FABP3 methylation as a novel biomarker for the differentiation and classification of benign and malignant thyroid nodules

FABP3 methylation as a novel biomarker for the differentiation and classification of benign and malignant thyroid nodules | 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 FABP3 methylation as a novel biomarker for the differentiation and classification of benign and malignant thyroid nodules Haixia Huang, Yifei Yin, Hong Li, Junjie Li, Mengxia Li, Yi Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5794576/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: Differentiation between benign and malignant thyroid nodules has been a challenge in clinical practice. We aim to explore a novel biomarker to determine the malignancy of thyroid nodules. Materials and methods: In the discovery study, 32 tissue samples from benign thyroid nodule (BTN) and thyroid cancer (TC) patients were analyzed by Methylation 850K array and RNA-Sequencing. TC associated FABP3 methylation was further verified by mass spectrometry in two independent studies (221 BTN vs. 222 TC in Validation I and 191 BTN vs. 256 TC in Validation II). Logistic regression analysis and non-parametric tests were used for the analysis between groups. Results: Altered and inversely correlated methylation and expression in FABP3 gene in TC was found in the discovery study (P = 2.90E-05 for the methylation and P = 0.040 for the expression), and verified in the two validation studies (P values range from 0.012 to 6.30E-10-12). FABP3 methylation could sufficiently differentiate TC from BTN (AUC = 0.77), and could be further improved when combined with the BRAFV600E mutations (AUC = 0.87). The association between FABP3 hypomethylation and TC was enhanced in women, in patients with younger age, with larger tumor size and with lower FT3. FABP3 methylation was varied in BTN and TC subtypes, with the highest level in adenoma and the lowest in anaplastic thyroid cancer. Conclusion: Our study suggested that altered FABP3 methylation in tissue samples as a potential biomarker to distinguish malignant and benign thyroid nodules, and might be helpful for the pathological classification of TC. benign thyroid nodule thyroid cancer DNA methylation FABP3 biomarker classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Thyroid cancer (TC) is the most common endocrine malignant tumors in adults, with an estimated total of more than 9.2 million new cases in 2020 worldwide 1 . In China, the incidence of TC increased from 2.40/100,000 in 2003 to 13.75/100,000 in 2012, with an average annual increase of 20% 2, 3 . In some provinces, such as Zhejiang Province, the incidence rate of TC ranks first among all cancers in women 4 , 5 . Differentiated thyroid cancer (DTC) has two subtypes, papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC), which are the most common subtype of TC. The rarer subtypes are medullary thyroid cancer (MTC) and anaplastic thyroid cancer (ATC) 6 – 8 . Thyroid nodules have a high incidence and a low malignant rate in the population. Although 60% of ultrasonography showed the presence of thyroid nodules, only 5% were eventually confirmed as malignant 9 . Accurate diagnosis of TC, including the judgment of benign and malignant thyroid nodules and classification of TC, is one of the current clinical challenges. Ultrasound-guided fine-needle aspiration biopsy (FNAB) remains crucial in the preoperative diagnosis of thyroid nodules 10 . However, up to 15–30% of thyroid nodules evaluated by FNAB were cytologically indeterminate, which were usually diagnosed by postoperative pathological diagnosis 11 . Patients with uncertain nodules will be at risk of overdiagnosis or misdiagnosis. Therefore, reliable and practical biomarkers are urgently needed. Determining the malignancy of thyroid nodules is becoming increasingly dependent on molecular pathological techniques 12 , including genetic detection and epigenetic detection 13 , 14 . The role of BRAF, RAS, and RET gene mutations in TC has been widely recognized. BRAF mutation is known to be specific for PTC, and the reported positive rate ranges from 29 to 83% 15, 16 . Because of the low sensitivity, compensating molecular diagnostic methods are needed 17 . Afirma gene expression classifiers and ThyroSeq v3 are suitable for fresh aspirates and are expensive 18 , 19 . In addition, they are associated with a high rate of overdiagnosis due to low positive predictive value 20 . These facts point to the need for a highly accurate diagnostic test for thyroid nodules using time-insensitive materials. DNA methylation catalyzed by DNA methyltransferases (DNMTs) is one of the important epigenetic modifications controlling gene expression and maintaining genome structure. Alterations of DNA methylation are early molecular changes in human cancers and play an important role in tumorigenesis. As a potential biomarker, changes in the methylation profile have been found in all types of cancer including TC 21 . For example, Stephen et al. used quantitative methylation-specific polymerase chain reaction (PCR) to detect the promoter methylation status of 21 candidate genes on 329 formalin-fixed paraffin-embedded (FFPE), demonstrating that combined abnormal gene methylation helps clinically differentiate FTC and PTC from benign thyroid nodules (BTN) 22 . Yim et al. evaluated DNA methylation in 109 thyroid specimens form BTN, PTCs and adjacent normal thyroid tissues using the Reduced Representation Bisulfite Sequencing (RRBS) and then performed validation in a retrospective cohort containing 65 thyroid nodules, suggesting epigenetic testing as a new molecular approach for thyroid diagnostics 23 . Nevertheless, these studies were based on candidate approaches and were also limited by small sample size. So far, there is few screenings for the variant methylation signatures aiming to differentiate malignant and benign thyroid tumors, especially in large sample size. Here, we intended to discover and validate the abnormal DNA methylation alterations in TC compared to BTN in the Chinese population. Combining the genome-wide screening assay of Methylation 850K BeadChip array and RNA-Sequencing, we discovered TC associated differential methylation in FABP3 gene and performed further validations via mass spectrometry by case-control studies with a total of 890 patients from two clinical centers. Materials and methods Study design and population The studies were approved by the Ethics Committee of the Nanjing Medical University. Informed consent was obtained from each recruited participant. In the discovery study, we collected 32 fresh-frozen tissue samples from 17 BTN subjects and 15 TC cases at the Affiliated Huai’an Hospital of Xuzhou Medical University from 2019 to 2020. Two independent studies were conducted on 890 FFPE tissue samples for validation. Validation Ⅰ: 222 TC patients and 221 age- and gender-matched BTN subjects were collected from the Affiliated Huai’an Hospital of Xuzhou Medical University and the Jiangsu Provincial Hospital of Chinese Medicine from 2013 to 2023. In the BTN group, 78.70% (174/221) were female, with a median (interquartile range, IQR) age of 53.00 (45.50–60.00) years; while in the TC group, 72.10% (160/222) were female, with a median (IQR) age of 50.00 (41.75–58.00) years. Validation Ⅱ: a total of 256 TC cases and 191 age- and gender-matched BTN subjects were collected from the Affiliated Hospital of Nantong University from 2017 to 2022. The median (IQR) age of BTN subjects and TC cases were 49.00 (35.00–55.00) and 50.00 (38.00–57.00) years old, and the proportions of females were 77.50% (148/191) and 77.30% (198/256), respectively. The detailed clinical characteristics of the participants were shown in Table 1 , including tumor length, tumor size, lymph node involvement, tumor stage, thyroid-stimulating hormone (TSH) levels, free triiodothyronine (FT3) levels, free tetraiodothyronine acid (FT4) levels, and classification of BTN and TC. Table 1 Clinical characteristics of the participants in two independent validations Variables Group Validation Ⅰ Validation Ⅱ BTN (n = 221) TC (n = 222) P value BTN (n = 191) TC (n = 256) P value Age (years) 53.00 (45.50–60.00) 50.00 (41.75–58.00) 0.017 * 49.00 (35.00–55.00) 50.00 (38.00–57.00) 0.531 * Gender Female 174 (78.70%) 160 (72.10%) 0.104 $ 148 (77.50%) 198 (77.30%) 0.971 $ Male 47 (21.30%) 62 (27.90%) 43 (22.50%) 58 (22.70%) Tumor length (cm) - 1.30 (0.90–2.00) - 1.50 (1.20–2.00) Tumor size T1 - 163 (73.40%) - 161 (62.90%) T2&3&4 - 56 (25.20%) - 56 (21.90%) Unknown - 3 (1.40%) - 39 (15.20%) Lymph node involvement pN0 - 103 (46.40%) - 73 (28.50%) pN1 - 107 (48.20%) - 130 (50.80%) Unknown - 12 (5.40%) - 53 (20.70%) Tumor stage Stage Ⅰ - 172 (77.50%) - 164 (64.10%) Stage Ⅱ&Ⅲ&Ⅳ - 49 (22.10%) - 39 (15.20%) Unknown - 1 (0.40%) - 53 (20.70%) TSH (µIU/mL) 1.46 (0.89–2.36) 1.88 (1.20–3.15) 0.001 * 1.71 (1.09–2.52) 2.09 (1.45–2.92) 0.002 * FT3 (pmol/L) 4.34 (3.60–4.84) 4.26 (3.67–4.82) 0.525 * 4.93 (4.60–5.31) 4.88 (4.52–5.33) 0.663 * FT4 (pmol/L) 15.84 (13.00–18.33) 15.06 (10.92–17.52) 0.029 * 11.56 (10.52–12.66) 11.56 (10.46–12.65) 0.746 * Classification of BTN adenoma 90 (40.72%) - 106 (55.50%) - goiter 127 (57.47%) - 68 (35.60%) - subacute thyroiditis - - 3 (1.57%) - lymphatic thyroiditis 4 (1.81%) - 14 (7.33%) - Classification of TC PTC - 171 (77.03%) - 197 (76.95%) FTC - 19 (8.56%) - 31 (12.11%) MTC - 27 (12.16%) - 24 (9.38%) ATC - 5 (2.25%) - 4 (1.56%) P values * were calculated by the Mann-Whitney U test. P values $ were calculated by Chi-square test. Significant P values are in bold. Abbreviation: BTN, benign thyroid nodule; TC, thyroid cancer; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free tetraiodothyronine acid; PTC, papillary thyroid cancer; FTC, follicular thyroid cancer; MTC, medullary thyroid cancer; ATC, anaplastic thyroid cancer. In the discovery study and two independent validations, the inclusion criteria for malignant nodules were as follows: (1) no distant metastases or other co-occurring cancers, (2) before any related treatment, and (3) complete clinical records. BTN patients were matched to TC cases by age, gender, and the year of diagnosis. Histopathological diagnosis was performed by two qualified pathologists in all cases, and the clinical TNM stage of each malignant case was determined according to the 8th edition American Joint Committee on Cancer Staging System 24 . Illumina Methylation EPIC 850K BeadChip array and RNA-Sequencing The study design and flow chart were shown in Fig. 1 . In the discovery round, FastPure Blood/Cell/Tissue/Bacteria DNA Isolation Kit (DC112, Vazyme, Nanjing, China) and FastPure Cell/Tissue Total RNA Isolation Kit (RC101, Vazyme, Nanjing, China) were used to isolate genomic DNA and total RNA from 32 fresh-frozen tissue samples, respectively. Genome-wide DNA methylation profiles were analyzed at single nucleotide resolution by the Illumina Methylation EPIC 850K BeadChip array. Probes that meet the following criteria were considered differentially methylated: (1) methylation difference between BTN and TC groups (|delta-beta|) ≥ 0.25, (2) P value < 0.001, (3) no adjacent single nucleotide polymorphisms (SNPs), and (4) on the promoter region or the 1st exon of gene body. At the same time, mRNA expression was measured by RNA-Sequencing. Genes with a fold change of expression ≥ 1.5 and P value < 0.05 were thought to be differentially expressed. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry DNA was extracted from FFPE tissue samples using FastPure FFPE DNA Isolation Kit (DC105, Vazyme, Nanjing, China). The isolated DNA was further bisulfite converted by EZ-96 DNA Methylation Gold Kit (D5007, Zymo Research, Orange, USA) according to the manufacturer’s protocol. After bisulfite treatment, all non-methylated cytosine (C) bases in CpG sites were converted to uracil (U), whereas all methylated (C) bases remained unchanged. The CpG methylation levels of FFPE tissue samples were then determined by MALDI-TOF mass spectrometry, as described by Yang et al. and Yin et al . 