Marker, kit and method for thyroid papillary carcinoma lymphatic metastasis assessment

Abstract

The invention discloses a marker, a kit and a method for thyroid papillary carcinoma lymphatic metastasis assessment, and belongs to the technical field of carbohydrate chemistry. The marker comprises a miRNA (micro Ribonucleic Acid) biomarker and an IgG (Immunoglobulin G) N-glycan, wherein the miRNA biomarkers are miR-616-3p and miR-1285-5p, and the miRNA biomarkers are miR-616-3p and miR-1285- And the IgG N-glycans are GP4, GP10, GP13 and GP24. A biomarker model is constructed by jointly detecting six key indexes of miR-616-3p, miR-1285-5p, GP4, GP10, GP13 and GP24, the problem of multicollinearity is effectively solved, the AUC value reaches 0.930, the general efficiency level of a clinical conventional diagnosis method is remarkably exceeded, and a high-precision judgment tool is provided for PTC LNM clinical decision. The numerical value shows extremely high discrimination capability; and the constructed model is based on internal correlation analysis of miRNA and initial glycan which are remarkably related to PTC LNM, and has good specificity and reliability.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of genetic technology, and particularly relates to a biomarker for evaluating lymph node metastasis of papillary thyroid carcinoma, a detection kit and a detection method. BACKGROUND

[0002] Thyroid carcinoma (TC) is one of the most common malignant tumors in the endocrine system, and occupies an important position in head and neck malignant tumors, with an incidence of about 3-4% of all malignant tumors.

[0003] According to histological classification, TC can be mainly divided into three types, (1) differentiated thyroid carcinoma including papillary, follicular and oxyphilic; (2) anaplastic thyroid carcinoma; (3) medullary thyroid carcinoma. Among the various types, papillary thyroid carcinoma (PTC) is the most common, with a prevalence of about 80-90% of all TC types. PTC is a kind of indolent tumor, and patients usually have a good prognosis after clinical treatment, with a 10-year overall survival rate of more than 90%. However, PTC has obvious lymphoid characteristics and is prone to lymph node metastasis (LNM), and studies have shown that 40-90% of PTC patients in clinical practice will have cervical lymph node metastasis, and even a small number of patients will have multiple or distant metastasis, which greatly increases the recurrence rate of PTC patients after surgery and significantly reduces the survival rate. The potential risk of PTC LNM makes a large number of patients receive prophylactic lymph node dissection surgery or even total thyroidectomy surgery, but this treatment method may cause complications such as permanent hypoparathyroidism or laryngeal recurrent nerve injury, and patients need to take medication for life, which has a serious impact on the quality of life. At present, preoperative ultrasound as an effective detection method for PTCLNM diagnosis has been widely used in clinical practice, but its clinical performance is not satisfactory, and only less than 30% of PTC patients can detect the occurrence of LNM early, and patients who change the final surgical method account for only 20%, which is far from meeting the needs. Therefore, there is an urgent need for a reliable non-invasive biomarker to improve the early diagnosis and prognosis prediction of PTC LNM.

[0004] Exosomes are extracellular vesicles surrounded by a lipid bilayer membrane and secreted by eukaryotic cells, with a diameter of 40-160 nm (average 100 nm) and a morphology of tea tray, whose biological functions depend on the internal carrying of bioactive substances such as lipids, metabolites, proteins, mRNA, miRNA and lncRNA, etc. Exosomes can participate in the influence of intercellular signal communication and various biological processes through three ways: direct fusion with target cells, release of soluble molecules into contact with target cells, and dependence on special membrane proteins to bind to target cell receptors. Exosomes play an important role in the occurrence, development, metastasis and even antagonism of cancer treatment. The production rate and quantity of exosomes in cancer cells can even reach more than ten times in normal cells. Exosomes can stimulate strong signal response at the metastatic site, thereby efficiently remodeling the distal tumor microenvironment and promoting the metastasis process of tumor. In the development process of tumor, cancer cells exchange substances in exosomes with stromal cells, creating an environment conducive to tumor growth, thereby regulating the occurrence and development of tumor. In addition, exosomes also participate in the pathological process of tumor growth and metastasis by regulating angiogenesis and extracellular matrix remodeling. As a key regulator of gene expression in animals and plants, microRNA (miRNA) is a highly conserved non-coding single-stranded RNA with a length of about 18-27 nt, which plays a regulatory role in the key transcription process of animals and plants. miRNA can control the expression function of about half of the protein-coding genes of eukaryotes. Studies have shown that miRNA is involved in the regulation of almost all cellular processes including cell growth and apoptosis, tissue differentiation, information communication, etc., and its abnormal expression is related to many human diseases. miRNA is closely related to tumor progression, metastasis, recurrence and uncontrolled proliferation, and is expressed abnormally in most cancers, affecting the expression of cancer-related genes by activating or inhibiting cancer-related signaling pathways, thereby affecting the occurrence and development of tumor and showing the potential as a marker for cancer prognosis evaluation. It is worth noting that miRNA carried by exosomes has excellent biological stability compared to intracellular miRNA due to the protection of the vesicle structure, and can be transported over long distances through body fluids, and can be detected far away from the source organ or tissue, which provides a new idea for early diagnosis of tumor. However, the specificity of single exosomal miRNA for clinical diagnosis is insufficient, and the concentration of exosomal miRNA obtained by different extraction and enrichment methods is different, so other biomarkers need to be combined for diagnosis to improve the accuracy and specificity of exosomes as a means of disease diagnosis.

[0005] Glycosylation is an important post-translational modification process of proteins, which is catalyzed by glycosyltransferases to combine sugar chains with specific amino acid residues to form glycoproteins with functional regulation. According to the difference of sugar-peptide connection mode, this process is mainly divided into N-linked and O-linked two forms, and more than half of the glycoproteins undergo N-glycosylation modification process. Immunoglobulin G (IgG) is one of the specific antibodies secreted by B cells after being stimulated by external stimuli, and is the most important component of immunoglobulin antibodies. It is also the most abundant glycoprotein, accounting for 75% of the total amount of peripheral blood immunoglobulin. IgG molecule is composed of four polypeptide chains, including two heavy chains and two light chains, connected by disulfide bonds to form a "Y" shaped symmetrical framework. These peptide chains form three structurally independent protein fragments, including two variable fragments (Fab) and one crystallizable fragment (Fc) connected by a flexible hinge region. Each of the two heavy chains on the Fc segment of IgG contains a conserved N-glycosylation site at the asparagine (Asn) 297 site, which is used to connect various sugar residues including galactose, sialic acid, core fucose and bisecting acetylglucose. IgG Fc N-glycosylation can stabilize the structure of Fc fragment, and the difference in the type and number of connected sugar residues affects the functional properties of IgG. In addition, the IgG Fab segment also contains N-glycan, but only accounts for a small part of the total N-glycosylation, about 10-25%, and IgG Fab N-glycosylation mainly plays a role in the antigen affinity and serum half-life of the antibody.

