Application of REG1A gene and RUNX3 gene as biomarkers for diabetic nephropathy

Abstract

The application discloses application of REG1A gene and RUNX3 gene as biomarkers of diabetic nephropathy. Specifically disclosed are application of biomarkers and / or substances for detecting the biomarkers in diagnosis of diabetic kidney disease (DKD) or preparation of products for the diagnosis of DKD, the biomarkers being any one of the following: B1) REG1A gene and RUNX3 gene; B2) REG1A gene; B3) RUNX3 gene. The application develops the DKD biomarkers based on high expression of the REG1A gene and the RUNX3 gene in blood and kidneys of DKD patients. Experiments show that the biomarkers have the characteristics of high sensitivity and high specificity, can provide fast and effective tools for diagnosis, prognosis and risk assessment of DKD patients, and further provide precise treatment schemes for the patients, are favorable for realizing optimal disease management, and have good clinical application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the application of REG1A gene and RUNX3 gene as biomarkers of diabetic nephropathy in the field of molecular diagnosis. BACKGROUND

[0002] Diabetic kidney disease (DKD) is a chronic kidney disease (CKD) caused by diabetes mellitus (DM), and is clinically characterized by persistent albuminuria and / or progressive decline in glomerular filtration rate (GFR), and can progress to end-stage renal disease (ESRD). Early DKD has a certain reversibility, and early and accurate diagnosis can prevent further deterioration of the patient's kidney. However, DKD in the early stage has no obvious symptoms and is easily ignored by patients. When DKD patients seek medical treatment, they are often in an irreversible state. Kidney function will gradually deteriorate over time until complete loss. Therefore, exploring and identifying high sensitivity and specificity biomarkers of DKD is one of the problems to be solved in clinical practice, and has important significance for the prediction, diagnosis and prognosis of DKD.

[0003] The diagnosis of DKD is mainly based on kidney pathology and / or clinical manifestations, in addition to the duration of diabetes mellitus, diabetic retinopathy and urine protein level, including the determination of glomerular filtration rate (GFR). Although traditional clinical features are easy to obtain, their diagnostic efficiency is limited. With the continuous development of analysis technology, high-throughput technology and bioinformatics database, exploring genes related to diseases and discovering new biomarkers for evaluating the risk of DKD have become the current research hotspot. So far, there is no specific treatment for DKD, and the emphasis is on prevention, early diagnosis and early intervention treatment. The sensitivity and specificity of the commonly used diagnostic markers still cannot meet the clinical needs of DKD diagnosis and prediction. In view of this, the research and development of more reliable, more sensitive and more specific biomarkers have important clinical significance for the diagnosis, prediction and prognosis of DKD. SUMMARY

[0004] The technical problem to be solved by the present application is how to diagnose DKD and / or how to assess the risk of DKD progression. The technical problem to be solved is not limited to the technical subject described, and other technical subjects not mentioned herein can be clearly understood by those skilled in the art through the following description.

[0005] To solve the above technical problems, the present application first provides the following application of any one of the biomarkers and / or substances for detecting the biomarkers:

[0006] A1) use in the diagnosis of diabetic nephropathy or in the preparation of a product for the diagnosis of diabetic nephropathy;

[0007] A2) use in the auxiliary diagnosis of diabetic nephropathy or in the preparation of a product for the auxiliary diagnosis of diabetic nephropathy;

[0008] A3) use in the screening of diabetic nephropathy or in the preparation of a product for the screening of diabetic nephropathy;

[0009] A4) use in the prediction or evaluation of the risk of diabetic nephropathy or in the preparation of a product for the prediction or evaluation of the risk of diabetic nephropathy;

[0010] A5) use in the prognosis evaluation of diabetic nephropathy or in the preparation of a product for the prognosis evaluation of diabetic nephropathy;

[0011] The biomarker can be any one of the following:

[0012] B1) REG1A gene and RUNX3 gene;

[0013] B2) REG1A gene;

[0014] B3) RUNX3 gene.

[0015] The REG1A gene described herein can be a human REG1A gene, and the RUNX3 gene can be a human RUNX3 gene.

[0016] Herein, "REG1A" and "REG1A gene" have the same meaning and can be used interchangeably, and "RUNX3" and "RUNX3 gene" have the same meaning and can be used interchangeably.

[0017] The nucleotide sequence of the REG1A gene is 79120488-79123409 of GenBank Accession No. P05451.3 (Update Date 13-May-2022). The REG1A gene includes a polynucleotide of the REG1A gene and any functional equivalent of the REG1A gene.

[0018] The nucleotide sequence of the RUNX3 gene is 24899511-24965138 of GenBank Accession No. Q13761.2 (Update Date 23-Jun-2022). The RUNX3 gene includes a polynucleotide of the REG1A gene and any functional equivalent of the REG1A gene.

[0019] In the above uses, the substance for detecting the biomarker can be any one of the following:

[0020] C1) reagents for detecting the mRNA expression level of the REG1A gene and / or reagents for detecting the mRNA expression level of the RUNX3 gene;

[0021] C2) reagents for detecting the protein expression level of the REG1A gene and / or reagents for detecting the protein expression level of the RUNX3 gene.

