The diagnostic efficacy of serum galectin-3 and other markers in papillary thyroid carcinoma

The diagnostic efficacy of serum galectin-3 and other markers in papillary thyroid carcinoma | 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 The diagnostic efficacy of serum galectin-3 and other markers in papillary thyroid carcinoma Xiaohong Zhang, Xin Song, Yu Li, Xiangyi Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3823254/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 Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer, representing approximately 85–90% of cases. Galectin-3 (GAL-3) is a well-established histologic marker of thyroid cancer that is not expressed by normal thyroid cells. The potential utility of serum GAL-3 in differentiating benign thyroid tumors from PTC remains to be studied. Methods According to the postoperative pathology results, patients were divided into the PTC group (165 cases) and the benign thyroid tumor group (95 cases). Serum GAL-3 was detected by chemiluminescence immunoassay (CLIA). Additionally, other markers including human epidermal growth factor receptor 2 (HER2), Ki-67, cytokeratin 19 (CK19), thyroid peroxidase (TPO) and CD56 were detected by enzyme-linked immunosorbent assay (ELISA). Serum levels were compared between patients with PTC and those with benign tumors using SPSS 22.0. Results In patients with PTC, serum GAL-3 levels were significantly higher than those in patients with benign thyroid tumors (p = 0.045). Additionally, serum HER-2 and Ki-67 levels in PTC patients were significantly higher than those in patients with benign tumors (p 0.05). The Receiver Operating Characteristic (ROC) curve analysis revealed that GAL-3 had an area under the curve (AUC) of 0.645 (p = 0.000) for distinguishing between benign and malignant thyroid tumors. When combined with HER2 and Ki-67, the AUC increased to 0.787 (p = 0.000). Conclusions Our research results indicate that the combination of GAL-3, HER2, and Ki-67 can be used to differentiate between benign and malignant thyroid diseases. Trial registration Not applicable papillary thyroid carcinoma galectin-3 diagnostic efficacy Figures Figure 1 Figure 2 Introduction Thyroid cancer is a malignant tumor originating from the thyroid follicular epithelium or follicular epithelial cells, and it is also the most common malignancy in the head and neck region [ 1 ]. In recent years, the incidence of thyroid cancer has rapidly increased on a global scale [ 2 ]. According to the National Cancer Registry, thyroid cancer is the fourth most common malignant tumor in women in urban areas of China. And thyroid cancer is expected to increase at an annual rate of 20% in China [ 3 ]. Based on origin and differentiation, thyroid cancer is further categorized into papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC). Among these, PTC is the most common, accounting for approximately 85–90% of all thyroid cancers. PTC and FTC together are referred to as differentiated thyroid carcinomas (DTC) [ 4 ]. Thyroid hormones, thyroid autoantibodies, and tumor marker tests are the most commonly used in thyroid examinations [ 5 ]. High-resolution ultrasound, recognized for its convenience, non-invasiveness, and cost-effectiveness, serves as the preferred imaging method for thyroid cancer. It can effectively detect thyroid nodules with a diameter greater than 2mm, providing detailed information about their boundaries, morphology, and internal structure [ 6 ]. However, its diagnostic accuracy relies on the clinical expertise of the sonographer. For the preoperative assessment of thyroid nodules' benign or malignant nature, fine-needle aspiration biopsy (FNAB) emerges as the most sensitive and specific method [ 7 ]. Nevertheless, numerous studies indicate that accurately diagnosing benign or malignant thyroid nodules remains a formidable challenge in clinical pathology, with up to 15% of thyroid cancer cases ultimately eluding detection through FNAB [ 8 ]. Galectin-3 (beta-galactoside-binding lectin-3, GAL-3), implicated in cell adhesion, is associated with the initiation and progression of tumors [ 9 ]. Existing research reveals that GAL-3 is absent in normal thyroid tissues but markedly expressed in thyroid cancer tissues [ 10 ]. Animal studies indicate that radiolabeled antibodies targeting GAL-3 accumulate in thyroid cancer tissues [ 11 ]. It is also suggested that the combined action of GAL-3 and thyroid peroxidase aids in differentiating and diagnosing thyroid tumors [ 12 ]. Numerous studies suggest that GAL-3 plays a role in predicting lymph node metastasis in primary thyroid cancer [ 13 ]. In the current practice of immunohistochemical diagnosis following the excision of thyroid cancer tissue, GAL-3, CK19, and Hector Battifora Mesothelial-1 (HBME-1) are the most commonly used markers for identifying papillary thyroid carcinoma [ 14 ]. Therefore, our study aims to explore the diagnostic significance of serum GAL-3 in thyroid cancer, providing insights into the differential diagnosis of PTC and benign thyroid tumors. Materials and Methods Aim and design This study aims to investigate the diagnostic efficacy of serum GAL-3 in thyroid cancer through a retrospective observational study design. Patients and sample collection Data were collected from 260 patients with thyroid tumors admitted to Beijing Tongren Hospital, Capital Medical University, from January 2023 to May 2023. Inclusion criteria were as follows: 1) complete clinical data; 2) no special treatments (e.g., radiotherapy, chemotherapy, hormone replacement therapy, etc.); 3) postoperative pathology confirming PTC or benign thyroid tumor; 4) clear pathological staging. Exclusion criteria were as follows: 1) concurrent other cancers; 2) concurrent kidney diseases; 3) concurrent other infectious diseases; 4) concurrent cardiovascular system diseases. The tumor-lymph node-metastasis (TNM) staging followed the 8th edition classification system of the American Joint Committee on Cancer. In addition, to assess the serum GAL-3 levels in a population of healthy individuals undergoing normal physical examination, we collected samples from healthy individuals without cardiovascular, liver, kidney, diabetes, thyroid, hypertension, tumor-related conditions, pregnancy, etc. Prior to surgery, fasting peripheral venous blood samples of 3 mL were collected from all patients and immediately centrifuged at 3000 rpm for 10 minutes. Clinical biochemical and thyroid function indicators were promptly analyzed. The remaining serum was stored at -80°C for subsequent detection of GAL-3 and other markers, including human epidermal growth factor receptor 2 (HER2), Ki-67, cytokeratin 19 (CK19), thyroid peroxidase (TPO), and CD56. Detection of clinical biochemical and thyroid function indicators Beckman AU5811 biochemical analyzer and its corresponding reagents (Beckman Coulter Inc., Brea, CA) were used to measure blood glucose (GLU), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), creatinine (CRE), apolipoprotein AI (ApoA1), and apolipoprotein B (ApoB). Additionally, on the Beckman AU5811 instrument, the Kyowa Medex Co., Ltd. reagents from Japan were employed to determine triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c). Serum apolipoprotein E (ApoE) was assessed using external reagents (Meikang, Ningbo, China) on the Beckman AU5811 instrument, and high-sensitivity C-reactive protein (hs-CRP) in serum was determined using reagents from Desai, Shanghai, China. Beckman DXI800 chemiluminescent immunoassay analyzer and its associated reagents (Beckman Coulter Inc., Brea, CA) were utilized for the detection of thyroid function indicators, including total triiodothyronine (TT3), total thyroxine (TT4), free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), thyroglobulin antibody (TG-Ab), thyroid peroxidase antibody (TPO-Ab), parathyroid hormone (PTH), interleukin-6 (IL-6), and ferritin (FER). Detection of GAL-3 and other markers The serum GAL-3 levels were measured using the Abbott Diagnostics fully automated immunoassay analyzer (I2000SR, Abbott Diagnostics, USA) and its corresponding reagents. Other indicators, including HER2, Ki-67, CK19, TPO, and CD56, were assessed using enzyme-linked immunosorbent assay (ELISA). Serum HER2 and Ki-67 content were determined using the CUSABIO kit (Wuhan, China); serum CK19 and TPO levels were measured using ELISA kits from Jianglai (Shanghai, China). The determination of serum CD56 content was performed using