A peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis and its application, detection reagent and kit

Abstract

The present invention discloses a peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, and its application, detection reagent and kit, belonging to the field of biomedical technology. The amino acid sequence of the peripheral blood TCR marker of the present invention is selected from the group consisting of all amino acid sequences shown in SEQ ID NO.1~100. The peripheral blood TCR marker provided by the present invention can be used for auxiliary diagnosis of the prognosis risk of traditional Chinese medicine for granulomatous mastitis, with good detection specificity, high sensitivity and accuracy, so as to be used for the research and development of related products (including reagents, kits, test strips or instrument platforms, etc.), and the above-mentioned peripheral blood TCR marker can also be used as a target for screening related drugs for the prevention and treatment of granulomatous mastitis.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, and an application thereof, a detection reagent and a kit. Background Art

[0002] The information disclosed in the background of the invention is only intended to enhance the understanding of the overall background of the invention and should not be necessarily regarded as an acknowledgment or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art.

[0003] Granulomatous mastitis is a chronic inflammatory disease of the breast characterized by non-bacterial infection, non-caseous necrosis, confined to the breast lobules, and the formation of granulomas. If not treated properly, it is very easy to relapse and not heal for a long time. It is a difficult-to-treat breast disease second only to breast cancer. It has a strong destructive effect on the breast, often causing great physical and mental trauma to patients, greatly reducing the quality of life of patients. Patients often present with irregular breast lumps. In the early stage, there is no skin redness and swelling, unclear boundaries, and it is easy to be confused with breast cancer; in the middle stage, some patients have local inflammation, skin redness and swelling, mostly not involving the nipple and areola, and antibiotic treatment is ineffective; in the late stage, the disease progresses rapidly, abscesses, ulcers and sinus tracts may form and do not heal for a long time, and most patients have no systemic symptoms.

[0004] Once granulomatous mastitis is diagnosed, the treatment is mainly hormone, surgery and traditional Chinese medicine. Traditional Chinese medicine is effective in treating granulomatous mastitis, shortening the course of the disease and improving the quality of life of patients. However, there are few studies on the treatment of granulomatous mastitis with traditional Chinese medicine, and there is currently no accurate clinical test method for the prognosis of granulomatous mastitis. Therefore, it is urgent to find a new biomarker to evaluate the prognosis of patients, improve the accuracy of clinical diagnosis, and reduce the risk of recurrence of the disease.

[0005] T cells are the main components of lymphocytes. They are derived from pluripotent stem cells in the bone marrow. During the embryonic and neonatal periods of the human body, a portion of pluripotent stem cells or pre-T cells in the bone marrow migrate to the thymus and differentiate and mature under the induction of thymic hormones to become immune-active T cells. The specific immune response produced by T cells is cellular immunity. Specific immune response refers to the activation, proliferation, and differentiation of antigen-specific T lymphocytes in the body into effector cells after receiving antigen stimulation, producing a series of immune physiological effects. Each T cell has a unique T cell antigen receptor (T cell receptor, TCR) on its surface to specifically recognize antigens and perform immune functions, making TCR a reliable biomarker for identifying the uniqueness of T cells.

[0006] The T cell antigen receptor repertoire (TCR repertoire) is the sum of T lymphocyte clones in the body's immune circulation at a specific time, which can reflect the body's ability to respond to external stimuli at a specific time point. Its diversity is related to the strength of the body's immunity. The TCR repertoire will change with the occurrence and development of the disease, reflecting the state of the body's immune system under different disease conditions; the development of second-generation sequencing technology has made high-throughput sequencing of the immune repertoire possible. However, there are still few reports on the use of immune molecule TCR markers for the prediction of the efficacy of traditional Chinese medicine for granulomatous mastitis. Summary of the invention

[0007] In view of this, the present invention provides a peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis and its application, detection reagent and kit. The present invention starts from the immune perspective, by analyzing the specific TCR sequence of the prognosis and efficacy differences of granulomatous mastitis, to explore the markers for predicting the prognosis and efficacy of granulomatous mastitis, which has higher specificity and accuracy.

[0008] In a first aspect, the present invention provides a peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, wherein the amino acid sequence of the peripheral blood TCR marker is selected from the group consisting of all amino acid sequences shown in SEQ ID NO. 1 to 100.

