Metabolomics-Based Analysis of the Correlation Between Metabolites and Thyroid Carcinoma

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The gut microbiome, a long-overlooked risk factor, may play a significant role in TC development. Studies indicate that gut dysbiosis, bacterial outer membrane components such as LPS, and metabolites like Short-Chain Fatty Acids (SCFAs)and Trimethylamine N-Oxide (TMAO) are pivotal in mediating or influencing both gastrointestinal and extra-gastrointestinal tumors. However, reports on the multi-omics analysis of the correlation between gut microbiota, metabolites, and thyroid carcinoma remain scarce. Methods: This study employs a case-control and cohort design, integrating 16S rDNA sequencing, and metabolomics for multi-omics joint analysis of fecal samples from thyroid cancer patients before and after surgery. The aim was to identify associations between the gut microbiome, functional genes, and metabolites, as well as potential disease biomarkers. Results: By combining existing clinical diagnostic indicators for thyroid carcinoma, this research aims to screen for novel biomarkers to aid in the diagnosis of thyroid carcinoma. Conclusions :This paper investigates the correlation between metabolites and thyroid carcinoma through metabolomics-based analysis. We speculate that the lipid metabolite cholesterol sulfate may affect thyroid cancer through key metabolic pathways such as steroid hormone synthesis and arachidonic acid metabolism. Metabolomics Thyroid carcinoma Biomarkers Diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Thyroid carcinoma (TC), one of the most prevalent malignancies of the head and neck, originates pathologically from thyroid follicular epithelial or parafollicular cells [ 1 ] . Histologically, 89.8% of cases are classified as papillary thyroid carcinoma (PTC), 4.5% as follicular thyroid carcinoma, 1.8% as Hürthle cell carcinoma, while the remaining cases comprise medullary thyroid carcinoma (1.6%) and anaplastic thyroid carcinoma (0.8%) [ 2 ] . Ultrasonography-guided fine-needle aspiration biopsy (FNAB) with subsequent pathological evaluation represents the gold-standard diagnostic approach for thyroid nodules. FNAB results are categorized into six tiers: (I) non-diagnostic/unsatisfactory, (II) benign, (III) atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), (IV) follicular neoplasm (FN) or suspicious for FN, (V) suspicious for malignancy, and (VI) malignant. Notably, 15–30% of FNAB interpretations demonstrate limited diagnostic reliability in distinguishing benign from malignant lesions [ 3 ] For indeterminate cases, repeat FNAB is mandatory; when diagnostic uncertainty persists, lobectomy or total thyroidectomy becomes necessary. Globally, TC incidence has exhibited rapid escalation in recent decades. National cancer registry data reveal that TC now ranks as the fourth most common malignancy among urban Chinese women [ 4 ] with an alarming annual growth rate of 20%, imposing substantial societal burdens and healthcare resource strain [ 5 ] . Emerging evidence implicates gut microbiota composition and metabolic activity as critical environmental determinants in both gastrointestinal and extra-intestinal tumorigenesis [ 6 , 7 ] . Dysbiosis of gut microbiota modulates host metabolic and immune homeostasis, influences drug pharmacokinetics (absorption, metabolism, and biotransformation), and consequently impacts carcinogenesis, progression, and therapeutic response. These findings position gut microbiota as a promising diagnostic and therapeutic target in oncology [ 8 ] . Two principal mechanisms underlie microbial contributions to carcinogenesis. The first involves direct genotoxicity, where enteric bacteria (e.g., Escherichia coli) compromise genomic integrity through DNA damage, mutagenesis, and apoptotic dysregulation, ultimately promoting colorectal carcinogenesis [ 9 ] . The second mechanism centers on inflammation modulation, wherein oncogenic microbiota activate pattern recognition receptors (e.g., Toll-like receptors, TLRs), perpetuating NF-κB signaling cascades within the tumor microenvironment [ 10 ] . Substantial evidence confirms that gut microbiota collectively influence tumorigenesis through metabolic products [ 11 – 13 ] . Investigating gut microbial and metabolic profiles in cancer patients may unveil novel biomarkers and elucidate pathways linking microbiota to malignant transformation. Current Methodologies for Gut Microbiota Profiling and Metabolomics Applications Multiple approaches are available for analyzing the overall composition of gut microbiota, with 16S rDNA sequencing and metagenomic sequencing being the most widely adopted gold-standard method [ 14 ] . The 16S rDNA gene sequence consists of variable and conserved regions with sufficient interspecies polymorphism of genes, whose conserved sequence regions reflect the interspecies affinities of bacteria, while the variable regions show the interspecies differences that can provide statistically valid measurements for distinguishing different bacteria. With the advancement of PCR and DNA sequencing technologies, 16S rDNA sequencing of enteric flora by existing technologies can obtain information on the overall composition of the microbial system of the enteric flora, functional genes and their abundance, microbe-host interactions, and the effects of different treatments on species and genes. Qunye Zhang et al. used 16S, qPCR, high-resolution metabolome and qRT-PCR to investigate the mechanism of gut flora mediating the production of high salt diet-induced hypertension [ 15 ] ; Shun Lu et al. found that the diversity of gut flora in non-small-cell lung cancer patients was correlated to the good results of anti-PD-1 immunotherapy through 16S gene sequencing and FACS detection of peripheral blood immune cells correlation [ 16 ] . Metabolomics is an important branch of systems biology, and as an emerging genomics technology after genomics and proteomics, it mainly focuses on the qualitative and quantitative analysis of small-molecule metabolites (usually with a molecular weight of less than 1,500) in living organisms and explores the association between these metabolites and the physiological or pathological state of the organism. This technology is not only able to discover potential disease biomarkers, but also through multi-omics integration with genomics and proteomics, it can comprehensively reveal the intrinsic physiological mechanisms and pathological changes of organisms from the two dimensions of metabolic regulation (genes/proteins) and the end effect (metabolites), which will push the research of systems biology to a higher level. 2. Materials and methods 2.1 Experimental materials 2.1.1 Experimental Objects This study strictly adhered to the inclusion criteria to recruit and screen 12 patients with thyroid cancer. Samples and baseline data were collected from thyroid cancer volunteers before and after surgery while taking levothyroxine sodium as prescribed, including age, gender, and various physiological indicators, and the volunteers were followed up. The obtained stool samples were subjected to 16S rDNA sequencing, macro gene sequencing and metabolomic sequencing, and the sequencing results were analyzed by multi-omics analysis to obtain the association of intestinal flora-functional genes-metabolites and potential disease markers, which were combined with the existing clinical diagnostic indexes of thyroid cancer, to provide a new disease marker for assisting in the diagnosis of thyroid cancer, and to validate it clinically. Subjects should complete the collection of relevant medical history information within 24 hours. Comprehensive and detailed collection of basic information, general physical examination, neurological examination, measurement of temperature, blood pressure, respiration and pulse rate, and recording of age, gender, history of hypertension, diabetes mellitus, smoking, alcohol consumption, white blood cell counts, blood glucose levels, homocysteine levels, total cholesterol levels, triglyceride levels, LDL cholesterol levels, HDL cholesterol levels, and other clinical indicators for clinical diagnosis of thyroid cancer. cholesterol level, HDL cholesterol level, cerebral white matter lesion score and other relevant indicators were fully documented on the case report form for backup. 