the ELB kit (Wuhan, China). For the enzyme-linked immunosorbent assay (ELISA) procedure, 100 µL of serum was added to each well of the enzyme-coated plate, sealed, and incubated at 37°C for 120 minutes. After five washes, 100 µL of enzyme-labeled reagent was added, followed by incubation at 37°C for 60 minutes. After washing, 90 µL of chromogenic agent was added, and the mixture was incubated at 37°C in the dark for 15 minutes. The reaction was terminated by adding 50 µL of stop solution to each well. The absorbance was measured at 450 nm using an enzyme label reader (MB530, Huisong, China), and the levels of the other markers were calculated using a standard curve. Statistical analysis The statistical analysis was conducted using SPSS 22.0 software, and graphing was performed using GraphPad Prism 8.0 software. Normally distributed metric data were expressed as X ± standard deviation (SD). Differences between two groups were compared using independent sample t-tests, while differences among three groups were assessed using one-way analysis of variance (ANOVA). For skewed distribution measurement data, the median (interquartile range) (M (P25, P75)) was used, and the Mann-Whitney U test was employed to compare differences between two groups. Count data were presented as cases or percentages, and inter-group comparisons were made using the chi-square test. Receiver operating characteristic (ROC) curve analysis was applied to assess the diagnostic value of GAL-3 and other indicators in combination for distinguishing between PTC and benign thyroid tumors, with a higher area under the curve (AUC) indicating better discriminatory ability. A significance level of p < 0.05 was considered statistically significant. Results Patient characteristics A total of 260 patients were included, with 165 in the PTC group and 95 in the benign thyroid tumor group based on postoperative pathological results. There were no significant differences in age and sex between the two groups. Among the 165 PTC patients, 121 cases (73.33%) had a tumor diameter greater than 1cm, 108 cases (65.45%) were solitary, and 57 cases (34.55%) were multifocal. Tumor staging revealed 137 cases (83.03%) in stages 1–2 and 28 cases (16.97%) in stages 3–4. Lymph node metastasis was absent in 90 cases (54.55%) and present in 75 cases (45.45%). Among the 95 patients in benign thyroid tumor group, 8 cases were benign papillary hyperplasia, 9 cases were thyroid adenoma, and 78 cases were nodular thyroid goiter. The healthy group included 138 individuals, with 79 males and 59 females, aged between 18–65 years old (mean age 41.8 ± 9.3 years). Although the GLU in patients with benign thyroid diseases is slightly higher than in PTC patients, there is no significant difference between the two groups. This may be because benign thyroid diseases often present with clinical symptoms (such as chest tightness, dry cough, and difficulty breathing) and require surgery for thyroid nodule patients. These patients tend to be slightly older than PTC patients, resulting in a mild increase in blood glucose levels. There were no significant differences between the two groups in liver function (ALT, AST), kidney function (BUN, CRE), lipid profile (TG, TC, LDL-c, HDL-c), hs-CRP, ApoE, ApoA1, and ApoB. In the thyroid function tests, there were no significant differences between the PTC group and the benign thyroid disease group in TT3, TT4, FT3, FT4, TSH, TPO-Ab, and PTH. However, the benign disease group exhibited significantly higher levels of thyroglobulin compared to the PTC group, while the PTC group showed significantly higher levels of TG-Ab than the benign disease group (Table 1 ). Table 1 General characteristics of patients with papillary thyroid carcinoma and benign thyroid tumor Parameters PTC (n = 165) (median, interquartile range) Benign (n = 95) (median, interquartile range) P value Age (years) ＜50 106 51 0.094 ≥ 50 59 44 Sex Male 52 31 0.852 Female 113 64 Tumor size < 1cm 44 - ≥ 1cm 121 - Multifocality Unifocal 108 - Multifocal 57 - Tumor stage T1-T2 137 - T3-T4 28 - Lymph node metastasis N0 90 - N1 75 - Clinical biochemistry GLU (mmol/L) 5.22 (4.91–5.72) 5.35 (5.00-5.81) 0.144 BUN (mmol/L) 4.7 (3.9–5.4) 4.7 (3.8–5.7) 0.790 CRE (umol/L) 61 (52–71) 62 (53–70) 0.338 ALT(U/L) 18 (13–31) 16 (12–23) 0.127 AST (U/L) 21 (17–25) 20 (17–23) 0.352 Triglyceride (mmol/L) 1.05 (0.69–1.67) 1.17 (0.80–1.71) 0.184 TC (mmol/L) 4.65 (4.01–5.23) 4.81 (4.23–5.36) 0.205 LDL-c (mmol/L) 2.85 (2.23–3.34) 2.88 (2.24–3.42) 0.521 HDL-c (mmol/L) 1.29 (1.10–1.58) 1.38 (1.13–1.56) 0.389 Hs-CRP (mg/L) 0.9 (0.3–1.9) 1.0 (0.3–1.9) 0.935 ApoE (mg/dL) 3.5 (2.7–4.3) 3.6 (2.7–4.6) 0.652 ApoA1 (g/L) 1.43 (1.23–1.62) 1.40 (1.17–1.63) 0.454 ApoB (g/L) 0.82 (0.64–0.99) 0.83 (0.67-1.00) 0.716 FER (ng/ml) 53.70 (23.25-115.28) 65.30 (38.70-135.50) 0.252 IL-6 (ng/ml) 1.4 (0.7–2.7) 1.6 (0.9–3.4) 0.094 Thyroid function markers TT3(nmol/L) 1.66 (1.44–1.88) 1.68 (1.47–1.85) 0.393 TT4(nmol/L) 109.70 (95.00-126.58) 112.85 (99.78–128.40) 0.273 FT3(pmol/L) 5.34 (4.92–5.88) 5.30 (4.85–5.80) 0.419 FT4(pmol/L) 11.00 (10.03–12.13) 11.19 (10.16–12.28) 0.298 TSH (mIU/L) 1.70 (1.15–2.69) 1.58 (1.05–2.49) 0.243 Thyroglobulin (ng/ml) 8.84 (3.05–21.35) 16.82 (5.25–51.32) 0.001 TG-Ab (IU/mL) 0.2 (0.1–1.6) 0.0 (0.0-0.3) 0.000 TPO-Ab (IU/mL) 1.1 (0.6–4.4) 1.0 (0.5–2.2) 0.263 PTH (pg/ml) 52.90 (40.25–70.45) 54.50 (38.55–70.38) 0.752 ALT alanine aminotransferase, ApoA1 apolipoprotein AI, ApoB apolipoprotein B, ApoE apolipoprotein E, AST aspartate aminotransferase, BUN blood urea nitrogen, CRE creatinine, FER ferritin, FT3 free triiodothyronine, FT4 free thyroxine, GLU blood glucose, HDL-c high-density lipoprotein cholesterol, Hs-CRP high-sensitivity C-reactive protein, IL-6 interleukin-6, LDL-c low-density lipoprotein cholesterol, PTC papillary thyroid carcinoma, PTH parathyroid hormone, TC total cholesterol, TG-Ab thyroglobulin antibody, TPO-Ab thyroid peroxidase antibody, TSH thyroid-stimulating hormone, TT3 total triiodothyronine, TT4 total thyroxine n refers to number of patients in group Establishment of the reference interval of serum GAL-3 in normal physical examination population The Kolmogorov-Smirnov test for normality indicated that data of the healthy group were normally distributed (p = 0.081). The mean serum GAL-3 level of the healthy group was 8.5 ± 2.7. The 2.5th percentile (95% confidence interval (CI)) was 3.0 (2.7–3.3), and the 97.5th percentile (95% CI) was 14.3 (13.4–15.2). Therefore, the reference range for serum GAL-3 in the healthy group was 3.0–14.3 ng/ml. The serum GAL-3 levels in both males and females were found to follow a normal distribution. Although males exhibited higher GAL-3 levels, the difference between male and female GAL-3 levels was not statistically significant (t-test, p = 0.236, Table 2 ). Table 2 Serum galectin-3 level of normal physical examination population Number of individuals Age (mean ± standard deviation, years) Galectin-3 (mean ± standard deviation, ng/ml) Male 79 42.8 ± 9.0 8.7 ± 2.4 Female 59 40.6 ± 9.5 8.1 ± 3.0 Total 138 41.8 ± 9.3 8.5 ± 2.7 Results of serum GAL-3 and other markers in PTC and benign thyroid tumor patients As shown in Table 3 , the serum GAL-3 levels in PTC patients were significantly higher than those in patients with benign thyroid tumors, indicating a significant difference between the two groups ( p = 0.045). Additionally, serum HER-2 and Ki-67 levels in PTC patients were also significantly higher than those in benign tumor patients ( p 0.05). Furthermore, according to results from t-test and ANOVA, serum GAL-3 levels in patients with PTC and benign thyroid tumors were significantly elevated compared to those in normal healthy individuals (Fig. 1 ). Table 3 Serum galectin-3 and other markers between patients with papillary thyroid carcinoma and benign thyroid tumor Parameters PTC (median, interquartile range) Benign (median, interquartile range) p value GAL-3 (ng/ml) 15.6 (12.8–18.5) 13.6 (11.4–15.2) 0.045* HER2 (ng/ml) 6.54 (3.12–9.79) 4.25 (2.23–7.35) 0.005* Ki-67 (ng/ml) 0.97 (0.38–1.55) 0.44 (0.12–0.93) 0.000* Cytokeratin 19 (ng/ml) 522.26 (347.64-779.39) 476.24 (301.76-689.96) 0.113 Thyroid peroxidase (ng/ml) 0.36 (0.17–0.76) 0.29 (0.08–0.76) 0.186 CD56 (ng/ml) 156.49 (92.93.16-226.27) 160.48 (96.93-236.27) 0.520 GAL-3 galectin-3, HER2 human epidermal growth factor receptor 2, PTC papillary thyroid carcinoma *significant difference between patients with papillary thyroid carcinoma and benign thyroid tumor Enhanced