[0006] IgG N-glycosylation in healthy individuals is relatively stable, but it will fluctuate with changes in sex hormones and age. When facing diseases such as infection, autoimmune or cancer, the IgG N-glycan profile can change significantly beyond the above-mentioned mixed effects. So far, many studies have shown the relationship between IgG N-glycosylation and cancer. Pan et al. found that IgG N-glycosylation is a biomarker for early identification of esophageal cancer, which has great research potential. Studies have shown that IgG N-glycosylation is related to the occurrence and development of colorectal cancer and can be used as a biomarker for early diagnosis and prognosis prediction of colorectal cancer. It can be seen that IgG N-glycosylation shows its potential as an early biomarker in various diseases, but also reflects its lack of specificity in cancer screening and potential diagnosis, which has serious limitations and is difficult to function as a single biomarker. SUMMARY

[0007] The purpose of the present application is to provide a biomarker, a detection kit and a non-invasive detection method for diagnosing lymph node metastasis of papillary thyroid carcinoma. The problem to be solved by the present application is.

[0008] The application provides a biomarker for thyroid papillary carcinoma lymph node metastasis evaluation, which comprises two types of miRNA biomarkers and IgG N-glycan; wherein the miRNA biomarkers are miR-616-3p and miR-1285-5p; and the IgG N-glycan is GP4, GP10, GP13 and GP24.

[0009] GP4 is one of the main components in IgG, representing a "no galactose (G0)" state. During rheumatoid arthritis or aging, the level of GP4 is usually increased, and this state is usually associated with pro-inflammatory activity.

[0010] GP10 can significantly affect the binding force of IgG and its receptor, thereby enhancing antibody-dependent cellular cytotoxicity.

[0011] GP13 is a fully galactosylated structure. In many inflammatory diseases, the proportion of GP13 tends to decrease with the overall decrease of galactosylation.

[0012] GP24 represents a highly complex modification. The higher the GP24 is, the more balanced the body's immune state is or the lighter the inflammation is.

[0013] The application also provides a detection kit for thyroid papillary carcinoma lymph node metastasis, which comprises an miRNA detection module and an IgG N-glycosylation detection module.

[0014] The miRNA detection module comprises a reverse transcription reaction solution, a quantitative PCR reaction solution, and specific stem-loop reverse transcription primers, upstream and downstream PCR primers, and a fluorescently labeled TaqMan probe for miR-616-3p and miR-1285-5p;

[0015] The IgG N-glycosylation detection module comprises 2-AB fluorescent labeling reagent, standard sample and loading buffer;

[0016] The standard sample is a glycan standard of GP4, GP10, GP13 and GP24, and the glycan composition and structure of the GP4, GP10, GP13 and GP24 are respectively:

[0017] The glycan composition of the GP4 is H3N4F1, and the glycan structure is named FA2;

[0018] The glycan composition of the GP10 is H4N5F1(6), and the glycan structure is named FA2(6)BG1;

[0019] The glycan composition of the GP13 is H5N5, and the glycan structure is named A2BG2;

[0020] The glycan composition of the GP24 is H5N5F1S2, and the glycan structure is named FA2BG2S2.

[0021] The application further provides a detection method for evaluating lymph node metastasis of thyroid papillary carcinoma, comprising the following steps:

[0022] A sample processing step: fasting peripheral venous blood of a PTC patient is collected, serum is separated by centrifugation, and the serum is stored at-80°;

[0023] A marker detection step:

[0024] Exosomal miRNA in the serum is extracted, and the relative expression amounts of miR-616-3p and miR-1285-5p are detected by qRT-PCR;

[0025] Serum IgG is purified, IgG is digested by PNGase F enzyme, 2-AB fluorescent labeling reagent is used, and the relative peak area percentages of GP4, GP10, GP13 and GP24 are detected;

[0026] A determination step: the data of the six biomarkers are substituted into a Logistic regression equation, a risk probability value (P) is calculated, and when P is greater than or equal to 0.527, it is determined to be positive.

[0027] By means of the above technical solutions, the application has the following beneficial effects:

[0028] The application deeply explores the changes of exosomal miRNA and IgG N-glycosyl when PTC patients have lymph node metastasis. This not only helps to screen more accurate biomarkers, but also constructs a more scientific and forward-looking biomarker composite model. The construction of the model will provide a solid scientific basis for the early diagnosis and intervention treatment of PTC LNM population, and is expected to make up for the shortcomings of existing diagnostic methods, significantly improve the diagnostic accuracy and specificity, thereby improving the prognosis of patients and reducing the disease burden. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a relative expression amount comparison column chart of miR-616-3p in PTC LNM+ and LNM- groups provided by the embodiment of the application;

[0030] Figure 2 is a relative expression amount comparison column chart of miR-1285-5p in PTC LNM+ and LNM- groups provided by the embodiment of the application;

[0031] Figure 3 is a comparison chart of receiver operating characteristic (ROC) curves of three diagnostic models provided by the embodiment of the application;

[0032] Figure 4 is the composition and structure diagram of 24 initial glycans provided by the embodiment of the application, the square represents a bisected acetylglucosamine glycosyl; the dark gray circle represents mannose; the triangle represents fucose; the light gray circle represents galactose; and the rhombus represents sialic acid. DETAILED DESCRIPTION

[0033] In order to fully illustrate the preparation idea of the application, the process of the present scheme will be described below in combination with specific examples, and the examples are only for illustration and should not be interpreted or understood as limiting the protection of the application.

