Immunoglobulin A T cell diagnostic marker for nephropathy

Abstract

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

Technical Field

[0001] The present application relates to the technical field of kidney disease detection, and in particular to an immunoglobulin A kidney disease T cell diagnostic marker. Background Art

[0002] Immunoglobulin A (IgA) nephropathy is the most common primary glomerular disease, which is caused by the deposition of IgA complexes in the kidneys, leading to local autoimmune reactions in the kidneys and causing renal tissue lesions. More than 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. Currently, the gold standard for diagnosing IgA nephropathy is a pathological tissue biopsy by renal puncture. However, invasive renal puncture has several defects: (1) Renal puncture cannot be used for early diagnosis and can only detect patients whose renal damage has already occurred. (2) Renal puncture is risky because many patients have relative contraindications to renal puncture, or the hospital does not have the conditions for renal puncture pathological diagnosis, resulting in the inability of patients to obtain a clear diagnosis and targeted treatment. (3) The medical cost of renal puncture is high, equivalent to an operation, and requires hospitalization for one week. Therefore, there is an urgent need to develop non-invasive biomarkers that can help diagnose IgA nephropathy or judge the condition.

[0003] Biomarkers for the diagnosis of IgA nephropathy can be roughly divided into two categories: immune diagnostic markers and genetic diagnostic markers. Immune diagnostic markers refer to proteins or antibodies, while genetic diagnostic markers refer to DNA detection, mRNA gene expression, and miRNA that regulates gene expression, including gene mutations and genotyping of hereditary IgA nephropathy. Existing immune diagnostic markers for IgA nephropathy usually have a specificity of 25%-75% and a sensitivity of 60%-90%. Among them, the most studied ones are: (1) galactose-deficient IgA1 (Gd-IgA1) molecules; (2) anti-sugar antibodies against Gd-IgA1; (3) IgA / C3 ratio, complement C3 of the complement pathway; and (4) the total signal of all IgA complexes. However, the specificity of these immune diagnostic markers is not high, so it is necessary to find markers with more diagnostic value through new methods. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application proposes a marker for immunoglobulin A nephropathy with good diagnostic value.

[0005] In a first aspect of the present application, a reagent for quantitatively detecting at least one of the following markers is provided for use in preparing a diagnostic kit for IgA nephropathy: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.

[0006] According to the application of the embodiment of the present application, there are at least the following beneficial effects:

[0007] The pathogenesis of immunoglobulin A nephropathy is related to five gene axes. The present application starts from the T cell immune axis (TCell Immunity Axis) and screens from relevant mRNA gene expression data based on T cell-specific genes or proteins to obtain the above-mentioned 8 markers. Quantitative detection of the 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.

[0008] Among them, CD4 refers to CD4 membrane glycoprotein of T lymphocytes, which is an auxiliary receptor of T cell receptor and can recognize antigens displayed by antigen presenting cells in class II MHC molecules.

[0009] CD8 is a cell surface glycoprotein present on most cytotoxic T lymphocytes that mediates efficient cell-cell interactions within the immune system. The CD8 antigen acts as a co-receptor with the T cell receptor on T lymphocytes, recognizing antigens displayed by antigen presenting cells in class I MHC molecules. CD8 is composed of a homodimer consisting of two α chains or a heterodimer consisting of one α and one β chain. CD8A encodes the CD8 α chain.

[0010] The protein encoded by GATA3 (GATA Binding Protein 3) belongs to the GATA family. It contains two GATA-type zinc fingers and is an important regulator of T cell development. At the same time, as a transcriptional activator that binds to the enhancers of T cell receptor α and δ genes, it binds to the consensus sequence 5'-AGATAG-3' and is required for the Th2 differentiation process after immune and inflammatory responses.

[0011] GZMA (Granzyme A) is a protease in the cytoplasmic granules of cytotoxic T cells and NK cells. When it enters target cells through the immune synapse, it activates caspase-independent apoptosis.

[0012] HDAC7 (Histone Deacetylase 7) is a histone deacetylase 7 that can positively regulate the transcriptional repressive activity of FOXP3, which is essential for the development and suppressive function of Treg.