[0022] Specifically, the mRNA expression level refers to the abundance of mRNA of gene transcription detected at the transcription level;

[0023] The protein expression level refers to the abundance of protein encoded by the gene detected at the translation level.

[0024] The present application can detect the expression level of the REG1A gene and / or the RUNX3 gene based on sequencing technology of cDNA and RNA. The sequencing technology is nucleic acid sequencing technology, including chain terminator (Sanger) sequencing technology and dye terminator sequencing technology, and it is known to those skilled in the art that RNA is less stable in cells and more susceptible to nuclease attack in experiments, so RNA is usually reverse transcribed into DNA before sequencing. In addition, the sequencing technology also includes next-generation sequencing technology (i.e. deep sequencing / high-throughput sequencing technology), which is a single-molecule cluster-based sequencing-by-synthesis technology based on a proprietary reversible termination chemical reaction principle. During sequencing, random fragments of DNA of the genome are attached to an optically transparent glass surface. After extension and bridge amplification, these DNA fragments form hundreds of millions of clusters on the glass surface, each cluster is a single-molecule cluster with thousands of copies of the same template. Then, using four special deoxyribonucleotides with fluorescent groups, the template DNA to be sequenced is sequenced by reversible sequencing-by-synthesis technology. The present application can use second-generation sequencing and third-generation sequencing to detect the transcriptome in cDNA, and further detect the expression level of the REG1A gene and / or the RUNX3 gene. The present application can also use Nanopore sequencing to directly detect the RNA expression level, thereby quantifying the expression level of the REG1A gene and / or the RUNX3 gene.

[0025] In the above application, the substance for detecting the biomarker includes a reagent for detecting the biomarker by reverse transcription-polymerase chain reaction, real-time fluorescent quantitative PCR, transcriptome sequencing technology, Northern blot, in situ hybridization technology, gene chip technology, Nanopore sequencing technology, PacBio sequencing technology, immunoblotting, immunohistochemistry, immunofluorescence, radioimmunoassay, co-immunoprecipitation, enzyme-linked immunosorbent assay, enzyme immunoassay, flow cytometry, high-performance liquid chromatography, capillary gel electrophoresis, near-infrared spectroscopy, mass spectrometry, immunochemiluminescence, colloidal gold immunotechnology, fluorescent immunochromatography, surface plasmon resonance technology, immuno-PCR technology, or biotin-avidin technology.

[0026] In the above application, the product can include a kit, a gene chip, a protein chip, an immunochromatographic diagnostic test paper, a high-throughput sequencing platform, or a biosensor.

[0027] The detection sample of the product can be derived from a diabetic patient or a suspected diabetic patient.

[0028] The detection sample of the product can be a blood sample or a tissue sample, the blood sample can be a peripheral blood sample or a peripheral blood mononuclear cell sample, and the tissue sample can be a kidney sample.

[0029] The kit can be a genetic detection kit or a protein immunodetection kit; the genetic detection kit includes a reagent for detecting the transcription level of the REG1A gene and / or the RUNX3 gene; and the protein immunodetection kit includes a specific antibody of the REG1A gene encoded protein and / or the RUNX3 gene encoded protein.

[0030] The gene chip includes a solid phase carrier and an oligonucleotide probe fixed on the solid phase carrier, and the oligonucleotide probe includes an oligonucleotide probe for detecting the REG1A gene and / or the RUNX3 gene.

[0031] The protein chip includes a solid phase carrier and a specific antibody of the REG1A gene encoded protein and / or a specific antibody of the RUNX3 gene encoded protein fixed on the solid phase carrier.

[0032] The immunochromatographic diagnostic test paper includes a specific antibody of the REG1A gene encoded protein and / or a specific antibody of the RUNX3 gene encoded protein.

[0033] The high-throughput sequencing platform includes a reagent for detecting the REG1A gene and / or the RUNX3 gene;

[0034] The biosensor includes a reagent for detecting the REG1A gene and / or the RUNX3 gene.

[0035] The antibody described herein can be a monoclonal antibody, a polyclonal antibody, a genetically engineered antibody, and can also be an antibody fragment such as an antibody variable region Fv, a single-chain antibody ScFv, an antigen-binding fragment Fab or Fab', F(ab')2, Fab'-SH, and an antibody derivative, etc.

[0036] In the above application, the product includes a substance for detecting the biomarker.

[0037] In the above application, the substance for detecting the biomarker can include at least any one of the following:

[0038] M1) a primer specifically amplifying the REG1A gene and / or the RUNX3 gene;

[0039] M2) a probe specifically recognizing the REG1A gene and / or the RUNX3 gene;

[0040] M3) an antibody binding to a REG1A gene-encoded protein and / or a RUNX3 gene-encoded protein.

[0041] Further, the primer can include at least any one of the following:

[0042] P1) a forward primer specifically amplifying the RUNX3 gene with a nucleotide sequence of SEQ ID No. 1;

[0043] P2) a reverse primer specifically amplifying the RUNX3 gene with a nucleotide sequence of SEQ ID No. 2;

[0044] P3) a forward primer specifically amplifying the REG1A gene with a nucleotide sequence of SEQ ID No. 3;

[0045] P4) a reverse primer specifically amplifying the REG1A gene with a nucleotide sequence of SEQ ID No. 4.

