CN114324887A - Immunoglobulin a nephropathy T cell diagnostic marker - Google Patents

Immunoglobulin a nephropathy T cell diagnostic marker Download PDF

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CN114324887A
CN114324887A CN202111198418.4A CN202111198418A CN114324887A CN 114324887 A CN114324887 A CN 114324887A CN 202111198418 A CN202111198418 A CN 202111198418A CN 114324887 A CN114324887 A CN 114324887A
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饶皑炳
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Shenzhen Luwei Biotechnology Co ltd
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Abstract

The invention discloses an immunoglobulin A nephropathy T cell diagnostic marker. In a first aspect of the present application, there is provided a use of a reagent for quantitatively detecting at least one of the following markers for 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 application, at least the following beneficial effects are achieved: the pathogenesis of the immunoglobulin A nephropathy is related to five gene axes (Axis), the application starts from T Cell Immunity Axis (T Cell Immunity Axis), the 8 markers are obtained by screening relevant mRNA gene expression data based on T Cell specific genes or proteins, and whether the IgA nephropathy exists can be effectively and accurately diagnosed by quantitatively detecting a subject based on at least one of the 8 markers, and the specificity and the sensitivity are good.

Description

Immunoglobulin a nephropathy T cell diagnostic marker
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, the most common primary glomerular disease, results from deposition of IgA complexes in the kidney, resulting in local autoimmune responses in the kidney, causing lesions in the renal 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, the IgA nephropathy diagnosis gold standard is pathological tissue biopsy of renal puncture, however, the invasive renal puncture has several defects: (1) renal puncture does not allow early diagnosis, and can only detect patients in whom the onset of renal injury has developed. (2) Renal puncture presents a risk because many patients have relative contraindications of renal puncture or hospitals do not have the condition of pathological diagnosis of renal puncture, so that the patients cannot obtain definite diagnosis and perform targeted treatment. (3) Renal puncture is a costly procedure, equivalent to a single operation, requiring one week of hospitalization. Therefore, there is a great clinical need for the development of noninvasive biomarkers that contribute to the diagnosis or judgment of the condition of IgA nephropathy.
Biomarkers for IgA nephropathy diagnosis can be roughly divided into two categories: immunodiagnostic markers and genetic diagnostic markers. Immunodiagnostic markers refer to proteins or antibodies, and genetic diagnostic markers refer to DNA detection, mRNA gene expression, miRNA that regulates gene expression, and the like, including genetic IgA nephropathy gene mutation and genotyping. The existing IgA nephropathy immunodiagnostic markers usually have the specificity of 25% -75% and the sensitivity of 60% -90%. Among them, the most studied are: (1) a galactose-deficient IgA1(Gd-IgA1) molecule; (2) an anti-sugar antibody against Gd-IgA 1; (3) IgA/C3 ratio, complement of complement pathway C3; (4) total signal for all IgA complexes. However, these immunodiagnostic markers are not highly specific and therefore, it is necessary to find more diagnostically valuable markers by new methods.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. To this end, the present application proposes a marker for immunoglobulin a nephropathy with good diagnostic value.
In a first aspect of the present application, there is provided a use of a reagent for quantitatively detecting at least one of the following markers for 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 application, at least the following beneficial effects are achieved:
the pathogenesis of the immunoglobulin A nephropathy is related to five gene axes (Axis), the application starts from T Cell Immunity Axis (T Cell Immunity Axis), the 8 markers are obtained by screening relevant mRNA gene expression data based on T Cell specific genes or proteins, and whether the IgA nephropathy exists can be effectively and accurately diagnosed by quantitatively detecting a subject based on at least one of the 8 markers, and the specificity and the sensitivity are good.
Among them, CD4 refers to the membrane glycoprotein of T lymphocyte CD4 (CD4 membrane glycoproteins of T lymphocytes), which is a helper receptor for T cell receptors and can recognize antigens displayed by antigen presenting cells in MHC class II molecules.
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 the antigen 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. Whereas CD8A encodes the CD8 α chain.