[0013] VEGFC (Vascular Endothelial Growth Factor C) is vascular endothelial growth factor C. The protein encoded by this gene is a member of the platelet-derived growth factor / vascular endothelial growth factor (PDGF / VEGF) family. It can promote angiogenesis of the venous and lymphatic vascular systems in the embryonic period, as well as the maintenance of differentiated lymphatic endothelium in adults.

[0014] CCR3 (CC Motif Chemokine Receptor 3) is a CC-type chemokine receptor that can bind and respond to a variety of chemokines, including CCL11, CCL26, MCP-3 (CCL7), MCP-4 (CCL13), and RANTES (CCL5). It is highly expressed in eosinophils and basophils, and is also detected in TH1 and TH2 cells and airway epithelial cells.

[0015] RORA (RAR Related Orphan Receptor A) is an orphan receptor A related to RAR, and is a member of the nuclear hormone receptor NR1 subfamily. It is downstream of IL6 and TGFB, and synergizes with RORC subtype 2, which is manifested in differentiating undetermined CD4+ helper T cells Th into Th17 and inhibiting differentiation into Th1.

[0016] In some embodiments of the present application, the agent is detected at the transcription level or protein level.

[0017] In some embodiments of the present application, the reagent is quantitatively detected by any one of the second-generation sequencing, third-generation sequencing, fluorescent quantitative PCR, digital PCR, gene chip, mass spectrometry, electrophoresis, immunoadsorption, etc.

[0018] In some embodiments of the present application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, or at least eight of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC.

[0019] In some embodiments of the present application, the reagent quantitatively detects at least one of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.

[0020] In some embodiments of the present application, the reagent quantitatively detects at least one of GATA3 and VEGFC.

[0021] In some preferred embodiments, the reagent quantitatively detects any two of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any three of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any four of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any five of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any six of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any seven of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects all eight of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.

[0022] In a second aspect of the present application, a diagnostic kit for IgA nephropathy is provided, the diagnostic kit comprising a reagent for quantitatively detecting at least one of the following markers: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.

[0023] In some embodiments of the present application, the agent is detected at the transcription level or protein level.

[0024] In some embodiments of the present application, the reagent is quantitatively detected by any one of the second generation sequencing, third generation sequencing, fluorescence quantitative PCR, digital PCR, gene chip, mass spectrometry, electrophoresis, immunoadsorption, etc. According to different detection requirements, the sample can be quantitatively detected by different detection platforms or detection methods.

[0025] In some embodiments of the present application, the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, or all eight of the above-mentioned markers.

[0026] According to a third aspect of the present application, a computer-readable storage medium is provided, 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:

[0027] 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.

[0028] Step 2: The expression levels are mathematically correlated to obtain a score; the score is used to indicate the subject's risk of developing IgA nephropathy.

[0029] The subject refers to a person to be tested whose risk of IgA nephropathy is to be assessed, and the subject sample refers to a sample of the person to be tested containing information on the expression level of the above-mentioned markers, specifically including but not limited to peripheral blood samples, urine samples, tissue samples (such as puncture samples), etc. Performing mathematical association to obtain a score means obtaining the relationship between the risk of disease and the expression level of these marker genes by means such as modeling, and the risk of disease is reflected in the form of a score.

[0030] In some embodiments of the present application, the expression level is the transcription level or protein level of the marker. Depending on the actual source of the sample, the expression of the gene can be detected at the transcription level or protein level.

[0031] In some embodiments of the present application, step 1 further includes standardizing the expression level to further avoid possible errors in the diagnostic results.

[0032] In some embodiments of the present application, the operation further includes step 3: evaluating the risk of immunoglobulin A nephropathy of the subject according to the score. Specifically, the score threshold for distinguishing normal people from patients can be obtained by the difference in scores between the patient group and normal people, and the risk of IgA nephropathy is evaluated according to the relationship between the subject's score and the score threshold. For example, if the subject's score reaches the set threshold or is higher than it, it is judged that the subject is more likely to suffer from IgA nephropathy.