[0046] The present application also provides a composition, which can be any one of the following:

[0047] D1) the composition contains a reagent for detecting the mRNA expression level of the REG1A gene and a reagent for detecting the mRNA expression level of the RUNX3 gene;

[0048] D2) the composition contains a reagent for detecting the protein expression level of the REG1A gene and a reagent for detecting the protein expression level of the RUNX3 gene.

[0049] The present application also provides an application of the composition, which can be any one of the following:

[0050] F1) use in diagnosis of diabetic nephropathy or in preparation of a product for diagnosis of diabetic nephropathy;

[0051] F2) use in auxiliary diagnosis of diabetic nephropathy or in preparation of a product for auxiliary diagnosis of diabetic nephropathy;

[0052] F3) use in screening of diabetic nephropathy or in preparation of a product for screening of diabetic nephropathy;

[0053] F4) use in prediction or evaluation of risk of diabetic nephropathy or in preparation of a product for prediction or evaluation of risk of diabetic nephropathy;

[0054] F5) use in prognosis evaluation of diabetic nephropathy or in preparation of a product for prognosis evaluation of diabetic nephropathy.

[0055] The present application also provides a kit, which can be any one of the following:

[0056] G1) the kit contains reagents for detecting mRNA expression level of REG1A gene and / or reagents for detecting mRNA expression level of RUNX3 gene;

[0057] G2) the kit contains reagents for detecting protein expression level of REG1A gene and / or reagents for detecting protein expression level of RUNX3 gene.

[0058] The sample to be detected by the kit can be a blood sample or a tissue sample, the blood sample can be a peripheral blood sample or a peripheral blood mononuclear cell sample, and the tissue sample can be a kidney sample.

[0059] The various reagent components of the kit can be present in separate containers, or can be pre-combined into a reagent mixture in whole or in part.

[0060] Further, the kit can be a genetic detection kit or a protein immunodetection kit.

[0061] Further, the kit can be an ELISA kit, a qPCR kit, an electrochemiluminescence detection kit, an immunoblotting detection kit, an immunochromatographic detection kit, a flow cytometry analysis kit, or an immunohistochemical detection kit, but not limited thereto.

[0062] Further, the genetic detection kit (such as a qPCR kit) can contain at least any one of the following:

[0063] P1) the nucleotide sequence is a forward primer specific for amplifying the RUNX3 gene of SEQ ID No. 1;

[0064] P2) the nucleotide sequence of SEQ ID No. 2 is a reverse primer for specifically amplifying the RUNX3 gene;

[0065] P3) the nucleotide sequence of SEQ ID No. 3 is a forward primer for specifically amplifying the REG1A gene;

[0066] P4) the nucleotide sequence of SEQ ID No. 4 is a reverse primer for specifically amplifying the REG1A gene.

[0067] Further, the genetic detection kit can further comprise one or more of Taq DNA polymerase, dNTP, PCR buffer, Mg 2+ .

[0068] Further, the genetic detection kit can further comprise an internal reference gene detection reagent, which can be a GAPDH gene and / or a β-actin gene, but is not limited thereto.

[0069] The protein immunodetection kit can contain antibodies that bind to the protein encoded by the REG1A gene and / or the protein encoded by the RUNX3 gene.

[0070] The present application also provides a device for the diagnosis, assisted diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy, the device comprising any of the detection substances for the biomarkers described herein and a computer-readable storage medium having stored thereon a computer program, which causes a computer to perform any of the following steps:

[0071] H1) diagnosing, assisted diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy according to the expression levels of the REG1A gene and the RUNX3 gene;

[0072] H2) diagnosing, assisted diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy according to the expression level of the REG1A gene;

[0073] H3) diagnosing, assisted diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy according to the expression level of the RUNX3 gene.

[0074] The aim of the present invention is to establish a simplified blood biomarker to predict the risk of DKD. Microarray data obtained from the Gene Expression Omnibus (GEO) database were first analyzed, and subsequently REG1A and RUNX3 were identified as diagnostic biomarkers for DKD in the obtained dataset. Blood samples from healthy participants, diabetic patients without DKD and DKD patients were further collected and subjected to qPCR analysis to determine the transcriptional levels of RUNX3 and REG1A. The obtained blood samples were then used to validate the diagnostic effect of these biomarkers in the GEO database and to predict the risk of developing DKD. In summary, RUNX3 and REG1A were shown to have the potential to predict the risk of DKD and can contribute to the prevention and management of DKD.