[0034] EMBODIMENT

[0035] I. Extraction and identification of serum exosomes

[0036] 1. Extraction steps of serum exosomes

[0037] (1) The serum sample stored at -80°C was taken out and quickly thawed in a 25°C water bath, and then mixed uniformly by vortex oscillation and quickly placed on ice to maintain its stability;

[0038] (2) The serum sample was centrifuged at 2000g for 30 minutes to remove cell residues and larger fragments;

[0039] (3) 1000µL of supernatant was aspirated from the centrifuged serum sample using a pipette and transferred to a 1.5mL enzyme-free EP tube, and stored on ice for standby;

[0040] (4) 0.2 times the volume of supernatant (500µL) of exosome separation reagent was added, and the mixture was quickly and fully shaken until a yellow suspension was formed;

[0041] (5) The mixed liquid was incubated at 4°C for 30min, and the EP tube was kept upright during the incubation to promote the precipitation of exosomes;

[0042] (6) The incubated sample was centrifuged at 10000g at room temperature for 10 minutes, and the sample after centrifugation showed obvious stratification, and the exosomes mainly existed in the lower layer of yellow or off-white precipitate;

[0043] (7) The supernatant was discarded by gradient aspiration using a pipette of different scales, and as much supernatant as possible was discarded without touching the lower layer of precipitate;

[0044] (8) 100μL of 1×PBS buffer was added to the precipitate, and the precipitate was gently blown and dispersed by a pipette to ensure that the precipitate was completely and uniformly dispersed;

[0045] (9) The isolated exosomes can be stored at 2-8°C for a short period of one week or at -80°C for a long period of time, but repeated freezing and thawing should be avoided.

[0046] 2. Identification steps of serum exosomes

[0047] (1) The isolated exosomes were taken out from the -80°C ultra-low temperature refrigerator and placed on ice for thawing. Then, the sample was uniformly mixed by vortex oscillation and centrifuged;

[0048] (2) 10 μL of the exosome sample was taken by a pipette and placed on a copper mesh support film, and then left to stand for 1 to 3 minutes;

[0049] (3) The liquid remaining on the edge of the copper mesh support film was absorbed by filter paper;

[0050] (4) 10 μL of ultrapure water was accurately extracted by a pipette and uniformly dropped on the surface of the support film, and then quickly flushed. This process was repeated three times to completely remove the surface impurities;

[0051] (5) 10 μL of 2% uranyl acetate solution was added to the sample for staining, and left to stand in the dark for 3 minutes;

[0052] (6) The residual staining agent on the support film was absorbed by filter paper, and the exosome sample after staining was naturally dried;

[0053] (7) Whether the sample morphology meets the requirements was observed under the field of view of the transmission electron microscope. Under the field of view of the transmission electron microscope, the diameter of the exosomes was 40-160 nm, and the morphology presented a tea tray shape. The criteria for determining that the sample does not meet the typical exosome morphological characteristics under the transmission electron microscope are as follows: the morphology is regular spherical, long rod-shaped or irregular, without typical "tea tray" indentation; the size continuously exceeds the range of 40-160 nm; the membrane structure is incomplete, broken or attached to non-membrane substances; or there are serious aggregation, staining precipitation and other preparation artifacts. If the particle population in the field of view generally meets the above exclusion characteristics, it is determined that the identification result does not meet the requirements.

[0054] II. Extraction and quality inspection of total RNA of exosomes

[0055] 1. Extraction of total RNA of exosomes

[0056] The serum exosome RNA was extracted according to the steps of the ultrapure RNA extraction kit:

[0057] (1) The exosome sample was taken out from the -80°C ultra-low temperature refrigerator and placed on ice for dissolution under the condition that the sample was not degraded. The dissolved sample was mixed by oscillation to ensure uniformity and centrifuged to clarify the solution;

[0058] (2) Add 1000 μL of Trizol lysis reagent to the centrifuged sample for lysis. Note that after adding Trizol, flocculent precipitate may appear in the EP tube, which needs to be dissolved by thorough vortexing. Incubate at room temperature for 5 minutes to ensure the separation of proteins and nucleic acids;

[0059] (3) Add 200 μL of chloroform to the incubated sample and gently shake for 15 seconds to achieve uniform mixing. The mixed solution should be turbid. Let stand at room temperature for 2 minutes, then centrifuge at 4°C and 12000 r / min for 10 minutes. The sample will show obvious stratification: the bottom layer is the organic phase, the middle layer is separated by white proteins and DNA, and the top layer is a clear aqueous phase containing RNA;

[0060] (4) After tilting the test tube, carefully use a pipette to draw about 450-500 μL of the upper aqueous phase and transfer it to a 1.5 mL enzyme-free EP centrifuge tube, making sure not to disturb the white boundary layer in the middle.

[0061] (5) Then add an equal volume (450-500 μL) of 70% ethanol and gently invert the tube several times to mix thoroughly;

[0062] (6) Install the adsorption column into the enzyme-free tube and add 450 μL of the mixed solution. Run the adsorption column at 12000 r / min.

[0063] Centrifuge for 20 seconds, discard the waste liquid at the bottom and repeat until the remaining 450 μL of solution is also processed;

[0064] (7) After adding 700 μL of Buffer RW1 to the adsorption column, centrifuge at 12000 r / min for 20 seconds to allow the liquid to settle at the bottom of the tube and then discard it.

[0065] (8) Place 500 μL of Buffer RW2 into the adsorption column and centrifuge at 12000 r / min for 20 seconds. Discard the waste liquid at the bottom. This operation needs to be repeated twice.

[0066] (9) Centrifuge the empty adsorption column at 12000 r / min for 2 min, and then open the lid and let it air dry for 5 min;

[0067] (10) Place 30 μL of enzyme-free water evenly onto the white filter membrane in the center of the adsorption column. Always use a fresh pipette tip for each sample to prevent RNA cross-contamination. Incubate the sample at room temperature for 1 minute, then centrifuge at 12000 r / min for 1 minute to collect the filtrate.

[0068] (11) To improve the RNA concentration, the RNA solution obtained by centrifugation in the previous step is reloaded into the adsorption column, and after incubation at room temperature for 1 min, the centrifugal treatment is repeated. The RNA is placed on ice to prevent degradation, and after measuring the concentration, it is labeled and stored in a ultra-low temperature freezer at -80°C for long-term storage.

[0069] 2. Quality control of total RNA of exosomes

[0070] NanoDrop 2000 microspectrophotometer is used for quality control of RNA, and the steps are as follows:

[0071] (1) After starting the NanoDrop software, enter the nucleic acid detection module;

[0072] (2) Select RNA in the detection category, and set the concentration unit to ng / μL;

[0073] (3) First, clean and wipe the base with 2 μL of enzyme-free water;

[0074] (4) Take 1 μL of enzyme-free water and place it on the base, and click "blank" and "measure" in turn to calibrate the blank, and ensure that the reading is maintained at 0 or ±0.1 ng / μL;

[0075] (5) Place 1 μL of sample RNA on the base, select "measure" to determine the exosome RNA concentration, and record the OD260 / 280 ratio.

[0076] (6) At the same time, the RNA integrity is detected by Agilent 2100 bioanalyzer. The threshold for RNA quality control is set as follows: RNA integrity number (RIN value) ≥7.0, and OD260 / 280 ratio between 1.8-2.1. Only samples meeting the threshold are used for subsequent experiments.

