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CN114324887B - Immunoglobulin A T cell diagnostic marker for nephropathy - Google Patents

Immunoglobulin A T cell diagnostic marker for nephropathy Download PDF

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CN114324887B
CN114324887B CN202111198418.4A CN202111198418A CN114324887B CN 114324887 B CN114324887 B CN 114324887B CN 202111198418 A CN202111198418 A CN 202111198418A CN 114324887 B CN114324887 B CN 114324887B
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饶皑炳
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Shenzhen Luwei Biotechnology Co ltd
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Abstract

本发明公开了免疫球蛋白A肾病T细胞诊断标志物。本申请的第一方面，提供定量检测以下至少一种标志物的试剂在制备IgA肾病的诊断试剂盒中的应用：CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。根据本申请实施例的应用，至少具有如下有益效果：免疫球蛋白A肾病的发病机理与五个基因轴(Axis)相关，本申请从T细胞免疫轴(T Cell Immunity Axis)出发，基于T细胞特定的基因或蛋白，从相关mRNA基因表达数据中进行筛选，得到上述8个标志物，基于这8个标志物中至少一种对受试者进行定量检测都能够可以高效准确地诊断出是否患有IgA肾病，并且具有良好的特异性和灵敏度。

The present invention discloses a T cell diagnostic marker for immunoglobulin A nephropathy. In the first aspect of the present application, a reagent for quantitatively detecting at least one of the following markers is provided for use in the preparation of a diagnostic kit for IgA nephropathy: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC. According to the application of the embodiment of the present application, at least the following beneficial effects are achieved: the pathogenesis of immunoglobulin A nephropathy is related to five gene axes. The present application starts from the T cell immune axis, based on T cell-specific genes or proteins, and screens from relevant mRNA gene expression data to obtain the above-mentioned 8 markers. Quantitative detection of a subject based on at least one of these 8 markers can efficiently and accurately diagnose whether the subject suffers from IgA nephropathy, and has good specificity and sensitivity.

Description

Immunoglobulin A nephropathy T cell diagnostic markers
Technical Field
The application relates to the technical field of kidney disease detection, in particular to an immunoglobulin A kidney disease T cell diagnosis marker.
Background
Immunoglobulin a (IgA) nephropathy, a primary glomerular disease most commonly seen, is caused by deposition of IgA complexes in the kidneys, resulting in local autoimmune reactions of the kidneys, causing lesions in the kidney tissue. Over 30% of patients progress to end-stage renal disease (ESRD) 10-20 years after onset, making IgA nephropathy one of the most common causes of uremia. At present, igA nephropathy diagnosis gold standard is pathological tissue biopsy of kidney puncture, however, invasive kidney puncture has several defects that (1) kidney puncture cannot be diagnosed early, and only patients with onset kidney injury can be detected. (2) Renal puncture is a risk because many patients have a relative contraindication of renal puncture or the condition of a hospital without pathological diagnosis of renal puncture, which results in patients not being able to obtain definitive diagnosis and to perform targeted therapy. (3) The medical cost of kidney puncture is high, which is equivalent to one operation, and needs to be hospitalized for one week. There is therefore a clinical need to develop noninvasive biomarkers that aid in diagnosis or diagnosis of IgA nephropathy.
Biomarkers for IgA nephropathy diagnosis can be roughly classified into two categories, immunodiagnostic markers and genetic diagnostic markers. The immunodiagnostic marker refers to a protein or an antibody, and the genetic diagnostic marker refers to DNA detection including genetic mutation and genotyping of genetic IgA nephropathy, mRNA gene expression, miRNA regulating gene expression, and the like. Existing IgA nephropathy immunodiagnostic markers generally have a specificity of 25% -75% and a sensitivity of 60% -90%. Among these, there are many studies of (1) galactose-deficient IgA1 (Gd-IgA 1) molecules, (2) anti-glycoantibodies against Gd-IgA1, (3) IgA/C3 ratio, complement C3 of complement pathway, and (4) total signal of all IgA complexes. However, the specificity of these immunodiagnostic markers is not high, and therefore, it is necessary to find markers of more diagnostic value by new methods.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a marker of immunoglobulin A nephropathy with good diagnostic value.