[0009] In a second aspect, the present invention provides an application of a substance for detecting the above-mentioned peripheral blood TCR marker, wherein the application is the application of the substance for detecting the peripheral blood TCR marker in the preparation of a product for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis.

[0010] In a third aspect, the present invention provides a detection reagent, which is used to detect the above-mentioned peripheral blood TCR markers.

[0011] In a fourth aspect, the present invention provides a kit, comprising the above-mentioned detection reagent, and the kit is used for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis.

[0012] Compared with the prior art, the present invention has achieved the following beneficial effects:

[0013] (1) The present invention provides a peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, which can be used for auxiliary diagnosis of the prognosis risk of traditional Chinese medicine for granulomatous mastitis, with good detection specificity, high sensitivity and accuracy, and thus can be used for the research and development of related products (including reagents, kits, test strips or instrument platforms, etc.). At the same time, the above-mentioned peripheral blood TCR marker can also be used as a target for screening related drugs for the prevention and treatment of granulomatous mastitis.

[0014] (2) The present invention can simultaneously compare a large number of specific TCR sequences for the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, and has higher specificity and accuracy, and high diagnostic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their description are used to explain the present invention and do not constitute an improper limitation of the present invention. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 is a box plot of the distribution of high-quality data size in a good efficacy group and a poor efficacy group in an embodiment of the present invention;

[0017] Figure 2 The CDR3 quantity graph (a) and the VJ combination quantity violin graph (b) between the two groups with good efficacy and poor efficacy in the embodiment of the present invention;

[0018] Figure 3 It is a dilution curve of an example sample of an embodiment of the present invention; the horizontal axis is the number of sequencing reads, and the vertical axis is the number of CDR3s measured;

[0019] Figure 4 The D50 diversity coefficient box plot (a) and the Shannon coefficient box plot (b) drawn for the diversity of the immune group according to the embodiment of the present invention; red represents a group with good efficacy, and green represents a group with poor efficacy;

[0020] Figure 5 This is a diagram of the abundance of V gene fragment clones in the samples of the embodiment of the present invention; wherein the ordinate is the sample number, the abscissa is the V gene fragment, and the status bar on the right side of the hotspot map represents the relative abundance of the gene fragment;

[0021] Figure 6 This is a diagram of the abundance of J gene fragment clones in the samples of the embodiment of the present invention; wherein the ordinate is the sample number, the abscissa is the J gene fragment, and the status bar on the right side of the hotspot map represents the relative abundance of the gene fragment;

[0022] Figure 7 This is a bar graph drawn for the difference between the J gene in the good efficacy group and the poor efficacy group according to the embodiment of the present invention; wherein the abscissa is the J gene fragment, and the ordinate is the J gene expression percentage; red represents the good efficacy group, green represents the poor efficacy group, ns indicates no significant difference, and *** indicates an extremely significant difference between the two groups;

[0023] Figure 8This is a bar graph drawn for the difference between some V genes in the good efficacy group and the poor efficacy group according to the embodiment of the present invention; wherein the abscissa is the J gene segment, and the ordinate is the V gene expression percentage; red represents the good efficacy group, green represents the poor efficacy group, ns indicates no significant difference, * indicates significant difference between the two groups, ** indicates very significant difference between the two groups, and *** indicates extremely significant difference between the two groups;

[0024] Fig. 9 This is a bar graph drawn for the difference between some V genes in the good efficacy group and the poor efficacy group according to the embodiment of the present invention; wherein the abscissa is the J gene segment, and the ordinate is the V gene expression percentage; red represents the good efficacy group, and green represents the poor efficacy group; ns indicates no significant difference, * indicates significant difference between the two groups, and ** indicates very significant difference between the two groups;

[0025] Fig.10 The heat map of the difference between the good and poor efficacy groups reflected by some characteristic sequences in the embodiment of the present invention; wherein each row represents a CDR3 sequence of a TCR, each column represents a sample, and the color blocks in the figure represent the relative abundance of the X sequence in the Y sample, and the darker the color, the higher its abundance;

[0026] Fig.11 This is a volcano plot of differential sequences in an embodiment of the present invention; yellow dots are differential clones with increased abundance in the good efficacy group compared to the poor efficacy group, and blue dots are differential clones with decreased abundance in the good efficacy group compared to the poor efficacy group;

[0027] Fig.12 The ROC curve diagram of the random forest model established in the embodiment of the present invention for diagnosing the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis;

[0028] Fig.13 The figure is a ROC curve diagram of the random forest model of the validation set of the embodiment of the present invention for diagnosing the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis. DETAILED DESCRIPTION

[0029] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0030] The "and / or" used in the present invention should be understood as "one or both" of the associated components, that is, the components exist in combination in some cases and separately in other cases. Multiple components listed with "and / or" should be understood in the same way, that is, "one or more" associated components.