[0077] Three, serum exosome miRNA sequencing and data analysis

[0078] 1. Serum exosome miRNA sequencing

[0079] Through random sampling, 10 cases were selected from each of PTC LNM+ and PTC LNM- groups, a total of 20 cases. The age of the 20 research subjects was 50.70±9.45 years, including 2 males and 18 females. There was no significant difference in age and gender between the two groups of research subjects (P>0.05). This study commissioned Tianjia Gene Technology (Suzhou) Co., Ltd. to conduct high-throughput sequencing of serum exosome miRNA of the samples.

[0080] 2. Sequencing data analysis

[0081] (1) Base quality score (Phred quality score, Q) is an index for evaluating the accuracy of each base identification in high-throughput sequencing. Among them, Q20 means that the accuracy of base identification reaches 99%; and Q30 means that the accuracy is as high as 99.9%. In the process of quality control of the original sequencing data, the fastp software is used to screen the sequences, and the screening standards include: removing sequences containing adapter sequences; removing sequences without adapters or with insert deletion at the 3' end; excluding sequences with Q20 base ratio less than 60%; and filtering out sequences with length not in the range of 18-36bp. The quality of the screened sequences is qualified and suitable for subsequent analysis;

[0082]

[0083] (2) The blastn software is used for further analysis of the screened sequences, and only those sequences matched with the mature miRNA sequences in the miRBase and Rfam databases are reserved, and the sequences matched with the ncRNA library are excluded, so as to be used for subsequent research;

[0084] (3) With the help of miRDeep2 program, the RNA sequence data is matched with the miRBase database, and then the expression of known miRNA in each sample is quantified;

[0085] (4) Deseq2 is applied to perform differential analysis on the miRNA expression data. The determination criteria are |log2FC|>1 and P<0.05, wherein log2FC>1 represents up-regulated expression, and log2FC<-1 represents down-regulated expression. The miRNAs not meeting these conditions are regarded as non-differentially expressed. The R software is used to draw a clustering heat map and a volcano plot to visualize the analysis results;

[0086] (5) The miRanda and RNAhybrid software are used for miRNA target prediction, and the intersection of the results of the two is taken to screen the final candidate target genes;

[0087] ​(6) Adopt Gene Ontology (GO) analysis to classify the differential expression genes from three dimensions of biological process, cell component and molecular function, so as to reveal the functional characteristics thereof; meanwhile, the signal pathways in which the genes are involved are analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) database, so as to clarify the roles of the genes in biological processes. The cluster Profiler package of R language is adopted to complete the analysis of the above two aspects: the enrichment of genes in BP, CC and MF is displayed by a bar chart, and the length of the bar chart is inversely proportional to the P value; the enrichment analysis results of KEGG pathways are displayed by a bubble chart, and the size of the bubble represents the number of target genes enriched into the pathway, and the color change (from blue to red) represents that the P value changes from large to small, reflecting the enrichment degree and credibility.

[0088] The GO / KEGG enrichment analysis of the application finds that the predicted target genes of miR-616-3p and miR-1285-5p are significantly enriched in glycosyltransferase related pathways (including MGAT family genes). The result shows that the above miRNAs may participate in the mechanism of PTC lymph node metastasis by regulating IgG N-glycosylation modification process. The regulation pathway and the expression change of GP4 and other glycan markers echo each other in biological processes, and jointly point to the role of abnormal glycosylation modification in metastasis, thereby providing a mechanism level basis for subsequent integration of miRNA and glycan markers and construction of a joint diagnostic model.

[0089] IV. qRT-PCR verification of differential expression miRNAs

[0090] 1. Primer design

[0091] The downstream primer of the miRNA is the reverse universal primer Universal reverse Q primer in the miRNA tailing reverse transcription kit, and the annealing temperature is about 66℃. The synthesis of the upstream primer of the miRNA and the U6 primer is performed by Shengong Bioengineering (Shanghai) Co., Ltd.:

[0092] Table 3 miRNA upstream primer sequence

[0093]

[0094] 2. Reverse transcription to synthesize cDNA

[0095] The miRNA reverse transcription uses the miRNA tailing reverse transcription kit of Nuaidian Company to synthesize the first strand cDNA, and the whole reverse transcription experiment uses enzyme-free experimental consumables in a biological safety cabinet to prevent cross contamination and degradation of the RNA sample. The specific steps are as follows:

[0096] (1) Dilute the concentration of the sample to the same level;

[0097] (2) Prepare the reverse transcription reaction system in RNase-free centrifuge tube (0.2 mL enzyme-free centrifuge tube) according to the following ingredients:

[0098] Table 4 miRNA first-strand cDNA reverse transcription reaction system

[0099]

[0100] (3) Gently blow the prepared mixture with a pipette to ensure uniform mixing, and remove any air bubbles by centrifugation to avoid affecting the experimental results;

[0101] (4) Perform cDNA synthesis reaction according to the following conditions:

[0102] Table 5 miRNA first-strand cDNA reverse transcription reaction program

[0103]

[0104] (5) After the reverse transcription reaction, the cDNA obtained for subsequent experiments can be stored at -20°C for one week, and if long-term storage is required, it should be placed in a -80°C environment.

[0105] Five, qRT-PCR reaction

[0106] Use the SYBR Green I chimeric fluorescence method special premix from Novozyme Company, and use the cDNA obtained in the above experiment as the template for qRT-PCR reaction. The experiment should be carried out in the dark and on ice, and it should be noted that the premix thawed on ice may have white precipitate at the bottom of the tube, which needs to be shaken and mixed until the white precipitate is completely dissolved before the experiment.

[0107] (1) Prepare the reaction system in 0.2 mL enzyme-free centrifuge tube according to the following contents:

[0108] Table 6 qRT-PCR reaction system

[0109]

[0110] (2) Shake and centrifuge the mixture, and transfer it to a light-proof eight-row array, tap the bottom and centrifuge to ensure there are no air bubbles. To avoid errors, set 3 replicate wells for each miRNA in the sample verification to ensure biological repetition;

[0111] (3) Perform qRT-PCR reaction according to the following reaction:

[0112] Table 7 qRT-PCR reaction program

[0113]

[0114] (4) After obtaining the CT value, the fluctuation of the CT data of three repetitions should be verified not to exceed 0.5. The method of 2 -ΔΔCT was used to calculate the relative expression of PTC LNM group.

[0115] The qRT-PCR analysis results showed that miR-616-3p was significantly up-regulated in PTC LNM+ subjects, and the expression of miR-616-3p in LNM+ group was 2.52 times that of LNM- group, while miR-1285-5p was down-regulated, and the expression of miR-1285-5p in LNM+ group was 0.6 times that of LNM-, and the difference between the two groups was statistically significant (P < 0.05).