25 – 27 . Briefly, the target sequence was amplified by PCR using bisulfite-specific primers, forward primer: aggaagagagTTATAGTGATGTTGGGTTAGGTTGA, reverse primer: cagtaatacgactcactatagggagaaggctCAACCCCTCCTAAATAAACCCT. Upper case letters present the sequence-specific primer regions, and non-specific tags are shown in lower case letters. The sequence of the amplicon was presented in Table S1 . There are no SNPs overlapped with any of the CpG loci in the amplicon. Next, the amplification products were treated with Shrimp Alkaline Phosphatase and followed by T-cleavage using RNase A. After cleaning residual ions with resin, the methylation level of each sample was quantified and collected by the MassARRAY system. BRAF mutation detection A single hotspot mutation in nucleotide 1799 of the BRAF gene (corresponding to p.V600E) has been recognized as the most frequent genetic event in PTC, with an incidence of 29–83%, and accounts for more than 90% of BRAF-mutated TC 28 . A total of 711 patients from two validations were detected for BRAF gene mutations. Forward primer: 5'-TCATAATGCTTGCTGATAGGA-3' and reverse primer: 5'-GGCCAAAAATTTAATCAGTGGA-3' were used for amplification by PCR. The sequences of the amplified fragments were then analyzed by Sanger-Sequencing. Statistical analyses All data analyses were performed using SPSS (version 25.0) and GraphPad Prism (version 8.0). Mann-Whitney U test and Chi-square test were used to compare the differences between TC and BTN groups. Spearman correlation was used to determine the relationship between variables. Binary logistic regression analysis was performed to calculate odds ratios (ORs) and their 95% confidence intervals (95% CIs), adjusted for covariates. Kruskal-Wallis test and Mann-Whitney U test were used to analyze the correlation between FABP3 methylation levels and different clinical characteristics. Receiver operating characteristic (ROC) curve was used to evaluate goodness of fit. A two-tailed P value < 0.05 was considered statistically significant. Results Discovery of TC associated FABP3 hypomethylation in tissue Genome-wide Illumina Methylation 850K BeadChip array and RNA-Sequencing were used to screen methylation sites and genes with significant differences between TC and BTN in a total of 32 fresh-frozen samples (15 TC and 17 BTN). Cg18368411 within the transcription start site 200 bp (TSS200) of the FABP3 gene was identified (Fig. 1 ). The CpG site showed the most significant difference between TC cases and BTN subjects (median methylation: TC cases = 0.28, BTN subjects = 0.56, P = 2.90E-05; Fig. 2 A). Alterations in DNA methylation may affect gene expression. Consistently, we found that compared with BTN subjects, TC cases showed increased expression of FABP3 mRNA ( P = 0.040; Fig. 2 B). In addition, the methylation level of cg18368411 was significantly negatively correlated with the expression level of FABP3 with the Spearman correlation coefficient value of -0.62 ( P = 1.34E-04; Fig. 2 C). Validation of the differential FABP3 methylation in BTN and TC by two independent case-control studies To validate FABP3 hypomethylation in TC cases compared to BTN subjects, two independent case-control studies were conducted using FFPE tissue samples. A 183 bp amplicon containing cg18368411 site and flanking CpG sites was designed for analyses by MALDI-TOF mass spectrometry (Fig. 3 A). In the amplicon, the EpiTyper assay detected the methylation levels of 7 CpG sites and yielded 5 distinguishable mass peaks. CpG_9, CpG_10, and CpG_11 sites were located at the same fragment, and thus the mass peak shows the average methylation level of the three loci, which was presented as CpG_9.10.11. The other mass peaks have only one CpG locus, and cg18368411 was referred to as CpG_7. In Validation I (222 TC cases and 221 age- and gender-matched BTN subjects), all the seven CpG sites in the FABP3 amplicon showed significantly lower methylation levels in TC than in BTN, among which CpG_8 was the most significant loci (methylation values of BTN and TC: 0.27 vs. 0.17, P = 5.83E-10 adjusted for age, gender, TSH, FT3, and FT4). Moreover, there was a significant association between FABP3 hypomethylation and TC cases. After adjusting for covariates, the odds ratios (ORs) per 10% reduced methylation of all FABP3 CpG sites ranged from 1.24 to 1.81 ( P ≤ 0.002 for all; Fig. 3 B, Table S2). Consistent results were observed in Validation II (256 TC and 191 BTN). All seven FABP3 CpG sites were hypomethylated in TC cases than those in BTN patients. Similarly, CpG_8 showed the most significant reduction (methylation values of BTN and TC: 0.37 vs. 0.21, P = 6.30E-12). Hypomethylation of all FABP3 CpG sites was significantly associated with TC (the ORs per − 10% methylation ranged from 1.17 to 1.79, P ≤ 0.012 for all by binary logistic regression adjusted for age, gender, TSH, FT3, and FT4; Fig. 3 C, Table S2). The association between FABP3 hypomethylation in tissues and TC stratified by gender and age To eliminate the confounding effects of gender and age, we further evaluated the association between FABP3 methylation and TC cases by stratified regression analyses. To avoid possible bias due to the small sample size, the subjects in the two validations were combined (478 TC vs. 412 BTN). When stratified by gender, in males, four out of seven FABP3 CpG sites presented a significant association with TC (CpG_8 and CpG_9.10.11, the ORs per − 10% methylation ranged from 1.39 to 1.56, all the P values ≤ 0.011; Fig. 4 A, Table S3). In females, all CpG sites showed significantly lower methylation levels in TC cases than in BTN subjects (the ORs per − 10% methylation ranged from 1.20 to 1.85, all the P values ≤ 0.001; Fig. 4 B, Table S3) by logistic regression adjusted for age, TSH, FT3, and FT4. Moreover, the methylation differences between the cases and controls were larger in females than in males. When stratified by the age of 55 years old, which is the cutoff age for staging DTC, there was a significant association between all the seven CpG sites methylation and TC in the group less than 55 years old (the ORs per 10% reduced methylation ranged from 1.26 to 1.82, all the P values ≤ 3.70E-05) by logistic regression adjusted for covariates (Fig. 4 C, Table S4). In contrast, in subjects older than or equal to 55 years old, only CpG_7/cg18368411, CpG_8, and CpG_9.10.11 sites showed association with TC (the ORs per − 10% methylation ranged from 1.22 to 1.72, all the P values ≤ 0.009; Fig. 4 D, Table S4). The diagnostic efficiency of FABP3 hypomethylation alone and combined with BRAF V600E in differentiating TC from BTN To estimate the potential clinical utility of FABP3 methylation as a biomarker for TC, ROC curve analyses were performed and adjusted for possible confounding effects by logistic regression. As shown in Figs. 5 A, FABP3 hypomethylation based on FFPE tissue samples combining two validations has high credibility and accuracy in distinguishing TC cases from BTN patients (the area under the ROC curve (AUC) = 0.77, 95% CI: 0.73–0.80). The BRAF V600E mutation has been reported to be a potential biomarker for PTC. Sanger-Sequencing was performed to evaluate the BRAF V600E mutation status of the patients in two validations (Figure S1 ). A total of 711 patients (381 BTN patients and 330 TC cases) were detected for mutations in the BRAF gene. No mutations were found in any of the BTN patients, while BRAF mutations were detected in 180 (54.5%) of the TC cases. We observed good predictability of BRAF V600E to distinguish TC cases from BTN subjects (AUC = 0.77, 95% CI: 0.74–0.81; Fig. 5 B). Next, whether FABP3 methylation in combination with BRAF V600E could improve the diagnostic value of differentiating TC from BTN was investigated. The combined application of FABP3 hypomethylation and BRAF V600E achieved higher diagnostic accuracy (AUC = 0.87, 95% CI: 0.85–0.90; Fig. 5 C). Histological classification of BTN and TC subtypes by FABP3 methylation in tissue Furthermore, we analyzed the methylation levels of FABP3 in 478 TC cases and 412 BTN patients stratified by subtype. All seven FABP3 CpG sites were hypomethylated in PTC, MTC, and ATC cases than those in adenoma patients (all the P values ≤ 5.90E-05; Fig. 6 , Table S5). However, we did not observe a difference in FABP3 methylation between adenoma subjects and FTC cases (all the P values > 0.05; Fig. 6 , Table S5). As for BTN, the methylation level of FABP3 in patients with lymphatic thyroiditis was lower than that in patients with adenoma (all the P values ≤ 0.002; Fig. 6 , Table S5). The most significant reduction was in CpG_7/cg18368411 (methylation values in adenoma and lymphatic thyroiditis: 0.53 vs. 0.32, P = 1.41E-04; Fig. 6 , Table S5). In addition, we observed a correlation between five FABP3 CpG sites methylation and tumor size (CpG_7/cg18368411, CpG_8, and CpG_9.10.11, all the P values ≤ 0.016; Table 2 ). Next, we evaluated the correlation between FABP3 methylation and thyroid-related hormones with their median values as cutoff points (1.95 µIU/mL for TSH, 4.70 pmol/L for FT3, and 12.22 pmol/L for FT4). A positive correlation was observed between FT3 levels and the methylation levels of CpG_1, CpG_2, CpG_8, and CpG_9.10.11 sites (all the P values ≤ 0.046; Table 2 ). Table 2 Correlation between FABP3 methylation and the clinical characteristics of TC Clinical characteristics Group (n) Median of methylation levels CpG_1 CpG_2 CpG_7/cg18368411 CpG_8 CpG_9.10.11 Tumor length (cm) ≤ 1.0 (126) 0.65 (0.55–0.72) 0.48 (0.37–0.58) 0.36 (0.29–0.46) 0.20 (0.15–0.25) 0.17 (0.13–0.22) 1.0–2.0 (202) 0.68 (0.57–0.77) 0.51 (0.37–0.64) 0.38 (0.26–0.51) 0.19 (0.13–0.28) 0.18 (0.12–0.24) > 2.0 (94) 0.67 (0.48–0.81) 0.49 (0.28–0.67) 0.36 (0.17–0.54) 0.15 (0.09–0.28) 0.16 (0.07–0.27) P value $ 0.087 0.226 0.641 0.136 0.481 Tumor size T1 (324) 0.67 (0.57–0.75) 0.50 (0.39–0.62) 0.37 (0.28–0.49) 0.20 (0.14–0.26) 0.18 (0.13–0.24) T2&3&4 (112) 0.62 (0.47–0.78) 0.45 (0.29–0.65) 0.30 (0.15–0.53) 0.15 (0.08–0.25) 0.15 (0.07–0.25) P value * 0.113 0.071 0.006 0.001 0.016 Lymph node involvement pN0 (176) 0.68 (0.55–0.76) 0.48 (0.37–0.61) 0.36 (0.25–0.50) 0.20 (0.14–0.30) 0.19 (0.12–0.25) pN1 (237) 0.67 (0.54–0.76) 0.51 (0.40–0.63) 0.36 (0.27–0.51) 0.19 (0.13–0.24) 0.17 (0.12–0.23) P value * 0.966 0.257 0.607 0.033 0.775 Tumor stage Stage Ⅰ (336) 0.67 (0.55–0.75) 0.50 (0.39–0.62) 0.37 (0.27–0.50) 0.19 (0.14–0.27) 0.18 (0.12–0.24) Stage Ⅱ&Ⅲ&Ⅳ (88) 0.67 (0.51–0.78) 0.48 (0.33–0.63) 0.33 (0.16–0.53) 0.16 (0.09–0.25) 0.16 (0.07–0.24) P value * 0.524 0.199 0.050 0.024 0.076 TSH (µIU/mL) ≤ 1.95 (183) 0.69 (0.58–0.78) 0.53 (0.41–0.65) 0.39 (0.30–0.51) 0.21 (0.14–0.28) 0.18 (0.13–0.25) > 1.95 (181) 0.68 (0.53–0.75) 0.50 (0.39–0.63) 0.37 (0.27–0.52) 0.19 (0.14–0.26) 0.18 (0.13–0.22) P value * 0.159 0.252 0.371 0.117 0.508 FT3 (pmol/L) ≤ 4.70 (184) 0.67 (0.55–0.74) 0.47 (0.37–0.62) 0.36 (0.28–0.50) 0.18 (0.13–0.25) 0.17 (0.12–0.23) > 4.70 (180) 0.70 (0.57–0.79) 0.56 (0.43–0.65) 0.40 (0.31–0.53) 0.21 (0.15–0.30) 0.19 (0.13–0.26) P value * 0.011 0.002 0.060 0.019 0.046 FT4 (pmol/L) ≤ 12.22 (183) 0.71 (0.58–0.79) 0.53 (0.41–0.67) 0.39 (0.30–0.55) 0.20 (0.14–0.28) 0.18 (0.13–0.24) > 12.22 (181) 0.66 (0.55–0.74) 0.50 (0.38–0.61) 0.37 (0.27–0.47) 0.19 (0.14–0.26) 0.18 (0.13–0.24) P value * 0.011 0.104 0.052 0.185 0.680 P values * were calculated by the Mann-Whitney U test. P values $ were calculated by the Kruskal-Wallis test. Significant P values are in bold. Abbreviation: TC, thyroid cancer; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free tetraiodothyronine acid. Discussion Fatty acid-binding proteins ( FABPs ) is expressed in most major tissues 29 , and have been proposed to be central regulators of lipid metabolism and energy homeostasis through their control of fatty acid transport 30 . FABP3 , which is mainly expressed in skeletal muscle, the heart, and the placenta, plays a role in the intracellular transport of long-chain fatty acids and their acyl-CoA esters 31 . Recent reports have shown that FABP3 may be involved in the pathogenesis of various diseases 32 , 33 . For example, knockdown of FABP3 caused mitochondrial dysfunction and increased the apoptosis of cardiac cell lines 34 . Bensaad et al. found that FABP3 knockdown impaired the growth of glioblastoma xenograft by reducing fatty acid uptake and oxidation 35 . The methylation of FABP3 is associated with insulin, lipids, and cardiovascular phenotypes of the metabolic syndrome 36 . Till now, there have been no report on FABP3 methylation alterations in TC. Here, we discovered hypomethylation of the FABP3 gene in TC cases compared to BTN subjects together with a markedly elevated FABP3 mRNA expression via joint analyses of 850K BeadChip array and RNA-Sequencing, and performed further validations using mass spectrometry in two independent studies with large sample size. Age and gender have been identified as risk factors for the incidence of TC 37 . TC is the only malignancy with age as a prognostic indicator in the majority of staging systems 38 , which is the most frequently diagnosed malignancy among adolescents and young adults 39 , 40 . Deng et al. reported that the most common onset age in persons who developed TC decreased, and the age at death of those with TC increased worldwide 41 . A retrospective cohort evaluation of TC cases and deaths during 2005–2015 showed the rate of increasing incidence trend was higher in the younger age group and lower in the older age group 42 . Our results suggested that the altered FABP3 methylation was statistically significantly associated with TC risk, especially in younger people. As for gender, TC is the only non-reproductive cancer that occurs more often in women than in men, with a 3–4-fold higher incidence among women than men 43 . We observed the methylation differences of the FABP3 gene between the TC cases and BTN subjects were larger in females than in males. Estrogen may participate in the initiation of tumorigenesis in TC. It has been reported that estrogen has a positive effect on the proliferation of thyroid cells, which is essential for the development of TC, and may contribute to the mechanisms that cause DNA damage 44 . In addition, estrogen has also been shown to enhance the secretion of VEGF (vascular endothelial growth factor) by thyroid cells 45 , 46 , thereby regulating the vascular environment and allowing further tumor growth 47 , 48 . Moreover, estrogen receptor plays a role in cell migration and invasion in TC. Not only for the differentiation of BTN and TC, we also found interesting variation of FABP3 methylation in variant subtypes of BTN (adenoma, goiter, subacute thyroiditis, lymphatic thyroiditis) and TC (PTC, FTC, MTC, ATC). Adenoma showed the highest FABP3 methylation among all the BTN and TC subgroups, followed by goiter and subacute thyroiditis. To our surprise, the methylation level of FABP3 in most lymphocytic thyroiditis is similar to that of malignancy but not as other BTN subgroups. Chronic inflammation has been considered as a pro-tumor effect in TC 49 , 50 . Lymphocytic thyroiditis is the most common autoimmune disorder. Several studies have shown an epidemiological correlation between lymphocytic thyroiditis and TC, especially PTC 51 , 52 . Our study hereby supported that chronic inflammation may be a precancerous lesion or at least a highly correlated risk factor. In addition, we found that the methylated level of FABP3 was correlated with the grade of malignancy. The methylation of FABP3 gradually declined from low malignant PTC to the highly aggressive ATC. The methylation level of FABP3 of MTC, the intermediate subtypes of TC, is around 50% lower than PTC, but higher than ATC. Follicular adenoma is the early stage of FTC with a slightly different DNA methylation pattern than that observed in the normal thyroid, implying a role in the initiation of malignancy 53 . Sharing a common genetic background, follicular adenoma and FTC represents a significant diagnostic challenge at pre- and postsurgical differentiation 54 . Our study showed similar FABP3 methylation level in FTC with adenoma, indicating that follicular adenoma may share common epigenetic background with FTC as well. Taken together, the epigenetic alterations in TC subtypes could be helpful for the diagnosis and classification of tumors, and may even indicate the precancerous lesion and the malignant grade of TC. In our study, we observed lower levels of FABP3 methylation in TC patients with larger tumors, higher stages, and lymph node metastases, suggesting that the methylation level of FABP3 may be associated with tumor progression. Previous study has shown that gastric cancer highly expresses FABPs and its expression is associated with disease progression, tumor aggressiveness, and poor patient survival 55 . However, these correlations were weak, and further investigation in larger studies is necessary to validate the relation of changed FABP3 methylation and TC progression. Interestingly, we discovered that the methylation level of FABP3 steadily rose as FT3 levels rose and fell as FT4 levels rose. The relationship between changed thyroid hormone levels and the risk of TC has been widely studied 56 – 58 . Low FT3 and high FT4 concentrations are associated with shorter survival in patients with TC 59 . Moreover, serum FT3 levels are negatively correlated with inflammation, which is also associated with TC 60 . FT4 levels play different roles in different cancer types. In liver cancer, reduced FT4 levels indicate an increased risk of death; whereas in patients with primary breast cancer, elevated FT4 levels are associated with poor prognosis 58 . To date, few studies have been conducted on the relationship between thyroid-related hormones and alterations in gene methylation 61 . Thus, future prospective studies are warranted. The present study is among the largest studies on differential diagnosis of TC and BTN with a total of 890 samples, which suggests hypomethylation of the FABP3 gene might be biomarkers to differentiate benign and malignant thyroid tumors. Moreover, by combining FABP3 methylation with existing diagnostic techniques, such as BRAF V600E mutation, the diagnostic efficacy can be significantly improved. Nevertheless, our study has limitations that are related to retrospective study cohorts. We were unable to obtain the FABP3 methylation levels in fresh-frozen tissues measured by MALDI-TOF mass spectrometry due to the limited materials. To identify and explore further the relationship between FABP3 methylation changes and TC, prospective multicenter cohorts and functional studies are warranted. Conclusion To sum up, this study revealed and proved hypomethylation of FABP3 gene, together with a markedly elevated FABP3 mRNA expression in TC cases compared to BTN subjects. FABP3 showed significant methylation differences between TC and BTN, and among TC subtypes, suggesting the potential of DNA methylation as a novel pathological biomarker not only in discriminating malignant and BTN, but also in distinguishing between TC subtypes. Moreover, by combining FABP3 methylation with BRAF V600E mutation, the diagnostic efficacy can be significantly improved. Further investigation in a prospective multi-center study is needed before clinical practice. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This work was supported by the Research Grant of Nanjing TANTICA Co. Ltd [Grant No. 2021TC01.1]. Author Contribution Authors’contributionsHaixia Huang, Chenxia Jiang and Rongxi Yang conceived and designed this study; Haixia Huang and Yifei Yin wrote the main manuscript and prepared figures and tables; Haixia Huang, Junjie Li and Mengxia Li conducted major experiments; Hong Li, Yi Zhang, Xuandong Huang and YiFen Zhang were responsible for data interpretation and analysis; Haixia Huang drafted and Rongxi Yang revised the manuscript. All authors reviewed the manuscript. Data availability statement The datasets generated or analyzed in the present study are included in this publication and are available on reasonable request. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. Du L, Li R, Ge M, Wang Y, Li H, Chen W, He J. Incidence and mortality of thyroid cancer in China, 2008-2012. 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Bensaad K, Favaro E, Lewis CA, Peck B, Lord S, Collins JM, Pinnick KE, Wigfield S, Buffa FM, Li JL, Zhang Q, Wakelam MJO, et al. Fatty acid uptake and lipid storage induced by HIF-1alpha contribute to cell growth and survival after hypoxia-reoxygenation. Cell Rep 2014;9:349-65. Zhang Y, Kent JW, 2nd, Lee A, Cerjak D, Ali O, Diasio R, Olivier M, Blangero J, Carless MA, Kissebah AH. Fatty acid binding protein 3 (fabp3) is associated with insulin, lipids and cardiovascular phenotypes of the metabolic syndrome through epigenetic modifications in a Northern European family population. BMC Med Genomics 2013;6:9. Carling T, Udelsman R. Thyroid cancer. Annu Rev Med 2014;65:125-37. Dean DS, Hay ID. Prognostic indicators in differentiated thyroid carcinoma. Cancer Control 2000;7:229-39. Miller KD, Fidler-Benaoudia M, Keegan TH, Hipp HS, Jemal A, Siegel RL. Cancer statistics for adolescents and young adults, 2020. CA Cancer J Clin 2020;70:443-59. Di Giuseppe G, Zuk AM, Wasserman JD, Pole JD. 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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-5794576","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":406069797,"identity":"133ecd0a-3b19-48e4-9004-4282272b085b","order_by":0,"name":"Haixia Huang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haixia","middleName":"","lastName":"Huang","suffix":""},{"id":406069798,"identity":"2497be03-dfa5-4706-8cb2-651da8e78fd5","order_by":1,"name":"Yifei Yin","email":"","orcid":"","institution":"The Affiliated Huai'an Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yifei","middleName":"","lastName":"Yin","suffix":""},{"id":406069799,"identity":"a1495ae9-13cd-4ce3-b648-2cbc0eda6074","order_by":2,"name":"Hong Li","email":"","orcid":"","institution":"The Affiliated Huai'an Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Li","suffix":""},{"id":406069800,"identity":"d3d9c7f6-1506-4130-b5b2-9a33561de330","order_by":3,"name":"Junjie Li","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Li","suffix":""},{"id":406069801,"identity":"6fb4310c-0fc3-4fc9-9acf-cce4a467a952","order_by":4,"name":"Mengxia Li","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengxia","middleName":"","lastName":"Li","suffix":""},{"id":406069802,"identity":"2c28d1ec-5f98-4335-8be4-aa894f789da0","order_by":5,"name":"Yi Zhang","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":406069803,"identity":"a70e140e-2552-4561-9775-e0fd2808bd03","order_by":6,"name":"Xuandong Huang","email":"","orcid":"","institution":"The Affiliated Huai'an Hospital of Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuandong","middleName":"","lastName":"Huang","suffix":""},{"id":406069804,"identity":"785f31b2-aafe-4e2a-b028-e1a17b48f131","order_by":7,"name":"Yifen Zhang","email":"","orcid":"","institution":"Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yifen","middleName":"","lastName":"Zhang","suffix":""},{"id":406069805,"identity":"d1a33667-8b35-4045-8043-9631d24cb0e4","order_by":8,"name":"Chenxia Jiang","email":"","orcid":"","institution":"The Affiliated Hospital of Nantong University","correspondingAuthor":false,"prefix":"","firstName":"Chenxia","middleName":"","lastName":"Jiang","suffix":""},{"id":406069806,"identity":"dbdef07f-1237-49dd-9a30-fba9d843c476","order_by":9,"name":"Rongxi Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYNACAxDBfIBBAsxLIFoLWwIpWsCAxwDKIKDFvL338GuegsPy5vxrvj2wzDnMwM+eY8DwcwduLTJnzqVZzjA4bLhzxtvtBpLbDjNI9rwxYOw9g1uLhESOmcEHg8OMG26c3SYB0mJwI8eAmbGNgJYEg8P2G26ceQbWYk+EFuMHQFsSN5zvYYPYIkFIC88ZM8YZBunJG26wmQG1pPNInHlWcLAXnxb2HuPPPH+sbTecP/xMWnKbtRx/e/LGBz/xaAECNmAENgM1JzAwA1k8IKEDeDUAE8oHBoY6Bgb+AwyMHwgoHQWjYBSMgpEJAOC/UVgpHYJUAAAAAElFTkSuQmCC","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Rongxi","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-01-09 08:23:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5794576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5794576/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74691714,"identity":"bab3f57a-8887-405c-bb80-46f5b66f6f91","added_by":"auto","created_at":"2025-01-24 18:43:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":95391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and flow chart. The 32 fresh-frozen tissue samples in the discovery study were subjected to Illumina Methylation EPIC 850K BeadChip array and RNA-Sequencing.