The Protein encoded by GATA3(GATA Binding Protein 3) belongs to the GATA family, contains two GATA-type zinc fingers, and is an important regulator of T cell development. Meanwhile, as a transcription activator combined with T cell receptor alpha and delta gene enhancers, combined with the consensus sequence 5 '-AGATAG-3' is essential for Th2 differentiation process after immune and inflammatory response.
Gzma (granzyme a) is a protease in the cytoplasmic granules of cytotoxic T and NK cells that activates caspase-independent apoptosis when entering target cells through immune synapses.
HDAC7(Histone deacylase 7) is Histone Deacetylase 7, which is capable of positively modulating the transcriptional repression activity of FOXP3, whereas FOXP3 is crucial for the development and repression function of tregs.
VEGFC (vascular Endothelial Growth Factor C) is vascular Endothelial Growth Factor C, and the 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 venous and lymphatic vascular systems, and maintenance of adult differentiated lymphatic endothelium.
CCR3(C-C Motif Chemokine Receptor 3) is a C-C type Chemokine Receptor that binds to and responds to a variety of chemokines, including CCL11, CCL26, MCP-3(CCL7), MCP-4(CCL13), and RANTES (CCL 5). It is highly expressed in eosinophils and basophils, and is also detected in TH1 and TH2 cells as well as airway epithelial cells.
RORA (RAR Related Orphan Receptor A) is 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 shown to differentiate the unidentified CD4+ helper T cell Th into Th17, inhibiting differentiation towards Th 1.
In some embodiments of the present application, the agent is detected at the transcriptional level or at the protein level.
In some embodiments of the present 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 present application, the reagent 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, GATA3, GZMA, HDAC7, RORA, and VEGFC.
In some embodiments of the present application, the reagent quantitatively detects at least one of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC.
In some embodiments of the present application, the reagent quantitatively detects at least one of GATA3, VEGFC.
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.
In a second aspect of the present application, there is provided a diagnostic kit for IgA nephropathy, comprising reagents for quantitatively detecting at least one of the following markers: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC.
In some embodiments of the present application, the agent is detected at the transcriptional level or at the protein level.
In some embodiments of the present 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 through different detection platforms or detection methods.
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, all eight of the above markers.
In a third aspect of the present application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer-executable instructions for causing a computer to:
step 1: obtaining information from the expression level in a sample from the subject of at least one of the following markers: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC.
Step 2: mathematically correlating the expression levels to obtain a score; the score is used to indicate the risk of IgA nephropathy in the subject.
The subject refers to a person to be assessed for the risk of IgA nephropathy, and the subject sample refers to a sample of the person to be assessed for the expression level of the marker, and specifically includes, but is not limited to, a peripheral blood sample, a urine sample, a tissue sample (e.g., a puncture sample), and the like. The mathematical association to obtain the score means that the relationship between the risk of disease and the expression levels of these marker genes is obtained by means such as modeling, and the risk of disease is expressed in a scoring manner.
In some embodiments of the present application, the expression level is the transcriptional level or the protein level of the marker. Depending on the source of the sample, the expression of the gene may be detected at the transcription level or the protein level.
In some embodiments of the present application, step 1 further comprises normalizing the expression level. And further avoids the possible error of the diagnosis result by the standardization process.
In some embodiments of the present application, the operations further comprise step 3: the risk of immunoglobulin a nephropathy in the subject is assessed according to the score. Specifically, a score threshold for distinguishing a normal person from a patient can be obtained by the difference in score between a patient group and a normal person, and the risk of IgA nephropathy is evaluated based on the relationship between the score of the subject and the score threshold. For example, if the subject's score reaches a set threshold or is higher, the subject is judged to have a greater likelihood of having IgA nephropathy.