[0116] (5) Six, IgG N-glycan laboratory index detection

[0117] 1. Isolation of serum IgG

[0118] (1) Sample preparation

[0119] 1) Take out the Protein G extraction plate pre-stored in the buffer from the cold storage environment at 4°C, and take out the solution that needs to be stored at 4°C together, and balance the temperature at room temperature for 30 minutes;

[0120] 2) Take out the required serum sample from the ultra-low temperature freezer at -80°C, thaw at room temperature, and shake well after thawing. Centrifuge 100 μL of the upper serum sample using a 100 μL range pipette and transfer it to a 2 mL collection plate;

[0121] 3) Place the experimental solution balanced to room temperature in a disposable pipette tank, and label it for use. Add 700 μL of 1x PBS buffer to the serum sample in the collection plate for dilution;

[0122] 4) Place the 0.45 μm GHP filter plate above a stable and well-sealed vacuum pump, and connect the waste collection tank below. Add 200 μL of ultrapure water to the required filter plate hole and filter under a vacuum pressure of less than 5 inHg, repeat three times, and pay attention to the amount of waste liquid in the waste tank at all times and clean it in time when reaching the water level;

[0123] 5) Transfer the diluted serum to the 0.45 μm GHP filter plate that has been cleaned using a 1000 μL range pipette, place the filter plate above the vacuum pump, and connect a new 2 mL collection plate below, filter and collect the serum under a vacuum pressure of less than 5 inHg to remove impurities.

[0124] 6) Hemolysis (release of hemoglobin) and lipemia, which are common in clinical serum samples, can interfere with the detection system based on exosomal miRNA and IgG N-glycosylation. Specifically, hemoglobin not only can degrade miRNA due to its endogenous nuclease activity, but also can non-specifically interfere with the binding efficiency of IgG to the solid-phase carrier (such as Protein G); lipid particles can co-precipitate during the exosome extraction process, affecting the yield and purity of miRNA, and their residues can also contaminate the ultra-performance liquid chromatography (UPLC) system, affecting the accuracy of subsequent glycan analysis and the performance of the chromatographic column.

[0125] To this end, the following methods are used to exclude: for hemolysis, a color card standard is established to grade the color of serum: light yellow (qualified), orange red (slight hemolysis, use with caution), deep red (severe hemolysis, reject); for lipid interference, after serum separation, an additional step of high-speed centrifugation (such as 16,000 g, 4°C, 15 minutes) is added, and after centrifugation, the lipid will float on the uppermost layer to form a white oily layer, and the lower clear liquid is carefully aspirated for subsequent experiments to avoid aspiration of the upper lipid.

[0126] (2) Pre-treatment of Protein G extraction plate

[0127] Place the Protein G extraction plate equilibrated to room temperature above the vacuum pump, connect the waste collection tank below, first filter and discard the storage buffer in the hole, then wash the required holes of the extraction plate with 2mL ultrapure water, 2mL 1×PBS buffer, 1mL 0.1M formic acid solution, 2mL 10×PBS buffer, 2mL 1×PBS buffer, and 2mL 1×PBS buffer in the order of 2mL ultrapure water, 2mL 1×PBS buffer, 1mL 0.1M formic acid solution, 2mL 10×PBS buffer, 2mL 1×PBS buffer, and 2mL 1×PBS buffer. Similarly, pour out the waste liquid in time to prevent back suction and damage the vacuum pump.

[0128] (3) Binding and washing of IgG

[0129] Transfer the filtered and impurity-free serum sample to the treated Protein G extraction plate, and stand for more than 10 minutes to ensure that the IgG in the sample is fully combined with the Protein G extraction plate. Place the Protein G extraction plate on the vacuum pump, connect the waste collection tank below, and filter and discard the serum sample with a vacuum pressure of 5 inHg to complete the binding of IgG. Then add 1mL 1×PBS buffer to the extraction plate three times in succession, and filter and remove the waste liquid with a vacuum pressure of 15 inHg to complete the washing.

[0130] (4) Elution and collection process of IgG N-glycan

[0131] After the Protein G extraction plate is tightly combined with IgG and the cleaning is completed, place the plate on a vacuum pump and use a gun to add 1 mL of 0.1M formic acid. Let it stand for more than 5 minutes, connect a new 2 mL collection plate below, and perform vacuum filtration to collect the eluent at a vacuum pressure of 5 inHg. After the filtration is completed, add 170 μL of 1M ammonium bicarbonate to the eluent to neutralize the acid, and mix gently to obtain the IgG extraction solution.

[0132] (5) IgG concentration detection and preservation

[0133] Use the Nanodrop 8000 to detect the concentration of the IgG extraction solution. After turning it on, wait for 1-2 minutes for the instrument to self-check, select Proteomics-CY3, take 2 μL of ultrapure water for Blank and repeat twice, then take 2 μL of ultrapure water for Sample to detect the concentration, and ensure that the concentration is around 0.001 for normal. Then take the IgG extraction solution for Sample to detect the concentration, and add a standard water sample every eight samples for instrument cleaning. After all the extraction solutions to be tested are detected, take 2 μL of ultrapure water for instrument cleaning again, and turn off the software and machine after completion.

[0134] According to the detection concentration of the IgG extraction solution, the subsequent determination of the evaporation sample volume is as follows: if the concentration is greater than 1000 μg / mL, the evaporation sample volume is 300 μL; if the concentration is greater than 500 μg / mL but less than 1000 μg / mL, the evaporation sample volume is 350 μL; if the concentration is greater than 200 μg / mL but less than 500 μg / mL, the evaporation sample volume is 400 μL; and if the concentration is less than 200 μg / mL, the evaporation sample volume is 600 μL. The evaporation temperature is 60°C, and the evaporation time varies depending on the number of evaporation samples. After evaporation, a white powder can be seen at the bottom of the EP tube, which is the concentrated IgG sample, which can be stored at -80°C for a long time. The remaining liquid IgG can be stored at -40°C.

[0135] (6) Regeneration and preservation of Protein G extraction plate

[0136] The Protein G extraction plate can be reused, but it needs to be cleaned, regenerated, and preserved according to strict procedures. Place the extraction plate above the vacuum pump, connect the waste collection tank below, first add 2 mL of 0.1M formic acid solution and discard by filtration, the formic acid in the extraction plate should be less than 10 minutes to prevent damage to the extraction plate, then add 2 mL of 10×PBS buffer, 2 mL of 1×PBS buffer, 2 mL of 1×PBS buffer, and 1 mL of storage buffer in turn, and add storage buffer again after all cleaning steps are completed, and store at 4°C for long-term preservation.