[0031] The term "biomarker" in the present invention refers to a substance that can specifically mark cells or indicate a disease state. In the present invention, biomarkers can also be represented by TCR markers. The full name of TCR is T cell antigen receptor (T cell receptor), which is a characteristic marker present on the surface of T cells. TCR is divided into intracellular region, transmembrane region and extracellular region; the variable region (V region) is located outside the cell and is mainly responsible for recognizing antigens. It contains 3 complementary determining regions (CDRs) and 4 framework regions (FRs), of which the CDR3 region directly binds to the antigen and determines the specificity of TCR.

[0032] The present invention provides a peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, wherein the amino acid sequence of the peripheral blood TCR marker is selected from the group consisting of all amino acid sequences shown in SEQ ID NOs. 1 to 100.

[0033] In the present invention, the peripheral blood TCR markers are derived from the peripheral blood of a subject, and the subject is a patient with granulomatous mastitis, more preferably a patient with granulomatous mastitis with a prognosis of traditional Chinese medicine.

[0034] The present invention also provides an application of a substance for detecting the above-mentioned peripheral blood TCR marker, wherein the application is the application of the substance for detecting the peripheral blood TCR marker in the preparation of a product for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis.

[0035] In the present invention, the substance includes a substance for detecting the expression level of the peripheral blood TCR marker based on a high-throughput sequencing method and / or based on a quantitative PCR method and / or based on a probe hybridization method. The high-throughput sequencing method of the present invention is performed by a second-generation sequencing method or a third-generation sequencing method. The means for sequencing is not particularly limited, and sequencing by a second-generation or third-generation sequencing method can achieve rapid and efficient sequencing.

[0036] In the present invention, the product for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis includes a product for detecting the content of the peripheral blood TCR biomarker. Furthermore, the product for detecting the content of the peripheral blood TCR biomarker includes a reagent, a kit, a test paper or an instrument platform. The product for detecting the content of the peripheral blood TCR biomarker can be based on the principle of cell counting technology, for example, resistance method, light scattering method, special cell staining method, microscopic imaging method, volume conductivity light scattering combined detection method, impedance radio frequency technology combined detection method, light scattering and cell chemistry technology, etc.

[0037] The present invention also provides a detection reagent, which is used to detect the above peripheral blood TCR markers.

[0038] The present invention also provides a kit, which comprises the above-mentioned detection reagent, and the kit is used for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis.

[0039] The above peripheral blood TCR markers of the present invention can also be used as targets for screening drugs for preventing and treating granulomatous mastitis. The effects of candidate drugs on these peripheral blood TCR markers before and after use can be used to determine whether the candidate drugs can be used to prevent and treat granulomatous mastitis.

[0040] The present invention is further described below in conjunction with specific examples, which are only for the purpose of explaining the present invention and are not intended to limit the content thereof. If the specific experimental conditions are not specified in the examples, they are usually carried out according to conventional conditions or the conditions recommended by the sales company; unless otherwise specified in the present invention, they can all be purchased through commercial channels.

[0041] Example

[0042] In this example, two sample groups with different clinical characteristics, granulomatous mastitis with good prognosis and efficacy of traditional Chinese medicine and granulomatous mastitis with poor prognosis, were used. Based on a high-throughput sequencing method, a small amount of peripheral blood was collected from the two sample groups, RNA was extracted, and sequencing libraries were constructed for the samples. After high-throughput sequencing and immune group data analysis, disease-related sequences of the two sample groups were found. Through these sequences, markers for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis were explored.

[0043] The specific steps are as follows:

[0044] 1. Source of samples:

[0045] According to clinical guidelines, 41 patients were diagnosed with granulomatous mastitis through pathology, imaging and other clinical diagnoses. After treatment with traditional Chinese medicine, the samples were divided into a good efficacy group of 15 cases and a poor efficacy group of 26 cases according to the differences in prognosis and efficacy of the patients. Peripheral blood samples were collected from the two groups of patients.