2.1.2 Inclusion exclusion criteria (1) Inclusion criteria Thyroid cancer volunteers: ① First diagnosed and judged as primary papillary thyroid cancer by pathological results; ② None of the volunteers had received any treatment before admission; ③ Patients gave informed consent, volunteered to participate and signed an informed consent form. (2) Exclusion criteria ① Volunteers have cancers other than primary thyroid cancer; ② Volunteers have a history of alcohol or narcotic abuse, drug abuse, or a history of psychiatric disorders (e.g., schizophrenia, obsessive-compulsive disorder, depression), antagonistic personality, poor motivation, paranoia, or other emotional or intellectual problems that may affect the informed validity of participation in this study; ③ Volunteers have other endocrine-related disorders. This study has been reviewed and approved by the Clinical Research Ethics Review Committee of the Affiliated Hospital of Guangdong Medical University, with ethics approval number: PJ2021-079. All participants have signed informed consent forms. 2.1.3 Methods of fecal sample collection (1) Distribute one sterilized fecal cup to the subject in advance; (2) Use the spoon in the sampling tube to intercept one spoonful of the inside of the middle section of the feces; (3) Place the spoon in the fecal cup and tighten the lid; (4) Hand the sample to the person in charge, who registers the subject's information on the wall of the cup and quickly places the sample in a -80°C refrigerator for storage. The preoperative group (FF) consisted of fecal samples collected from 12 patients 3 days prior to surgery, while the postoperative group (BF) consisted of fecal samples collected from the same 12 patients within 72 hours after surgery. 2.2 Experimental Methods 2.2.1 DNA extraction from fecal samples DNA extraction was carried out by the powerful fecal DNA extraction kit method. Take 0.15g of feces and vortex with Bead Solution; add C1 lysis, heating at 65℃ + vortex; centrifugation to get the supernatant; C2/C3 precipitate impurities, centrifugation to retain the supernatant; C4 binding DNA, divided into 3 times through the column; C5 washing; C6 elution, detection of concentration (≥ 10ng/µl, total amount of ≥ 1 µg) and purity (AGE / Qubit); qualified samples were sent to sequencing. 2.2.2 Amplification, sequencing and analysis of 16Sr DNAV3-V4 of fecal DNA Sample extraction, amplification, sequencing and analysis were done by Shanghai Zhongke New Life Technology Co. The basic experimental flow of sequencing was as follows: (1) Primer design and synthesis: The amplification region was selected as V3-V4 region. The primers 341F and 806R were used to synthesize the sequences applicable to Illumina Miseq. The sequences of Illumina Miseq were added to the 5' end of 341F and 806R to make them into specific primers with barcode. Primer sequences: Forward primer (338F, 5'-3'): ACTCCTACGGGGAGGCAGCA Reverse primer (806R, 5'-3'): GGACTACHVGGGGTWTCTAAT (2) PCR amplification and purification: All samples were performed according to the standard procedure, with three replicates for each group of samples. The PCR products of the same sample were mixed and detected by 2% agarose electrophoresis, followed by cutting and recovery of PCR products using AxyPrepDNA Gel Recovery Kit (AXYGEN), and the recovered products were detected by 2% agarose gel electrophoresis. (3) Miseq library construction: ① PCR amplification was used to introduce the Illumina official specific junction sequence into the outer end of the target region; ② the target bands were separated by agarose gel electrophoresis, and the specific fragment was gel recovered; ③ the purity of the target sequences was verified by 2% agarose gel electrophoresis after elution with Tris-HCl buffer; ④ single-stranded DNA templates were obtained by denaturing with sodium hydroxide for subsequent sequencing. template for subsequent sequencing. (4) Miseq sequencing: Bridge PCR amplification of DNA clusters, sequencing while synthesizing (fluorescent labeled dNTP), and reading the sequence. (5) Bioinformatic analysis of 16S rDNA data. 3. Results 3.1 Baseline characteristics of the preoperative and postoperative thyroid cancer groups A total of 12 patients with thyroid cancer were enrolled in this study, and their preoperative and postoperative fecal samples were collected, and their general data and the values of the relevant blood biochemical tests are shown in Table 1 .There was no difference between the preoperative and postoperative groups in terms of FT3, TSH, A-TG, and HTG, and there was a significant difference between the two groups in terms of FT4 and PTH(The P-value was obtained through a T-test). Table 1 Baseline characteristics of patients Variant FF(n = 12) BF(n = 12) P-value FT3 (pmol/L, χ ± s) 4.82 ± 0.7 4.65 ± 0.89 0.63236 FT4 (pmol/L, χ ± s) 16.98 ± 1.46 20.35 ± 3.63 0.00991** TSH(mIU/L, χ ± s) 1.53 ± 1.21 1.79 ± 2.55 0.76213 A-TG(mmol/L,χ ± s) 42.13 ± 77.43 36.59 ± 64.95 0.85766 PTH (pg/ml, χ ± s) 45.97 ± 19.87 24.9 ± 11.39 0.00629** HTG(ng/ml, χ ± s) 36.03 ± 51.87 4.66 ± 6.49 0.06038 3.2 Results of 16s rDNA sequencing of fecal samples 3.2.1 PCA and PLS-DA analysis PCA and PLS-DA analyses were performed on the two groups of samples in the positive and negative ion modes, respectively. It can be seen that both the two modes, whether supervised dimensionality reduction (Fig. 1 A) or unsupervised dimensionality reduction (Fig. 1 B), present the phenomenon of similarity of the samples within the groups and the existence of a high degree of heterogeneity of the samples between the groups. 3.2.2 Between-group difference metabolite analysis Differential analysis between groups was performed by lipid metabolites in positive and negative ion modes. We found that in positive ion mode (Fig. 2 A, Fig. 2 C), 112 metabolites were down-regulated and 171 metabolites were up-regulated in the BF group relative to the FF group (|logFC|>0.5, P 0.5, P < 0.05). 3.2.3 Identification of metabolites differing between groups After we identified the lipid metabolites that varied significantly between groups, we merged the up- and down-regulated metabolites of the positive and negative ion modes, respectively, and performed de-emphasis to obtain 92 up-regulated metabolites,104 down-regulated metabolites, respectively, and we demonstrated the categorization of up- and down-regulated metabolites of the positive and negative ion modes (Fig. 3 A), of which 40 were down-regulated metabolites: Fatty Acyls [FA] 40 kinds of metabolites; Glycerol lipids [GL] 7 species; Glycerophospholipids [GP] 11 species; Polyketides [PR] 19 species; Prenol Lipids [PR] 8 species; Sphingolipids [SP] 9 species; Sterols [ST] 10 species. Up-regulated metabolites: Fatty Acyls [FA] 24; Glycerol lipids [GL] 2; Glycerophospholipids [GP] 9; Polyketides [PR] 10; Prenol Lipids [PR] 12; Sphingolipids [SP] 15 Sterols [ST] 20 species. After identifying the lipid metabolites that changed significantly between groups, we show the top 15 lipid metabolites with VIP scores in the PLS-DA analysis in the positive and negative ion mode, and all of them had scores of 1 or more (Fig. 3 B). They may be the key differential lipid metabolites before and after surgery. 3.2.4 Metabolic pathway composition and differential analysis KEGG functional enrichment was performed for lipid metabolites that differed significantly between groups (Fig. 4 A). As shown, the lipid metabolites upregulated in the BF group were significantly enriched to the Steroid hormone biosynthesis pathway, while the lipid metabolites downregulated by BF were significantly enriched to the Arachidonic acid metabolism pathway. And we found that the BF group was significantly enriched to the Steroid hormone biosynthesis pathway due to up-regulation of cholesterol sulfate; to the Arachidonic acid metabolism pathway due to significant down-regulation of the 2,3-dinor-5,6-dihydro-15-F2t-IsoP metabolism pathway. Among them, cholesterol sulfate is a sulfated derivative of cholesterol, formed by the binding of cholesterol to a sulfate group catalyzed by sulfotransferase (SULT). The role of cholesterol sulfate in cancer has not been fully clarified, with some studies suggesting that cholesterol sulfate may be able to promote cancer metastasis, but some studies suggest that it exerts anti-tumor effects by regulating T cells. 