[0033] According to a fourth aspect of the present application, an electronic device is provided, the electronic device comprising a processor and a memory, the memory storing a computer program executable on the processor, the processor implementing the following operations when executing the computer program:

[0034] 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;

[0035] 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.

[0036] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs, such as the marker screening method described in the embodiments of the present application or the assessment of the risk of IgA nephropathy in the subject. The processor implements the above-mentioned marker screening method or the assessment of the risk of IgA nephropathy in the subject by running the non-transient software program and instructions stored in the memory.

[0037] The memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store and execute the above-mentioned marker screening method. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some specific embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0038] The non-transitory software program and instructions required to implement the above-mentioned marker screening method are stored in the memory, and when executed by one or more processors, the above-mentioned marker screening method is executed.

[0039] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0040] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that 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 may include any information delivery media.

[0041] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a box plot of the 8 genetic diagnostic markers screened out in Example 1 of the present application for different sample types.

[0043] Figure 2 This is the ROC curve obtained by modeling the 8 genes screened out in Example 1 of the present application as diagnostic markers.

[0044] Figure 3 This is the ROC curve obtained by modeling GATA3 and VEGFC screened out in Example 1 of the present application as diagnostic markers.

[0045] Figure 4 This is the ROC curve obtained by modeling VEGFC, CD8A and RORA screened out in Example 1 of the present application as diagnostic markers.

[0046] Figure 5 This is the ROC curve obtained by modeling CD8A, RORA, CCR3 and VEGFC screened out in Example 1 of the present application as diagnostic markers.

[0047] Figure 6 This is the ROC curve obtained by modeling VEGFC, HDAC7, CD4, RORA and GZMA screened out in Example 1 of the present application as diagnostic markers.

[0048] Figure 7 This is the ROC curve obtained by modeling GZMA, CD8A, HDAC7, RORA, VEGF and CCR3 screened out in Example 1 of the present application as diagnostic markers.

[0049] Figure 8 This is the ROC curve obtained by modeling GATA3, RORA, VEGFC, CD8A, CCR3, CD4 and GZMA screened out in Example 1 of the present application as diagnostic markers. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the concept of the present application and the technical effects produced in combination with the embodiments, so as to fully understand the purpose, features and effects of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of them. Based on the embodiments of the present application, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present application.

[0051] The embodiments of the present application are described in detail below. The described embodiments are exemplary and are only used to explain the present application, and should not be understood as limiting the present application.

[0052] In the description of this application, "several" means more than one, "more" means more than two, "greater than", "less than", "exceed", etc. are understood to exclude the number itself, and "above", "below", "within", etc. are understood to include the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0053] In the description of the present application, the description with reference to the terms "one embodiment", "some embodiments", "illustrative embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0054] Example 1: Screening of markers

[0055] The present application embodiment involves the screening of diagnostic markers. Previous studies have shown that mRNA gene expression has great potential for molecular diagnosis of kidney disease. At the same time, the pathogenesis of immunoglobulin A nephropathy may be related to some gene axes including the T cell immune axis. Therefore, this scheme uses the expression of specific genes or proteins based on T cells to screen potential diagnostic markers.

[0056] The genes related to five types of T cells that were pre-selected and may be involved in the pathogenesis of IgA nephropathy are as follows:

[0057] 1. Helper T Cell 1 Th1-related genes: CCL4, CCR2, CCR5, CXCR3, IFNG, IFNGR1, IFNGR2, IL2, IL20RA, JAK2, JAK3, STAT1, STAT4, TBR1, TNF;

[0058] 2. Helper T Cell 2 Th2-related genes: BCL2, BCL2L1, CCR3, CCR4, CCR8, CD28, CXCL11, CXCL12, GATA3, IL10, IL13, IL33, IL4, IL4R, IL5, NFATC2IP, STAT6;

[0059] 3. Helper T Cell 17 Th17-related genes: CCL20, IL17A, IL17B, IL17RA, IL17RB, IL6, IL6R, IL6ST, NOTCH1, NOTCH2, NOTCH3, NOTCH4, RORA, RORB, RORC, STAT3, VEGFA, VEGFB, VEGFC;