[0046] 2. Research Methods

[0047] 1. Sample collection

[0048] For each patient in the good efficacy group and the poor efficacy group, 3-5 mL of peripheral blood sample was collected using EDTA anticoagulation blood collection tubes. The tubes were turned upside down to mix the liquid in the tubes thoroughly, the tube walls were marked with sample information, and the tubes were temporarily stored in a low temperature place.

[0049] 2. Extract RNA

[0050] (1) Transfer the sample to a sterile 5 mL RNase-free centrifuge tube; add 3 volumes of Trizol Universal total RNA extraction reagent to the centrifuge tube containing the sample and mix well;

[0051] (2) Place the homogenate sample at room temperature for 5 min to allow the nucleic acid-protein complex to be completely separated;

[0052] (3) Centrifuge at 4°C and 12,000 rpm for 10 min and collect the supernatant;

[0053] (4) Add 0.2 mL of chloroform for every 1 mL of Trizol Universal reagent, cover the tube, shake vigorously for 15 seconds, and leave at room temperature for 3 minutes;

[0054] (5) Centrifuge at 4°C and 12,000 rpm for 15 min. The sample will separate into three layers: a pink organic phase, a middle layer, and an upper colorless aqueous phase. RNA is mainly in the aqueous phase. Transfer the aqueous phase (about 500 μL) to a new centrifuge tube.

[0055] (6) Add an equal volume of isopropanol to the aqueous solution, mix well, and leave at room temperature for 10 min;

[0056] (7) Centrifuge at 4°C and 12,000 rpm for 10 min. A gelatinous precipitate will form on the sides and bottom of the tube. Remove the supernatant and keep the precipitate.

[0057] (8) Add 1 mL of 75% (v / v) ethanol (prepared with RNase-free ddH2O) to wash the precipitate. For every 1 mL of Trizol Universal reagent used, at least 1 mL of 75% (v / v) ethanol should be used to wash the precipitate.

[0058] (9) Centrifuge at 4°C, 10,000 rpm for 5 min. Pour out the liquid, briefly centrifuge the remaining small amount of liquid, and then aspirate it with a pipette;

[0059] (10) Allow to dry at room temperature (2–3 min), add 50 μL RNase-Free ddH2O, and repeatedly pipette and mix to fully dissolve the RNA.

[0060] 3. Reverse transcription of RNA into cDNA:

[0061] (1) Take 1 μg of RNA and reverse transcribe it into cDNA according to the following steps;

[0062] (2) Add RNA and reagents to a PCR tube (total volume 13 μL) according to the following system:

[0063] RNA: 11 μL; Random primers (20 μM): 1 μL; dNTP (deoxynucleoside triphosphate) solution (10 mM): 1 μL;

[0064] (3) Mix well on a vortex mixer and centrifuge briefly;

[0065] (4) Set the PCR instrument (polymerase chain reaction instrument) conditions to 65°C for 10 min (80°C hot cover), place the PCR tube on the PCR instrument at 65°C for 5 min, and quickly place on ice after the reaction;

[0066] (5) Add the following reagents to the PCR tube:

[0067] 5X ProtoScript II Buffer (NEB): 4 μL; 0.1M DTT (1,4-dimercaptothreitol, NEB): 2 μL; ProtoScript II Reverse Transcriptase (200U / μL): 1 μL;

[0068] (6) Mix well on a vortex mixer and centrifuge briefly;

[0069] (7) Place the PCR tube on a PCR instrument and perform the following reactions: react at 20°C for 10 min, then heat to 42°C for 1 h; then heat to 65°C for 10 min;

[0070] (8) Take 10 μL of reverse transcribed cDNA product and proceed to the next step of multiplex amplicon library construction.

[0071] 4. TCR library construction:

[0072] (1) First round of multiplex PCR reaction:

[0073] ① Take out the EM808 polymerase mixture and primer reagent from the kit stored at -20℃ in advance, put them on an ice box to dissolve, mix them well and put them on ice for later use;

[0074] ②The reaction system is prepared as follows:

[0075] DNA: 750ng; EM808 polymerase mixture: 10μL; Primer: 2μL; Nuclease-free water: 30μL;

[0076] ③ Use a pipette to gently blow and mix, then centrifuge briefly;

[0077] ④ Place the sample on the PCR instrument (heat cover temperature is 105°C) and start the PCR program as follows:

[0078] 95℃, 4 min; 95℃, 30 s; 60℃, 4 min; repeat “95℃, 30 s; 60℃, 4 min” for 30 cycles; 72℃, 5 min; keep at 4℃.