efficacy in the combined assessment of GAL-3 with HER2 and Ki-67 for differential diagnosis of PTC and benign thyroid tumors To further evaluate the diagnostic efficacy of serum GAL-3, HER2, and Ki-67 levels in differentiating PTC and benign thyroid tumors, we employed ROC curve analysis. The results revealed that the area under the ROC curve for GAL-3 in discriminating between benign and malignant thyroid diseases was 0.645 (95% CI: 0.577–0.714), with a sensitivity of 60.9% and specificity of 76.8% ( p = 0.000). The AUC for serum HER2 and Ki-67 in diagnosing benign and malignant thyroid tumors were 0.621 (95% CI: 0.553–0.689) and 0.764 (95% CI: 0.707–0.822), respectively. When the three markers (GAL-3, HER2, and Ki-67) were combined for diagnostic purposes, the AUC increased to 0.787 (95% CI: 0.727–0.856), with a sensitivity of 72.8% and specificity of 84.5% ( p = 0.000). Discussion The incidence of thyroid cancer is steadily increasing, with PTC being the most prevalent subtype. However, current laboratory tests lack a reliable indicator for PTC screening. GAL-3 is commonly employed in the pathological diagnosis of thyroid cancer. Consequently, we investigated the feasibility of preoperatively distinguishing between benign and malignant thyroid nodules through the detection of the serum biomarker GAL-3. The results suggest that serum GAL-3 detection holds promise for differentiating between benign and malignant thyroid diseases. Additionally, the combined detection of GAL-3 with HER2 and Ki-67 can significantly enhance the diagnostic efficacy of thyroid diseases. In the assessment of thyroid function markers, including TT3, TT4, FT3, FT4, TSH, TPO-Ab, and PTH, no significant differences were observed between the PTC and benign thyroid disease groups. However, there was a notable elevation in thyroglobulin levels in the benign disease group compared to the PTC group. While, elevated serum thyroglobulin levels across various thyroid disorders contribute to the consideration by both the European and American Thyroid Associations that preoperative thyroglobulin testing is insensitive and non-specific for thyroid cancer [ 15 , 16 ]. Similarly, the 2017 Chinese Expert Consensus on the Clinical Application of Serum Markers in Thyroid Cancer does not recommend using thyroglobulin to differentiate between benign and malignant thyroid tumors [ 17 ]. Aligned with the conclusions of Rok P’s study, our findings also reveal a significant elevation in serum TG-Ab levels in PTC patients compared to those with benign thyroid diseases [ 18 ]. In this study, we preliminarily established a reference range for serum GAL-3 in a normal healthy population, which ranged from 3.0 to 14.3 ng/ml. This range is lower than the serum GAL-3 levels reported in Sonia L's 2013 study, where the 97.5th percentile was noted as 17.9 ng/ml. Consistently, we observed higher serum GAL-3 levels in males compared to females, although there was no significant difference between the two groups [ 19 ]. In recent years, there has been a growing body of research on the correlation between serum GAL-3 and PTC. Corresponding to our study findings, Li 2021 [ 20 ] proposed that a combined assessment of serum long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) and GAL-3 could serve for the discrimination of benign and malignant thyroid diseases. In Li 2021, the area under the serum GAL-3 curve was 0.817, surpassing our curve's area. However, it is worth noting that the control group in Li 2021 predominantly comprised patients with thyroid adenomas, while ours mainly featured individuals with nodular thyroid goiters. Similarly, Makki FM 2013 [ 21 ] discovered no significant difference in serum CK19 levels between PTC and benign thyroid tumor patients. Nonetheless, the study suggests that further exploration is warranted to investigate potential disparities in serum GAL-3 levels between benign and malignant thyroid diseases. In contrast to another study, Yu 2020 [ 22 ], which suggests a significant difference in serum GAL-3 levels between PTC patients with lymph node metastasis and those without, our research results indicate no significant difference between these two groups. The different findings may be explained by the inclusion of a limited sample size with 27 cases in the lymph node metastasis group and 23 cases in the non-metastasis group in Yu 2020. Compared with Li 2021 which also demonstrated a significant difference in serum GAL-3 levels between PTC patients with and without lymph node metastasis, our study primarily included patients with smaller tumors (≤ 1cm) and those without lymph node metastasis, where Li 2021 included PTC patients with larger tumor ((≥ 2cm) and those with lymph node metastasis. One of the limitations of our study is that the control group primarily consisted of patients with benign thyroid diseases who required surgery for thyroid nodules. These nodules were typically larger, often exceeding 3cm, and in some cases, presented with features such as bleeding and calcification. Therefore, we plan to select patients with thyroid nodules smaller than 3cm to investigate whether there are differences in serum GAL-3 levels between PTC and patients with benign thyroid nodules in the future. Additionally, we aim to explore the association between serum GAL-3 levels in patients with PTC and tumor size as well as lymph node metastasis. Specifically, our focus will be on patients with a tumor diameter of ≥ 2cm and those with lymph node metastasis in the next phase of our research. Conclusions Our study results indicate that the combination of GAL-3 with HER2 and Ki-67 can be employed to differentiate between benign and malignant thyroid diseases. Abbreviations alanine aminotransferase (ALT) analysis of variance (ANOVA) anaplastic thyroid carcinoma (ATC) apolipoprotein AI (ApoA1) apolipoprotein B (ApoB) apolipoprotein E (ApoE) area under the curve (AUC) aspartate aminotransferase (AST) blood glucose (GLU) blood urea nitrogen (BUN) chemiluminescence immunoassay (CLIA) confidence interval (CI) creatinine (CRE) cytokeratin 19 (CK19) differentiated thyroid carcinomas (DTC) enzyme-linked immunosorbent assay (ELISA) ferritin (FER) fine-needle aspiration biopsy (FNAB) follicular thyroid carcinoma (FTC) free triiodothyronine (FT3) free thyroxine (FT4) galectin-3 (GAL-3) hector Battifora Mesothelial-1 (HBME-1) high-density lipoprotein cholesterol (HDL-c) high-sensitivity C-reactive protein (hs-CRP) human epidermal growth factor receptor 2 (HER2) interleukin-6 (IL-6) low-density lipoprotein cholesterol (LDL-c) medullary thyroid carcinoma (MTC), papillary thyroid carcinoma (PTC) parathyroid hormone (PTH) Receiver operating characteristic (ROC) standard deviation (SD) thyroglobulin antibody (TG-Ab) thyroid peroxidase (TPO) thyroid peroxidase antibody (TPO-Ab) thyroid-stimulating hormone (TSH) total cholesterol (TC) total triiodothyronine (TT3) total thyroxine (TT4) triglycerides (TG) tumor-lymph node-metastasis (TNM) Declarations Ethics approval and consent to participate The design and implementation of the clinical research protocol adhere to the relevant provisions of the Helsinki Declaration regarding the protection of the rights of research subjects. The study is authorized by the Academic Ethics Committee of Beijing Tongren Hospital, Capital Medical University (Approval Number: TRECKY2021-131). All procedures are conducted strictly in accordance with ethical standards. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are not publicly available due to individual privacy but are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by Abbott Laboratories. Authors’ Contributions Xiaohong Zhang carried out the specimen collection, galectin-3 detection, ELISA experiments and drafted the manuscript. Xin Song participated in the clinical biochemical and thyroid function indicators detection. Yu Li provided the information of health check-ups. 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Performance characteristics of the ARCHITECT Galectin-3 assay. Clinical biochemistry. 2013;46(1-2):119-22. http://doi.org/10.1016/j.clinbiochem.2012.09.014 Li L, Wang J, Li Z, Qiu S, Cao J, Zhao Y, et al. Diagnostic Value of Serum lncRNA HOTAIR Combined with Galectin-3 in Benign and Papillary Thyroid Carcinoma. Cancer management and research. 2021;13:6517-25. http://doi.org/10.2147/cmar.S312784 Makki FM, Taylor SM, Shahnavaz A, Leslie A, Gallant J, Douglas S, et al. Serum biomarkers of papillary thyroid cancer. Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale. 2013;42(1):16. http://doi.org/10.1186/1916-0216-42-16 Yu W, Ma B, Zhao W, Liu J, Yu H, Tian Z, et al. The combination of circRNA-UMAD1 and Galectin-3 in peripheral circulation is a co-biomarker for predicting lymph node metastasis of thyroid carcinoma. American journal of translational research. 2020;12(9):5399-415. 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-3823254","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264650258,"identity":"38ae6b34-8fcf-4f9b-92a0-a153404f9ea7","order_by":0,"name":"Xiaohong Zhang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohong","middleName":"","lastName":"Zhang","suffix":""},{"id":264650259,"identity":"7eda2341-b053-4ad7-a333-17f9595919eb","order_by":1,"name":"Xin Song","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Song","suffix":""},{"id":264650260,"identity":"85509f15-b613-4fb3-a490-27ac81b71de6","order_by":2,"name":"Yu Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Li","suffix":""},{"id":264650261,"identity":"5ccb91cc-ffa0-4171-bd86-5dbf7f5cdd63","order_by":3,"name":"Xiangyi Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACPmYgkQBiMTMfgIol4NfChtDCBlNKSAuCyWNApBZ2HjOJBzV37DYc5/n44WfOYQZ+9hwDhp878DmMx9gg4diz5A2HeTdL9m47zCDZ88aAsfcMXi2GDxLYDicbHObdxswI1GJwI8eAmbENrxaDAwn/QFp4noG12BOhxfBBYtthO6AWNogtEgS1sBUbJPYdTpA8zGYM9Es6j8SZZwUHe/Fo4ec/vE3yx7fD9nznDz/88HObtRx/e/LGBz/xaIGBxAUHIAweEHGAsAYGBnv5BmKUjYJRMApGwYgEAHMHTAhCtjOAAAAAAElFTkSuQmCC","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiangyi","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-12-30 05:59:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3823254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3823254/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49146909,"identity":"e9607e95-53f3-4e37-b6fb-cc0dc6dcf4c7","added_by":"auto","created_at":"2024-01-03 20:36:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSerum galectin-3 level in patients with papillary thyroid carcinoma, benign thyroid tumor, and healthy population.\u003c/strong\u003e *\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e","description":"","filename":"Figure1zhangxiaohong.png","url":"https://assets-eu.researchsquare.com/files/rs-3823254/v1/8f927c49bf5b8b86d9d9d0e5.png"},{"id":49146908,"identity":"828cd1d5-9c93-4203-9d00-3512aff5c150","added_by":"auto","created_at":"2024-01-03 20:36:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58303,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis of diagnostic efficacy of biomarkers. \u003c/strong\u003e(A) galectin-3 (GAL-3); (B) human epidermal growth factor receptor 2 (HER2); (C) Ki-67; (D) the combination of GAL-3, HER2 and Ki-67. AUC refers to area under the curve.\u003c/p\u003e","description":"","filename":"Figure2zhangxiaohong.png","url":"https://assets-eu.researchsquare.com/files/rs-3823254/v1/7babca46e0dd3d41af4f4dc7.png"},{"id":50378414,"identity":"3ac67f0e-bdd8-4bdf-a7e8-e6cc020c9efd","added_by":"auto","created_at":"2024-01-30 15:56:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":548105,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3823254/v1/60b8bbca-1191-42fa-8d7c-9ad0e8811aee.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The diagnostic efficacy of serum galectin-3 and other markers in papillary thyroid carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer is a malignant tumor originating from the thyroid follicular epithelium or follicular epithelial cells, and it is also the most common malignancy in the head and neck region [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In recent years, the incidence of thyroid cancer has rapidly increased on a global scale [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to the National Cancer Registry, thyroid cancer is the fourth most common malignant tumor in women in urban areas of China. And thyroid cancer is expected to increase at an annual rate of 20% in China [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on origin and differentiation, thyroid cancer is further categorized into papillary thyroid carcinoma (PTC), follicular thyroid carcinoma (FTC), medullary thyroid carcinoma (MTC), and anaplastic thyroid carcinoma (ATC). Among these, PTC is the most common, accounting for approximately 85\u0026ndash;90% of all thyroid cancers. PTC and FTC together are referred to as differentiated thyroid carcinomas (DTC) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThyroid hormones, thyroid autoantibodies, and tumor marker tests are the most commonly used in thyroid examinations [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. High-resolution ultrasound, recognized for its convenience, non-invasiveness, and cost-effectiveness, serves as the preferred imaging method for thyroid cancer. It can effectively detect thyroid nodules with a diameter greater than 2mm, providing detailed information about their boundaries, morphology, and internal structure [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, its diagnostic accuracy relies on the clinical expertise of the sonographer. For the preoperative assessment of thyroid nodules' benign or malignant nature, fine-needle aspiration biopsy (FNAB) emerges as the most sensitive and specific method [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Nevertheless, numerous studies indicate that accurately diagnosing benign or malignant thyroid nodules remains a formidable challenge in clinical pathology, with up to 15% of thyroid cancer cases ultimately eluding detection through FNAB [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGalectin-3 (beta-galactoside-binding lectin-3, GAL-3), implicated in cell adhesion, is associated with the initiation and progression of tumors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Existing research reveals that GAL-3 is absent in normal thyroid tissues but markedly expressed in thyroid cancer tissues [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Animal studies indicate that radiolabeled antibodies targeting GAL-3 accumulate in thyroid cancer tissues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is also suggested that the combined action of GAL-3 and thyroid peroxidase aids in differentiating and diagnosing thyroid tumors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Numerous studies suggest that GAL-3 plays a role in predicting lymph node metastasis in primary thyroid cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In the current practice of immunohistochemical diagnosis following the excision of thyroid cancer tissue, GAL-3, CK19, and Hector Battifora Mesothelial-1 (HBME-1) are the most commonly used markers for identifying papillary thyroid carcinoma [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, our study aims to explore the diagnostic significance of serum GAL-3 in thyroid cancer, providing insights into the differential diagnosis of PTC and benign thyroid tumors.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAim and design\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the diagnostic efficacy of serum GAL-3 in thyroid cancer through a retrospective observational study design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatients and sample collection\u003c/h2\u003e \u003cp\u003eData were collected from 260 patients with thyroid tumors admitted to Beijing Tongren Hospital, Capital Medical University, from January 2023 to May 2023. Inclusion criteria were as follows: 1) complete clinical data; 2) no special treatments (e.g., radiotherapy, chemotherapy, hormone replacement therapy, etc.); 3) postoperative pathology confirming PTC or benign thyroid tumor; 4) clear pathological staging. Exclusion criteria were as follows: 1) concurrent other cancers; 2) concurrent kidney diseases; 3) concurrent other infectious diseases; 4) concurrent cardiovascular system diseases. The tumor-lymph node-metastasis (TNM) staging followed the 8th edition classification system of the American Joint Committee on Cancer.\u003c/p\u003e \u003cp\u003eIn addition, to assess the serum GAL-3 levels in a population of healthy individuals undergoing normal physical examination, we collected samples from healthy individuals without cardiovascular, liver, kidney, diabetes, thyroid, hypertension, tumor-related conditions, pregnancy, etc.