In a fourth aspect of the present application, an electronic device is provided, which includes 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:
step 1: obtaining information from the expression level of at least one of the following markers in a sample from the subject: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC;
step 2: mathematically correlating said expression levels to obtain a score; the score is indicative of the subject's risk of 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 methods described in the examples herein or assessing a subject's risk of IgA nephropathy. The processor implements the marker screening method described above or assesses a subject's risk of IgA nephropathy by executing a non-transitory software program and instructions stored in memory.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data for performing the marker screening method described above. Further, 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 optionally includes memory located remotely from the processor, and the remote memory may be coupled to the processor via 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 needed to implement the marker screening methods described above are stored in memory and, when executed by one or more processors, perform the marker screening methods described above.
The above-described embodiments of the apparatus 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 also 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 the present embodiment.
One 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 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 is well known to those of ordinary skill 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 accessed by a computer. In addition, 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 as known to those skilled in the art.
Additional aspects and advantages of the present 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 present application.
Drawings
FIG. 1 is a box plot of 8 gene diagnostic markers screened in example 1 of the present application against different sample types.
FIG. 2 is a ROC curve modeled by 8 genes selected in example 1 of the present application alone as diagnostic markers.
FIG. 3 is a ROC curve modeled by GATA3 and VEGFC screened as diagnostic markers in example 1 of the present application.
FIG. 4 is a ROC curve modeled by VEGFC, CD8A and RORA screened as diagnostic markers in example 1 of the present application.
FIG. 5 is a ROC curve modeled by CD8A, RORA, CCR3 and VEGFC as diagnostic markers, screened in example 1 of the present application.
FIG. 6 is a ROC curve modeled by VEGFC, HDAC7, CD4, RORA and GZMA screened as diagnostic markers in example 1 of the present application.
FIG. 7 is a ROC curve modeled by GZMA, CD8A, HDAC7, RORA, VEGF, and CCR3 screened as diagnostic markers in example 1 of the present application.
FIG. 8 is a ROC curve modeled by GATA3, RORA, VEGFC, CD8A, CCR3, CD4 and GZMA screened as diagnostic markers in example 1 of the present application.
Detailed Description
The conception and the resulting technical effects of the present application will be clearly and completely described below in conjunction with the embodiments to fully understand the objects, features and effects of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all embodiments, and other embodiments obtained by those skilled in the art without inventive efforts based on the embodiments of the present application belong to the protection scope of the present application.
The following detailed description of embodiments of the present application is provided for the purpose of illustration only and is not intended to be construed as a limitation of the application.
In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and the above, below, exceeding, etc. are understood as excluding the present number, and the above, below, within, etc. are understood as including the present number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood 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, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means 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, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. 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 for markers
The present examples relate to the screening of diagnostic markers, and prior studies have shown that mRNA gene expression has great potential for molecular diagnosis of renal disease, while the pathogenesis of immunoglobulin a nephropathy may be related to several gene axes (Axis) including the T Cell Immunity Axis (T Cell Immunity Axis). Thus, the present protocol utilizes screening for potential diagnostic markers based on T cell specific gene or protein expression profiles.
The genes associated with five pre-selected T cell types that may be associated with the pathogenesis of immunoglobulin a nephropathy are as follows:
1. helper T Cell type 1 (Helper T Cell 1) Th 1-related gene: CCL4, CCR2, CCR5, CXCR3, IFNG, IFNGR1, IFNGR2, IL2, IL20RA, JAK2, JAK3, STAT1, STAT4, TBR1, TNF;
2. helper T Cell type 2 (Helper T Cell 2) Th 2-related gene: BCL2, BCL2L1, CCR3, CCR4, CCR8, CD28, CXCL11, CXCL12, GATA3, IL10, IL13, IL33, IL4, IL4R, IL5, NFATC2IP, STAT 6;
3. helper T Cell type 17 (Helper T Cell 17) Th 17-related gene: CCL20, IL17A, IL17B, IL17RA, IL17RB, IL6, IL6R, IL6ST, NOTCH1, NOTCH2, NOTCH3, NOTCH4, RORA, RORB, RORC, STAT3, VEGFA, VEGFB, VEGFC;
4. follicular Helper T Cell (follicullar Helper T Cell) Tfh-related gene: BCL6, CCR6, CD3D, CD3E, CD3G, CD4, CD40, CD40LG, CD80, CD86, CD8A, CXCR5, ICOS, IL21R, PDCD1, PDCD1LG 2;
5. regulatory T Cell (Regulatory T Cell) Treg-associated gene: GZMA, GZMH, HDAC7, IKZF4, IL23A, IL2RA, IL2RB, IL2RG, KAT5, KITLG, NFATC1, NFATC3, NFATC4, RELA, STAT5A, STAT5B, TGFB1, TNFRSF 11A.