[0079] (2) First round of magnetic bead purification:

[0080] ① Add 27 μL of Agencourt AMPure XP magnetic beads to the PCR tube after reaction (1) and mix well with a pipette to avoid bubbles.

[0081] ② Incubate at room temperature for 10 minutes, and place the PCR tube on a magnetic rack for 3 minutes to clarify the solution;

[0082] ③ Completely remove the supernatant, remove the PCR tube from the magnetic stand, add 50 μL YF buffer B (type 2 PCR buffer) into the tube, and mix well with a pipette to avoid bubbles;

[0083] ④ Incubate at room temperature for 5 minutes, and place the PCR tube on a magnetic rack for 3 minutes to clarify the solution;

[0084] ⑤ Remove the supernatant, keep the PCR tube on the magnetic rack, add 200 μL 80% (v / v) ethanol solution into the PCR tube, and let it stand for 30 seconds;

[0085] ⑥ Remove the supernatant and add 200 μL 80% (v / v) ethanol solution to the PCR tube. After standing for 30 seconds, completely remove the supernatant (it is recommended to use a 10 μL pipette to remove the residual ethanol solution at the bottom);

[0086] ⑦ Leave it at room temperature for about 4 minutes to allow the residual ethanol to evaporate completely;

[0087] ⑧Add 22 μL of Nuclease-free Water, remove the PCR tube from the magnetic stand, gently pipette to resuspend the magnetic beads to avoid bubbles, and let stand at room temperature for 3 minutes;

[0088] ⑨ Place the PCR tube on a magnetic rack for 2 minutes to clarify the solution;

[0089] ⑩ Use a pipette to aspirate 20 μL of supernatant, transfer it to a new PCR tube, and mark the sample number on the reaction tube.

[0090] (3) Second round of linker sequence PCR reaction:

[0091] ① Take out the EM808 polymerase mixture, MGI-Dual1.0 Index (MGI sequencing primer adapter 1.0) and MGI-Dual2.0 Index reagent (MGI sequencing primer adapter 2.0) from the kit stored at -20℃ in advance, place them on ice to dissolve, mix well and place on ice for later use;

[0092] ②The reaction system is prepared as follows:

[0093] Sample from (2) step ⑩: 15 μL;

[0094] EM808 polymerase mixture: 10 μL;

[0095] MGI-Dual1.0 Index (10 μM): 1μL;

[0096] MGI-Dual2.0 Index (10 μM): 1μL;

[0097] Nuclease-free water: add to 30 μL;

[0098] ③ Use a pipette to gently blow and mix, then centrifuge briefly;

[0099] ④ Place the sample on the PCR instrument (heat cover temperature is 105°C) and start the PCR program as follows:

[0100] 98℃, 2 min; 98℃, 20 s; 60℃, 1 min; 72℃, 1 min; repeat “98℃, 20 s; 60℃, 1 min; 72℃, 1 min” for 9 cycles; 72℃, 5 min; keep at 4℃.

[0101] (4) Second round of magnetic bead purification

[0102] a. Add 27 μL of Agencourt AMPure XP magnetic beads to the PCR tube after reaction (3) and mix well with a pipette to avoid bubbles.

[0103] b. Incubate at room temperature for 10 minutes, place the PCR tube on a magnetic rack for 3 minutes to clarify the solution;

[0104] c. Remove the supernatant completely, remove the PCR tube from the magnetic stand, add 50 μL YF buffer B (type 2 PCR buffer) into the tube, and mix well with a pipette to avoid bubbles;

[0105] d. Incubate at room temperature for 5 minutes and place the PCR tube on a magnetic rack for 3 minutes to clarify the solution;

[0106] e. Remove the supernatant, keep the PCR tube on the magnetic rack, add 200 μL 80% (v / v) ethanol solution to the PCR tube, and let it stand for 30 seconds;

[0107] f. Remove the supernatant and add 200μL 80% (v / v) ethanol solution to the PCR tube. Let it stand for 30 seconds and then completely remove the supernatant (it is recommended to use a 10μL pipette to remove the residual ethanol solution at the bottom);

[0108] g. Let stand at room temperature for 5 minutes to allow the residual ethanol to evaporate completely;