[0060] 4. Follicular Helper T Cell Tfh-related genes: BCL6, CCR6, CD3D, CD3E, CD3G, CD4, CD40, CD40LG, CD80, CD86, CD8A, CXCR5, ICOS, IL21R, PDCD1, PDCD1LG2;

[0061] 5. Regulatory T Cell Treg-related genes: GZMA, GZMH, HDAC7, IKZF4, IL23A, IL2RA, IL2RB, IL2RG, KAT5, KITLG, NFATC1, NFATC3, NFATC4, RELA, STAT5A, STAT5B, TGFB1, TNFRSF11A.

[0062] Dataset preparation

[0063] 1. Download the gene transcriptome gene chip datasets GSE37460 and GSE93798 from the Gene Expression Omnibus (GEO). GSE37460 contains 27 kidney tissue samples from healthy people and patients with IgA nephropathy, while GSE93798 contains 22 kidney tissue samples from healthy people and 20 kidney tissue samples from patients with IgA nephropathy, both with more than 20,000 gene probes.

[0064] 2. Data normalization: Data normalization is divided into two steps: (i) First, for each sample, the median expression of all genes is calculated, and the normalized expression is the original expression minus the calculated median. This normalization method removes the difference in the input amount of mRNA in the samples; (ii) In order to facilitate the integration of the two data sets, each data set is quartile normalized, that is, the first and third quartiles of each sample (or gene) are linearly mapped to 0 and 1.

[0065] 3. Finally, the gene intersection of the two was selected to stack the expression data to form a comprehensive data set with 49 healthy people and 47 IgA nephropathy patients. The intersection has more than 10,000 genes and includes the 84 T cell genes selected above.

[0066] Marker screening

[0067] This embodiment uses a multiple iterative linear regression method to establish a model (it is understandable that other supervised machine learning nonlinear algorithms can also be used instead, such as classic SVM, PCA, neural network, etc. or deep learning algorithms):

[0068] Step 1: Since the establishment of a linear regression model is more suitable for several to dozens of input parameters, the number of model input parameters S is selected, and the genome is evenly divided into gene subsets consisting of S genes. A linear regression model is established for each subset, where the genes are input parameters, the sample type code, HC (healthy people) = 0, IgAN (IgA nephropathy patients) = 1, is the target variable, and the genes with p values ​​less than 0.10 in the model are retained. The threshold of 0.10 here is higher than the traditional 0.05 because these genes may also meet the statistically significant p value in the next round of models.

[0069] Step 2: Merge all the genes selected in this way. If the total number is greater than S, repeat the first step for the merged genes until the number of genes after the merger does not exceed S.

[0070] During the modeling process, all reasonable model sizes, S = 10, 11, ..., 60, were traversed, and the above-mentioned multiple iterative linear regression modeling steps were performed. Finally, the maximum value of the R-squared value (rsq) obtained by each S was taken as the optimal model size, and S = 16 was selected. The optimal model obtained consisted of 8 genes: CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC. The optimal linear regression model composed of 8 genes is shown in Table 1. It can be seen from the table that the p value corresponding to each gene in the model is less than 0.05.

[0071] Table 1. The optimal linear regression model and functional annotation of the 8 genes

[0072]

[0073]

[0074] The expression levels of 8 genes in different groups were tested separately, and the results are shown in Figure 1 , is a box plot of t test, where 0 on the horizontal axis represents the control group of normal people, and 1 represents the patient group with IgA nephropathy. In the above box plot, there are significant differences in the expression of each gene in the two groups (p<0.05). This result shows that these 8 genes have good separation for IgA nephropathy. Therefore, at least one of these 8 genes can be used as a diagnostic marker for IgA nephropathy, and the expression level of at least one of the markers can be detected in the subject, and the risk of IgA nephropathy in the subject can be evaluated based on the results.