In a first aspect of the application there is provided the use of a reagent for the quantitative detection of at least one marker selected from CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC in the preparation of a diagnostic kit for IgA nephropathy.
The application according to the embodiment of the application has at least the following beneficial effects:
The pathogenesis of immunoglobulin A nephropathy is related to five gene axes (Axis), and the application starts from a T cell immune Axis (T Cell Immunity Axis), screens from related mRNA gene expression data based on specific genes or proteins of T cells to obtain the 8 markers, and can diagnose whether the subject has IgA nephropathy or not with high efficiency and accuracy by quantitatively detecting at least one of the 8 markers, and has good specificity and sensitivity.
Wherein CD4 refers to the T lymphocyte CD4 membrane glycoprotein (CD 4 membrane glycoprotein of T lymphocytes), which is a helper receptor for the T cell receptor, capable of recognizing antigens displayed by antigen presenting cells in MHC class II molecules.
CD8 is a cell surface glycoprotein that is present on most cytotoxic T lymphocytes and mediates potent intercellular interactions within the immune system. The CD8 antigen acts as a co-receptor with T cell receptors on T lymphocytes, recognizing antigens displayed by antigen presenting cells in MHC class I molecules. CD8 is a homodimer consisting of two alpha chains or a heterodimer consisting of one alpha and one beta chain. While CD8A encodes the CD8A chain.
The protein encoded by GATA3 (GATA Binding Protein) belongs to the GATA family, which contains two GATA-type zinc fingers, and is an important regulator of T cell development. Meanwhile, as a transcriptional activator binding to T cell receptor alpha and delta gene enhancers, binding to the consensus sequence 5'-AGATAG-3' is necessary for Th2 differentiation processes following immune and inflammatory responses.
GZMA (Granzyme A) are proteases in the cytoplasmic granules of cytotoxic T cells and NK cells that activate caspase-independent apoptosis when entering target cells through immune synapses.
HDAC7 (Histone Deacetylase) is histone deacetylase 7, which is capable of upregulating the transcriptional inhibitory activity of FOXP3, whereas FOXP3 is critical for Treg development and inhibitory function.
VEGFC (Vascular Endothelial Growth Factor C) is vascular endothelial growth factor C, a protein encoded by this gene is a member of the platelet derived growth factor/vascular endothelial growth factor (PDGF/VEGF) family. Can promote angiogenesis of embryonic vein and lymphatic vasculature, and maintain differentiated lymphatic endothelium of adult.
CCR3 (C-C Motif Chemokine Receptor) is a C-C chemokine receptor that binds to and responds to a variety of chemokines, including CCL11, CCL26, MCP-3 (CCL 7), MCP-4 (CCL 13) and RANTES (CCL 5). It is highly expressed in eosinophils and basophils, and is also detected in TH1 and TH2 cells and airway epithelial cells.
RORA (RAR RELATED Orphan Receptor A) is an RAR-related orphan receptor a, a member of the NR1 subfamily of nuclear hormone receptors. Downstream of IL6 and TGFB, and in synergy with RORC subtype 2, is reflected in the differentiation of undetermined cd4+ helper T cells Th to Th17, inhibiting differentiation to Th 1.
In some embodiments of the application, the agent is detected at the transcriptional level or the protein level.
In some embodiments of the application, the reagents are quantitatively detected by any one of second generation sequencing, third generation sequencing, fluorescent quantitative PCR, digital PCR, gene chip, mass spectrometry, electrophoresis, immunoadsorption, and the like.
In some embodiments of the application, the agent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, at least eight of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC.
In some embodiments of the application, the agent quantitatively detects at least one of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC.
In some embodiments of the application, the reagent quantitatively detects at least one of GATA3, VEGFC.
In some preferred embodiments thereof, the agent quantitatively detects any two of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC. In some preferred embodiments thereof, the agent quantitatively detects any three of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC. In some preferred embodiments thereof, the agent quantitatively detects any four of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC. In some preferred embodiments thereof, the agent quantitatively detects any five of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC. In some preferred embodiments thereof, the agent quantitatively detects any six of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC. In some preferred embodiments thereof, the agent quantitatively detects any seven of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC. In some preferred embodiments thereof, the reagent quantitatively detects all eight of CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA, and VEGFC.