4. Discussion It has been concluded that thyroid carcinoma (TC) is mainly a disease caused by a combination of hormonal-environmental factors [ 17 ] , and intestinal microorganisms may be an important environmental risk factor that has been overlooked. In this study, we used a case-control design to analyze the gut flora-functional gene-metabolite associations with new disease markers through a combined multi-omics analysis to assist in the diagnosis of thyroid cancer in conjunction with existing clinical diagnostic indexes of thyroid cancer, thus improving the efficiency of preoperative diagnosis. Although there have been a large number of reports on multi-omics combined head and neck tumor diagnostic models [ 18 ] , there are still few studies related to TC, and even fewer studies discussing the changes in intestinal flora, metabolic profiling, and related diagnostic models in preoperative and postoperative TC. Our team took this as an entry point to explore, through metabolomics sequencing, data analysis to obtain gut flora-functional gene-metabolite associations and potential disease markers, combined with the existing clinical diagnostic indicators of thyroid cancer, to provide new disease markers to assist in the diagnosis of thyroid cancer, and its clinical validation. In recent years, the investigation of the correlation between the microbiology of the human gut and various diseases has deepened, and our research team was prompted to think about the possible connection between the pathogenesis of thyroid cancer and metabolites. Metabolomics was chosen as the entry point for this study. Of interest is the potential influence of structural changes in the gut microbial community on the development of thyroid cancer [ 19 ] , which was revealed through the systematic analysis of metabolic pathways and their regulatory networks. Examples have shown that the use of metabolomics approaches not only enables the identification of key differential metabolites, but also provides a deeper understanding of the chain of molecular events during disease progression [ 20 ] . Thus, this research strategy will provide new theoretical basis and therapeutic targeting direction for clinical intervention in thyroid cancer. Metabolomics has emerged from genomics and proteomics research, and metabolomics is a new technology field that is regarded as an indispensable component of systems biology. The existence of various small molecule metabolites and their quantitative changes in the organism constitute the main observation object of this technology. Fluctuations in the physiological functions of the organism and the underlying mechanisms of pathological changes are revealed by the dynamics of these metabolites. Examples have shown the value of this type of research for the advancement of the life sciences. Gut flora is an important microbial component of the human body, and its impact on human health has received widespread attention [ 21 ] . Microorganisms present in the gut may have a potential impact on the development of thyroid cancer. We collected 12 pre- and post-operative fecal samples from thyroid cancer patients for sequencing analysis, both in supervised and unsupervised downscaling the two groups of samples were similar, indicating a high degree of heterogeneity between the two groups of samples. The metabolites that differed between groups were identified by intergroup difference analysis, which resulted in the top 15 lipid metabolites with VIP scores in the PLS-DA analysis in positive and negative ion mode, and they may be the lipid metabolites that are the key differences between pre- and post-surgery. Subsequently, we performed KEGG functional enrichment on the above metabolites with intergroup differences, and the results of KEGG functional enrichment analysis were presented, which showed that lipid metabolites with significant intergroup differences were specifically distributed in different metabolic pathways. The present study showed that the up-regulated lipid metabolites in the BF group were mainly enriched in the steroid hormone biosynthesis pathway (Steroid hormone biosynthesis) [ 22 ] ; while the down-regulated metabolites were significantly enriched in the arachidonic acid metabolism pathway (Arachidonic acid metabolism) [ 23 ] . The experimental data showed that the significant up-regulation of cholesterol sulfate correlated with the enrichment of the BF group in the steroid hormone biosynthesis pathway. The significant down-regulation of 2,3-dinor-5,6-dihydro-15-F2t-IsoP was found to correlate with the enrichment of the arachidonic acid metabolism pathway in this group. The formation of cholesterol sulfate was elucidated as a derivative of cholesterol catalyzed by sulfotransferase (SULT) and combined with a sulfate group. It is widely distributed in human tissues such as skin, intestine, adrenal glands, liver and brain [ 24 ] . It plays an important role in a variety of physiological activities, including T cell signaling regulation, glucose metabolism regulation, and lipid homeostasis [ 25 ] .At the current stage of research, controversy still exists regarding its biological effects in cancer, with some studies suggesting that it may promote tumor progression and influence tumor value-addition and survival through metabolic regulation in the tumor microenvironment. However, some studies have also suggested that it may exert anti-tumor effectivity through the modulation of T cell function [ 26 ] . 5. Conclusions In summary, this study systematically analyzed the role of microorganisms and metabolites in the gut microbiota on the occurrence and development of thyroid cancer using metabolomics technology. It was found that there were specifically distributed lipid metabolites—cholesterol sulfate esters—in the pre- and post-surgical grouped samples, We speculate that these metabolites may influence thyroid cancer through key metabolic pathways such as the steroid hormone synthesis pathway and the arachidonic acid metabolism pathway. This suggests that there may be potential metabolic intervention targets that can be used for the clinical treatment of thyroid cancer.. However, due to the limited number of experimental samples, this study still has its limitations. Nevertheless, it provides a relatively clear direction for future research. The research team will collect more samples to conduct more in-depth experimental validation. Abbreviations TC Thyroid Carcinoma SCFAs Short-Chain Fatty Acid TMAO Trimethylamine N-Oxides TLRs Toll-like receptors LDL Low-Density Lipoprotein‌ HDL High-Density Lipoprotein‌ FA Fatty Acyls GL Glycerol lipids GP Glycerophospholipids PR Polyketides SP Sphingolipids ST Sterols SULT Sulfotransferase Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and was approved by the Clinical Research Ethics Committee of the Affiliated Hospital of Guangdong Medical University (approval number: PJ2021-079). ● Consent for publication Not applicable ● Availability of data and materials All data within the literature are available for use. The sequence data supporting the results of this study have been deposited in Metabolights with the primary accession code MTBLS12769.The following is access link https://www.ebi.ac.uk/metabolights/MTBLS12769. ● Competing Interests The authors declare no conflicts of interest. ● Funding This study was supported by the following two projects: Clinical Research Project of the Affiliated Hospital of Guangdong Medical University (LCYJ2021B010)) 2024 Unsubsidized Science and Technology Tackling Program of Zhanjiang City, Guangdong Province, China (2024B01154). ● Authors' contributions JH and JL: Writing–original draft, Writing–review & editing, Data curation, Visualization. XX: Data curation. SL: Formal analysis. HL: Project administration. YW, WC and ZC: Validation. CZ, ML, BL, GZ and ZH: Investigation. JL and BL: Resources. ZZ: Writing–review & editing, Supervision, Methodology. 