[0137] 2. IgG deglycosylation process

[0138] IgG powder was taken out from the ultra-low temperature refrigerator, and was balanced to room temperature. 30 μL of 1.33% sodium dodecyl sulfate (SDS) was added, and the white powder was completely dissolved in the SDS solution by gently stirring with a gun head. The sample was wrapped with tin paper, and was incubated in a 65°C oven for 10 minutes, and then was incubated in a 60°C shaker for 10 minutes. After the incubation, the sample was taken out and was cooled to room temperature. 10 μL of 4% Igepal was added to each sample, and was mixed by gently stirring with a gun head. The sample was placed on a shaker for five minutes. After the denaturation and incubation, 20 μL of 5×PBS was added to the sample, and was mixed by shaking for two minutes. Then, 30 μL of 0.1 mol / L NaOH solution was added to adjust the pH value, until the pH value was 8 as determined by using pH paper. The sample was mixed by shaking again.

[0139] 1 μL of PNGase F enzyme was added to the sample with the adjusted pH value, and was mixed by gently blowing with a pipette. The sample was placed on a shaker for 15 minutes. After the incubation, the sample was transferred to a water bath at 37°C for 18-20 hours. After the incubation, the sample was placed in a drying machine at 60°C for 3 hours. After the drying, white powder was observed at the bottom of the EP tube. The sample was stored in a -40°C environment until the next step.

[0140] 3. Labeling of IgG N-glycan

[0141] The labeling system was configured according to the number of samples to be labeled. 35 μL of labeling mixture was required for each sample. The labeling system was configured for at least 3 samples each time. The labeling system included 2.5 mg of 2-AB labeling reagent, 6 mg of sodium cyanoborohydride (NaBH3CN), 37.5 μL of acetic acid solution, and 87.5 μL of dimethyl sulfoxide (DMSO) solution. It should be noted that when the acetic acid-DMSO mixture is prepared, the DMSO solution should be added to the acetic acid, and the NaBH3CN should be weighed as quickly as possible to prevent water absorption and denaturation. The mixture was wrapped with tin paper and shaken for 5 minutes. The mixture was dissolved in a 65°C oven for 30 minutes, and was cooled to room temperature. The labeling reagent was added to the sample powder, and was mixed by gently stirring with a gun head. Then, the sample was placed on a shaker for 10 minutes. The sample was wrapped with tin paper and was incubated in a 65°C oven for 3 hours. The sample was taken out and was cooled to room temperature, and was ready for the next step.

[0142] 4. Detection of IgG N-glycan

[0143] A 0.22 pm GHP filter plate was placed on top of a vacuum pump, with a waste collection tank connected underneath. 200 pL of 70% ethanol solution was first added, followed by two repeated 200 pL ultra-pure water washes, and finally 200 pL of 96% acetonitrile solution for pretreatment. The vacuum pressure throughout the filtration process should be kept below 5 inHg. After the filtration was completed, the 0.2 pm GHP filter plate was nested with a PCR plate underneath, and the excess pretreatment solution was removed by centrifugation for 15 minutes using the CEFU mode of the evaporator. 700 pL of 100% acetonitrile solution was added to the labeled sample, which was mixed and then transferred to a 0.2 pm GHP filter plate. A new PCR collection plate was nested underneath the filter plate to collect the filtered sample, and then the sample was centrifuged for 15 minutes at room temperature using the CEFU mode of the evaporator. The centrifuged sample was transferred to a glass vial, the bottom was tapped to remove air bubbles, and it was left to stand for the next step of detection.

[0144] The UPLC was used to detect the fluorescence-labeled IgG N-glycan peak spectrum. Before detection, the UPLC instrument was balanced using four liquid phases A (100 mM ammonium formate), B (100% acetonitrile solution), C (10% methanol solution), and D (50% methanol solution). First, 0.4 mL / min of A, B, C, and D were injected for 10 minutes. Then, 1:1 ratio of B and C was injected at a flow rate of 0.2 mL / min for 30 minutes. After the pressure fluctuation was less than 10 Psi, 1:1 ratio of A and B was injected at a flow rate of 0.2 mL / min for 30 minutes. Similarly, after the pressure was balanced, 1:1 ratio of A and B was injected at a flow rate of 0.4 mL / min for 30 minutes until the pressure was balanced for the next sample detection. After the sample detection, the glycan peak spectrum was generated according to the system's self-setting algorithm, and the area of the chromatographic peak, the relative proportion of each peak, and the retention time were automatically generated. Then, based on the standard chromatographic peak set by previous research, manual adjustment was performed. The retention time of the main chromatographic peaks (G0F, G1F, G2F) of IgG N-glycan was less than 0.2% (SD < 0.03 min) in terms of intra-day and inter-day RSD. In terms of quantitative repeatability, the RSD of the peak area of glycan peaks with a relative abundance greater than 1% was controlled within 5%, and the RSD of high-abundance peaks (>10%) was less than 2%, meeting the general requirements of biological analysis method validation.

[0145] 5. Initial glycan and derivative indicators of IgG N-glycan

[0146] (1) Initial glycan of IgG N-glycan

[0147] The UPLC can generate 24 initial glycan chromatographic peaks, which represent different glycans, the composition and structure of which can be represented by different numbers and letters, and the intuitive structure composition can be displayed by graphical combination. The detection results are normalized, that is, the area of each chromatographic peak is divided by the sum of the total area of all glycosyl peaks, to obtain the normalized initial glycan relative expression, and the result is in percentage form, see Figure 4 .

[0148] Figure 4 The numbers in the glycan composition and glycan structure represent the number of various glycan residues, H represents hexose, N represents acetylglucosamine, F represents fucose, S represents sialic acid, (3) is the 3-position hydroxyl connection site identifier of mannose, (6) represents the 6-position hydroxyl connection site identifier of mannose, A represents a double-antenna core structure, B represents a bisecting acetylglucosamine, and G represents galactose.

[0149] 6. Derivative index of IgG N-glycan

[0150] GP1-GP24 represent different initial glycan structures, which contain the same center structure composed of mannose and acetylglucosamine, but in addition to the same center structure, the different types and numbers of glycan residues connected determine the different biological functions of glycosylation. Therefore, the present application combines and calculates the 24 initial glycans, and divides them into five derivative indexes, including neutral glycosylation index, sialic acid glycosylation index, bisecting acetylglucosamine glycosylation index, galactosylation index and core fucosylation index. The combination calculation formula is shown in Table 9.