[0109] h. Add 30 μL of nuclease-free water, remove the PCR tube from the magnetic stand, and use a pipette to gently pipette and resuspend the magnetic beads;

[0110] i. Let stand at room temperature for 3 minutes, and place the 200 μL PCR tube on a magnetic stand for 2 minutes to clarify the solution;

[0111] j. Use a pipette to transfer 27 μL of supernatant into a new 200 μL PCR tube (placed on ice), and mark the tube with the sample number;

[0112] k. Take 1 μL of sample and use Qubit 3.0 Fluorometer (Qubit dsDNA HS Assay Kit, double-stranded DNA high-sensitivity fluorescent quantitative kit) to measure the library concentration and record the library concentration;

[0113] l. Take 1 μL of sample and use Agilent 2100 Bioanalyzer system to measure fragment length.

[0114] 5. High-throughput sequencing of immune group:

[0115] After passing the quality inspection, the TCRβ chain library was sequenced using the MGI high-throughput sequencing platform.

[0116] 6. Data processing:

[0117] (1) Data filtering after sequencing: The raw image data obtained by high-throughput sequencing is converted into raw test data, namely Raw Reads, after base recognition analysis. Each sample in the TCRβ chain library has a unique label corresponding to the patient's specimen; Raw Reads are filtered to obtain Clean Reads (filtered data), and then subsequent data analysis is performed.

[0118] (2) Upload the filtered data to the miXCR software and IMGT / High-QUEST database for analysis.

[0119] (3) After identifying the V and J gene segments of all TCRβ chains, the CDR3 sequence can be extracted, and then subsequent clone identification and calculation analysis can be performed.

[0120] (4) Introduction of methods for identifying differences between groups:

[0121] When analyzing the differences between the VJ combinations and sequences of the two groups, small sample t-test and rank sum test were used for identification, and box plots and violin plots were drawn for intuitive analysis of the number of VJ combinations and diversity index. In both identification methods, the significance level was 0.05. If the test p value was less than the significance level, it was considered that the data was significantly different between the good and poor efficacy groups.

[0122] (5) Extraction of intra-group feature sequences:

[0123] The original data obtained by sequencing is filtered and cleaned to obtain cleaned data, which is then aligned to the reference genome, and a secondary alignment is performed specifically on the TCR region to filter out impurity results; based on the above alignment process, the sequence is assembled and the corresponding CDR3 abundance is calculated; then, based on the calculated TCR CDR3 sequence abundance results, the CDR3 sequences of TCRs with significant differential abundance are screened through differential analysis, and after final filtration, purification and integration, the characteristic sequences within the group are obtained.

[0124] (6) Construction of a model to predict the efficacy of treatment:

[0125] The CDR3 sequences of the characteristic TCRs in the groups with good or bad efficacy obtained in the difference analysis were used as input, and the models were constructed using the random forest algorithm, support vector machine algorithm, and regression algorithm respectively. The weights of the corresponding sequences in the model were adjusted according to the degree of influence of each characteristic sequence in the results on the model. Finally, various models of good or bad efficacy based on the CDR3 sequences of the characteristic TCRs in the groups with good or bad efficacy were established, and the classification effect of the model was tested. The most accurate random forest model was selected as the prediction model for good or bad efficacy. Then, samples were collected again as the validation set to verify the accuracy of the established random forest model in diagnosing the good or bad efficacy of granulomatous mastitis.

[0126] 7. Statistical analysis:

[0127] All statistical analyses were performed using R software, and normal distribution parameters were expressed as "mean ± standard deviation". The differences between the good efficacy group and the poor efficacy group were compared using the paired Wilcoxon signed rank test; the counting data were tested using the t test. The test level was ɑ=0.05, and P<0.05 was considered statistically significant.

[0128] 3. Research results:

[0129] 1. High-throughput sequencing

[0130] (1) Advantages of high-throughput sequencing

[0131] High-throughput sequencing technology is also known as second-generation sequencing technology. Compared with first-generation sequencing technology, second-generation sequencing technology can provide higher-throughput data at a lower cost. This embodiment uses high-throughput sequencing to sequence hundreds of thousands or even millions of gene molecules in parallel at one time, greatly reducing the cost and time required for sequencing; at the same time, high-throughput sequencing can perform deep sequencing, and even CDR3 sequences with very low expression levels can still be detected.