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University","correspondingAuthor":false,"prefix":"","firstName":"Baibei","middleName":"","lastName":"Li","suffix":""},{"id":492231849,"identity":"d87c190b-e4e4-4174-a401-73c6a191c97f","order_by":11,"name":"Guiping Zhou","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guiping","middleName":"","lastName":"Zhou","suffix":""},{"id":492231850,"identity":"c6cdc59d-89df-4fcd-8be2-42d56cba7098","order_by":12,"name":"Zhibin Huang","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Huang","suffix":""},{"id":492231851,"identity":"b6037c1b-722d-46a0-ad78-114c32814198","order_by":13,"name":"Jinquan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACPmaGhANgFnsDA0NCAUMCRJwNtxY2uBYeIJVgQIwWOEsCpJgoLewMDw8XlB2WN5d8nfjhgQFDnnz7GQOGD2WHGfhnN+B02OEZ5w4b7pydu1kC6LBixp4cA0agCIPEnQO4tfC2HWbccDt3A0hLYjNDjgEzUITBQCIBrxb7DTfPbv4B0tLG/8aA+S8RWhI33ODdBralRwJoCyMhLTzn0pM3nMndZpFgIJE4Q+JZwcGec+k8Ejewa+HnP5P8mafM2nbD8bObb/6osEmc35+88cGPMms5/hnYtQCjMAEldsDkAZA4DvVAwH4Ab8SNglEwCkbBKGAAACZ2XFX94oldAAAAAElFTkSuQmCC","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jinquan","middleName":"","lastName":"Li","suffix":""},{"id":492231852,"identity":"80bba8bb-b6f9-46c4-930d-82a8efa074f5","order_by":14,"name":"Bin Liu","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Liu","suffix":""},{"id":492231853,"identity":"a46b420d-181f-4417-9511-1edd0a49e0c3","order_by":15,"name":"Haiqing Luo","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haiqing","middleName":"","lastName":"Luo","suffix":""},{"id":492231854,"identity":"300387f4-63aa-4c1c-996d-a32b09958be7","order_by":16,"name":"Wenyi Qin","email":"","orcid":"","institution":"Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2025-07-07 10:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7064330/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7064330/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12896-025-01045-6","type":"published","date":"2025-09-26T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88000907,"identity":"c76cc60c-5df4-4bb2-9844-55cbf8ff755a","added_by":"auto","created_at":"2025-07-31 10:21:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1100147,"visible":true,"origin":"","legend":"\u003cp\u003ePCA vs. PLS-DA analysis in positive and negative ion mode\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7064330/v1/db51a881ce022fa15f808baf.png"},{"id":88000908,"identity":"db8adb75-87d4-4f94-b21c-ebefe25ff87e","added_by":"auto","created_at":"2025-07-31 10:21:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1563254,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano versus thermograms of differential lipid metabolites in positive and negative ion mode\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7064330/v1/2648ed5180173973632b7306.png"},{"id":88000910,"identity":"ecbce129-1c01-4632-a997-070d8ddb06b0","added_by":"auto","created_at":"2025-07-31 10:21:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1431364,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of metabolites differing between groups\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7064330/v1/91b44117eae34cbe90fed047.png"},{"id":88003024,"identity":"3fbff516-790b-40ca-829a-07559de819bd","added_by":"auto","created_at":"2025-07-31 10:29:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":536066,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG functional enrichment-related pathways\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7064330/v1/d723767d8f87d02aa17e393c.png"},{"id":92430569,"identity":"af3a3745-8200-4e83-b900-5d73217c61a8","added_by":"auto","created_at":"2025-09-29 16:05:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5497691,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7064330/v1/d05b69ec-ad51-43dd-9b46-bb1f86b32a84.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomics-Based Analysis of the Correlation Between Metabolites and Thyroid Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThyroid carcinoma (TC), one of the most prevalent malignancies of the head and neck, originates pathologically from thyroid follicular epithelial or parafollicular cells\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Histologically, 89.8% of cases are classified as papillary thyroid carcinoma (PTC), 4.5% as follicular thyroid carcinoma, 1.8% as H\u0026uuml;rthle cell carcinoma, while the remaining cases comprise medullary thyroid carcinoma (1.6%) and anaplastic thyroid carcinoma (0.8%)\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Ultrasonography-guided fine-needle aspiration biopsy (FNAB) with subsequent pathological evaluation represents the gold-standard diagnostic approach for thyroid nodules. FNAB results are categorized into six tiers: (I) non-diagnostic/unsatisfactory, (II) benign, (III) atypia of undetermined significance (AUS) or follicular lesion of undetermined significance (FLUS), (IV) follicular neoplasm (FN) or suspicious for FN, (V) suspicious for malignancy, and (VI) malignant. Notably, 15\u0026ndash;30% of FNAB interpretations demonstrate limited diagnostic reliability in distinguishing benign from malignant lesions\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e For indeterminate cases, repeat FNAB is mandatory; when diagnostic uncertainty persists, lobectomy or total thyroidectomy becomes necessary.\u003c/p\u003e\u003cp\u003eGlobally, TC incidence has exhibited rapid escalation in recent decades. National cancer registry data reveal that TC now ranks as the fourth most common malignancy among urban Chinese women\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003ewith an alarming annual growth rate of 20%, imposing substantial societal burdens and healthcare resource strain\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Emerging evidence implicates gut microbiota composition and metabolic activity as critical environmental determinants in both gastrointestinal and extra-intestinal tumorigenesis\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Dysbiosis of gut microbiota modulates host metabolic and immune homeostasis, influences drug pharmacokinetics (absorption, metabolism, and biotransformation), and consequently impacts carcinogenesis, progression, and therapeutic response. These findings position gut microbiota as a promising diagnostic and therapeutic target in oncology\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTwo principal mechanisms underlie microbial contributions to carcinogenesis. The first involves direct genotoxicity, where enteric bacteria (e.g., Escherichia coli) compromise genomic integrity through DNA damage, mutagenesis, and apoptotic dysregulation, ultimately promoting colorectal carcinogenesis\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. The second mechanism centers on inflammation modulation, wherein oncogenic microbiota activate pattern recognition receptors (e.g., Toll-like receptors, TLRs), perpetuating NF-κB signaling cascades within the tumor microenvironment\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Substantial evidence confirms that gut microbiota collectively influence tumorigenesis through metabolic products\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Investigating gut microbial and metabolic profiles in cancer patients may unveil novel biomarkers and elucidate pathways linking microbiota to malignant transformation.\u003c/p\u003e\u003cp\u003eCurrent Methodologies for Gut Microbiota Profiling and Metabolomics Applications Multiple approaches are available for analyzing the overall composition of gut microbiota, with 16S rDNA sequencing and metagenomic sequencing being the most widely adopted gold-standard method\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The 16S rDNA gene sequence consists of variable and conserved regions with sufficient interspecies polymorphism of genes, whose conserved sequence regions reflect the interspecies affinities of bacteria, while the variable regions show the interspecies differences that can provide statistically valid measurements for distinguishing different bacteria. With the advancement of PCR and DNA sequencing technologies, 16S rDNA sequencing of enteric flora by existing technologies can obtain information on the overall composition of the microbial system of the enteric flora, functional genes and their abundance, microbe-host interactions, and the effects of different treatments on species and genes.