In a second aspect of the application, there is provided a diagnostic kit for IgA nephropathy comprising reagents for quantitatively detecting at least one marker selected from CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.
In some embodiments of the application, the agent is detected at the transcriptional level or the protein level.
In some embodiments of the application, the reagents are quantitatively detected by any one of second generation sequencing, third generation sequencing, fluorescent quantitative PCR, digital PCR, gene chip, mass spectrometry, electrophoresis, immunoadsorption, and the like. According to different detection requirements, the sample can be quantitatively detected by different detection platforms or detection methods.
In some embodiments of the application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, all eight of the above-described markers.
In a third aspect of the present application, there is provided a computer-readable storage medium storing computer-executable instructions for causing a computer to:
Step 1 information is obtained from the expression level of at least one marker selected from CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA and VEGFC in a sample from a subject.
Step2, mathematically relating the expression levels to obtain a score, the score being indicative of the risk of developing IgA nephropathy in the subject.
Wherein, the subject refers to a person to be tested for evaluating the disease risk of IgA nephropathy, and the subject sample refers to a sample of the person to be tested containing the information of the expression level of the marker, and specifically includes, but is not limited to, a peripheral blood sample, a urine sample, a tissue sample (such as a puncture sample) and the like. Mathematical correlation to obtain a score refers to deriving the relationship of the risk of disease to the expression levels of these marker genes, such as by modeling, where the risk of disease is represented by a score.
In some embodiments of the application, the expression level is the transcriptional level or the protein level of the marker. Depending on the actual sample source, the gene expression may be detected at the transcriptional level or at the protein level.
In some embodiments of the application, step 1 further comprises normalizing the expression level. To further avoid possible errors in the diagnostic results by normalization.
In some embodiments of the application, the operations further comprise step 3 of assessing the risk of developing immunoglobulin A kidney disease in the subject based on the score. The scoring threshold value for distinguishing normal people from patients can be obtained through the difference of the scores between the patient group and normal people, and the disease risk of IgA nephropathy is evaluated according to the relation between the scores of the subjects and the scoring threshold value. For example, if the subject's score reaches or is higher than a set threshold, the subject is judged to be more likely to have IgA nephropathy.
In a fourth aspect of the present application, there is provided an electronic device comprising a processor and a memory, the memory having stored thereon a computer program executable on the processor, the processor when executing the computer program effecting the following operations:
step 1, obtaining information on the expression level of at least one marker selected from CCR3, CD4, CD8A, GATA, GZMA, HDAC7, RORA and VEGFC in a sample from a subject;
And 2, carrying out mathematical correlation on the expression levels to obtain scores, wherein the scores are used for indicating the risk of the patients suffering from the immunoglobulin A nephropathy.
The memory, as a non-transitory computer readable storage medium, may be used to store a non-transitory software program and a non-transitory computer executable program, such as the marker screening method described in embodiments of the application or to evaluate a subject for risk of IgA nephropathy. The processor implements the marker screening method described above or evaluates the subject's risk of IgA nephropathy by running a non-transitory software program and instructions stored in memory.
The memory may include a memory program area for storing an operating system, an application program required for at least one function, and a memory data area for storing a program for executing the above-described marker screening method. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The non-transitory software programs and instructions required to implement the above-described marker screening methods are stored in memory and when executed by one or more processors, perform the above-described marker screening methods.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
FIG. 1 is a box plot of the 8 gene diagnostic markers screened for in example 1 of the present application versus different sample types.
FIG. 2 is a ROC curve obtained by modeling 8 genes screened in example 1 of the present application alone as diagnostic markers.
FIG. 3 is a ROC curve modeled by GATA3 and VEGFC as diagnostic markers screened in example 1 of the present application.
Fig. 4 is a ROC curve modeled by VEGFC, CD8A and RORA screened in example 1 of the present application as diagnostic markers.
Fig. 5 is a ROC curve modeled by CD8A, RORA, CCR and VEGFC screened in example 1 of the present application as diagnostic markers.
Fig. 6 is a ROC curve modeled by VEGFC, HDAC7, CD4, RORA and GZMA as diagnostic markers screened in example 1 of the present application.