[0132] (2) Sequencing data results

[0133] After sequencing, the original test data, namely Raw Reads, were obtained. After filtering the raw data, the remaining data with low-quality data were removed to obtain high-quality data. Subsequent data analysis was based on high-quality data, namely Clean Reads. A total of 737.05153 (M) raw data were obtained after sequencing of all samples, of which 283.663452 (M) were obtained in the good efficacy group, the maximum amount obtained was 28.858828 (M), the minimum amount obtained was 10.929862 (M), and an average of 18.9108968 (M) Raw Reads were obtained for each sample; 453.388078 (M) were obtained in the poor efficacy group, the maximum amount obtained was 27.906408 (M), the minimum amount obtained was 10.190164 (M), and an average of 17.438003 (M) Raw Reads were obtained for each sample. Finally, after data cleaning and filtering, a total of 737.03975 (M) high-quality data were obtained. Figure 1 This is a box plot of high-quality data between the two groups. A small sample t-test showed that there was no significant difference in the quality results of the sequencing files between the two groups (P value was 0.13, greater than 0.05).

[0134] 2. Number of TCRβ chains in the immune system

[0135] The sequencing data of the samples were obtained by high-throughput sequencing. After data filtering and processing, the data results were sorted out, and an average of 855,284 TCRβ chain sequences were obtained for each sample. Subsequently, the miXCR program was used to compare the Vβ and Jβ genes of the obtained effective TCR sequences. On the basis of identifying the qualified CDR3 region, the CDR3 clone type was identified and counted. Furthermore, the analysis showed that there were 737 independent VJ fragments in the samples. Box plots and violin plots were drawn for important data such as the number of VJ combinations and the number of short sequences for the grouped samples, and a dilution curve was drawn for each sample to understand the sample situation, avoid the bias of poor-quality data on subsequent analysis results, and ensure the accuracy of the analysis results. Figure 2 The figure is a violin plot of the number of CDR3 and the number of VJ combinations between the good efficacy group and the poor efficacy group. The violin plot can be used to intuitively observe the distribution of the number of CDR3 and the number of VJ combinations and the differences between the good efficacy group and the poor efficacy group. In addition, a small sample t-test was performed on the two types of data to analyze the differences between the two groups. According to the test results, there were significant differences between the two groups in the number of CDR3 (P<0.05) and the number of VJ combinations (P<0.01), which may be the reason for the difference in treatment effects between the two groups. Figure 3 This is the dilution curve of the example sample. The curve in the figure shows that the sample is close to saturation and the quality of the sample will not cause a large error in the subsequent analysis.

[0136] (1) The number of TCRβ from the group with good efficacy

[0137] In this experiment, the offline data was obtained by high-throughput sequencing. After data filtering and processing, an average of 35,687 TCRβ chain sequences were obtained for each sample. The miXCR program was then used to compare the Vβ and Jβ genes of the obtained valid TCR sequences. After identifying the qualified CDR3 region, the CDR3 clonal type was identified and counted. Furthermore, the analysis showed that there were 8,312 independent VJ segments in the sample.

[0138] (2) Number of TCRβ from the poor efficacy group

[0139] After data processing and filtering, an average of 17,603 TCRβ chain sequences were obtained for each sample. Using the miXCR program, 13,182 VJ gene fragments were identified in the poor efficacy group.

[0140] 3. Differences in immune group level

[0141] The principal component analysis was performed on the sample sequences and the diversity index (D50 diversity index and Shannon coefficient) was calculated. Figure 4The distribution of D50 diversity coefficient and fragrance coefficient in the good efficacy group and the poor efficacy group. According to the information in the above figure, it can be concluded that the samples in the good efficacy group and the poor efficacy group have differences in immune diversity.

[0142] In order to understand the T cell immunity of patients with good and poor treatment effects during treatment, especially the usage pattern of Vβ and Jβ gene fragments by T cell surface receptor TCR, a hotspot map was made based on the abundance of V gene and J gene fragments between the two groups to intuitively display the expression of gene fragments between samples. The results are shown in Figure 2. Figure 5 and Figure 6 As shown. Figure 5 It is a heat map of the abundance of V gene fragments, with the horizontal axis representing the V gene fragments and the vertical axis representing the sample number; Figure 6 The J gene fragment abundance heat map is shown in the figure. The horizontal axis represents the J gene fragment and the vertical axis represents the sample number. From the information in the figure, we can see that the relative abundance of V gene and J gene are between 0-0.2. In order to clarify the difference between the two groups, a difference VJ bar chart is drawn and tested. The results are shown in the figure. Figure 7 , Figure 8 and Fig. 9 As shown. Figure 7 The horizontal axis represents different J genes, and the vertical axis represents the expression abundance of J genes; Figure 8 and Fig. 9 As for the differences in V genes, according to the analysis results, it can be concluded that there are extremely significant differences in genes TRBJ2-5, TRBV19 and TRBV24-1 between the two groups.