\u003c/p\u003e\u003cp\u003eQunye Zhang et al. used 16S, qPCR, high-resolution metabolome and qRT-PCR to investigate the mechanism of gut flora mediating the production of high salt diet-induced hypertension\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e; Shun Lu et al. found that the diversity of gut flora in non-small-cell lung cancer patients was correlated to the good results of anti-PD-1 immunotherapy through 16S gene sequencing and FACS detection of peripheral blood immune cells correlation\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Metabolomics is an important branch of systems biology, and as an emerging genomics technology after genomics and proteomics, it mainly focuses on the qualitative and quantitative analysis of small-molecule metabolites (usually with a molecular weight of less than 1,500) in living organisms and explores the association between these metabolites and the physiological or pathological state of the organism. This technology is not only able to discover potential disease biomarkers, but also through multi-omics integration with genomics and proteomics, it can comprehensively reveal the intrinsic physiological mechanisms and pathological changes of organisms from the two dimensions of metabolic regulation (genes/proteins) and the end effect (metabolites), which will push the research of systems biology to a higher level.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Experimental materials\u003c/h2\u003e\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\u003ch2\u003e2.1.1 Experimental Objects\u003c/h2\u003e\u003cp\u003eThis study strictly adhered to the inclusion criteria to recruit and screen 12 patients with thyroid cancer. Samples and baseline data were collected from thyroid cancer volunteers before and after surgery while taking levothyroxine sodium as prescribed, including age, gender, and various physiological indicators, and the volunteers were followed up. The obtained stool samples were subjected to 16S rDNA sequencing, macro gene sequencing and metabolomic sequencing, and the sequencing results were analyzed by multi-omics analysis to obtain the association of intestinal flora-functional genes-metabolites and potential disease markers, which were combined with the existing clinical diagnostic indexes of thyroid cancer, to provide a new disease marker for assisting in the diagnosis of thyroid cancer, and to validate it clinically.\u003c/p\u003e\u003cp\u003eSubjects should complete the collection of relevant medical history information within 24 hours. Comprehensive and detailed collection of basic information, general physical examination, neurological examination, measurement of temperature, blood pressure, respiration and pulse rate, and recording of age, gender, history of hypertension, diabetes mellitus, smoking, alcohol consumption, white blood cell counts, blood glucose levels, homocysteine levels, total cholesterol levels, triglyceride levels, LDL cholesterol levels, HDL cholesterol levels, and other clinical indicators for clinical diagnosis of thyroid cancer. cholesterol level, HDL cholesterol level, cerebral white matter lesion score and other relevant indicators were fully documented on the case report form for backup.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\u003ch2\u003e2.1.2 Inclusion exclusion criteria\u003c/h2\u003e\u003cp\u003e(1) Inclusion criteria Thyroid cancer volunteers:\u003c/p\u003e\u003cp\u003e① First diagnosed and judged as primary papillary thyroid cancer by pathological results;\u003c/p\u003e\u003cp\u003e② None of the volunteers had received any treatment before admission;\u003c/p\u003e\u003cp\u003e ③ Patients gave informed consent, volunteered to participate and signed an informed consent form.\u003c/p\u003e\u003cp\u003e(2) Exclusion criteria\u003c/p\u003e\u003cp\u003e① Volunteers have cancers other than primary thyroid cancer;\u003c/p\u003e\u003cp\u003e② Volunteers have a history of alcohol or narcotic abuse, drug abuse, or a history of psychiatric disorders (e.g., schizophrenia, obsessive-compulsive disorder, depression), antagonistic personality, poor motivation, paranoia, or other emotional or intellectual problems that may affect the informed validity of participation in this study;\u003c/p\u003e\u003cp\u003e③ Volunteers have other endocrine-related disorders.\u003c/p\u003e\u003cp\u003e This study has been reviewed and approved by the Clinical Research Ethics Review Committee of the Affiliated Hospital of Guangdong Medical University, with ethics approval number: PJ2021-079. All participants have signed informed consent forms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.1.3 Methods of fecal sample collection\u003c/h2\u003e\u003cp\u003e(1) Distribute one sterilized fecal cup to the subject in advance;\u003c/p\u003e\u003cp\u003e(2) Use the spoon in the sampling tube to intercept one spoonful of the inside of the middle section of the feces;\u003c/p\u003e\u003cp\u003e(3) Place the spoon in the fecal cup and tighten the lid;\u003c/p\u003e\u003cp\u003e(4) Hand the sample to the person in charge, who registers the subject's information on the wall of the cup and quickly places the sample in a -80\u0026deg;C refrigerator for storage.\u003c/p\u003e\u003cp\u003eThe preoperative group (FF) consisted of fecal samples collected from 12 patients 3 days prior to surgery, while the postoperative group (BF) consisted of fecal samples collected from the same 12 patients within 72 hours after surgery.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental Methods\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.2.1 DNA extraction from fecal samples\u003c/h2\u003e\u003cp\u003eDNA extraction was carried out by the powerful fecal DNA extraction kit method. Take 0.15g of feces and vortex with Bead Solution; add C1 lysis, heating at 65℃ + vortex; centrifugation to get the supernatant; C2/C3 precipitate impurities, centrifugation to retain the supernatant; C4 binding DNA, divided into 3 times through the column; C5 washing; C6 elution, detection of concentration (\u0026ge;\u0026thinsp;10ng/\u0026micro;l, total amount of \u0026ge;\u0026thinsp;1 \u0026micro;g) and purity (AGE / Qubit); qualified samples were sent to sequencing.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.2.2 Amplification, sequencing and analysis of 16Sr DNAV3-V4 of fecal DNA\u003c/h2\u003e\u003cp\u003eSample extraction, amplification, sequencing and analysis were done by Shanghai Zhongke New Life Technology Co. The basic experimental flow of sequencing was as follows:\u003c/p\u003e\u003cp\u003e(1) Primer design and synthesis:\u003c/p\u003e\u003cp\u003eThe amplification region was selected as V3-V4 region. The primers 341F and 806R were used to synthesize the sequences applicable to Illumina Miseq.\u003c/p\u003e\u003cp\u003eThe sequences of Illumina Miseq were added to the 5' end of 341F and 806R to make them into specific primers with barcode.\u003c/p\u003e\u003cp\u003ePrimer sequences: Forward primer (338F, 5'-3'): ACTCCTACGGGGAGGCAGCA\u003c/p\u003e\u003cp\u003eReverse primer (806R, 5'-3'): GGACTACHVGGGGTWTCTAAT\u003c/p\u003e\u003cp\u003e(2) PCR amplification and purification:\u003c/p\u003e\u003cp\u003eAll samples were performed according to the standard procedure, with three replicates for each group of samples. The PCR products of the same sample were mixed and detected by 2% agarose electrophoresis, followed by cutting and recovery of PCR products using AxyPrepDNA Gel Recovery Kit (AXYGEN), and the recovered products were detected by 2% agarose gel electrophoresis.\u003c/p\u003e\u003cp\u003e(3) Miseq library construction:\u003c/p\u003e\u003cp\u003e① PCR amplification was used to introduce the Illumina official specific junction sequence into the outer end of the target region; ② the target bands were separated by agarose gel electrophoresis, and the specific fragment was gel recovered; ③ the purity of the target sequences was verified by 2% agarose gel electrophoresis after elution with Tris-HCl buffer; ④ single-stranded DNA templates were obtained by denaturing with sodium hydroxide for subsequent sequencing. template for subsequent sequencing.\u003c/p\u003e\u003cp\u003e(4) Miseq sequencing: Bridge PCR amplification of DNA clusters, sequencing while synthesizing (fluorescent labeled dNTP), and reading the sequence.\u003c/p\u003e\u003cp\u003e(5) Bioinformatic analysis of 16S rDNA data.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline characteristics of the preoperative and postoperative thyroid cancer groups\u003c/h2\u003e\u003cp\u003eA total of 12 patients with thyroid cancer were enrolled in this study, and their preoperative and postoperative fecal samples were collected, and their general data and the values of the relevant blood biochemical tests are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.There was no difference between the preoperative and postoperative groups in terms of FT3, TSH, A-TG, and HTG, and there was a significant difference between the two groups in terms of FT4 and PTH(The P-value was obtained through a T-test).