[0151] Table 9. Calculation formula of IgG N-glycan derivative index

[0152]

[0153]

[0154] The method of the present application is convenient and efficient, and can realize the detection of thyroid papillary carcinoma lymph node

[0155] The accurate identification of metastasis (PTC LNM) does not depend on complex or expensive instruments and equipment during the operation process; the diagnostic performance is excellent, the biomarker model is constructed by jointly detecting six key indicators of miR-616-3p, miR-1285-5p, GP4, GP10, GP13 and GP24, the multiple collinearity problem is effectively overcome, the AUC value reaches 0.930 (95% CI: 0.900-0.960), in the field of diagnosis of lymph node metastasis of thyroid papillary microcarcinoma, the value not only significantly surpasses the general performance level of the current clinical routine diagnostic method (such as ultrasound), but also provides an unprecedented high-precision judgment tool for clinical decision-making, which represents the leading technical level in this field. This quantifiable and unexpected excellent effect fully proves the non-obviousness and creativity of the technical scheme of the application. The value shows high discrimination ability; the specificity is strong, the model constructed based on the miRNA and the internal correlation analysis of the initial glycan significantly related to PTC LNM has good specificity and reliability. The application has important reference value for early diagnosis, disease assessment and clinical treatment strategy of PTC LNM, and has good popularization and application prospect.

[0156] The kit for diagnosing PTC LNM, the use method and effect verification thereof:

[0157] The embodiment describes in detail the composition, use method of the kit for diagnosing thyroid papillary carcinoma lymph node metastasis (PTC LNM) provided by the application, and verifies the excellent diagnostic efficiency through clinical test data.

[0158] (1) Kit composition

[0159] The core of the kit is to jointly detect six key biomarkers: miR-616-3p, miR-1285-5p, GP4, GP10, GP13 and GP24. The kit comprises the following components:

[0160] (2) miRNA detection module:

[0161] Reverse transcription reaction solution: comprising reverse transcriptase, dNTPs, RNAase inhibitor and random primer.

[0162] Quantitative PCR reaction solution: comprising Taq DNA polymerase, dNTPs, MgCl2 and PCR buffer.

[0163] Specific primer probe mixture: contains specific stem-loop reverse transcription primers for miR-616-3p and miR-1285-5p, upstream and downstream PCR primers, and fluorescently labeled TaqMan probes. U6 snRNA or miR-16 is used as an internal reference gene. Primer sequences can be routinely designed by those skilled in the art according to published miRNA sequences.

[0164] (3) IgG N-glycosylation detection module:

[0165] Glycosylation profiling reagents: a complete set of reagents for cutting, fluorescent labeling, and purification of N-glycan chains of serum IgG, including but not limited to peptide N-glycosidase F (PNGase F), fluorescent labeling dyes (such as 2-aminobenzamide, 2-AB), and standards and loading buffers required for high-performance liquid chromatography (HPLC) or capillary electrophoresis analysis.

[0166] (4) Positive control and negative control:

[0167] Positive control: a mixed standard containing known high expression levels of miR-616-3p, miR-1285-5p, and known concentrations of GP4, GP10, GP13, and GP24.

[0168] Negative control: nuclease-free water or a mixed serum sample from healthy individuals.

[0169] (4) Detection method

[0170] First step: sample collection and processing

[0171] Collect 3-5 mL of fasting peripheral venous blood from suspected PTC patients, and after standing, centrifuge to obtain serum. The serum is aliquoted and stored at -80°C for later use.

[0172] Second step: detection of biomarkers A. Detection of serum exosome miRNA:

[0173] Use commercially available exosome extraction kits (such as ExoQuick™) to extract exosomes from serum. Use the TRIzol method or a specialized miRNA extraction kit to extract total RNA from the exosomes. Use the reverse transcription reaction solution and specific primers in the kit to reverse transcribe the miRNA into cDNA. Perform quantitative PCR (qRT-PCR) reaction. The reaction system (20 μL) includes: quantitative PCR reaction solution 10 μL, cDNA template 2 μL, specific primer probe mixture 0.5 μL each, supplemented with nuclease-free water to a total volume of 20 μL. The reaction is performed on a quantitative PCR instrument, with the program set as: 95°C pre-denaturation for 10 minutes; then 45 cycles of 95°C for 15 seconds and 60°C for 1 minute. Collect the fluorescence signal, and use 2^(-ΔΔCt) The relative expression of miR-616-3p and miR-1285-5p is calculated.

[0174] B. Detection of serum IgG N-glycosylation:

[0175] IgG is purified from serum using protein A / G affinity chromatography column. IgG is enzymatically digested using PNGase F to release N-glycan chains. The released N-glycan chains are labeled using fluorescent dye 2-AB. The labeled glycan chains are analyzed using HPLC system (equipped with fluorescence detector) or capillary electrophoresis instrument. By comparison with standard, the relative peak height or area percentage of GP4, GP10, GP13, GP24 are identified and quantified.

[0176] Step 3: Result analysis and diagnosis

[0177] The present application found that there was a significant correlation between the miRNAs associated with PTC LNM and the initial glycans. To solve the problem of multiple collinearity, three diagnostic models were screened and constructed by stepwise logistic regression analysis: PTC LNM biomarker model 1 including miR-616-3p and miR-1285-5p (Table 10, Model 1), with an AUC value of 0.825 (95% CI: 0.771-0.879); PTC LNM pathogenesis diagnostic model 2 including GP4, GP10, GP13 and GP24 4 initial glycans (Table 10, Model 2), with an AUC value of 0.867 (95% CI: 0.822-0.912). In addition, the diagnostic indicators of Model 1 and Model 2 are combined to form a new Model 3 (Table 10, Model 3), and the AUC value of Model 3 is 0.930 (95% CI: 0.900-0.960), which is higher than the other two models and has better diagnostic performance. The sensitivity, specificity and classification performance evaluation of the three models in distinguishing LNM and non-LNM in PTC, and the comparison of diagnostic performance are shown in Tables 11, 12, and the receiver operating characteristic curves of the three models are constructed and drawn based on the above analysis, as shown in Figure 3

[0178] Table 10 Diagnostic models of PTC LNM based on multivariate logistic stepwise regression analysis

[0179]

[0180] Note: *P<0.05, P value is calculated by stepwise logistic regression analysis.

[0181] Table 11 Classification performance evaluation of three models in distinguishing LNM and non-LNM in PTC

[0182] Table 11 Classification performance evaluation of three models in distinguishing LNM and non-LNM in PTC

[0183] Note: Model 1 contains miR-616-3p and miR-1285-5p, Model 2 contains GP4, GP10, GP13 and GP24, Model 3 contains miR-616-3p, miR-1285-5p, GP4, GP10, GP13 and GP24.