\u003c/p\u003e \u003cp\u003ePrior to surgery, fasting peripheral venous blood samples of 3 mL were collected from all patients and immediately centrifuged at 3000 rpm for 10 minutes. Clinical biochemical and thyroid function indicators were promptly analyzed. The remaining serum was stored at -80\u0026deg;C for subsequent detection of GAL-3 and other markers, including human epidermal growth factor receptor 2 (HER2), Ki-67, cytokeratin 19 (CK19), thyroid peroxidase (TPO), and CD56.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDetection of clinical biochemical and thyroid function indicators\u003c/h2\u003e \u003cp\u003eBeckman AU5811 biochemical analyzer and its corresponding reagents (Beckman Coulter Inc., Brea, CA) were used to measure blood glucose (GLU), alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), creatinine (CRE), apolipoprotein AI (ApoA1), and apolipoprotein B (ApoB). Additionally, on the Beckman AU5811 instrument, the Kyowa Medex Co., Ltd. reagents from Japan were employed to determine triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), and high-density lipoprotein cholesterol (HDL-c). Serum apolipoprotein E (ApoE) was assessed using external reagents (Meikang, Ningbo, China) on the Beckman AU5811 instrument, and high-sensitivity C-reactive protein (hs-CRP) in serum was determined using reagents from Desai, Shanghai, China.\u003c/p\u003e \u003cp\u003eBeckman DXI800 chemiluminescent immunoassay analyzer and its associated reagents (Beckman Coulter Inc., Brea, CA) were utilized for the detection of thyroid function indicators, including total triiodothyronine (TT3), total thyroxine (TT4), free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), thyroglobulin antibody (TG-Ab), thyroid peroxidase antibody (TPO-Ab), parathyroid hormone (PTH), interleukin-6 (IL-6), and ferritin (FER).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDetection of GAL-3 and other markers\u003c/h2\u003e \u003cp\u003eThe serum GAL-3 levels were measured using the Abbott Diagnostics fully automated immunoassay analyzer (I2000SR, Abbott Diagnostics, USA) and its corresponding reagents. Other indicators, including HER2, Ki-67, CK19, TPO, and CD56, were assessed using enzyme-linked immunosorbent assay (ELISA). Serum HER2 and Ki-67 content were determined using the CUSABIO kit (Wuhan, China); serum CK19 and TPO levels were measured using ELISA kits from Jianglai (Shanghai, China). The determination of serum CD56 content was performed using the ELB kit (Wuhan, China). For the enzyme-linked immunosorbent assay (ELISA) procedure, 100 \u0026micro;L of serum was added to each well of the enzyme-coated plate, sealed, and incubated at 37\u0026deg;C for 120 minutes. After five washes, 100 \u0026micro;L of enzyme-labeled reagent was added, followed by incubation at 37\u0026deg;C for 60 minutes. After washing, 90 \u0026micro;L of chromogenic agent was added, and the mixture was incubated at 37\u0026deg;C in the dark for 15 minutes. The reaction was terminated by adding 50 \u0026micro;L of stop solution to each well. The absorbance was measured at 450 nm using an enzyme label reader (MB530, Huisong, China), and the levels of the other markers were calculated using a standard curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted using SPSS 22.0 software, and graphing was performed using GraphPad Prism 8.0 software. Normally distributed metric data were expressed as X\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Differences between two groups were compared using independent sample t-tests, while differences among three groups were assessed using one-way analysis of variance (ANOVA). For skewed distribution measurement data, the median (interquartile range) (M (P25, P75)) was used, and the Mann-Whitney U test was employed to compare differences between two groups. Count data were presented as cases or percentages, and inter-group comparisons were made using the chi-square test. Receiver operating characteristic (ROC) curve analysis was applied to assess the diagnostic value of GAL-3 and other indicators in combination for distinguishing between PTC and benign thyroid tumors, with a higher area under the curve (AUC) indicating better discriminatory ability. A significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePatient characteristics\u003c/h2\u003e \u003cp\u003eA total of 260 patients were included, with 165 in the PTC group and 95 in the benign thyroid tumor group based on postoperative pathological results. There were no significant differences in age and sex between the two groups. Among the 165 PTC patients, 121 cases (73.33%) had a tumor diameter greater than 1cm, 108 cases (65.45%) were solitary, and 57 cases (34.55%) were multifocal. Tumor staging revealed 137 cases (83.03%) in stages 1\u0026ndash;2 and 28 cases (16.97%) in stages 3\u0026ndash;4. Lymph node metastasis was absent in 90 cases (54.55%) and present in 75 cases (45.45%). Among the 95 patients in benign thyroid tumor group, 8 cases were benign papillary hyperplasia, 9 cases were thyroid adenoma, and 78 cases were nodular thyroid goiter. The healthy group included 138 individuals, with 79 males and 59 females, aged between 18\u0026ndash;65 years old (mean age 41.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3 years).\u003c/p\u003e \u003cp\u003eAlthough the GLU in patients with benign thyroid diseases is slightly higher than in PTC patients, there is no significant difference between the two groups. This may be because benign thyroid diseases often present with clinical symptoms (such as chest tightness, dry cough, and difficulty breathing) and require surgery for thyroid nodule patients. These patients tend to be slightly older than PTC patients, resulting in a mild increase in blood glucose levels. There were no significant differences between the two groups in liver function (ALT, AST), kidney function (BUN, CRE), lipid profile (TG, TC, LDL-c, HDL-c), hs-CRP, ApoE, ApoA1, and ApoB.\u003c/p\u003e \u003cp\u003eIn the thyroid function tests, there were no significant differences between the PTC group and the benign thyroid disease group in TT3, TT4, FT3, FT4, TSH, TPO-Ab, and PTH. However, the benign disease group exhibited significantly higher levels of thyroglobulin compared to the PTC group, while the PTC group showed significantly higher levels of TG-Ab than the benign disease group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e General characteristics of patients with papillary thyroid carcinoma and benign thyroid tumor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTC (n\u0026thinsp;=\u0026thinsp;165)\u003c/p\u003e \u003cp\u003e(median, interquartile range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003cp\u003e(median, interquartile range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e＜50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifocality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnifocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1-T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3-T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node metastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical biochemistry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGLU (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.22 (4.91\u0026ndash;5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35 (5.00-5.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.7 (3.9\u0026ndash;5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7 (3.8\u0026ndash;5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRE (umol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (52\u0026ndash;71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (53\u0026ndash;70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (13\u0026ndash;31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (12\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (17\u0026ndash;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (17\u0026ndash;23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05 (0.69\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.80\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65 (4.01\u0026ndash;5.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.81 (4.23\u0026ndash;5.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.85 (2.23\u0026ndash;3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.88 (2.24\u0026ndash;3.