[0143] 4. Differences in amino acid levels

[0144] The amino acid hydrophobicity ratio, amino acid class ratio, amino acid sequence length distribution, and base sequence length distribution between the two groups of samples with different treatment efficacy for granulomatous mastitis were calculated and analyzed. The analysis results showed that there was no obvious difference in the properties of amino acids between the two groups of samples with different treatment effects.

[0145] 5. Sequence of differences between good and bad effects of mastitis treatment

[0146] The clone abundance information results were analyzed, and the differences were found through comparison with statistical software. The TCR sequences with significant differences between the two groups with good and poor efficacy were captured so that the efficacy of the patients could be predicted based on the differences in the TCR sequences and a volcano map was drawn to show the difference results. Some of the difference sequences between the two groups with good and poor efficacy are shown in SEQ ID NO. 1-100. Fig.10The figure shows the differences between the good and poor efficacy groups reflected by some of the characteristic sequences. Each row represents a TCR CDR3 sequence (X sequence), and each column represents a sample (Y sample). The color blocks in the figure represent the relative abundance of X sequence in Y sample. Fig.11 The figure shows the changes in the abundance of differential clones in the sequences of the two groups. The yellow dots are differential clones with increased abundance in the good efficacy group compared to the poor efficacy group, and the blue dots are differential clones with decreased abundance in the good efficacy group compared to the poor efficacy group.

[0147] 6. Therapeutic effect prediction model

[0148] The model was evaluated and the threshold was set to a random probability of 0.5. If it was greater than 0.5, it was judged as a positive class, otherwise it was a negative class. The receiver operating characteristic curve (ROC) was used to analyze the generalization performance of the established random forest based on the probability of the predicted value. In the ROC curve, the vertical axis is the true positive rate and the horizontal axis is the false positive rate. The area enclosed by the ROC curve and the horizontal axis is called AUC (Area Under ROC curve). The closer the value is to 1, the better the algorithm model is. Fig.12 The analysis results shown in the figure have an AUC of 0.821 and a confidence interval of 0.604~1. It can be considered that the established random forest model can achieve good prediction results.

[0149] The data of 10 re-collected samples (5 samples with good prognosis of granulomatous mastitis treated with traditional Chinese medicine, and 5 samples with poor prognosis of granulomatous mastitis treated with traditional Chinese medicine) were used as the validation set for model construction to evaluate the accuracy of the sample model. The original sample data (15 samples with good efficacy and 26 samples with poor efficacy) were used as the training set data to establish the random forest model. The predicted results of the validation set data were compared with the actual results. Fig.13 As shown, the AUC of the prediction model for therapeutic efficacy established this time was calculated to be 0.917, and the confidence interval was 0.72~1. Therefore, it can be considered that the constructed random forest model has a good prediction effect.

[0150] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A peripheral blood TCR marker for predicting the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis, characterized in that: The peripheral blood TCR marker is a group consisting of all amino acid sequences shown in SEQ ID NOs. 1 to 100.

2. A method for detecting the peripheral blood TCR marker of claim 1, characterized in that: The application is the use of a substance for detecting the peripheral blood TCR marker in the preparation of a product for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis.

3. The use according to claim 2, characterized in that The substance includes a substance for detecting the expression level of the peripheral blood TCR marker based on a high-throughput sequencing method and / or based on a quantitative PCR method and / or based on a probe hybridization method.

4. The use according to claim 2, characterized in that The product for predicting and / or evaluating the prognosis and efficacy of traditional Chinese medicine for granulomatous mastitis includes a product for detecting the content of the peripheral blood TCR biomarker.

5. The use according to claim 2, characterized in that The product for detecting the content of the peripheral blood TCR biomarker includes a reagent, a kit, a test paper or an instrument platform.

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