[0075] DKD remains the leading cause of ESRD worldwide. Approximately one-fourth of DM patients eventually develop DKD. Therefore, identifying the characteristics of DKD patients can help predict the risk of disease progression. Clinical characteristics are the most widely used DKD predictors. The community-based atherosclerosis risk in communities study found that eGFR levels declined most rapidly in diabetic patients with high-risk apolipoprotein L1 genotype, insulin use, high systolic blood pressure, and high glycated hemoglobin levels. Another longitudinal observation study at the University of Washington Hospital showed that age, obesity, hypertension, high levels of glycated hemoglobin, and proteinuria were independent risk factors for eGFR reduction in type 2 diabetes patients. Data from the Swedish National Diabetes Register also showed that independent risk factors for proteinuria in diabetic patients included advanced age, male gender, smoking, high BMI, systolic blood pressure, glycated hemoglobin, TC, and LDL-C levels. With the advancement of histological analysis and sequencing technology, a large number of new biomarkers have been developed to make up for the low predictive efficacy of clinical characteristics. The cactus study identified four plasma biomarkers for predicting eGFR reduction in type 1 diabetes patients: beta-2-microglobulin, cystatin C, neutrophil gelatinase-associated lipocalin, and osteopontin. Metabolomics analysis further identified 7 blood metabolites (octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3,5-dimethoxy mannurenamide, and N-acetyl glutamine) as predictors of eGFR reduction in diabetic patients. The present invention analyzed human DKD datasets obtained from the GEO database and identified 38 differentially expressed genes (DEGs) shared in blood and kidney samples. Sixteen DEGs were further identified by LASSO regression. Nine of these genes had consistent expression trends in blood and kidney samples. These DEGs were used to compare the diagnostic efficacy of lupus nephritis, ANCA-associated nephritis, focal segmental glomerulosclerosis, IgA nephropathy, and minimal change nephropathy. The results found that only REG1A and RUNX3 were specific for the diagnosis of DKD. Therefore, REG1A and RUNX3 are potential biomarkers for distinguishing DKD from DM with kidney disease (except DKD). Membranous nephropathy was excluded from our analysis because anti-phospholipase A2 receptor antibodies have been clearly identified as specific markers of membranous nephropathy. The transcriptional levels of REG1A and RUNX3 had high diagnostic efficacy in blood samples (AUC = 0.917) and kidney biopsy samples (AUC = 0.929) from DKD patients. We selected the GSE142153 dataset (RNAseq from blood samples of DKD patients) as the development set because blood samples are generally the first choice for marker detection. Finally, blood samples from DKD patients and healthy individuals at Shenzhen People's Hospital were used for external validation. The results found that REG1A combined with RUNX3 had equally high diagnostic efficacy (AUC = 0.948).Accordingly, REG1A and RUNX3 are potential biomarkers for DKD.

[0076] In blood and kidney samples of DKD patients, we found that the expression levels of REG1A and RUNX3 were significantly increased, which indicated that these genes might have a pathogenic role in DKD. Blood expression levels might also reflect the damage of the kidney. REG1A and RUNX3 levels were positively correlated with serum creatinine and urine protein creatinine ratio, and negatively correlated with eGFR. Therefore, REG1A and RUNX3 levels in blood might be closely related to glomerular damage and decreased kidney function in DKD patients. REG1A levels were also positively correlated with C-peptide, glycosylated hemoglobin and fasting blood glucose levels, suggesting that the increase in REG1A levels might also be closely related to beta cell dysfunction. With the increase of extracellular glucose concentration, the expression level of REG1A increased rapidly, which might be an important physiological feedback loop in beta cell regulation. Therefore, REG1A and RUNX3 are potential biomarkers for the development of DKD.

[0077] To assess the risk of DKD development, we plotted the KM curves of REG1A, RUNX3 and clinical characteristics. The results showed that people with high expression of REG1A and RUNX3 had an increased risk of DKD after about 12 years and 8 years of diabetes, respectively. It was found that the increase in levels of TC, FBG, SCr, BMI and UACR was a risk factor for DKD, while the increase in levels of eGFR and HDL-C was a protective factor for DKD. Table 3 shows the results of KM analysis of REG1A and RUNX3 at different expression levels. Therefore, when the expression levels of REG1A and RUNX3 are both high, the risk of DKD is the highest (HR = 6.87) compared to other groups. There is no increased risk of DKD when the expression of REG1A or RUNX3 is low (HR = 1). Compared with patients with low expression of both REG1A and RUNX3, diabetic patients with high expression of both genes have a rapidly increased risk of DKD after 7-8 years of diabetes. Therefore, REG1A and RUNX3 are potential biomarkers for predicting the risk of DKD.

[0078] The differential expression of genes is caused by multiple factors and is closely related to the occurrence and development of many diseases. Bioinformatics and biostatistical analysis of differentially expressed genes can provide important theoretical basis for gene diagnosis and treatment. Screening of differential genes is to screen out genes with significant differences between samples from high-throughput gene data by statistical methods.

[0079] The present inventors have conducted extensive and in-depth research, screened and analyzed the gene expression profile of diabetic kidney disease (DKD), found that REG1A gene and RUNX3 gene are highly expressed in the blood and kidney of DKD patients, and developed a DKD biomarker based thereon to achieve the purpose of diagnosing, screening, prognosis evaluation and risk assessment of DKD at the mRNA level and the protein level. REG1A and / or RUNX3 have a high diagnostic effect on DKD, and are confirmed by external verification. The level of REG1A and / or RUNX3 is positively correlated with the level of UACR and negatively correlated with the level of eGFR. Therefore, the transcription level of REG1A and / or RUNX3 in the blood sample has the potential to predict the risk of DKD. The biomarker and the kit based on the detection of REG1A gene and / or RUNX3 gene of the present application have the characteristics of high sensitivity and high specificity, can provide a fast and effective tool for the diagnosis, prognosis and risk assessment of DKD patients, and thus provide precise treatment plans for patients, which is conducive to achieving the best disease management and has good clinical application value. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 Screening of DEGs shared in blood and kidney biopsy samples of DKD patients.