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.10\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.13\u0026ndash;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHs-CRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9 (0.3\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.3\u0026ndash;1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5 (2.7\u0026ndash;4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6 (2.7\u0026ndash;4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.652\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoA1 (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43 (1.23\u0026ndash;1.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.40 (1.17\u0026ndash;1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoB (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82 (0.64\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83 (0.67-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFER (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.70 (23.25-115.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.30 (38.70-135.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.4 (0.7\u0026ndash;2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6 (0.9\u0026ndash;3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid function markers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT3(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.66 (1.44\u0026ndash;1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68 (1.47\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTT4(nmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109.70 (95.00-126.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.85 (99.78\u0026ndash;128.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT3(pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34 (4.92\u0026ndash;5.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.30 (4.85\u0026ndash;5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFT4(pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.00 (10.03\u0026ndash;12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.19 (10.16\u0026ndash;12.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (mIU/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70 (1.15\u0026ndash;2.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.05\u0026ndash;2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroglobulin (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.84 (3.05\u0026ndash;21.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.82 (5.25\u0026ndash;51.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG-Ab (IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2 (0.1\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0 (0.0-0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTPO-Ab (IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.1 (0.6\u0026ndash;4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.5\u0026ndash;2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.90 (40.25\u0026ndash;70.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.50 (38.55\u0026ndash;70.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eALT\u003c/em\u003e alanine aminotransferase, \u003cem\u003eApoA1\u003c/em\u003e apolipoprotein AI, \u003cem\u003eApoB\u003c/em\u003e apolipoprotein B, \u003cem\u003eApoE\u003c/em\u003e apolipoprotein E, \u003cem\u003eAST\u003c/em\u003e aspartate aminotransferase, \u003cem\u003eBUN\u003c/em\u003e blood urea nitrogen, \u003cem\u003eCRE\u003c/em\u003e creatinine, \u003cem\u003eFER\u003c/em\u003e ferritin, \u003cem\u003eFT3\u003c/em\u003e free triiodothyronine, \u003cem\u003eFT4\u003c/em\u003e free thyroxine, \u003cem\u003eGLU\u003c/em\u003e blood glucose, \u003cem\u003eHDL-c\u003c/em\u003e high-density lipoprotein cholesterol, \u003cem\u003eHs-CRP\u003c/em\u003e high-sensitivity C-reactive protein, \u003cem\u003eIL-6\u003c/em\u003e interleukin-6, \u003cem\u003eLDL-c\u003c/em\u003e low-density lipoprotein cholesterol, \u003cem\u003ePTC\u003c/em\u003e papillary thyroid carcinoma, \u003cem\u003ePTH\u003c/em\u003e parathyroid hormone, \u003cem\u003eTC\u003c/em\u003e total cholesterol, \u003cem\u003eTG-Ab\u003c/em\u003e thyroglobulin antibody, \u003cem\u003eTPO-Ab\u003c/em\u003e thyroid peroxidase antibody, \u003cem\u003eTSH\u003c/em\u003e thyroid-stimulating hormone, \u003cem\u003eTT3\u003c/em\u003e total triiodothyronine, \u003cem\u003eTT4\u003c/em\u003e total thyroxine\u003c/p\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e refers to number of patients in group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of the reference interval of serum GAL-3 in normal physical examination population\u003c/h2\u003e \u003cp\u003eThe Kolmogorov-Smirnov test for normality indicated that data of the healthy group were normally distributed (p\u0026thinsp;=\u0026thinsp;0.081). The mean serum GAL-3 level of the healthy group was 8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7. The 2.5th percentile (95% confidence interval (CI)) was 3.0 (2.7\u0026ndash;3.3), and the 97.5th percentile (95% CI) was 14.3 (13.4\u0026ndash;15.2). Therefore, the reference range for serum GAL-3 in the healthy group was 3.0\u0026ndash;14.3 ng/ml. The serum GAL-3 levels in both males and females were found to follow a normal distribution. Although males exhibited higher GAL-3 levels, the difference between male and female GAL-3 levels was not statistically significant (t-test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.236, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Serum galectin-3 level of normal physical examination population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of individuals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGalectin-3\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, ng/ml)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e42.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e40.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResults of serum GAL-3 and other markers in PTC and benign thyroid tumor patients\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the serum GAL-3 levels in PTC patients were significantly higher than those in patients with benign thyroid tumors, indicating a significant difference between the two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). Additionally, serum HER-2 and Ki-67 levels in PTC patients were also significantly higher than those in benign tumor patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there were no significant differences between the two groups in CK19, TPO, and CD56 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eFurthermore, according to results from t-test and ANOVA, serum GAL-3 levels in patients with PTC and benign thyroid tumors were significantly elevated compared to those in normal healthy individuals (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e Serum galectin-3 and other markers between patients with papillary thyroid carcinoma and benign thyroid tumor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTC\u003c/p\u003e \u003cp\u003e(median, interquartile range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003cp\u003e(median, interquartile range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAL-3 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.6 (12.8\u0026ndash;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.6 (11.4\u0026ndash;15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.54 (3.12\u0026ndash;9.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25 (2.23\u0026ndash;7.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKi-67 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.38\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44 (0.12\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytokeratin 19 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e522.26 (347.64-779.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e476.24 (301.76-689.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThyroid peroxidase (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36 (0.17\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29 (0.08\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD56 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156.49 (92.93.16-226.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160.48 (96.93-236.