\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\u003eBaseline characteristics of patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFF(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBF(n\u0026thinsp;=\u0026thinsp;12)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT3 (pmol/L, χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.63236\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT4 (pmol/L, χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e16.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e20.35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00991**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH(mIU/L, χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.76213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA-TG(mmol/L,χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e42.13\u0026thinsp;\u0026plusmn;\u0026thinsp;77.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e36.59\u0026thinsp;\u0026plusmn;\u0026thinsp;64.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85766\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTH (pg/ml, χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e45.97\u0026thinsp;\u0026plusmn;\u0026thinsp;19.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00629**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTG(ng/ml, χ\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e36.03\u0026thinsp;\u0026plusmn;\u0026thinsp;51.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;6.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Results of 16s rDNA sequencing of fecal samples\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 PCA and PLS-DA analysis\u003c/h2\u003e\u003cp\u003ePCA and PLS-DA analyses were performed on the two groups of samples in the positive and negative ion modes, respectively. It can be seen that both the two modes, whether supervised dimensionality reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) or unsupervised dimensionality reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), present the phenomenon of similarity of the samples within the groups and the existence of a high degree of heterogeneity of the samples between the groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Between-group difference metabolite analysis\u003c/h2\u003e\u003cp\u003eDifferential analysis between groups was performed by lipid metabolites in positive and negative ion modes. We found that in positive ion mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), 112 metabolites were down-regulated and 171 metabolites were up-regulated in the BF group relative to the FF group (|logFC|\u0026gt;0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while in negative ion mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), 157 metabolites were down-regulated and 111 metabolites were up-regulated in the BF group relative to the FF group (|logFC|\u0026gt;0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 Identification of metabolites differing between groups\u003c/h2\u003e\u003cp\u003eAfter we identified the lipid metabolites that varied significantly between groups, we merged the up- and down-regulated metabolites of the positive and negative ion modes, respectively, and performed de-emphasis to obtain 92 up-regulated metabolites,104 down-regulated metabolites, respectively, and we demonstrated the categorization of up- and down-regulated metabolites of the positive and negative ion modes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), of which 40 were down-regulated metabolites: Fatty Acyls [FA] 40 kinds of metabolites; Glycerol lipids [GL] 7 species; Glycerophospholipids [GP] 11 species; Polyketides [PR] 19 species; Prenol Lipids [PR] 8 species; Sphingolipids [SP] 9 species; Sterols [ST] 10 species. Up-regulated metabolites: Fatty Acyls [FA] 24; Glycerol lipids [GL] 2; Glycerophospholipids [GP] 9; Polyketides [PR] 10; Prenol Lipids [PR] 12; Sphingolipids [SP] 15 Sterols [ST] 20 species. After identifying the lipid metabolites that changed significantly between groups, we show the top 15 lipid metabolites with VIP scores in the PLS-DA analysis in the positive and negative ion mode, and all of them had scores of 1 or more (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). They may be the key differential lipid metabolites before and after surgery.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.2.4 Metabolic pathway composition and differential analysis\u003c/h2\u003e\u003cp\u003eKEGG functional enrichment was performed for lipid metabolites that differed significantly between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). As shown, the lipid metabolites upregulated in the BF group were significantly enriched to the Steroid hormone biosynthesis pathway, while the lipid metabolites downregulated by BF were significantly enriched to the Arachidonic acid metabolism pathway. And we found that the BF group was significantly enriched to the Steroid hormone biosynthesis pathway due to up-regulation of cholesterol sulfate; to the Arachidonic acid metabolism pathway due to significant down-regulation of the 2,3-dinor-5,6-dihydro-15-F2t-IsoP metabolism pathway. Among them, cholesterol sulfate is a sulfated derivative of cholesterol, formed by the binding of cholesterol to a sulfate group catalyzed by sulfotransferase (SULT). The role of cholesterol sulfate in cancer has not been fully clarified, with some studies suggesting that cholesterol sulfate may be able to promote cancer metastasis, but some studies suggest that it exerts anti-tumor effects by regulating T cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt has been concluded that thyroid carcinoma (TC) is mainly a disease caused by a combination of hormonal-environmental factors\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, and intestinal microorganisms may be an important environmental risk factor that has been overlooked. In this study, we used a case-control design to analyze the gut flora-functional gene-metabolite associations with new disease markers through a combined multi-omics analysis to assist in the diagnosis of thyroid cancer in conjunction with existing clinical diagnostic indexes of thyroid cancer, thus improving the efficiency of preoperative diagnosis. Although there have been a large number of reports on multi-omics combined head and neck tumor diagnostic models\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, there are still few studies related to TC, and even fewer studies discussing the changes in intestinal flora, metabolic profiling, and related diagnostic models in preoperative and postoperative TC. Our team took this as an entry point to explore, through metabolomics sequencing, data analysis to obtain gut flora-functional gene-metabolite associations and potential disease markers, combined with the existing clinical diagnostic indicators of thyroid cancer, to provide new disease markers to assist in the diagnosis of thyroid cancer, and its clinical validation.\u003c/p\u003e\u003cp\u003eIn recent years, the investigation of the correlation between the microbiology of the human gut and various diseases has deepened, and our research team was prompted to think about the possible connection between the pathogenesis of thyroid cancer and metabolites. Metabolomics was chosen as the entry point for this study. Of interest is the potential influence of structural changes in the gut microbial community on the development of thyroid cancer\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which was revealed through the systematic analysis of metabolic pathways and their regulatory networks. Examples have shown that the use of metabolomics approaches not only enables the identification of key differential metabolites, but also provides a deeper understanding of the chain of molecular events during disease progression\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Thus, this research strategy will provide new theoretical basis and therapeutic targeting direction for clinical intervention in thyroid cancer. Metabolomics has emerged from genomics and proteomics research, and metabolomics is a new technology field that is regarded as an indispensable component of systems biology. The existence of various small molecule metabolites and their quantitative changes in the organism constitute the main observation object of this technology. Fluctuations in the physiological functions of the organism and the underlying mechanisms of pathological changes are revealed by the dynamics of these metabolites. Examples have shown the value of this type of research for the advancement of the life sciences.