[0081] Figure 2 Screening of DEGs consistently expressed in blood and kidney biopsy samples of DKD patients. Among them, Figure 2 Figure 1A is a lasso coefficient profile plot of 38 features. A coefficient profile plot is generated according to the log(λ) sequence. A vertical line is drawn at the value selected using 5-fold cross-validation, where the optimal lambda results in 16 features with non-zero coefficients. Figure 2 Figure 1B is the selection of optimal parameters (λ) in the lasso model using five-fold cross-validation by the minimum criterion. The partial likelihood bias (binomial bias) curve is plotted with the log(λ) curve. A vertical dotted line is drawn at the optimal value by using the minimum criterion and 1SE of the minimum criterion (1-SE criterion). Figure 2 Figure 1C and Figure 2 Figure 1D is the expression of 16 DEGs in blood and kidney. There are 9 DEGs consistently expressed in blood and kidney. Figure 2 Figure 1E is the distribution of the 9 DEGs in blood samples of DKD patients. Figure 2 Figure 1F is the distribution of the 9 DEGs in kidney biopsy samples of DKD patients. Lasso: Least Absolute Shrinkage and Selection Operator; SE: Standard Error.

[0082] Figure 3Validation of the diagnostic efficacy of REG1A and / or RUNX3 as diagnostic markers for DKD. Figure 3 REG1A and Figure 3 Fig. 1C. Boxplot of the expression levels of REG1A and RUNX3 in blood samples (GSE142153) for the analysis of the diagnostic efficacy of DKD. The boxplot shows that the expression levels of REG1A and RUNX3 are significantly increased in blood samples (DKD vs HC). Figure 3 Fig. 1C. Boxplot of the expression levels of REG1A and RUNX3 in blood samples (GSE142153) for the analysis of the diagnostic efficacy of DKD. The boxplot shows that the expression levels of REG1A and RUNX3 are significantly increased in blood samples (DKD vs HC). Figure 3 Fig. 1D. ROC curve of the diagnostic efficacy of REG1A and RUNX3 in blood samples. P<0.05 (*) indicates a statistically significant difference, P<0.01 (**) indicates a statistically significant difference, and P<0.001 (***) indicates a statistically extremely significant difference. Figure 3 Fig. 1E. Boxplot of the expression levels of REG1A and RUNX3 in kidney samples (GSE30122) for the analysis of the diagnostic efficacy of DKD. The boxplot shows that the expression levels of REG1A and RUNX3 are significantly increased in kidney samples (DKD vs HC). Figure 3 Fig. 1F. ROC curve of the diagnostic efficacy of REG1A and RUNX3 in kidney samples. P<0.05 (*) indicates a statistically significant difference, P<0.01 (**) indicates a statistically significant difference, and P<0.001 (***) indicates a statistically extremely significant difference.

[0083] Figure 4 Expression and diagnostic efficacy of the diagnostic markers in the validation cohort. Figure 4 Fig. 2A. Boxplot of the expression levels of REG1A and RUNX3 in the validation cohort. Figure 4 Fig. 2B. Boxplot of the expression levels of REG1A and RUNX3 in the validation cohort. The boxplot shows that the expression levels of REG1A and RUNX3 are significantly increased (DKD vs HC). Figure 4 Fig. 2C. ROC curve of REG1A and ROC curve of RUNX3 in the validation cohort. Figure 4 Fig. 2D. ROC curve of the diagnostic efficacy of REG1A and RUNX3 after fitting the two diagnostic indicators to one variable in the validation cohort. P<0.05 (*) indicates a statistically significant difference, P<0.01 (**) indicates a statistically significant difference, and P<0.001 (***) indicates a statistically extremely significant difference.

[0084] Figure 5 Calibration curves of the diagnostic markers in the development cohort and the validation cohort. Figure 5 Fig. 3A. Calibration curve of the diagnostic markers in the development cohort (development set). Figure 5 Fig. 3B. Calibration curve of the diagnostic markers in the validation cohort (validation set).

[0085] Figure 6 Expression of the diagnostic markers in DKD and DM cohorts and correlation thereof with clinical characteristics. Figure 6 Fig. 4A. Boxplot of the expression levels of REG1A and RUNX3 in DKD and DM cohorts. Figure 6Figure 6: Boxplot of the expression levels of REG1A and RUNX3. The boxplot shows that the expression levels of REG1A and RUNX3 are significantly elevated (DKD vs DM). Figure 6 Figure 7: Correlation of REG1A with clinical characteristics. Figure 6 Figure 8: Correlation of RUNX3 with clinical characteristics. P<0.05 (*) indicates statistically significant difference, P<0.01 (**) indicates statistically significant difference, and P<0.001 (***) indicates extremely statistically significant difference. DETAILED DESCRIPTION

[0086] The present application will be further described in conjunction with the preferred embodiments thereof, given only by way of illustration of the present application, and not intended to limit the scope of the present application. The following examples provided can serve as a guide for further improvement by those of ordinary skill in the art, and do not constitute any limitation on the present application in any way.