Data set preparation
1. Gene transcriptome gene chip datasets GSE37460 and GSE93798 were downloaded from a Gene expression integration database (GEO). GSE37460 comprises 27 cases of kidney tissue samples of healthy persons and IgA nephropathy patients, GSE93798 comprises 22 cases of healthy persons and 20 cases of kidney tissue samples of IgA nephropathy patients, and more than 20000 gene probes are provided.
2. Data Normalization (Normalization): data normalization was divided into two steps: firstly, respectively calculating the median of all gene expression quantities of each sample, and standardizing expression to subtract the calculated median from the original expression quantity, thereby removing the difference of mRNA input quantity of the samples by the standardized way; second, to facilitate the integration of the two data sets, an Interquartile normalization is performed on each data set, i.e., the first and third quartiles of each sample (or gene) are linearly mapped to 0 and 1.
3. Finally, the gene intersections of the two genes are selected and the expression data are stacked to form a comprehensive data set of 49 healthy people and 47 IgA nephropathy patients, wherein the intersection contains 10000 genes and the 84T cell genes selected above.
Marker screening
In this embodiment, a multiple iterative linear regression method is used to build a model (it is understood that other supervised machine learning nonlinear algorithms may be used instead, such as classical SVM, PCA, neural network, etc. or deep learning algorithm instead):
the first step is as follows: the establishment of a Linear Regression (Linear Regression) model is relatively suitable for several to dozens of input parameters, the number S of the input parameters of the model is selected, a genome is averagely divided into a base factor set consisting of S genes, and a Linear Regression model is respectively established for each subset, wherein the genes are the input parameters, the sample type codes, HC (healthy person) ═ 0, IgAN (IgA nephropathy patient) ═ 1, and the genes with the p value less than 0.10 in the model are reserved for target variables. The threshold value of 0.10 is higher than the conventional value of 0.05 here, because these genes may also satisfy statistically significant p-values in the model of the next round.
The second step is that: all the genes selected in this way are combined, and if the total number is greater than S, the first step is repeated for the combined genes until the number of the combined genes does not exceed S.
In the modeling process, all reasonable model sizes are traversed, S is 10, 11, … and 60, the multiple iterative linear regression modeling step is carried out, finally, the maximum value of the R square value (rsq) obtained by each S is taken as the optimal model size, so that S is 16 selected, and the obtained optimal model consists of 8 genes: CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC, an optimal linear regression model of 8 genes, as seen in table 1, the p-value for each gene in the model is less than 0.05.
Table 1.8 Gene composition optimal Linear regression model and functional annotation
Figure BDA0003303963830000081
Figure BDA0003303963830000091
The expression levels of 8 genes in different groups were examined individually and the results are shown in FIG. 1, which is a t-test box plot in which 0 on the abscissa represents a control group of normal persons and 1 represents a patient group of IgA nephropathy, and in which the expression of each gene in both groups is significantly different (p < 0.05). The results indicate that these 8 genes all have good separability from IgA nephropathy. Therefore, using at least one of these 8 genes 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 IgA nephropathy in the subject can be evaluated based on the result.
Model Cross Validation (Cross Validation)
The 49 healthy human samples and 47 IgAN patient samples are respectively divided into two data subsets with balanced HC and IgAN at random, one data subset is used for establishing a linear regression model by taking CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables, the other data subset is used for verifying a data set, an ROC graph is drawn, and AUC is calculated. AUC ranked after 20 replicates: 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98. The worst is 0.91, the best is 0.98, and the median is 0.96, and the specificity and the sensitivity are both calculated to be more than 90%. The results indicate that the diagnosis of IgAN using the combination of 8 markers, CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA, and VEGFC, has excellent results and good stability.