\u003c/p\u003e\u003cp\u003eGut flora is an important microbial component of the human body, and its impact on human health has received widespread attention\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Microorganisms present in the gut may have a potential impact on the development of thyroid cancer. We collected 12 pre- and post-operative fecal samples from thyroid cancer patients for sequencing analysis, both in supervised and unsupervised downscaling the two groups of samples were similar, indicating a high degree of heterogeneity between the two groups of samples. The metabolites that differed between groups were identified by intergroup difference analysis, which resulted in the top 15 lipid metabolites with VIP scores in the PLS-DA analysis in positive and negative ion mode, and they may be the lipid metabolites that are the key differences between pre- and post-surgery. Subsequently, we performed KEGG functional enrichment on the above metabolites with intergroup differences, and the results of KEGG functional enrichment analysis were presented, which showed that lipid metabolites with significant intergroup differences were specifically distributed in different metabolic pathways.\u003c/p\u003e\u003cp\u003eThe present study showed that the up-regulated lipid metabolites in the BF group were mainly enriched in the steroid hormone biosynthesis pathway (Steroid hormone biosynthesis)\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e; while the down-regulated metabolites were significantly enriched in the arachidonic acid metabolism pathway (Arachidonic acid metabolism)\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The experimental data showed that the significant up-regulation of cholesterol sulfate correlated with the enrichment of the BF group in the steroid hormone biosynthesis pathway. The significant down-regulation of 2,3-dinor-5,6-dihydro-15-F2t-IsoP was found to correlate with the enrichment of the arachidonic acid metabolism pathway in this group. The formation of cholesterol sulfate was elucidated as a derivative of cholesterol catalyzed by sulfotransferase (SULT) and combined with a sulfate group. It is widely distributed in human tissues such as skin, intestine, adrenal glands, liver and brain\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. It plays an important role in a variety of physiological activities, including T cell signaling regulation, glucose metabolism regulation, and lipid homeostasis\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e.At the current stage of research, controversy still exists regarding its biological effects in cancer, with some studies suggesting that it may promote tumor progression and influence tumor value-addition and survival through metabolic regulation in the tumor microenvironment. However, some studies have also suggested that it may exert anti-tumor effectivity through the modulation of T cell function\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, this study systematically analyzed the role of microorganisms and metabolites in the gut microbiota on the occurrence and development of thyroid cancer using metabolomics technology. It was found that there were specifically distributed lipid metabolites\u0026mdash;cholesterol sulfate esters\u0026mdash;in the pre- and post-surgical grouped samples, We speculate that these metabolites may influence thyroid cancer through key metabolic pathways such as the steroid hormone synthesis pathway and the arachidonic acid metabolism pathway. This suggests that there may be potential metabolic intervention targets that can be used for the clinical treatment of thyroid cancer.. However, due to the limited number of experimental samples, this study still has its limitations. Nevertheless, it provides a relatively clear direction for future research. The research team will collect more samples to conduct more in-depth experimental validation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eTC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThyroid Carcinoma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSCFAs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShort-Chain Fatty Acid\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTMAO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrimethylamine N-Oxides\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTLRs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eToll-like receptors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLow-Density Lipoprotein\u0026zwnj;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigh-Density Lipoprotein\u0026zwnj;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFatty Acyls\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlycerol lipids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlycerophospholipids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolyketides\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSphingolipids\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSterols\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSULT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSulfotransferase\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and was approved by the Clinical Research Ethics Committee of the Affiliated Hospital of Guangdong Medical University (approval number: PJ2021-079).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data within the literature are available for use. The sequence data supporting the results of this study have been deposited in Metabolights with the primary accession code MTBLS12769.The following is access link https://www.ebi.ac.uk/metabolights/MTBLS12769.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● Competing Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the following two projects: Clinical Research Project of the Affiliated Hospital of Guangdong Medical University (LCYJ2021B010)) 2024 Unsubsidized Science and Technology Tackling Program of Zhanjiang City, Guangdong Province, China (2024B01154).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJH and JL: Writing\u0026ndash;original draft, Writing\u0026ndash;review \u0026amp; editing, Data curation, Visualization. XX: Data curation. SL: Formal analysis. HL: Project administration. YW, WC and ZC: Validation. CZ, ML, BL, GZ and ZH: Investigation. JL and BL: Resources. ZZ: Writing\u0026ndash;review \u0026amp; editing, Supervision, Methodology. WQ: Writing\u0026ndash;review \u0026amp; editing, Funding acquisition, Project administration, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e● Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Professor Bin Liu for his assistance in writing the paper and analyzing the conclusions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhuang, J., et al., Thyroid-Disrupting Effects of Exposure to Fipronil and Its Metabolites from Drinking Water Based on Human Thyroid Follicular Epithelial Nthy-ori 3-1 Cell Lines. Environ Sci Technol, 2023. \u003cstrong\u003e57\u003c/strong\u003e(15): p. 6072-6084.\u003c/li\u003e\n\u003cli\u003eXu, B., et al., International Medullary Thyroid Carcinoma Grading System: A Validated Grading System for Medullary Thyroid Carcinoma. J Clin Oncol, 2022. \u003cstrong\u003e40\u003c/strong\u003e(1): p. 96-104.\u003c/li\u003e\n\u003cli\u003eCibas, E.S. and S.Z. Ali, The 2017 Bethesda System for Reporting Thyroid Cytopathology. Thyroid, 2017. \u003cstrong\u003e27\u003c/strong\u003e(11): p. 1341-1346.\u003c/li\u003e\n\u003cli\u003eSiegel, R.L., et al., Cancer statistics, 2023. CA Cancer J Clin, 2023. \u003cstrong\u003e73\u003c/strong\u003e(1): p. 17-48.\u003c/li\u003e\n\u003cli\u003eDeng, Y., et al., Global Burden of Thyroid Cancer From 1990 to 2017. JAMA Netw Open, 2020. \u003cstrong\u003e3\u003c/strong\u003e(6): p. e208759.\u003c/li\u003e\n\u003cli\u003eBelkaid, Y. and T.W. Hand, Role of the microbiota in immunity and inflammation. Cell, 2014. \u003cstrong\u003e157\u003c/strong\u003e(1): p. 121-41.