[0184] Table 12 Comparison of diagnostic performance between the three groups of models

[0185]

[0186] Note: Model 1 contains miR-616-3p and miR-1285-5p, Model 2 contains GP4, GP10, GP13 and GP24, Model 3 contains miR-616-3p, miR-1285-5p, GP4, GP10, GP13 and GP24.

[0187] The data of the six biomarkers obtained by detection are substituted into the Logistic regression equation of the pre-constructed diagnostic model 3 (joint model) as follows, and the risk probability value (P) of PTC LNM is calculated.

[0188] Diagnostic model 3 equation:

[0189] Logit(P) = -0.654 × (miR-616-3p expression amount) + 1.064 × (miR-1285-5p expression amount) + 0.398 × (GP4 level) + 0.652 × (GP10 level) - 0.894 × (GP13 level) - 1.147 × (GP24 level) -2.315;

[0190] Result determination:

[0191] When the calculated risk probability value P is greater than or equal to 0.527, the patient is determined to be at high risk of PTC LNM (i.e. positive); when P is less than 0.527, the patient is determined to be at low risk of PTC LNM (i.e. negative).

[0192] 3. Effect verification

[0193] In order to verify the performance of the kit and the diagnostic model, the present application carries out a clinical study.

[0194] Study Design: Samples were collected from 224 pathologically confirmed PTC patients, including 112 patients with lymph node metastasis (LNM+) and 112 patients without lymph node metastasis (LNM-). All samples were tested using the above-described kits and methods, and diagnostic model 3 was used for calculations.

[0195] Experimental results: The combined diagnostic model based on the above six biomarkers (Model 3) demonstrated excellent diagnostic performance:

[0196] like Figure 3 As shown, the area under the curve (AUC) was as high as 0.930 (95% confidence interval CI: 0.900–0.960). The sensitivity was 84.8%, and the specificity was 83.0%. The Youden index was 0.678. The diagnostic efficacy of this combined model (Model 3) was significantly better than that of Model 1 (AUC = 0.825) which only included two miRNAs and Model 2 (AUC = 0.867) which only included four glycosylation markers, and the difference was statistically significant (P < 0.001).

[0197] Conclusion: This embodiment fully demonstrates that the combined detection kit and diagnostic model based on six key indicators—miR-616-3p, miR-1285-5p, GP4, GP10, GP13, and GP24—provided by this invention can diagnose lymph node metastasis of papillary thyroid carcinoma with high accuracy, high sensitivity, and high specificity. miR-616-3p, miR-1285-5p, and GP4, GP10, GP13, and GP24 can all independently predict PTC LNM. However, these two types of biomarkers can reflect the metastatic status from two independent pathological pathways—gene regulation and protein post-translational modification—thus achieving a diagnostic effect of "1+1>2." This has extremely high clinical application value.

Claims

1. A biomarker for assessing lymph node metastasis in papillary thyroid carcinoma, characterized in that, The biomarkers include two categories: miRNA biomarkers and IgG N-glycans; wherein the miRNA biomarkers are miR-616-3p and miR-1285-5p; The IgG N-glycans are GP4, GP10, GP13 and GP24.

2. The biomarker according to claim 1, characterized in that, The nucleotide sequence of the miRNA biomarker is as follows: The nucleotide sequence of miR-616-3p is shown in SEQ ID NO. 1; The nucleotide sequence of miR-1285-5p is shown in SEQ ID NO.

2.

3. The biomarker according to claim 1, characterized in that, The polysaccharide composition of GP4 is H3N4F1, and the polysaccharide structure is named FA2; The polysaccharide composition of GP10 is H4N5F1(6), and the polysaccharide structure is named FA2(6)BG1; The polysaccharide composition of GP13 is H5N5, and the polysaccharide structure is named A2BG2. The polysaccharide composition of GP24 is H5N5F1S2, and the polysaccharide structure is named FA2BG2S2. Wherein, H represents hexose, N represents acetylglucosamine, F represents fucose, S represents sialic acid, the numbers represent the number of various polysaccharide residues, (6) represents the 6-hydroxyl linkage site of mannose, A represents the dual-antenna core structure, B represents the diacetylglucosamine, and G represents galactose.

4. A detection kit for lymph node metastasis in papillary thyroid carcinoma, characterized in that, The detection kit includes: a miRNA detection module and an IgG N-glycosylation detection module. The miRNA detection module includes a reverse transcription reaction solution, a quantitative PCR reaction solution, and specific stem-loop reverse transcription primers, upstream and downstream PCR primers, and fluorescently labeled TaqMan probes for miR-616-3p and miR-1285-5p. The IgG N-glycosylation detection module includes: 2-AB fluorescent labeling reagent, standard sample, and loading buffer; The standards are polysaccharide standards of GP4, GP10, GP13, and GP24, and the polysaccharide compositions and structures of GP4, GP10, GP13, and GP24 are as follows: The polysaccharide composition of GP4 is H3N4F1, and the polysaccharide structure is named FA2; The polysaccharide composition of GP10 is H4N5F1(6), and the polysaccharide structure is named FA2(6)BG1; The polysaccharide composition of GP13 is H5N5, and the polysaccharide structure is named A2BG2. The polysaccharide composition of GP24 is H5N5F1S2, and the polysaccharide structure is named FA2BG2S2.

5. The reagent kit according to claim 4, characterized in that, Also includes: Reverse transcriptase, dNTPs, RNase inhibitors, Taq DNA polymerase, MgCl2, PCR buffer.

6. A method for detecting biomarkers for lymph node metastasis in papillary thyroid carcinoma, characterized in that, Includes the following steps: Sample processing steps: Collect fasting peripheral venous blood from PTC patients, centrifuge to separate serum, and store at -80°C; Marker detection steps: Serum exosomal miRNAs were extracted, and the relative expression levels of miR-616-3p and miR-1285-5p were detected by qRT-PCR. Serum IgG was purified, and IgG was digested with PNGase F. The relative peak area percentages of GP4, GP10, GP13, and GP24 were detected using 2-AB fluorescent labeling reagent. Judgment steps: Substitute the data of the 6 biomarkers into the Logistic regression equation, calculate the risk probability value (P), and determine the positive result when P ≥ 0.

527.

7. The detection method according to claim 6, characterized in that, The logistic regression equation is: Logit(P) = -0.654 × (miR-616-3p expression level) + 1.064 × (miR-1285-5p expression level) +0.398 × (GP4 level) + 0.652 × (GP10 level) - 0.894 × (GP13 level) - 1.147 × (GP24 level) - 2.315.

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