\u003c/strong\u003e In the discovery study, there was a good correlation between DNA methylation and mRNA expression of \u003cem\u003eFABP3\u003c/em\u003e in fresh-frozen tissue samples. Further validations with FFPE tissue samples were conducted in two independent studies (Validation Ⅰ and Validation Ⅱ) by MALDI-TOF mass spectrometry.\u003c/p\u003e\n\u003cp\u003eAbbreviation: BTN, benign thyroid nodule; TC, thyroid cancer; SNP, single nucleotide polymorphism; FC, fold change; TSS200, transcription start site 200 bp; FFPE, formalin-fixed and paraffin-embedded; MALDI-TOF, matrix-assisted laser desorption ionization time-of-flight.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/6e4015f240f178eb0316cd41.png"},{"id":74691716,"identity":"30f7a102-4fb1-4742-aaf5-1053c9c105c4","added_by":"auto","created_at":"2025-01-24 18:43:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe discovery study showed significantly correlation between CpG_7/cg18368411 methylation and mRNA expression of the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFABP3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e gene in fresh-frozen tissue samples. (A) \u003c/strong\u003eBox plots for methylation levels of CpG_7/cg18368411 detected by Methylation 850K BeadChip array. \u003cstrong\u003e(B) \u003c/strong\u003eBox plots for mRNA expression levels of\u003cem\u003e FABP3\u003c/em\u003e gene measured by RNA-Sequencing. \u003cstrong\u003e(C) \u003c/strong\u003eCorrelations of \u003cem\u003eFABP3\u003c/em\u003e expression with methylation levels of CpG_7/cg18368411.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/9ae7c2b99628d52dc1c93a81.png"},{"id":74692069,"identity":"1eddee8f-edf6-413f-865c-dbb15da4e3c2","added_by":"auto","created_at":"2025-01-24 18:51:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80819,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFABP3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e hypomethylation in TC cases compared to BTN subjects in two independent studies.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eSchematic diagram of the target amplicon within the \u003cem\u003eFABP3\u003c/em\u003e gene. A 183bp amplicon covers sevenmeasurable CpG sites of the \u003cem\u003eFABP3\u003c/em\u003egene (Chr1: 31845858 - 31846040, build GRCh37/hg19, defined by the UCSC Genome Browser). Cg18368411 was referred to CpG_7.\u003cstrong\u003e (B, C)\u003c/strong\u003e Box plots for the methylation levels of the seven CpG sites in \u003cem\u003eFABP3\u003c/em\u003e amplicon in Validation Ⅰ (\u003cstrong\u003eB\u003c/strong\u003e) and Validation Ⅱ (\u003cstrong\u003eC\u003c/strong\u003e). All the \u003cem\u003eP\u003c/em\u003e values were calculated by logistic regression adjusted for covariant.\u003c/p\u003e\n\u003cp\u003eAbbreviation: BTN, benign thyroid nodule; TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/37b461089deaacaa8fdda3e3.png"},{"id":74692071,"identity":"41db9e05-8c11-4fe2-b4db-1db5a910175a","added_by":"auto","created_at":"2025-01-24 18:51:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176220,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombination analysis of the association between \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFABP3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e hypomethylation in FFPE tissues and TC stratified by age and gender.\u003c/strong\u003e \u003cstrong\u003e(A, B)\u003c/strong\u003e Box plots for the \u003cem\u003eFABP3\u003c/em\u003emethylation levels in the male group (\u003cstrong\u003eA\u003c/strong\u003e) and female group (\u003cstrong\u003eB\u003c/strong\u003e). \u003cstrong\u003e(C, D)\u003c/strong\u003e Box plots for the \u003cem\u003eFABP3\u003c/em\u003e methylation levels in the less than 55 years old group\u003cstrong\u003e (C)\u003c/strong\u003e and older than or equal to 55 years old group \u003cstrong\u003e(D)\u003c/strong\u003e. All the \u003cem\u003eP\u003c/em\u003e values were calculated by logistic regression with the adjustment of covariates.\u003c/p\u003e\n\u003cp\u003eAbbreviation: BTN, benign thyroid nodule; TC, thyroid cancer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/3ecae291904e735e92ea6fee.png"},{"id":74691721,"identity":"7d9995c7-df16-421d-90d3-e0b356da1861","added_by":"auto","created_at":"2025-01-24 18:43:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe diagnostic efficiency of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFABP3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e hypomethylation alone and combined with BRAF\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eV600E\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e in differentiating TC from BTN.\u003c/strong\u003e \u003cstrong\u003e(A) \u003c/strong\u003eROC curve analyses for the discriminatory power of the seven \u003cem\u003eFABP3\u003c/em\u003e CpG sites to distinguish TC cases from BTN subjects. \u003cstrong\u003e(B)\u003c/strong\u003e ROC curve analysis for the discriminatory power of BRAF\u003csup\u003eV600E\u003c/sup\u003e to distinguish TC cases from BTN subjects. \u003cstrong\u003e(C)\u003c/strong\u003e ROC curve analyses for the discriminatory power of \u003cem\u003eFABP3\u003c/em\u003e methylation in combined with BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation to differentiate TC cases from BTN subjects.\u003cstrong\u003e \u003c/strong\u003eAll the above 95% CI of AUC were calculated by logistic regression with covariates-adjusted.\u003c/p\u003e\n\u003cp\u003eAbbreviation:\u003cstrong\u003e \u003c/strong\u003eROC, receiver operating characteristic; AUC, the area under the ROC curve; CI, confidence interval.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/3bb5515ab6dc33bbfa212b2a.png"},{"id":74692070,"identity":"be604022-bfde-4fbc-88bf-1f19dd442212","added_by":"auto","created_at":"2025-01-24 18:51:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":145603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFABP3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e methylation in BTN and TC subtypes. (A-E) Box plots of methylation levels of seven CpG sites of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eFABP3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in BTN and TC subtypes.\u003c/strong\u003e All the \u003cem\u003eP\u003c/em\u003evalues were calculated by the Mann-Whitney U test referred to adenoma. *: \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **: \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***: \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eAbbreviation: BTN, benign thyroid nodule; TC, thyroid cancer; PTC, papillary thyroid cancer; FTC, follicular thyroid cancer; MTC, medullary thyroid cancer; ATC, anaplastic thyroid cancer.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/37e180517bf0da1cd8d4cb3a.png"},{"id":80836387,"identity":"dee956f3-6b21-4bcd-84ac-08e248dcde54","added_by":"auto","created_at":"2025-04-17 14:54:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2226381,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/09a16888-e58d-48ab-b5a1-0eea2d5362db.pdf"},{"id":74691717,"identity":"acc579e8-2208-4821-a8e1-cb9f07b8bb87","added_by":"auto","created_at":"2025-01-24 18:43:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":316293,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794576/v1/ad8342bdf2f517bab500e67c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FABP3 methylation as a novel biomarker for the differentiation and classification of benign and malignant thyroid nodules","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThyroid cancer (TC) is the most common endocrine malignant tumors in adults, with an estimated total of more than 9.2\u0026nbsp;million new cases in 2020 worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In China, the incidence of TC increased from 2.40/100,000 in 2003 to 13.75/100,000 in 2012, with an average annual increase of 20% \u003csup\u003e2, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In some provinces, such as Zhejiang Province, the incidence rate of TC ranks first among all cancers in women \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Differentiated thyroid cancer (DTC) has two subtypes, papillary thyroid cancer (PTC) and follicular thyroid cancer (FTC), which are the most common subtype of TC. The rarer subtypes are medullary thyroid cancer (MTC) and anaplastic thyroid cancer (ATC) \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Thyroid nodules have a high incidence and a low malignant rate in the population. Although 60% of ultrasonography showed the presence of thyroid nodules, only 5% were eventually confirmed as malignant \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAccurate diagnosis of TC, including the judgment of benign and malignant thyroid nodules and classification of TC, is one of the current clinical challenges. Ultrasound-guided fine-needle aspiration biopsy (FNAB) remains crucial in the preoperative diagnosis of thyroid nodules \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, up to 15\u0026ndash;30% of thyroid nodules evaluated by FNAB were cytologically indeterminate, which were usually diagnosed by postoperative pathological diagnosis \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Patients with uncertain nodules will be at risk of overdiagnosis or misdiagnosis. Therefore, reliable and practical biomarkers are urgently needed.\u003c/p\u003e \u003cp\u003eDetermining the malignancy of thyroid nodules is becoming increasingly dependent on molecular pathological techniques \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, including genetic detection and epigenetic detection \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The role of BRAF, RAS, and RET gene mutations in TC has been widely recognized. BRAF mutation is known to be specific for PTC, and the reported positive rate ranges from 29 to 83% \u003csup\u003e15, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Because of the low sensitivity, compensating molecular diagnostic methods are needed \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Afirma gene expression classifiers and ThyroSeq v3 are suitable for fresh aspirates and are expensive \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In addition, they are associated with a high rate of overdiagnosis due to low positive predictive value \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. These facts point to the need for a highly accurate diagnostic test for thyroid nodules using time-insensitive materials.