[0087] The experimental methods in the following examples are all conventional methods, and are carried out according to the techniques or conditions described in the literature in the art or according to the product instructions, unless otherwise specified. The materials, reagents, etc. used in the following examples can be obtained commercially, unless otherwise specified.

[0088] Example 1: Screening of biomarkers for diabetic nephropathy

[0089] 1. Screening of differentially expressed genes (DEGs) shared in blood and kidney samples

[0090] Three GEO datasets (GSE30122, GSE142153 and GSE72326) were obtained from the GEO database (Gene Expression Omnibus, https: / / www.ncbi.nlm.nih.gov / geo / ) for preliminary analysis, and gene ID and symbol conversion was performed by Perl script. GSE30122, GSE142153 and GSE72326 contained transcriptomic data from kidney samples of DKD patients, blood samples of DKD patients, blood samples of CKD patients and healthy control samples (HC), respectively.

[0091] Identification of differentially expressed genes (DEGs): R software (version 4.1.0) was used for data analysis and plotting. The “limma” R package in R software was used to screen DEGs, and the “ggplot2” package was used to construct heat maps and volcano plots of DEGs to visualize the expression levels of DEGs. A p value <0.05 was considered to be statistically significant, and was used as the standard for screening differentially expressed genes (DEGs).

[0092] The results are shown in Table 1.Figure 1 As shown, Figure 1 The screening process for shared DEGs in blood and kidney biopsy samples from DKD patients is shown. Based on healthy control samples (HC), 679 (GSE142153: HC, n=10, DKD, n=23) and 499 (GSE30122: HC, n=50, DKD, n=19) DEGs were identified in blood and kidney biopsy samples from DKD patients, respectively. A total of 38 shared DEGs were found in the blood and kidney biopsy samples from DKD patients.

[0093] 2. Screening for DEGs that express consistent expression in blood and kidney samples from DKD patients.

[0094] LASSO regression algorithm was used to further screen for shared DEGs in blood samples (GSE142153) from DKD patients. A total of 16 DEGs were identified as diagnostic markers for DKD. Figure 2 China A, Figure 2 In the B group, there were 5 downregulated genes and 11 upregulated genes. Figure 2 (C). In kidney biopsy samples (GSE30122) from DKD patients, two genes were downregulated, while 14 genes were upregulated. Figure 2 (D).

[0095] Ultimately, nine markers with consistent expression profiles were identified in blood and kidney biopsy samples from DKD patients: including alpha-2A adrenergic receptor (ADRA2A), CC motif chemokine 5 (CCL5), cholesterol 25-hydroxylase (CH25H), CXC chemokine receptor type 4 (CXCR4), hemoglobin subunit delta (HBD), hydroxycarboxylic acid receptor 3 (HCAR3), lysophosphatidylcholine acyltransferase 1 (LPCAT1), regenerated islet-derived protein 1A (REG1A), and runt-associated transcription factor 3 (RUNX3). The expression profiles of these DEGs in the blood and kidneys of DKD patients are shown in the figure. Figure 2 China E and Figure 2 The results showed that their expression was upregulated.

[0096] 3. Screening for diagnostically specific DEGs

[0097] The screening and verification of diagnostic markers used the least absolute shrinkage and selection operator (lasso) method implemented in the "glmnet" package for data dimensionality reduction and prediction feature selection in DKD patients. The receiver operating characteristic curve (ROC) method was used to evaluate the diagnostic performance. The "pROC" package was used to draw the receiver operating characteristic curve and calculate the area under the curve (AUC) of the single factor or multi-factor ROC. The area under the ROC curve is an important test accuracy index, and the larger the area under the ROC curve, the greater the diagnostic value of the test. The "rms" package was used to draw the calibration curve to evaluate whether the predicted probability of the model is close to the true probability.

[0098] Further specific screening of DEGs for DKD was performed using the above method, and two blood transcriptome datasets (GSE142153 and GSE72326) were analyzed to determine the value of DKD-specific DEGs for diagnosis.

[0099] In the GSE142153 dataset, several DEGs (ADRA2A, CCL5, CH25H, CXCR4, HBD, HCAR3, LPCAT1, REG1A, and RUNX3) were found to have high differences, and except for CH25H (AUC = 0.67) with low diagnostic efficiency, the AUC of blood samples of other DEGs were all greater than 0.7 (Table 1). The GSE72326 dataset [lupus nephritis = 48; ANCA vasculitis-associated nephritis = 10; focal segmental glomerulosclerosis = 3; IgA nephropathy = 5; minimal change disease = 3] was used to verify the specificity of these DEGs for DKD diagnosis, and except for HCAR3, which was excluded from the analysis because it was not found in the dataset, the other 8 DEGs had relatively low diagnostic efficiency for LN patients (Table 1). CXCR4, LPCAT1, and HBD had high diagnostic efficacy for ANCA-associated nephritis (Table 1). Significant diagnostic performance of CXCR4, CCL5, and HBD was observed for FSGS patients (Table 1). ADRA2A, LPCAT1, and HBD had high diagnostic efficacy for IgAN patients (Table 1). CH25H and HBD had good diagnostic efficacy for MCD patients (Table 1).