FIG. 7 is a ROC curve modeled by GZMA, CD8A, HDAC, RORA, VEGF and CCR3 screened in example 1 of the application as diagnostic markers.
FIG. 8 is a ROC curve modeled by GATA3, RORA, VEGFC, CD8A, CCR3, CD4 and GZMA as diagnostic markers screened in example 1 of the application.
Detailed Description
The conception and the technical effects produced by the present application will be clearly and completely described in conjunction with the embodiments below to fully understand the objects, features and effects of the present application. It is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments, and that other embodiments obtained by those skilled in the art without inventive effort are within the scope of the present application based on the embodiments of the present application.
The following detailed description of embodiments of the application is exemplary and is provided merely to illustrate the application and is not to be construed as limiting the application.
In the description of the present application, the meaning of a number is one or more, the meaning of a number is two or more, and greater than, less than, exceeding, etc. are understood to exclude the present number, and the meaning of a number is understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.
In the description of the present application, the descriptions of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Example 1 screening of markers
The present examples relate to the screening of diagnostic markers, and previous studies have shown great potential for molecular diagnosis of kidney disease by mRNA gene expression, while the pathogenesis of immunoglobulin A kidney disease may be associated with some gene axes (Axis) including the T-cell immune Axis (T Cell Immunity Axis). Thus, the present protocol utilizes the screening of potential diagnostic markers based on T cell specific gene or protein expression profiles.
The genes associated with the pre-selected five types of T cells that may be associated with the pathogenesis of immunoglobulin a nephropathy are as follows:
1. Helper T cell type 1 (HELPER T CELL 1) Th1 associated genes CCL4, CCR2, CCR5, CXCR3, IFNG, IFNGR1, IFNGR2, IL20RA, JAK2, JAK3, STAT1, STAT4, TBR1, TNF;
2. Helper T cell type 2 (HELPER T CELL 2) Th 2-related genes, BCL2L1, CCR3, CCR4, CCR8, CD28, CXCL11, CXCL12, GATA3, IL10, IL13, IL33, IL4R, IL5, NFATC2IP, STAT6;
3. helper T cell 17 type (HELPER T CELL 17) Th17 related gene ：CCL20、IL17A、IL17B、IL17RA、IL17RB、IL6、IL6R、IL6ST、NOTCH1、NOTCH2、NOTCH3、NOTCH4、RORA、RORB、RORC、STAT3、VEGFA、VEGFB、VEGFC;
4. The follicular helper T cell (Follicular HELPER T CELL) Tfh related genes, BCL6, CCR6, CD3D, CD3E, CD3G, CD, CD40LG, CD80, CD86, CD8A, CXCR5, ICOS, IL21R, PDCD1, PDCD1LG2;
5. Regulatory T cell (Regulatory T Cell) Treg-related genes ：GZMA、GZMH、HDAC7、IKZF4、IL23A、IL2RA、IL2RB、IL2RG、KAT5、KITLG、NFATC1、NFATC3、NFATC4、RELA、STAT5A、STAT5B、TGFB1、TNFRSF11A.
Data set preparation
1. The genome-transcriptome gene chip dataset GSE37460 and GSE93798 were downloaded from a gene expression integrated database (GEO). GSE37460 contains 27 cases of kidney tissue samples of healthy people and IgA nephropathy patients, while GSE93798 contains 22 cases of kidney tissue samples of healthy people and 20 cases of kidney tissue samples of IgA nephropathy patients, and has more than 20000 gene probes.
2. Data Normalization (Normalization) is carried out by (1) respectively calculating the median of all gene expression amounts for each sample, normalizing expression to be the original expression amount minus the calculated median, removing the difference of sample mRNA input amounts by the Normalization mode, and (ii) respectively carrying out quartile (Interquartile) Normalization on each data set in order to facilitate the integration of two data sets, namely, linearly mapping the first quartile and the third quartile of each sample (or gene) to 0 and 1.
3. Finally, the gene intersections of the two genes were selected and the expression data were stacked to construct a comprehensive data set of 49 healthy persons and 47 IgA nephropathy patients, and the intersections were 10000 genes in total and contained 84T cell genes selected above.