\u003c/p\u003e \u003cp\u003eDNA methylation catalyzed by DNA methyltransferases (DNMTs) is one of the important epigenetic modifications controlling gene expression and maintaining genome structure. Alterations of DNA methylation are early molecular changes in human cancers and play an important role in tumorigenesis. As a potential biomarker, changes in the methylation profile have been found in all types of cancer including TC \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. For example, Stephen \u003cem\u003eet al.\u003c/em\u003e used quantitative methylation-specific polymerase chain reaction (PCR) to detect the promoter methylation status of 21 candidate genes on 329 formalin-fixed paraffin-embedded (FFPE), demonstrating that combined abnormal gene methylation helps clinically differentiate FTC and PTC from benign thyroid nodules (BTN) \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Yim \u003cem\u003eet al.\u003c/em\u003e evaluated DNA methylation in 109 thyroid specimens form BTN, PTCs and adjacent normal thyroid tissues using the Reduced Representation Bisulfite Sequencing (RRBS) and then performed validation in a retrospective cohort containing 65 thyroid nodules, suggesting epigenetic testing as a new molecular approach for thyroid diagnostics \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Nevertheless, these studies were based on candidate approaches and were also limited by small sample size. So far, there is few screenings for the variant methylation signatures aiming to differentiate malignant and benign thyroid tumors, especially in large sample size.\u003c/p\u003e \u003cp\u003eHere, we intended to discover and validate the abnormal DNA methylation alterations in TC compared to BTN in the Chinese population. Combining the genome-wide screening assay of Methylation 850K BeadChip array and RNA-Sequencing, we discovered TC associated differential methylation in \u003cem\u003eFABP3\u003c/em\u003e gene and performed further validations via mass spectrometry by case-control studies with a total of 890 patients from two clinical centers.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003e The studies were approved by the Ethics Committee of the Nanjing Medical University. Informed consent was obtained from each recruited participant. In the discovery study, we collected 32 fresh-frozen tissue samples from 17 BTN subjects and 15 TC cases at the Affiliated Huai\u0026rsquo;an Hospital of Xuzhou Medical University from 2019 to 2020. Two independent studies were conducted on 890 FFPE tissue samples for validation. Validation Ⅰ: 222 TC patients and 221 age- and gender-matched BTN subjects were collected from the Affiliated Huai\u0026rsquo;an Hospital of Xuzhou Medical University and the Jiangsu Provincial Hospital of Chinese Medicine from 2013 to 2023. In the BTN group, 78.70% (174/221) were female, with a median (interquartile range, IQR) age of 53.00 (45.50\u0026ndash;60.00) years; while in the TC group, 72.10% (160/222) were female, with a median (IQR) age of 50.00 (41.75\u0026ndash;58.00) years. Validation Ⅱ: a total of 256 TC cases and 191 age- and gender-matched BTN subjects were collected from the Affiliated Hospital of Nantong University from 2017 to 2022. The median (IQR) age of BTN subjects and TC cases were 49.00 (35.00\u0026ndash;55.00) and 50.00 (38.00\u0026ndash;57.00) years old, and the proportions of females were 77.50% (148/191) and 77.30% (198/256), respectively. The detailed clinical characteristics of the participants were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, including tumor length, tumor size, lymph node involvement, tumor stage, thyroid-stimulating hormone (TSH) levels, free triiodothyronine (FT3) levels, free tetraiodothyronine acid (FT4) levels, and classification of BTN and TC.\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\u003eClinical characteristics of the participants in two independent validations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eValidation Ⅰ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eValidation Ⅱ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBTN (n\u0026thinsp;=\u0026thinsp;221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTC (n\u0026thinsp;=\u0026thinsp;222)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBTN (n\u0026thinsp;=\u0026thinsp;191)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTC (n\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.00 (45.50\u0026ndash;60.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.00 (41.75\u0026ndash;58.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49.00 (35.00\u0026ndash;55.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50.00 (38.00\u0026ndash;57.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.531\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e174 (78.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (72.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.104\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e148 (77.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e198 (77.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.971\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (21.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (27.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43 (22.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58 (22.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (0.90\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50 (1.20\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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 \u003cp\u003eT1\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 \u003cp\u003e163 (73.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e161 (62.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u0026amp;3\u0026amp;4\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 \u003cp\u003e56 (25.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56 (21.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\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 \u003cp\u003e3 (1.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39 (15.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymph node involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epN0\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 \u003cp\u003e103 (46.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73 (28.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epN1\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 \u003cp\u003e107 (48.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e130 (50.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\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 \u003cp\u003e12 (5.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53 (20.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\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 \u003cp\u003eStage Ⅰ\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 \u003cp\u003e172 (77.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e164 (64.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅱ\u0026amp;Ⅲ\u0026amp;Ⅳ\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 \u003cp\u003e49 (22.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39 (15.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnknown\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 \u003cp\u003e1 (0.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53 (20.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46 (0.89\u0026ndash;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88 (1.20\u0026ndash;3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.71 (1.09\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.09 (1.45\u0026ndash;2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.34 (3.60\u0026ndash;4.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.26 (3.67\u0026ndash;4.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.93 (4.60\u0026ndash;5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.88 (4.52\u0026ndash;5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.663\u003csup\u003e*\u003c/sup\u003e\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.84 (13.00\u0026ndash;18.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.06 (10.92\u0026ndash;17.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.56 (10.52\u0026ndash;12.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.56 (10.46\u0026ndash;12.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.746\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification of BTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadenoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (40.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e106 (55.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egoiter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (57.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68 (35.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esubacute thyroiditis\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 \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3 (1.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003elymphatic thyroiditis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14 (7.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification of TC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTC\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 \u003cp\u003e171 (77.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e197 (76.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFTC\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 \u003cp\u003e19 (8.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31 (12.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTC\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 \u003cp\u003e27 (12.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24 (9.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATC\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 \u003cp\u003e5 (2.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4 (1.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eP\u003c/em\u003e values\u003csup\u003e*\u003c/sup\u003e were calculated by the Mann-Whitney U test. \u003cem\u003eP\u003c/em\u003e values\u003csup\u003e$\u003c/sup\u003e were calculated by Chi-square test. Significant \u003cem\u003eP\u003c/em\u003e values are in bold.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviation: BTN, benign thyroid nodule; TC, thyroid cancer; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free tetraiodothyronine acid; PTC, papillary thyroid cancer; FTC, follicular thyroid cancer; MTC, medullary thyroid cancer; ATC, anaplastic thyroid cancer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the discovery study and two independent validations, the inclusion criteria for malignant nodules were as follows: (1) no distant metastases or other co-occurring cancers, (2) before any related treatment, and (3) complete clinical records. BTN patients were matched to TC cases by age, gender, and the year of diagnosis. Histopathological diagnosis was performed by two qualified pathologists in all cases, and the clinical TNM stage of each malignant case was determined according to the 8th edition American Joint Committee on Cancer Staging System \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIllumina Methylation EPIC 850K BeadChip array and RNA-Sequencing\u003c/h3\u003e\n\u003cp\u003eThe study design and flow chart were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the discovery round, FastPure Blood/Cell/Tissue/Bacteria DNA Isolation Kit (DC112, Vazyme, Nanjing, China) and FastPure Cell/Tissue Total RNA Isolation Kit (RC101, Vazyme, Nanjing, China) were used to isolate genomic DNA and total RNA from 32 fresh-frozen tissue samples, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGenome-wide DNA methylation profiles were analyzed at single nucleotide resolution by the Illumina Methylation EPIC 850K BeadChip array. Probes that meet the following criteria were considered differentially methylated: (1) methylation difference between BTN and TC groups (|delta-beta|)\u0026thinsp;\u0026ge;\u0026thinsp;0.25, (2) \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.001, (3) no adjacent single nucleotide polymorphisms (SNPs), and (4) on the promoter region or the 1st exon of gene body. At the same time, mRNA expression was measured by RNA-Sequencing. Genes with a fold change of expression\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were thought to be differentially expressed.