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGAL-3\u003c/em\u003e galectin-3, \u003cem\u003eHER2\u003c/em\u003e human epidermal growth factor receptor 2, \u003cem\u003ePTC\u003c/em\u003e papillary thyroid carcinoma\u003c/p\u003e \u003cp\u003e*significant difference between patients with papillary thyroid carcinoma and benign thyroid tumor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eEnhanced efficacy in the combined assessment of GAL-3 with HER2 and Ki-67 for differential diagnosis of PTC and benign thyroid tumors\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate the diagnostic efficacy of serum GAL-3, HER2, and Ki-67 levels in differentiating PTC and benign thyroid tumors, we employed ROC curve analysis. The results revealed that the area under the ROC curve for GAL-3 in discriminating between benign and malignant thyroid diseases was 0.645 (95% CI: 0.577\u0026ndash;0.714), with a sensitivity of 60.9% and specificity of 76.8% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000). The AUC for serum HER2 and Ki-67 in diagnosing benign and malignant thyroid tumors were 0.621 (95% CI: 0.553\u0026ndash;0.689) and 0.764 (95% CI: 0.707\u0026ndash;0.822), respectively. When the three markers (GAL-3, HER2, and Ki-67) were combined for diagnostic purposes, the AUC increased to 0.787 (95% CI: 0.727\u0026ndash;0.856), with a sensitivity of 72.8% and specificity of 84.5% (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe incidence of thyroid cancer is steadily increasing, with PTC being the most prevalent subtype. However, current laboratory tests lack a reliable indicator for PTC screening. GAL-3 is commonly employed in the pathological diagnosis of thyroid cancer. Consequently, we investigated the feasibility of preoperatively distinguishing between benign and malignant thyroid nodules through the detection of the serum biomarker GAL-3. The results suggest that serum GAL-3 detection holds promise for differentiating between benign and malignant thyroid diseases. Additionally, the combined detection of GAL-3 with HER2 and Ki-67 can significantly enhance the diagnostic efficacy of thyroid diseases.\u003c/p\u003e \u003cp\u003eIn the assessment of thyroid function markers, including TT3, TT4, FT3, FT4, TSH, TPO-Ab, and PTH, no significant differences were observed between the PTC and benign thyroid disease groups. However, there was a notable elevation in thyroglobulin levels in the benign disease group compared to the PTC group. While, elevated serum thyroglobulin levels across various thyroid disorders contribute to the consideration by both the European and American Thyroid Associations that preoperative thyroglobulin testing is insensitive and non-specific for thyroid cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, the 2017 Chinese Expert Consensus on the Clinical Application of Serum Markers in Thyroid Cancer does not recommend using thyroglobulin to differentiate between benign and malignant thyroid tumors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Aligned with the conclusions of Rok P\u0026rsquo;s study, our findings also reveal a significant elevation in serum TG-Ab levels in PTC patients compared to those with benign thyroid diseases [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we preliminarily established a reference range for serum GAL-3 in a normal healthy population, which ranged from 3.0 to 14.3 ng/ml. This range is lower than the serum GAL-3 levels reported in Sonia L's 2013 study, where the 97.5th percentile was noted as 17.9 ng/ml. Consistently, we observed higher serum GAL-3 levels in males compared to females, although there was no significant difference between the two groups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, there has been a growing body of research on the correlation between serum GAL-3 and PTC. Corresponding to our study findings, Li 2021 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] proposed that a combined assessment of serum long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) and GAL-3 could serve for the discrimination of benign and malignant thyroid diseases. In Li 2021, the area under the serum GAL-3 curve was 0.817, surpassing our curve's area. However, it is worth noting that the control group in Li 2021 predominantly comprised patients with thyroid adenomas, while ours mainly featured individuals with nodular thyroid goiters. Similarly, Makki FM 2013 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] discovered no significant difference in serum CK19 levels between PTC and benign thyroid tumor patients. Nonetheless, the study suggests that further exploration is warranted to investigate potential disparities in serum GAL-3 levels between benign and malignant thyroid diseases.\u003c/p\u003e \u003cp\u003eIn contrast to another study, Yu 2020 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which suggests a significant difference in serum GAL-3 levels between PTC patients with lymph node metastasis and those without, our research results indicate no significant difference between these two groups. The different findings may be explained by the inclusion of a limited sample size with 27 cases in the lymph node metastasis group and 23 cases in the non-metastasis group in Yu 2020. Compared with Li 2021 which also demonstrated a significant difference in serum GAL-3 levels between PTC patients with and without lymph node metastasis, our study primarily included patients with smaller tumors (\u0026le;\u0026thinsp;1cm) and those without lymph node metastasis, where Li 2021 included PTC patients with larger tumor ((\u0026ge;\u0026thinsp;2cm) and those with lymph node metastasis.\u003c/p\u003e \u003cp\u003eOne of the limitations of our study is that the control group primarily consisted of patients with benign thyroid diseases who required surgery for thyroid nodules. These nodules were typically larger, often exceeding 3cm, and in some cases, presented with features such as bleeding and calcification. Therefore, we plan to select patients with thyroid nodules smaller than 3cm to investigate whether there are differences in serum GAL-3 levels between PTC and patients with benign thyroid nodules in the future. Additionally, we aim to explore the association between serum GAL-3 levels in patients with PTC and tumor size as well as lymph node metastasis. Specifically, our focus will be on patients with a tumor diameter of \u0026ge;\u0026thinsp;2cm and those with lymph node metastasis in the next phase of our research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study results indicate that the combination of GAL-3 with HER2 and Ki-67 can be employed to differentiate between benign and malignant thyroid diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ealanine aminotransferase (ALT)\u003c/p\u003e\n\u003cp\u003eanalysis of variance (ANOVA)\u003c/p\u003e\n\u003cp\u003eanaplastic thyroid carcinoma (ATC)\u003c/p\u003e\n\u003cp\u003eapolipoprotein AI (ApoA1)\u003c/p\u003e\n\u003cp\u003eapolipoprotein B (ApoB)\u003c/p\u003e\n\u003cp\u003eapolipoprotein E (ApoE)\u003c/p\u003e\n\u003cp\u003earea under the curve (AUC)\u003c/p\u003e\n\u003cp\u003easpartate aminotransferase (AST)\u003c/p\u003e\n\u003cp\u003eblood glucose (GLU)\u003c/p\u003e\n\u003cp\u003eblood urea nitrogen (BUN)\u003c/p\u003e\n\u003cp\u003echemiluminescence immunoassay (CLIA)\u003c/p\u003e\n\u003cp\u003econfidence interval (CI)\u003c/p\u003e\n\u003cp\u003ecreatinine (CRE)\u003c/p\u003e\n\u003cp\u003ecytokeratin 19 (CK19)\u003c/p\u003e\n\u003cp\u003edifferentiated thyroid carcinomas (DTC)\u003c/p\u003e\n\u003cp\u003eenzyme-linked immunosorbent assay (ELISA)\u003c/p\u003e\n\u003cp\u003eferritin (FER)\u003c/p\u003e\n\u003cp\u003efine-needle aspiration biopsy (FNAB)\u003c/p\u003e\n\u003cp\u003efollicular thyroid carcinoma (FTC)\u003c/p\u003e\n\u003cp\u003efree triiodothyronine (FT3)\u003c/p\u003e\n\u003cp\u003efree thyroxine (FT4)\u003c/p\u003e\n\u003cp\u003egalectin-3 (GAL-3)\u003c/p\u003e\n\u003cp\u003ehector Battifora Mesothelial-1 (HBME-1)\u003c/p\u003e\n\u003cp\u003ehigh-density lipoprotein cholesterol (HDL-c)\u003c/p\u003e\n\u003cp\u003ehigh-sensitivity C-reactive protein (hs-CRP)\u003c/p\u003e\n\u003cp\u003ehuman epidermal growth factor receptor 2 (HER2)\u003c/p\u003e\n\u003cp\u003einterleukin-6 (IL-6)\u003c/p\u003e\n\u003cp\u003elow-density lipoprotein cholesterol (LDL-c)\u003c/p\u003e\n\u003cp\u003emedullary thyroid carcinoma (MTC),\u003c/p\u003e\n\u003cp\u003epapillary thyroid carcinoma (PTC)\u003c/p\u003e\n\u003cp\u003eparathyroid hormone (PTH)\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC)\u003c/p\u003e\n\u003cp\u003estandard deviation (SD)\u003c/p\u003e\n\u003cp\u003ethyroglobulin antibody (TG-Ab)\u003c/p\u003e\n\u003cp\u003ethyroid peroxidase (TPO)\u003c/p\u003e\n\u003cp\u003ethyroid peroxidase antibody (TPO-Ab)\u003c/p\u003e\n\u003cp\u003ethyroid-stimulating hormone (TSH)\u003c/p\u003e\n\u003cp\u003etotal cholesterol (TC)\u003c/p\u003e\n\u003cp\u003etotal triiodothyronine (TT3)\u003c/p\u003e\n\u003cp\u003etotal thyroxine (TT4)\u003c/p\u003e\n\u003cp\u003etriglycerides (TG)\u003c/p\u003e\n\u003cp\u003etumor-lymph node-metastasis (TNM)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe design and implementation of the clinical research protocol adhere to the relevant provisions of the Helsinki Declaration regarding the protection of the rights of research subjects. The study is authorized by the Academic Ethics Committee of Beijing Tongren Hospital, Capital Medical University (Approval Number: TRECKY2021-131). All procedures are conducted strictly in accordance with ethical standards.