Marker screening
The present embodiment uses a multiple iterative linear regression method to build a model (it will be appreciated that other supervised machine learning nonlinear algorithms may be used instead, such as classical SVM, PCA, neural network, etc. or deep learning algorithms):
The first step, because the establishment of the linear regression (Linear Regression) model is more suitable for several to tens of input parameters, the number S of the input parameters of the model is selected, the genome is averagely divided into gene subsets consisting of S genes, the linear regression model is respectively established for each subset, wherein the genes are the input parameters, the sample type codes, HC (healthy people) =0, igAN (IgA nephropathy patients) =1, the genes with the p value smaller than 0.10 in the model are reserved as target variables. The threshold value of 0.10 is higher than the conventional value of 0.05 here, since these genes may also satisfy statistically significant p-values in the next round of model.
And a second step of combining all the genes selected in this way, and repeating the first step on the combined genes if the total number is greater than S until the number of the combined genes does not exceed S.
In the modeling process, traversing all reasonable model sizes, s=10, 11, 60, performing the multiple iterative linear regression modeling steps, and finally, taking the maximum value of the R square value (rsq) obtained by each S as the optimal model size, thereby selecting s=16, wherein the obtained optimal model consists of 8 genes, namely an optimal linear regression model consisting of CD4, CD8A, CCR, GATA3, GZMA, HDAC7, RORA and VEGFC, and the p value corresponding to each gene in the model is less than 0.05 as can be seen from the table with reference to table 1.
TABLE 1.8 optimal linear regression model for Gene composition and functional labeling
The results of examining the expression levels of 8 genes under different groups alone refer to fig. 1, which is a box plot of t-test, wherein 0 on the abscissa represents a control group of normal persons, and 1 represents a patient group of IgA nephropathy, and the expression of each gene in the two groups in the box plot is significantly different (p < 0.05). The results show that the 8 genes have better separability on IgA nephropathy. Therefore, at least one of these 8 genes is used as a diagnostic marker for IgA nephropathy, the expression level of at least one of the markers can be detected in a subject, and the risk of developing IgA nephropathy in the subject can be evaluated based on the result.
Cross Validation (Cross Validation)
The 49 healthy human samples and the 47 IgAN patient samples are divided into two data subsets balanced with HC and IgAN at random, one of the data subsets is used for establishing a linear regression model by taking CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables, the other subset is used for verifying a data set, a ROC graph is drawn, and AUC is calculated. AUC：0.91、0.92、0.93、0.94、0.94、0.94、0.94、0.94、0.95、0.95、0.96、0.96、0.96、0.97、0.97、0.97、0.97、0.98、0.98、0.98. of the sequences after 20 repetitions had a worst of 0.91, most preferably 0.98, median of 0.96, and the specificity and sensitivity were calculated to be over 90%. The results show that the diagnosis of IgAN by using the 8 markers of CD4, CD8A, CCR, GATA3, GZMA, HDAC7, RORA and VEGFC as the combination has excellent results and good stability.
According to the same method, the samples are randomly divided into two data subsets, one subset is used for respectively establishing a linear regression model by taking CD4, CD8A, CCR, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables, the other subset is used for verifying the data sets, a ROC graph is drawn, the AUC is calculated, and the sequence is repeated for 20 times, so that the result is shown in figure 2, the AUC values of 8 marker single gene modeling are all above 0.6, wherein the AUC values of GATA3, VEGFC, HDAC7, RORA, CD4 and CD8A are all above 0.7, and the AUC values of GATA3 and VEGFC are more than 0.79.
The samples were randomly divided into two subsets of data according to the same method, one subset was used to build a linear regression model with any two or more of CD4, CD8A, CCR, GATA3, GZMA, HDAC7, RORA, and VEGFC as input variables, the other subset was used as validation dataset, ROC map was drawn and AUC was calculated, and the ranking after 20 repetitions was performed, with maximum, median, and minimum values as shown in table 2.
TABLE 2 AUC values for different numbers of diagnostic markers
The partial ROC curves are shown in fig. 3-8, and as can be seen from fig. 3-8 in combination with table 2, two, three, four, five, six and seven of the markers have good diagnostic value as diagnostic markers of IgA nephropathy.