[0100] In summary, the transcriptional levels of REG1A and RUNX3 in blood samples can serve as DKD-specific predictors of disease progression, with good diagnostic value.

[0101] Table 1: Specificity analysis of DKD diagnostic genes

[0102]

[0103] Table 1: Area under curve (AUC) of 9 differentially expressed genes in various chronic kidney diseases. Diabetic nephropathy data from GSE72326; Chronic kidney disease data from GSE72326. Aimed to screen genes with diagnostic specificity in DKD.

[0104] 4. Validation of DKD diagnostic potential of REG1A and / or RUNX3 as diagnostic markers

[0105] DKD diagnostic effect of RUNX3 and REG1A blood samples (GSE142153) was analyzed, and the expression levels of REG1A and RUNX3 were found to be significantly increased in DKD patient blood samples compared with healthy control samples (HC) Figure 3 A, Figure 3 B; GSE142153). The joint diagnostic effect of these genes (RUNX3 and REG1A) was also high (AUC = 0.917, 95% CI: 0.818-1) Figure 3 C).

[0106] Similar results were also obtained from DKD patient kidney samples (GSE30122), and the expression levels of REG1A and RUNX3 were significantly increased in DKD patient kidney samples compared with healthy control samples (HC) Figure 3 D, Figure 3 E; GSE30122). The joint diagnostic effect of RUNX3 and REG1A was also high (AUC = 0.929, 95% CI: 0.846-1) Figure 3 F). These results show that the diagnostic performance of REG1A and RUNX3 is excellent, and the diagnostic performance in blood is not inferior to that in the kidney.

[0107] Example 2, Validation and application of diabetic nephropathy biomarkers

[0108] On the basis of Example 1, which has screened the diabetic nephropathy biomarkers REG1A and / or RUNX3, this example further validates the diagnostic performance of the biomarkers (REG1A and / or RUNX3) of the present application.

[0109] 1. qPCR analysis validation of clinical samples

[0110] Clinical statistical data:

[0111] From the biological sample library of Shenzhen People's Hospital, a total of 141 human blood samples from DKD patients, diabetic patients (without DKD) and healthy individuals (HC) were collected.

[0112] Inclusion criteria:

[0113] Healthy controls (HC): > 18 years old; no liver and kidney function impairment; no history of tumor; no history of diabetes mellitus.

[0114] DKD patients: 1) macroalbuminuria; 2) diabetic retinopathy with microalbuminuria; 3) microalbuminuria in T1DM patients with more than 10 years of diabetes duration.

[0115] Diabetic patients (without DKD): 3 of the following 1: 1. diabetic symptoms + plasma glucose level > 11.1 mmol / l (200 mg / dl) at any time; 2. fasting plasma glucose level > 7.0 mmol / l (126 mg / dl); 3. 2-hour PG level > 11.1 mmol / l (200 ng / dl) in OGTT test.

[0116] All samples for experimental purposes were obtained with the informed consent of the patients and approved by the Ethics Committee of Shenzhen People's Hospital. For continuous variables, data were expressed as mean ± standard deviation (SD) or median and interquartile range, and as percentages of categorical variables. Mann-Whitney U test or t test was used to compare the differences between the two groups according to whether the data were normally distributed.

[0117] Real-time quantitative PCR (qPCR) analysis: Total RNA was extracted from peripheral blood mononuclear cells (PBMC) using Trizol (Invitrogen) according to the manufacturer's instructions. Reverse transcription of RNA was performed using the reverse transcription RT reverse transcription kit (Thermo Fisher Scientific). Quantitative PCR was performed using PowerUp SYBR Green Master Mix (Thermo Scientific). The results of the study were standardized using the GAPDH method. qPCR was performed using an ABI real-time fluorescent quantitative PCR system (Applied Biosystems, Foster City, CA, USA). Gene expression was calculated using the 2 -△△Ct method. The gene-specific PCR primers are shown in Table 2.

[0118] Table 2. Primer sequences for qRT-PCR analysis

[0119]

[0120] The evaluation of the diagnostic performance of REG1A and RUNX3 in the validation set was performed by qPCR analysis of REG1A and RUNX3 in 141 blood samples from the Human Biological Specimen Bank of Shenzhen People's Hospital.