\u003c/li\u003e\n\u003cli\u003eCastano-Rodriguez, N., et al., Dysbiosis of the microbiome in gastric carcinogenesis. Sci Rep, 2017. \u003cstrong\u003e7\u003c/strong\u003e(1): p. 15957.\u003c/li\u003e\n\u003cli\u003eLi, W., et al., Changes in Gut Microbiota and Metabolites in Papillary Thyroid Carcinoma Patients Following Radioactive Iodine Therapy. Int J Gen Med, 2023. \u003cstrong\u003e16\u003c/strong\u003e: p. 4453-4464.\u003c/li\u003e\n\u003cli\u003eArthur, J.C., et al., Microbial genomic analysis reveals the essential role of inflammation in bacteria-induced colorectal cancer. Nat Commun, 2014. \u003cstrong\u003e5\u003c/strong\u003e: p. 4724.\u003c/li\u003e\n\u003cli\u003eKostic, A.D., et al., Fusobacterium nucleatum potentiates intestinal tumorigenesis and modulates the tumor-immune microenvironment. Cell Host Microbe, 2013. \u003cstrong\u003e14\u003c/strong\u003e(2): p. 207-15.\u003c/li\u003e\n\u003cli\u003eDalmasso, G., et al., The bacterial genotoxin colibactin promotes colon tumor growth by modifying the tumor microenvironment. Gut Microbes, 2014. \u003cstrong\u003e5\u003c/strong\u003e(5): p. 675-80.\u003c/li\u003e\n\u003cli\u003eOhtani, N., S. Yoshimoto, and E. Hara, Obesity and cancer: a gut microbial connection. Cancer Res, 2014. \u003cstrong\u003e74\u003c/strong\u003e(7): p. 1885-9.\u003c/li\u003e\n\u003cli\u003eArthur, J.C., et al., Intestinal inflammation targets cancer-inducing activity of the microbiota. Science, 2012. \u003cstrong\u003e338\u003c/strong\u003e(6103): p. 120-3.\u003c/li\u003e\n\u003cli\u003eSanschagrin, S. and E. Yergeau, Next-generation sequencing of 16S ribosomal RNA gene amplicons. J Vis Exp, 2014(90).\u003c/li\u003e\n\u003cli\u003eYan, X., et al., Intestinal Flora Modulates Blood Pressure by Regulating the Synthesis of Intestinal-Derived Corticosterone in High Salt-Induced Hypertension. Circ Res, 2020. \u003cstrong\u003e126\u003c/strong\u003e(7): p. 839-853.\u003c/li\u003e\n\u003cli\u003eJin, Y., et al., The Diversity of Gut Microbiome is Associated With Favorable Responses to Anti-Programmed Death 1 Immunotherapy in Chinese Patients With NSCLC. J Thorac Oncol, 2019. \u003cstrong\u003e14\u003c/strong\u003e(8): p. 1378-1389.\u003c/li\u003e\n\u003cli\u003eFranchini, F., et al., Obesity and Thyroid Cancer Risk: An Update. Int J Environ Res Public Health, 2022. \u003cstrong\u003e19\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eWang, X. and B.B. Li, Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature. Front Genet, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 624820.\u003c/li\u003e\n\u003cli\u003eYu, X., et al., Gut microbiota changes and its potential relations with thyroid carcinoma. J Adv Res, 2022. \u003cstrong\u003e35\u003c/strong\u003e: p. 61-70.\u003c/li\u003e\n\u003cli\u003eBauermeister, A., et al., Mass spectrometry-based metabolomics in microbiome investigations. Nat Rev Microbiol, 2022. \u003cstrong\u003e20\u003c/strong\u003e(3): p. 143-160.\u003c/li\u003e\n\u003cli\u003eAdak, A. and M.R. Khan, An insight into gut microbiota and its functionalities. Cell Mol Life Sci, 2019. \u003cstrong\u003e76\u003c/strong\u003e(3): p. 473-493.\u003c/li\u003e\n\u003cli\u003eSchwartz, N., et al., Rapid steroid hormone actions via membrane receptors. Biochim Biophys Acta, 2016. \u003cstrong\u003e1863\u003c/strong\u003e(9): p. 2289-98.\u003c/li\u003e\n\u003cli\u003eKoundouros, N., et al., Metabolic Fingerprinting Links Oncogenic PIK3CA with Enhanced Arachidonic Acid-Derived Eicosanoids. Cell, 2020. \u003cstrong\u003e181\u003c/strong\u003e(7): p. 1596-1611 e27.\u003c/li\u003e\n\u003cli\u003eNam, L.B., et al., Cholesterol sulfate as a negative regulator of cellular cholesterol homeostasis. Mol Cells, 2025. \u003cstrong\u003e48\u003c/strong\u003e(6): p. 100209.\u003c/li\u003e\n\u003cli\u003ePrah, J., et al., Cholesterol sulfate alters astrocyte metabolism and provides protection against oxidative stress. Brain Res, 2019. \u003cstrong\u003e1723\u003c/strong\u003e: p. 146378.\u003c/li\u003e\n\u003cli\u003eTatsuguchi, T., et al., Pharmacological intervention of cholesterol sulfate-mediated T cell exclusion promotes antitumor immunity. Biochem Biophys Res Commun, 2022. \u003cstrong\u003e609\u003c/strong\u003e: p. 183-188.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbit","sideBox":"Learn more about [BMC Biotechnology](http://bmcbiotechnol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bbit/default.aspx","title":"BMC Biotechnology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolomics, Thyroid carcinoma, Biomarkers, Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-7064330/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7064330/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackgrouds:\u003c/strong\u003eThe pathogenesis of thyroid carcinoma (TC) involves various factors, with the interplay between hormonal and environmental influences being particularly critical. The gut microbiome, a long-overlooked risk factor, may play a significant role in TC development. Studies indicate that gut dysbiosis, bacterial outer membrane components such as LPS, and metabolites like Short-Chain Fatty Acids (SCFAs)and Trimethylamine N-Oxide (TMAO) are pivotal in mediating or influencing both gastrointestinal and extra-gastrointestinal tumors. However, reports on the multi-omics analysis of the correlation between gut microbiota, metabolites, and thyroid carcinoma remain scarce.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study employs a case-control and cohort design, integrating 16S rDNA sequencing, and metabolomics for multi-omics joint analysis of fecal samples from thyroid cancer patients before and after surgery. The aim was to identify associations between the gut microbiome, functional genes, and metabolites, as well as potential disease biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eBy combining existing clinical diagnostic indicators for thyroid carcinoma, this research aims to screen for novel biomarkers to aid in the diagnosis of thyroid carcinoma.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e:This paper investigates the correlation between metabolites and thyroid carcinoma through metabolomics-based analysis. We speculate that the lipid metabolite cholesterol sulfate may affect thyroid cancer through key metabolic pathways such as steroid hormone synthesis and arachidonic acid metabolism.\u003c/p\u003e","manuscriptTitle":"Metabolomics-Based Analysis of the Correlation Between Metabolites and Thyroid Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 10:21:50","doi":"10.21203/rs.3.rs-7064330/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-11T06:12:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-10T05:39:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134828392441170917653590452731604619174","date":"2025-07-31T20:28:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-30T21:36:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15295667943839087547578040461882475481","date":"2025-07-28T22:22:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-28T07:23:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T07:08:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-28T04:06:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-26T09:23:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Biotechnology","date":"2025-07-26T08:33:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bbit","sideBox":"Learn more about [BMC Biotechnology](http://bmcbiotechnol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bbit/default.aspx","title":"BMC Biotechnology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2629154-55fb-4591-be52-2da4ec0d67c3","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:01:57+00:00","versionOfRecord":{"articleIdentity":"rs-7064330","link":"https://doi.org/10.1186/s12896-025-01045-6","journal":{"identity":"bmc-biotechnology","isVorOnly":false,"title":"BMC Biotechnology"},"publishedOn":"2025-09-26 15:58:08","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2025-07-31 10:21:50","video":"","vorDoi":"10.1186/s12896-025-01045-6","vorDoiUrl":"https://doi.org/10.1186/s12896-025-01045-6","workflowStages":[]},"version":"v1","identity":"rs-7064330","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7064330","identity":"rs-7064330","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}