Example 2
The present embodiment provides an apparatus for IgA nephropathy risk assessment, the apparatus comprising a processor and a memory, the memory having stored thereon a computer program executable by the processor. The method for evaluating IgA nephropathy risk of a subject by using the device comprises the following steps:
1. Peripheral blood samples of the subjects were selected to extract exosome mRNA.
2. The extracted mRNA was sent to a detection device (e.g., standard qPCR platform) to quantify the expression of the 7 gene diagnostic markers provided in example 1, CD4, CD8A, CCR3, GATA3, GZMA, RORA, VEGFC.
3. The device is adopted to retrain a linear regression model by using clinical observation results (such as proteinuria, eGFR, pathological grading of renal puncture, 5-year or 10-year uremia risk, drug effectiveness prediction and drug resistance) as target variables, a parameter vector w n (n=0-7) aiming at a peripheral blood sample is determined according to the obtained optimal linear regression model, a linear regression model N＝w0+w1×CD4+w2×CD8A+w3×CCR3+w4×GATA3+w5×GATA3+w6×GZMA+w7×RORA+w8×VEGFC, between a risk score N and each gene expression level is obtained according to the parameter vector w n, the risk score of a subject is calculated, and a proper threshold value of the risk score is determined. And if the risk score of the subject is greater than a threshold value, judging that the subject is positive.
Example 3
This example provides a kit comprising reagents capable of quantifying mRNA levels of CD4, CD8A, CCR, GATA3, GZMA, HDAC7, RORA, and VEGFC, including reverse transcriptase, primers, taq enzyme, fluorescent dye, and the like.
Example 4
The embodiment provides a kit, which comprises a microfluidic chip, wherein the microfluidic chip comprises a liquid storage module, and reagents capable of quantifying mRNA levels of GATA3, VEGFC, HDAC7, CD4 and CD8A genes are respectively arranged in the liquid storage module. The kit can be applied to diagnosis of IgA nephropathy, and realizes more sensitive and accurate diagnosis.
The present application has been described in detail with reference to the embodiments, but the present application is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present application. Furthermore, embodiments of the application and features of the embodiments may be combined with each other without conflict.

Claims (5)

1.定量检测以下至少一种标志物的试剂在制备IgA肾病的诊断试剂盒中的应用：CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC，所述至少一种标志物包括VEGFC。1. Use of a reagent for quantitatively detecting at least one of the following markers in the preparation of a diagnostic kit for IgA nephropathy: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC, wherein the at least one marker includes VEGFC. 2.计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令用于使计算机执行以下操作：2. A computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to perform the following operations: 步骤1：获取来自受试者样本中的以下至少一种标志物的表达水平的信息：CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC，所述至少一种标志物包括VEGFC；Step 1: obtaining information on the expression level of at least one of the following markers in a sample from a subject: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC, wherein the at least one marker includes VEGFC; 步骤2：对所述表达水平进行数学关联以获得评分；所述评分用于指示受试者的免疫球蛋白A肾病的患病风险。Step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the risk of the subject developing immunoglobulin A nephropathy. 3.根据权利要求2所述的计算机可读存储介质，其特征在于，所述表达水平为所述标志物的转录水平或蛋白水平。3 . The computer-readable storage medium according to claim 2 , wherein the expression level is the transcription level or protein level of the marker. 4.根据权利要求2所述的计算机可读存储介质，其特征在于，所述步骤1还包括对所述表达水平进行标准化。4. The computer-readable storage medium according to claim 2, wherein step 1 further comprises standardizing the expression level. 5.电子设备，其特征在于，包括处理器和存储器，所述存储器上存储有可在处理器上运行的计算机程序，所述处理器在运行所述计算机程序时实现以下操作：5. An electronic device, characterized in that it comprises a processor and a memory, wherein the memory stores a computer program that can be run on the processor, and the processor implements the following operations when running the computer program: 步骤1：获取来自受试者样本中的以下至少一种标志物的表达水平的信息：CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC，所述至少一种标志物包括VEGFC；Step 1: obtaining information on the expression level of at least one of the following markers in a sample from a subject: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC, wherein the at least one marker includes VEGFC; 步骤2：对所述表达水平进行数学关联以获得评分；所述评分用于指示受试者的免疫球蛋白A肾病的患病风险。Step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the risk of the subject developing immunoglobulin A nephropathy.
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