\u003c/p\u003e\n\u003ch3\u003eMatrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry\u003c/h3\u003e\n\u003cp\u003eDNA was extracted from FFPE tissue samples using FastPure FFPE DNA Isolation Kit (DC105, Vazyme, Nanjing, China). The isolated DNA was further bisulfite converted by EZ-96 DNA Methylation Gold Kit (D5007, Zymo Research, Orange, USA) according to the manufacturer\u0026rsquo;s protocol. After bisulfite treatment, all non-methylated cytosine (C) bases in CpG sites were converted to uracil (U), whereas all methylated (C) bases remained unchanged. The CpG methylation levels of FFPE tissue samples were then determined by MALDI-TOF mass spectrometry, as described by Yang \u003cem\u003eet al.\u003c/em\u003e and Yin \u003cem\u003eet al\u003c/em\u003e. \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Briefly, the target sequence was amplified by PCR using bisulfite-specific primers, forward primer: aggaagagagTTATAGTGATGTTGGGTTAGGTTGA, reverse primer: cagtaatacgactcactatagggagaaggctCAACCCCTCCTAAATAAACCCT. Upper case letters present the sequence-specific primer regions, and non-specific tags are shown in lower case letters. The sequence of the amplicon was presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. There are no SNPs overlapped with any of the CpG loci in the amplicon. Next, the amplification products were treated with Shrimp Alkaline Phosphatase and followed by T-cleavage using RNase A. After cleaning residual ions with resin, the methylation level of each sample was quantified and collected by the MassARRAY system.\u003c/p\u003e\n\u003ch3\u003eBRAF mutation detection\u003c/h3\u003e\n\u003cp\u003eA single hotspot mutation in nucleotide 1799 of the BRAF gene (corresponding to p.V600E) has been recognized as the most frequent genetic event in PTC, with an incidence of 29\u0026ndash;83%, and accounts for more than 90% of BRAF-mutated TC \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. A total of 711 patients from two validations were detected for BRAF gene mutations. Forward primer: 5'-TCATAATGCTTGCTGATAGGA-3' and reverse primer: 5'-GGCCAAAAATTTAATCAGTGGA-3' were used for amplification by PCR. The sequences of the amplified fragments were then analyzed by Sanger-Sequencing.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eAll data analyses were performed using SPSS (version 25.0) and GraphPad Prism (version 8.0). Mann-Whitney U test and Chi-square test were used to compare the differences between TC and BTN groups. Spearman correlation was used to determine the relationship between variables. Binary logistic regression analysis was performed to calculate odds ratios (ORs) and their 95% confidence intervals (95% CIs), adjusted for covariates. Kruskal-Wallis test and Mann-Whitney U test were used to analyze the correlation between \u003cem\u003eFABP3\u003c/em\u003e methylation levels and different clinical characteristics. Receiver operating characteristic (ROC) curve was used to evaluate goodness of fit. A two-tailed \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eDiscovery of TC associated\u003c/b\u003e \u003cb\u003eFABP3\u003c/b\u003e \u003cb\u003ehypomethylation in tissue\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGenome-wide Illumina Methylation 850K BeadChip array and RNA-Sequencing were used to screen methylation sites and genes with significant differences between TC and BTN in a total of 32 fresh-frozen samples (15 TC and 17 BTN). Cg18368411 within the transcription start site 200 bp (TSS200) of the \u003cem\u003eFABP3\u003c/em\u003e gene was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The CpG site showed the most significant difference between TC cases and BTN subjects (median methylation: TC cases\u0026thinsp;=\u0026thinsp;0.28, BTN subjects\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.90E-05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Alterations in DNA methylation may affect gene expression. Consistently, we found that compared with BTN subjects, TC cases showed increased expression of \u003cem\u003eFABP3\u003c/em\u003e mRNA (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In addition, the methylation level of cg18368411 was significantly negatively correlated with the expression level of \u003cem\u003eFABP3\u003c/em\u003e with the Spearman correlation coefficient value of -0.62 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.34E-04; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eValidation of the differential\u003c/b\u003e \u003cb\u003eFABP3\u003c/b\u003e \u003cb\u003emethylation in BTN and TC by two independent case-control studies\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo validate \u003cem\u003eFABP3\u003c/em\u003e hypomethylation in TC cases compared to BTN subjects, two independent case-control studies were conducted using FFPE tissue samples. A 183 bp amplicon containing cg18368411 site and flanking CpG sites was designed for analyses by MALDI-TOF mass spectrometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In the amplicon, the EpiTyper assay detected the methylation levels of 7 CpG sites and yielded 5 distinguishable mass peaks. CpG_9, CpG_10, and CpG_11 sites were located at the same fragment, and thus the mass peak shows the average methylation level of the three loci, which was presented as CpG_9.10.11. The other mass peaks have only one CpG locus, and cg18368411 was referred to as CpG_7.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Validation I (222 TC cases and 221 age- and gender-matched BTN subjects), all the seven CpG sites in the \u003cem\u003eFABP3\u003c/em\u003e amplicon showed significantly lower methylation levels in TC than in BTN, among which CpG_8 was the most significant loci (methylation values of BTN and TC: 0.27 vs. 0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.83E-10 adjusted for age, gender, TSH, FT3, and FT4). Moreover, there was a significant association between \u003cem\u003eFABP3\u003c/em\u003e hypomethylation and TC cases. After adjusting for covariates, the odds ratios (ORs) per 10% reduced methylation of all \u003cem\u003eFABP3\u003c/em\u003e CpG sites ranged from 1.24 to 1.81 (\u003cem\u003eP\u003c/em\u003e \u0026le; 0.002 for all; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table S2). Consistent results were observed in Validation II (256 TC and 191 BTN). All seven \u003cem\u003eFABP3\u003c/em\u003e CpG sites were hypomethylated in TC cases than those in BTN patients. Similarly, CpG_8 showed the most significant reduction (methylation values of BTN and TC: 0.37 vs. 0.21, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.30E-12). Hypomethylation of all \u003cem\u003eFABP3\u003c/em\u003e CpG sites was significantly associated with TC (the ORs per \u0026minus;\u0026thinsp;10% methylation ranged from 1.17 to 1.79, \u003cem\u003eP\u003c/em\u003e \u0026le; 0.012 for all by binary logistic regression adjusted for age, gender, TSH, FT3, and FT4; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, Table S2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe association between\u003c/b\u003e \u003cb\u003eFABP3\u003c/b\u003e \u003cb\u003ehypomethylation in tissues and TC stratified by gender and age\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo eliminate the confounding effects of gender and age, we further evaluated the association between \u003cem\u003eFABP3\u003c/em\u003e methylation and TC cases by stratified regression analyses. To avoid possible bias due to the small sample size, the subjects in the two validations were combined (478 TC vs. 412 BTN).\u003c/p\u003e \u003cp\u003eWhen stratified by gender, in males, four out of seven \u003cem\u003eFABP3\u003c/em\u003e CpG sites presented a significant association with TC (CpG_8 and CpG_9.10.11, the ORs per \u0026minus;\u0026thinsp;10% methylation ranged from 1.39 to 1.56, all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.011; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, Table S3). In females, all CpG sites showed significantly lower methylation levels in TC cases than in BTN subjects (the ORs per \u0026minus;\u0026thinsp;10% methylation ranged from 1.20 to 1.85, all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, Table S3) by logistic regression adjusted for age, TSH, FT3, and FT4. Moreover, the methylation differences between the cases and controls were larger in females than in males. When stratified by the age of 55 years old, which is the cutoff age for staging DTC, there was a significant association between all the seven CpG sites methylation and TC in the group less than 55 years old (the ORs per 10% reduced methylation ranged from 1.26 to 1.82, all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;3.70E-05) by logistic regression adjusted for covariates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, Table S4). In contrast, in subjects older than or equal to 55 years old, only CpG_7/cg18368411, CpG_8, and CpG_9.10.11 sites showed association with TC (the ORs per \u0026minus;\u0026thinsp;10% methylation ranged from 1.22 to 1.72, all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.009; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, Table S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe diagnostic efficiency of\u003c/b\u003e \u003cb\u003eFABP3\u003c/b\u003e \u003cb\u003ehypomethylation alone and combined with BRAF\u003c/b\u003e\u003csup\u003e\u003cb\u003eV600E\u003c/b\u003e\u003c/sup\u003e \u003cb\u003ein differentiating TC from BTN\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo estimate the potential clinical utility of \u003cem\u003eFABP3\u003c/em\u003e methylation as a biomarker for TC, ROC curve analyses were performed and adjusted for possible confounding effects by logistic regression. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cem\u003eFABP3\u003c/em\u003e hypomethylation based on FFPE tissue samples combining two validations has high credibility and accuracy in distinguishing TC cases from BTN patients (the area under the ROC curve (AUC)\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.73\u0026ndash;0.80). The BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation has been reported to be a potential biomarker for PTC. Sanger-Sequencing was performed to evaluate the BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation status of the patients in two validations (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 711 patients (381 BTN patients and 330 TC cases) were detected for mutations in the BRAF gene. No mutations were found in any of the BTN patients, while BRAF mutations were detected in 180 (54.5%) of the TC cases. We observed good predictability of BRAF\u003csup\u003eV600E\u003c/sup\u003e to distinguish TC cases from BTN subjects (AUC\u0026thinsp;=\u0026thinsp;0.77, 95% CI: 0.74\u0026ndash;0.81; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Next, whether \u003cem\u003eFABP3\u003c/em\u003e methylation in combination with BRAF\u003csup\u003eV600E\u003c/sup\u003e could improve the diagnostic value of differentiating TC from BTN was investigated. The combined application of \u003cem\u003eFABP3\u003c/em\u003e hypomethylation and BRAF\u003csup\u003eV600E\u003c/sup\u003e achieved higher diagnostic accuracy (AUC\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.85\u0026ndash;0.90; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eHistological classification of BTN and TC subtypes by\u003c/b\u003e \u003cb\u003eFABP3\u003c/b\u003e \u003cb\u003emethylation in tissue\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFurthermore, we analyzed the methylation levels of \u003cem\u003eFABP3\u003c/em\u003e in 478 TC cases and 412 BTN patients stratified by subtype. All seven \u003cem\u003eFABP3\u003c/em\u003e CpG sites were hypomethylated in PTC, MTC, and ATC cases than those in adenoma patients (all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;5.90E-05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S5). However, we did not observe a difference in \u003cem\u003eFABP3\u003c/em\u003e methylation between adenoma subjects and FTC cases (all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S5). As for BTN, the methylation level of \u003cem\u003eFABP3\u003c/em\u003e in patients with lymphatic thyroiditis was lower than that in patients with adenoma (all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S5). The most significant reduction was in CpG_7/cg18368411 (methylation values in adenoma and lymphatic thyroiditis: 0.53 vs. 0.32, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.41E-04; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Table S5). In addition, we observed a correlation between five \u003cem\u003eFABP3\u003c/em\u003e CpG sites methylation and tumor size (CpG_7/cg18368411, CpG_8, and CpG_9.10.11, all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.016; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Next, we evaluated the correlation between \u003cem\u003eFABP3\u003c/em\u003e methylation and thyroid-related hormones with their median values as cutoff points (1.95 \u0026micro;IU/mL for TSH, 4.70 pmol/L for FT3, and 12.22 pmol/L for FT4). A positive correlation was observed between FT3 levels and the methylation levels of CpG_1, CpG_2, CpG_8, and CpG_9.10.11 sites (all the \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026le;\u0026thinsp;0.046; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eCorrelation between \u003cem\u003eFABP3\u003c/em\u003e methylation and the clinical characteristics of TC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClinical characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eMedian of methylation levels\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCpG_1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCpG_2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCpG_7/cg18368411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCpG_8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCpG_9.10.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eTumor length (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.0 (126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.55\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.37\u0026ndash;0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.29\u0026ndash;0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20 (0.15\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17 (0.13\u0026ndash;0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.0\u0026ndash;2.0 (202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.57\u0026ndash;0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 (0.37\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.38 (0.26\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19 (0.13\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.12\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.0 (94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.48\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49 (0.28\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.17\u0026ndash;0.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15 (0.09\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.07\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT1 (324)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.57\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.39\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37 (0.28\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20 (0.14\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.13\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT2\u0026amp;3\u0026amp;4 (112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62 (0.47\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45 (0.29\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.30 (0.15\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15 (0.08\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15 (0.07\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLymph node involvement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epN0 (176)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.55\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.37\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.25\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20 (0.14\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19 (0.12\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epN1 (237)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.54\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51 (0.40\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.27\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19 (0.13\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17 (0.12\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTumor stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅰ (336)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.55\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.39\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37 (0.27\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19 (0.14\u0026ndash;0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.12\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage Ⅱ\u0026amp;Ⅲ\u0026amp;Ⅳ (88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.51\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48 (0.33\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33 (0.16\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16 (0.09\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16 (0.07\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTSH (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.95 (183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.58\u0026ndash;0.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.41\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39 (0.30\u0026ndash;0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21 (0.14\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.13\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1.95 (181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.53\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.39\u0026ndash;0.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37 (0.27\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19 (0.14\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.13\u0026ndash;0.22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFT3 (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4.70 (184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.55\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47 (0.37\u0026ndash;0.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36 (0.28\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18 (0.13\u0026ndash;0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17 (0.12\u0026ndash;0.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;4.70 (180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.57\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56 (0.43\u0026ndash;0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40 (0.31\u0026ndash;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21 (0.15\u0026ndash;0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19 (0.13\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eFT4 (pmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;12.22 (183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.71 (0.58\u0026ndash;0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53 (0.41\u0026ndash;0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39 (0.30\u0026ndash;0.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20 (0.14\u0026ndash;0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.13\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;12.22 (181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.55\u0026ndash;0.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 (0.38\u0026ndash;0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37 (0.27\u0026ndash;0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19 (0.14\u0026ndash;0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.18 (0.13\u0026ndash;0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eP\u003c/em\u003e values\u003csup\u003e*\u003c/sup\u003e were calculated by the Mann-Whitney U test. \u003cem\u003eP\u003c/em\u003e values\u003csup\u003e$\u003c/sup\u003e were calculated by the Kruskal-Wallis test. Significant \u003cem\u003eP\u003c/em\u003e values are in bold.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviation: TC, thyroid cancer; TSH, thyroid stimulating hormone; FT3, free triiodothyronine; FT4, free tetraiodothyronine acid.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFatty acid-binding proteins (\u003cem\u003eFABPs\u003c/em\u003e) is expressed in most major tissues \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and have been proposed to be central regulators of lipid metabolism and energy homeostasis through their control of fatty acid transport \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eFABP3\u003c/em\u003e, which is mainly expressed in skeletal muscle, the heart, and the placenta, plays a role in the intracellular transport of long-chain fatty acids and their acyl-CoA esters \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Recent reports have shown that \u003cem\u003eFABP3\u003c/em\u003e may be involved in the pathogenesis of various diseases \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. For example, knockdown of \u003cem\u003eFABP3\u003c/em\u003e caused mitochondrial dysfunction and increased the apoptosis of cardiac cell lines \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Bensaad \u003cem\u003eet al.\u003c/em\u003e found that \u003cem\u003eFABP3\u003c/em\u003e knockdown impaired the growth of glioblastoma xenograft by reducing fatty acid uptake and oxidation \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The methylation of \u003cem\u003eFABP3\u003c/em\u003e is associated with insulin, lipids, and cardiovascular phenotypes of the metabolic syndrome \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Till now, there have been no report on \u003cem\u003eFABP3\u003c/em\u003e methylation alterations in TC. Here, we discovered hypomethylation of the \u003cem\u003eFABP3\u003c/em\u003e gene in TC cases compared to BTN subjects together with a markedly elevated \u003cem\u003eFABP3\u003c/em\u003e mRNA expression via joint analyses of 850K BeadChip array and RNA-Sequencing, and performed further validations using mass spectrometry in two independent studies with large sample size.\u003c/p\u003e \u003cp\u003eAge and gender have been identified as risk factors for the incidence of TC \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. TC is the only malignancy with age as a prognostic indicator in the majority of staging systems \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, which is the most frequently diagnosed malignancy among adolescents and young adults \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Deng \u003cem\u003eet al.