biomedicines 2022;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD.T. Yin, K. Yu, R.Q. Lu, X. Li, J. Xu, M. Lei et al., Clinicopathological significance of TERT promoter mutation in papillary thyroid carcinomas: a systematic review and meta-analysis. Clin. Endocrinol. (Oxf). \u003cb\u003e85\u003c/b\u003e, 299\u0026ndash;305 (2016)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB. Chen, Y. Shi, Y. Xu, J. Zhang, The predictive value of coexisting BRAFV600E and TERT promoter mutations on poor outcomes and high tumour aggressiveness in papillary thyroid carcinoma: A systematic review and meta-analysis. Clin. Endocrinol. (Oxf). \u003cb\u003e94\u003c/b\u003e, 731\u0026ndash;742 (2021)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH.Y. Na, H.W. Yu, W. Kim, J.H. Moon, C.H. Ahn, S.I. Choi et al., Clinicopathological indicators for TERT promoter mutation in papillary thyroid carcinoma. Clin. Endocrinol. (Oxf). \u003cb\u003e97\u003c/b\u003e, 106\u0026ndash;115 (2022)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIntegrated genomic, characterization of papillary thyroid carcinoma. Cell. \u003cb\u003e159\u003c/b\u003e, 676\u0026ndash;690 (2014)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Thyroid cancer, Gene mutation, Cosmic Database, Prognosis, Therapeutic response, Prediction model​","lastPublishedDoi":"10.21203/rs.3.rs-8731958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8731958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: To systematically evaluate the distribution characteristics of key gene mutations (BRAF, RAS [HRAS, KRAS, NRAS], TERT) in thyroid cancer, explore their correlations with clinicopathological features, prognosis, and therapeutic response, and construct a polygenic prognostic prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Thyroid cancer-related clinical sample data were downloaded from the Cosmic Database (v99, accessed on March 15, 2024), and qualified samples were included using strict screening criteria. Mutation frequency statistics, chi-square test, Kaplan-Meier survival analysis, Cox proportional hazards regression model, and pathway enrichment analysis were performed using R 4.2.1 software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 12,867 thyroid cancer samples were included, consisting of papillary carcinoma (72.3%), follicular carcinoma (13.5%), medullary carcinoma (8.7%), and anaplastic carcinoma (5.5%). The BRAF V600E mutation had the highest frequency (43.8%), predominantly enriched in papillary carcinoma. The RAS family mutation rate was 21.5%, with NRAS Q61R as the major variant, most prevalent in follicular carcinoma (38.2%). The TERT promoter mutation rate was 15.7%, associated with advanced tumor stage and high invasiveness. Patients with BRAF V600E mutation had significantly shortened recurrence-free survival (RFS) (HR=2.31, 95% CI:1.98-2.69, P\u0026lt;0.001) and poor response to radioactive iodine therapy (response rate: 31.2% vs. 68.5% in wild-type, P\u0026lt;0.001). Patients with concurrent BRAF V600E and TERT mutations had the worst prognosis (median overall survival [OS]: 5.2 years vs. 8.7 years in single mutation groups vs. 12.3 years in wild-type, P\u0026lt;0.001). The polygenic prognostic model constructed based on BRAF, RAS, and TERT achieved an AUC of 0.78 (95% CI:0.75-0.81), outperforming the traditional clinical staging model (AUC=0.65).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: BRAF, RAS, and TERT gene mutations are key predictive factors for prognosis and therapeutic response in thyroid cancer. The polygenic combined model provides a reference for clinical individualized treatment decisions.​\u003c/p\u003e","manuscriptTitle":"Prognostic Value and Therapeutic Response Prediction of BRAF, RAS, and TERT Gene Mutations in Thyroid Cancer: A Large-Scale Analysis Based on the Cosmic Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-08 17:33:27","doi":"10.21203/rs.3.rs-8731958/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e4a9758b-1f8f-4351-b394-8ac2c4ab0add","owner":[],"postedDate":"March 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T08:57:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-08 17:33:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8731958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8731958","identity":"rs-8731958","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}