The samples are randomly divided into two data subsets according to the same method, one subset 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 as a verification data set, a ROC graph is drawn and the AUC is calculated, and the ranking is repeated for 20 times, as shown in figure 2, the result is shown in figure 2, the AUC values of 8 marker single-gene modeling are all above 0.6, 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 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, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables, the other subset was used as validation dataset, ROC graphs were drawn and AUC was calculated, and the ranking was repeated 20 times with maximum, median and minimum values as shown in table 2.
TABLE 2 AUC values for different numbers of diagnostic markers
Figure BDA0003303963830000101
Figure BDA0003303963830000111
Figure BDA0003303963830000121
Wherein a part of the ROC curves are shown in FIGS. 3 to 8, and it can be seen from FIGS. 3 to 8 in combination with Table 2 that any two, optionally three, optionally four, optionally five, optionally six, optionally seven of the above markers have good diagnostic value as diagnostic markers for IgA nephropathy.
Example 2
The present embodiment provides an apparatus for IgA nephropathy risk assessment, comprising a processor and a memory, the memory having stored thereon a computer program executable by the processor. The method for assessing the risk of IgA nephropathy in a subject using the apparatus is as follows:
1. peripheral blood samples from the subjects were selected for exosome mRNA extraction.
2. The extracted mRNA is sent to a detection device (e.g., a standard qPCR platform) for quantitative data on the expression of the 7 genetic diagnostic markers provided in example 1: CD4, CD8A, CCR3, GATA3, GZMA, RORA, VEGFC.
3. Using this apparatus, the linear regression model is retrained with clinical observations (e.g., proteinuria, eGFR, pathological grade of renal puncture, 5-or 10-year risk of uremia, drug-effectiveness prediction, drug resistance) as target variables, and the parameter vector w for the peripheral blood sample is determined from the resulting optimal linear regression modeln(n is 0 to 7) based on the parameter vector wnObtaining a linear regression model between the risk score N and the expression level of each gene, wherein N is w0+w1×CD4+w2×CD8A+w3×CCR3+w4×GATA3+w5×GATA3+w6×GZMA+w7×RORA+w8X VEGFC, calculating the risk score for the subject and determining a suitable threshold for the risk score. And if the risk score of the subject is larger than the threshold value, judging the test result to be positive.
Example 3
This example provides a kit comprising reagents capable of quantifying mRNA levels of CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA, and VEGFC, including reverse transcriptase, primers, Taq enzyme, fluorescent dyes, 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 the mRNA levels of the GATA3, VEGFC, HDAC7, CD4 and CD8A genes are respectively arranged in the liquid storage module. The kit can be applied to the diagnosis of IgA nephropathy, and relatively sensitive and accurate diagnosis is realized.
The present application has been described in detail with reference to the embodiments, but the present application is not limited to the embodiments described above, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

Claims (6)

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.
2. A diagnostic kit for immunoglobulin a nephropathy, comprising reagents for quantitatively detecting at least one of the following markers: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC.
3. A computer-readable storage medium having computer-executable instructions stored thereon for causing a computer to:
step 1: obtaining information from the expression level of at least one of the following markers in a sample from the subject: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC;
step 2: mathematically correlating said expression levels to obtain a score; the score is indicative of the subject's risk of immunoglobulin a nephropathy.
4. The computer-readable storage medium of claim 3, wherein the expression level is a transcription level or a protein level of the marker.
5. The computer-readable storage medium of claim 3, wherein step 1 further comprises normalizing the expression level.
6. Electronic device, characterized in that it comprises a processor and a memory, said memory having stored thereon a computer program executable on the processor, said processor realizing the following operations when executing said computer program:
step 1: obtaining information from the expression level of at least one of the following markers in a sample from the subject: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC;
step 2: mathematically correlating said expression levels to obtain a score; the score is indicative of the subject's risk of immunoglobulin a nephropathy.
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