\u003c/em\u003e reported that the most common onset age in persons who developed TC decreased, and the age at death of those with TC increased worldwide \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. A retrospective cohort evaluation of TC cases and deaths during 2005\u0026ndash;2015 showed the rate of increasing incidence trend was higher in the younger age group and lower in the older age group \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Our results suggested that the altered \u003cem\u003eFABP3\u003c/em\u003e methylation was statistically significantly associated with TC risk, especially in younger people. As for gender, TC is the only non-reproductive cancer that occurs more often in women than in men, with a 3\u0026ndash;4-fold higher incidence among women than men \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. We observed the methylation differences of the \u003cem\u003eFABP3\u003c/em\u003e gene between the TC cases and BTN subjects were larger in females than in males. Estrogen may participate in the initiation of tumorigenesis in TC. It has been reported that estrogen has a positive effect on the proliferation of thyroid cells, which is essential for the development of TC, and may contribute to the mechanisms that cause DNA damage \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In addition, estrogen has also been shown to enhance the secretion of VEGF (vascular endothelial growth factor) by thyroid cells \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, thereby regulating the vascular environment and allowing further tumor growth \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Moreover, estrogen receptor plays a role in cell migration and invasion in TC.\u003c/p\u003e \u003cp\u003eNot only for the differentiation of BTN and TC, we also found interesting variation of \u003cem\u003eFABP3\u003c/em\u003e methylation in variant subtypes of BTN (adenoma, goiter, subacute thyroiditis, lymphatic thyroiditis) and TC (PTC, FTC, MTC, ATC). Adenoma showed the highest \u003cem\u003eFABP3\u003c/em\u003e methylation among all the BTN and TC subgroups, followed by goiter and subacute thyroiditis. To our surprise, the methylation level of \u003cem\u003eFABP3\u003c/em\u003e in most lymphocytic thyroiditis is similar to that of malignancy but not as other BTN subgroups. Chronic inflammation has been considered as a pro-tumor effect in TC \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Lymphocytic thyroiditis is the most common autoimmune disorder. Several studies have shown an epidemiological correlation between lymphocytic thyroiditis and TC, especially PTC \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Our study hereby supported that chronic inflammation may be a precancerous lesion or at least a highly correlated risk factor. In addition, we found that the methylated level of \u003cem\u003eFABP3\u003c/em\u003e was correlated with the grade of malignancy. The methylation of \u003cem\u003eFABP3\u003c/em\u003e gradually declined from low malignant PTC to the highly aggressive ATC. The methylation level of \u003cem\u003eFABP3\u003c/em\u003e of MTC, the intermediate subtypes of TC, is around 50% lower than PTC, but higher than ATC. Follicular adenoma is the early stage of FTC with a slightly different DNA methylation pattern than that observed in the normal thyroid, implying a role in the initiation of malignancy \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Sharing a common genetic background, follicular adenoma and FTC represents a significant diagnostic challenge at pre- and postsurgical differentiation \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Our study showed similar \u003cem\u003eFABP3\u003c/em\u003e methylation level in FTC with adenoma, indicating that follicular adenoma may share common epigenetic background with FTC as well. Taken together, the epigenetic alterations in TC subtypes could be helpful for the diagnosis and classification of tumors, and may even indicate the precancerous lesion and the malignant grade of TC.\u003c/p\u003e \u003cp\u003eIn our study, we observed lower levels of \u003cem\u003eFABP3\u003c/em\u003e methylation in TC patients with larger tumors, higher stages, and lymph node metastases, suggesting that the methylation level of \u003cem\u003eFABP3\u003c/em\u003e may be associated with tumor progression. Previous study has shown that gastric cancer highly expresses \u003cem\u003eFABPs\u003c/em\u003e and its expression is associated with disease progression, tumor aggressiveness, and poor patient survival \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. However, these correlations were weak, and further investigation in larger studies is necessary to validate the relation of changed \u003cem\u003eFABP3\u003c/em\u003e methylation and TC progression. Interestingly, we discovered that the methylation level of \u003cem\u003eFABP3\u003c/em\u003e steadily rose as FT3 levels rose and fell as FT4 levels rose. The relationship between changed thyroid hormone levels and the risk of TC has been widely studied \u003csup\u003e\u003cspan additionalcitationids=\"CR57\" citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Low FT3 and high FT4 concentrations are associated with shorter survival in patients with TC \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. Moreover, serum FT3 levels are negatively correlated with inflammation, which is also associated with TC \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. FT4 levels play different roles in different cancer types. In liver cancer, reduced FT4 levels indicate an increased risk of death; whereas in patients with primary breast cancer, elevated FT4 levels are associated with poor prognosis \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. To date, few studies have been conducted on the relationship between thyroid-related hormones and alterations in gene methylation \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Thus, future prospective studies are warranted.\u003c/p\u003e \u003cp\u003eThe present study is among the largest studies on differential diagnosis of TC and BTN with a total of 890 samples, which suggests hypomethylation of the \u003cem\u003eFABP3\u003c/em\u003e gene might be biomarkers to differentiate benign and malignant thyroid tumors. Moreover, by combining \u003cem\u003eFABP3\u003c/em\u003e methylation with existing diagnostic techniques, such as BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation, the diagnostic efficacy can be significantly improved. Nevertheless, our study has limitations that are related to retrospective study cohorts. We were unable to obtain the \u003cem\u003eFABP3\u003c/em\u003e methylation levels in fresh-frozen tissues measured by MALDI-TOF mass spectrometry due to the limited materials. To identify and explore further the relationship between \u003cem\u003eFABP3\u003c/em\u003e methylation changes and TC, prospective multicenter cohorts and functional studies are warranted.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo sum up, this study revealed and proved hypomethylation of \u003cem\u003eFABP3\u003c/em\u003e gene, together with a markedly elevated \u003cem\u003eFABP3\u003c/em\u003e mRNA expression in TC cases compared to BTN subjects. \u003cem\u003eFABP3\u003c/em\u003e showed significant methylation differences between TC and BTN, and among TC subtypes, suggesting the potential of DNA methylation as a novel pathological biomarker not only in discriminating malignant and BTN, but also in distinguishing between TC subtypes. Moreover, by combining \u003cem\u003eFABP3\u003c/em\u003e methylation with BRAF\u003csup\u003eV600E\u003c/sup\u003e mutation, the diagnostic efficacy can be significantly improved. Further investigation in a prospective multi-center study is needed before clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Research Grant of Nanjing TANTICA Co. Ltd [Grant No. 2021TC01.1].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors\u0026rsquo;contributionsHaixia Huang, Chenxia Jiang and Rongxi Yang conceived and designed this study; Haixia Huang and Yifei Yin wrote the main manuscript and prepared figures and tables; Haixia Huang, Junjie Li and Mengxia Li conducted major experiments; Hong Li, Yi Zhang, Xuandong Huang and YiFen Zhang were responsible for data interpretation and analysis; Haixia Huang drafted and Rongxi Yang revised the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe datasets generated or analyzed in the present study are included in this publication and are available on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49.\u003c/li\u003e\n\u003cli\u003eDu L, Li R, Ge M, Wang Y, Li H, Chen W, He J. Incidence and mortality of thyroid cancer in China, 2008-2012. Chin J Cancer Res 2019;31:144-51.\u003c/li\u003e\n\u003cli\u003eSeib CD, Sosa JA. Evolving Understanding of the Epidemiology of Thyroid Cancer. Endocrinol Metab Clin North Am 2019;48:23-35.\u003c/li\u003e\n\u003cli\u003eZhou Y, Du J, Pan L, Li H, Du L. 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Thyroid-Stimulating Hormone, Thyroid Hormones, and Risk of Papillary Thyroid Cancer: A Nested Case-Control Study. Cancer Epidemiol Biomarkers Prev 2017;26:1209-18.\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":"benign thyroid nodule, thyroid cancer, DNA methylation, FABP3, biomarker, classification","lastPublishedDoi":"10.21203/rs.3.rs-5794576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5794576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDifferentiation between benign and malignant thyroid nodules has been a challenge in clinical practice. We aim to explore a novel biomarker to determine the malignancy of thyroid nodules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods: \u003c/strong\u003eIn the discovery study, 32 tissue samples from benign thyroid nodule (BTN) and thyroid cancer (TC) patients were analyzed by Methylation 850K array and RNA-Sequencing. TC associated FABP3 methylation was further verified by mass spectrometry in two independent studies (221 BTN vs. 222 TC in Validation I and 191 BTN vs. 256 TC in Validation II). Logistic regression analysis and non-parametric tests were used for the analysis between groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAltered and inversely correlated methylation and expression in FABP3 gene in TC was found in the discovery study (P = 2.90E-05 for the methylation and P = 0.040 for the expression), and verified in the two validation studies (P values range from 0.012 to 6.30E-10-12). FABP3 methylation could sufficiently differentiate TC from BTN (AUC = 0.77), and could be further improved when combined with the BRAFV600E mutations (AUC = 0.87). The association between FABP3 hypomethylation and TC was enhanced in women, in patients with younger age, with larger tumor size and with lower FT3. FABP3 methylation was varied in BTN and TC subtypes, with the highest level in adenoma and the lowest in anaplastic thyroid cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur study suggested that altered FABP3 methylation in tissue samples as a potential biomarker to distinguish malignant and benign thyroid nodules, and might be helpful for the pathological classification of TC.\u003c/p\u003e","manuscriptTitle":"FABP3 methylation as a novel biomarker for the differentiation and classification of benign and malignant thyroid nodules","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-24 18:43:38","doi":"10.21203/rs.3.rs-5794576/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":"d135392f-f3bb-43b3-abff-b1de9d88e7f9","owner":[],"postedDate":"January 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-17T14:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-24 18:43:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5794576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5794576","identity":"rs-5794576","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}