[0075] Cross Validation

[0076] The above 49 healthy samples and 47 IgAN patient samples were randomly divided into two balanced HC and IgAN data subsets. One of them was used to establish a linear regression model with CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables, and the other subset was used as a validation data set to draw the ROC graph and calculate the AUC. After repeating 20 times, the AUC ranked: 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. The worst was 0.91, the best was 0.98, and the median was 0.96. At the same time, it was calculated that its specificity and sensitivity were both over 90%. The results show that the diagnosis of IgAN using a combination of eight markers, CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA, and VEGFC, has excellent results and good stability.

[0077] The above samples were randomly divided into two data subsets in the same way. One of the subsets was used to establish a linear regression model with CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables. The other subset was used as the validation data set to draw the ROC graph and calculate the AUC. After 20 repetitions, the data were ranked. The results are shown in the figure. Figure 2 As shown in the figure, it can be seen that the AUC values ​​of the single gene modeling of the eight markers are all above 0.6, among which the AUC values ​​of GATA3, VEGFC, HDAC7, RORA, CD4, and CD8A are all above 0.7, and the AUC values ​​of GATA3 and VEGFC even reach 0.79.

[0078] The above samples were randomly divided into two data subsets in the same way. A linear regression model was established with any two or more of CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables in one of the subsets. The other subset was used as the validation data set. The ROC graph was drawn and the AUC was calculated. The data were repeated 20 times and then sorted. The maximum, median and minimum values ​​are shown in Table 2.

[0079] Table 2. AUC values ​​of different numbers of diagnostic markers

[0080]

[0081]

[0082]

[0083] Some ROC curves are as follows: Figure 3 to Figure 8 As shown, from Figure 3 to Figure 8 From Table 2, it can be seen that any two, three, four, five, six or seven of the above markers have good diagnostic value as diagnostic markers for IgA nephropathy.

[0084] Example 2

[0085] This embodiment provides a device for assessing the risk of IgA nephropathy, the device comprising a processor and a memory, the memory storing a computer program that can be executed by the processor. The method for assessing the risk of IgA nephropathy of a subject using the device is as follows:

[0086] 1. Select peripheral blood samples of subjects to extract exosome mRNA.

[0087] 2. The extracted mRNA is sent to a detection device (e.g., a standard qPCR platform) to perform quantitative data on the expression of the seven gene diagnostic markers provided in Example 1: CD4, CD8A, CCR3, GATA3, GZMA, RORA, and VEGFC.

[0088] 3. The device is used to retrain the linear regression model using clinical observations as target variables (such as proteinuria, eGFR, pathological grade of renal puncture, 5-year or 10-year risk of uremia, drug effectiveness prediction, and drug resistance), and the parameter vector w for peripheral blood samples is determined based on the optimal linear regression model obtained. n (n=0~7), according to the parameter vector w n The linear regression model N=w0+w1×CD4+w2×CD8A+w3×CCR3+w4×GATA3+w5×GATA3+w6×GZMA+w7×RORA+w8×VEGFC between the risk score N and the expression level of each gene was obtained, and the risk score of the subject was calculated and the appropriate threshold value of the risk score was determined. If the risk score of the subject is greater than the threshold value, it is judged as positive.

[0089] Example 3

[0090] This embodiment provides a kit, including reagents capable of quantifying the mRNA levels of CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC, wherein the reagents include reverse transcriptase, primers, Taq enzyme, fluorescent dye, and the like.

[0091] Example 4

[0092] This embodiment provides a kit, which includes a microfluidic chip, the microfluidic chip includes a liquid storage module, and the liquid storage module is respectively equipped with reagents capable of quantifying the mRNA levels of GATA3, VEGFC, HDAC7, CD4, and CD8A genes. The kit can be applied to the diagnosis of IgA nephropathy to achieve a more sensitive and accurate diagnosis.

[0093] The present application is described in detail above in conjunction with the embodiments, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the purpose of the present application. In addition, the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

Claims

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. A computer-readable storage medium, characterized in that: 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: 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; 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. The computer-readable storage medium according to claim 2, wherein: The expression level is the transcription level or protein level of the marker.

4. The computer-readable storage medium according to claim 2, wherein: The step 1 also includes normalizing the expression level.

5. An electronic device, characterized in that The system 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: 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; 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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