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to individual privacy but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by Abbott Laboratories.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; Contributions\u003c/h2\u003e\n\u003cp\u003eXiaohong Zhang carried out the specimen collection, galectin-3 detection, ELISA experiments and drafted the manuscript. Xin Song participated in the clinical biochemical and thyroid function indicators detection. Yu Li provided the information of health check-ups. Xiangyi Liu planned the study protocol, coordinated the research and revised the final version of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThe English language editing was provided by Dr. Sitong Dong from Systematic Review Solutions, Ltd.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2017. 2017;67(1):7-30. http://doi.org/https://doi.org/10.3322/caac.21387\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA: a cancer journal for clinicians. 2020;70(1):7-30. http://doi.org/10.3322/caac.21590\u003c/li\u003e\n\u003cli\u003eZheng R, Zhang S, Zeng H, Wang S, Sun K, Chen R, et al. Cancer incidence and mortality in China, 2016. Journal of the National Cancer Center. 2022;2(1):1-9. http://doi.org/https://doi.org/10.1016/j.jncc.2022.02.002\u003c/li\u003e\n\u003cli\u003eRahbari R, Zhang L, Kebebew E. Thyroid cancer gender disparity. Future oncology (London, England). 2010;6(11):1771-9. http://doi.org/10.2217/fon.10.127\u003c/li\u003e\n\u003cli\u003eLi D, Wu J, Liu Z, Qiu L, Zhang Y. Novel circulating protein biomarkers for thyroid cancer determined through data-independent acquisition mass spectrometry. PeerJ. 2020;8:e9507. http://doi.org/10.7717/peerj.9507\u003c/li\u003e\n\u003cli\u003eKim SK, Woo JW, Park I, Lee JH, Choe JH, Kim JH, et al. Computed Tomography-Detected Central Lymph Node Metastasis in Ultrasonography Node-Negative Papillary Thyroid Carcinoma: Is It Really Significant? Annals of surgical oncology. 2017;24(2):442-9. http://doi.org/10.1245/s10434-016-5552-1\u003c/li\u003e\n\u003cli\u003eHaugen BR, Alexander EK, Bible KC, Doherty GM, Mandel SJ, Nikiforov YE, 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 : official journal of the American Thyroid Association. 2016;26(1):1-133. http://doi.org/10.1089/thy.2015.0020\u003c/li\u003e\n\u003cli\u003eSapio MR, Guerra A, Posca D, Limone PP, Deandrea M, Motta M, et al. Combined analysis of galectin-3 and BRAFV600E improves the accuracy of fine-needle aspiration biopsy with cytological findings suspicious for papillary thyroid carcinoma. Endocrine-related cancer. 2007;14(4):1089-97. http://doi.org/10.1677/erc-07-0147\u003c/li\u003e\n\u003cli\u003eLiu FT, Rabinovich GA. 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Neoplasma. 2020;67(1):164-70. http://doi.org/10.4149/neo_2019_190128N86\u003c/li\u003e\n\u003cli\u003eTang W, Huang C, Tang C, Xu J, Wang H. Galectin-3 may serve as a potential marker for diagnosis and prognosis in papillary thyroid carcinoma: a meta-analysis. OncoTargets and therapy. 2016;9:455-60. http://doi.org/10.2147/ott.S94514\u003c/li\u003e\n\u003cli\u003eXin Y, Guan D, Meng K, Lv Z, Chen B. Diagnostic accuracy of CK-19, Galectin-3 and HBME-1 on papillary thyroid carcinoma: a meta-analysis. International journal of clinical and experimental pathology. 2017;10(8):8130-40.\u003c/li\u003e\n\u003cli\u003ePacini F, Schlumberger M, Dralle H, Elisei R, Smit JW, Wiersinga W. European consensus for the management of patients with differentiated thyroid carcinoma of the follicular epithelium. European journal of endocrinology. 2006;154(6):787-803. http://doi.org/10.1530/eje.1.02158\u003c/li\u003e\n\u003cli\u003eCooper DS, Doherty GM, Haugen BR, Kloos RT, Lee SL, Mandel SJ, et al. 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Clinical biochemistry. 2013;46(1-2):119-22. http://doi.org/10.1016/j.clinbiochem.2012.09.014\u003c/li\u003e\n\u003cli\u003eLi L, Wang J, Li Z, Qiu S, Cao J, Zhao Y, et al. Diagnostic Value of Serum lncRNA HOTAIR Combined with Galectin-3 in Benign and Papillary Thyroid Carcinoma. Cancer management and research. 2021;13:6517-25. http://doi.org/10.2147/cmar.S312784\u003c/li\u003e\n\u003cli\u003eMakki FM, Taylor SM, Shahnavaz A, Leslie A, Gallant J, Douglas S, et al. Serum biomarkers of papillary thyroid cancer. Journal of otolaryngology - head \u0026amp; neck surgery = Le Journal d\u0026apos;oto-rhino-laryngologie et de chirurgie cervico-faciale. 2013;42(1):16. http://doi.org/10.1186/1916-0216-42-16\u003c/li\u003e\n\u003cli\u003eYu W, Ma B, Zhao W, Liu J, Yu H, Tian Z, et al. The combination of circRNA-UMAD1 and Galectin-3 in peripheral circulation is a co-biomarker for predicting lymph node metastasis of thyroid carcinoma. American journal of translational research. 2020;12(9):5399-415.\u003c/li\u003e\n\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":"papillary thyroid carcinoma, galectin-3, diagnostic efficacy","lastPublishedDoi":"10.21203/rs.3.rs-3823254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3823254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePapillary thyroid carcinoma (PTC) is the most common type of thyroid cancer, representing approximately 85\u0026ndash;90% of cases. Galectin-3 (GAL-3) is a well-established histologic marker of thyroid cancer that is not expressed by normal thyroid cells. The potential utility of serum GAL-3 in differentiating benign thyroid tumors from PTC remains to be studied.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAccording to the postoperative pathology results, patients were divided into the PTC group (165 cases) and the benign thyroid tumor group (95 cases). Serum GAL-3 was detected by chemiluminescence immunoassay (CLIA). Additionally, other markers including human epidermal growth factor receptor 2 (HER2), Ki-67, cytokeratin 19 (CK19), thyroid peroxidase (TPO) and CD56 were detected by enzyme-linked immunosorbent assay (ELISA). Serum levels were compared between patients with PTC and those with benign tumors using SPSS 22.0.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn patients with PTC, serum GAL-3 levels were significantly higher than those in patients with benign thyroid tumors (p\u0026thinsp;=\u0026thinsp;0.045). Additionally, serum HER-2 and Ki-67 levels in PTC patients were significantly higher than those in patients with benign tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as well. However, there were no significant differences between the two groups in CK19, TPO, and CD56 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The Receiver Operating Characteristic (ROC) curve analysis revealed that GAL-3 had an area under the curve (AUC) of 0.645 (p\u0026thinsp;=\u0026thinsp;0.000) for distinguishing between benign and malignant thyroid tumors. When combined with HER2 and Ki-67, the AUC increased to 0.787 (p\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur research results indicate that the combination of GAL-3, HER2, and Ki-67 can be used to differentiate between benign and malignant thyroid diseases.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTrial registration\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNot applicable\u003c/p\u003e","manuscriptTitle":"The diagnostic efficacy of serum galectin-3 and other markers in papillary thyroid carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-03 20:36:11","doi":"10.21203/rs.3.rs-3823254/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":"70385185-1a73-4ee5-9a54-d255526a3817","owner":[],"postedDate":"January 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-30T15:48:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-03 20:36:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3823254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3823254","identity":"rs-3823254","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}