[0121] DKD (n=50) group and HC (n=41) group were included in the validation set (Table 3). In the DKD group, the expression of REG1A and RUNX3 was significantly up-regulated and compared with the HC group Figure 4 Mid-A, Figure 4 Mid-B). The AUC of REG1A and RUNX3 was 0.912 and 0.859, respectively Figure 4 Mid-C). When REG1A and RUNX3 were fitted as single variables, the diagnostic efficiency of the development set was 0.917 Figure 3 Mid-C), and the diagnostic efficiency of the validation set (AUC=0.948, 95% CI: 0.989-0.998) Figure 4 Mid-D), indicating that REG1A and RUNX3 had high diagnostic value. In the development set and the validation set, the predicted values and the true values of the calibration curve were also highly consistent Figure 5 Mid-A, Figure 5 Mid-B), and the diagonal dashed line represented the perfect prediction of an ideal model. The solid line represented the performance of the model, and the closer the solid line to the dashed line, the more accurate the model. The fitting of the dashed line and the solid line was very good in the development set Figure 5 Mid-A) and the validation set Figure 5 Mid-B). The results showed that REG1A and RUNX3 had satisfactory prediction ability in the validation set and the development set, and had a significant effect on predicting DKD.

[0122] Table 3, Baseline information of diagnostic markers and clinical characteristics in healthy controls and DKD groups in the validation cohort

[0123]

[0124] Note: n is the number of samples. Data is expressed as mean (SD), median (25% quartile, 75% quartile), or number (percentage).

[0125] 2. Correlation between the expression levels of diagnostic DEGs and clinical characteristics

[0126] The correlation between the diagnostic markers and the clinical characteristics was analyzed using the "ggplot2" package in R software, and then the results were visualized using the "ggplot2" package in R software. The baseline data of the diagnostic markers and the clinical characteristics of the DKD and DM (without DKD) groups in the validation set are shown in Table 4.

[0127] Table 4, Baseline information of diagnostic markers and clinical characteristics in DM and DKD groups

[0128]

[0129] Note: n is the number of samples. Data are expressed as mean (SD), median (25% quartile, 75% quartile) or number (percentage). Abbreviations: BMI, body mass index; FBG, fasting blood glucose; C-P, c-peptide; HbA1c, glycated hemoglobin A1c; SCr, serum creatinine; eGFR, estimated glomerular filtration rate; UA, uric acid; UACR, urinary albumin creatinine ratio; TG, triglyceride; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

[0130] The analysis results found that the expression levels of REG1A and RUNX3 in the DKD group were significantly higher than those in the DM group (Fig. 6, A, B). REG1A was positively correlated with serum creatinine (SCr), c-peptide (C-P), HbA1c, fasting blood glucose (FBG), and urinary albumin creatinine ratio (UACR) (Fig. 6, C), and was negatively correlated with eGFR level (Fig. 6, C). RUNX3 was positively correlated with UACR and SCr levels (Fig. 6, D), and was negatively correlated with eGFR level (Fig. 6, D). The results showed that both REG1A and RUNX3 were positively correlated with the clinical characteristics of urinary albumin creatinine ratio (UACR) and serum creatinine (SCr), and were negatively correlated with the glomerular filtration rate (eGFR) level. At present, the diagnosis of DKD is mainly based on the clinical diagnosis of increased UACR or decreased eGFR, suggesting that the detection of the expression levels of REG1A and / or RUNX3 has guiding significance for the diagnosis of DKD. Figure 6 Figure 6 Figure 6 The results showed that both REG1A and RUNX3 were positively correlated with the clinical characteristics of urinary albumin creatinine ratio (UACR) and serum creatinine (SCr), and were negatively correlated with the glomerular filtration rate (eGFR) level. At present, the diagnosis of DKD is mainly based on the clinical diagnosis of increased UACR or decreased eGFR, suggesting that the detection of the expression levels of REG1A and / or RUNX3 has guiding significance for the diagnosis of DKD.

[0131] 3. KM analysis of diagnostic markers and clinical characteristics

[0132] ​​The prognostic potential analysis of Kaplan-Meier (KM) curves of the identified biomarkers was constructed using the R software "Survival" package to assess the probability of DKD occurrence at a specific time period, and the log-rank test was used to determine the difference between groups. The prognostic value of diagnostic markers was evaluated using univariate and multivariate Cox proportional hazards models (Table 5). The probability of DKD occurrence within the corresponding time period was analyzed using the Kaplan-Meier (KM) method, and the variables included diagnostic DEGs and clinical characteristics. With DKD as the endpoint event, the variables associated with poor prognosis included REG1A, RUNX3, total cholesterol (TC), FBG, SCr, body mass index (BMI), and UACR, while the increase in high-density lipoprotein cholesterol (HDL-C), age (Age), and eGFR all indicated a good prognosis (Table 5). Finally we divided REG1A and RUNX3 into 4 groups according to their expression levels, and the results showed that the patients in the high expression group of both had the worst prognosis (HR = 6.87) (Table 6). In summary, REG1A and RUNX3 not only can be used as diagnostic markers for DKD patients, but also are closely related to the prognosis of DKD.

[0133] Table 5 Univariate COX regression of diagnostic markers and clinical characteristics

[0134]

[0135] HR: hazard ratio; CI: confidence interval; TC: total cholesterol (mmol / L); FBG: fasting blood glucose (mmol / L); SCr: serum creatinine (pmol / L); HDL-C: high-density lipoprotein (mmol / L); Age: age (years); eGFR: estimated glomerular filtration rate (ml / min / 1.73m 2 ); BMI: body mass index (kg / cm 2 ); UCR: urinary albumin creatinine ratio (mg / g).

[0136] Table 6 HR values of different expression level combinations of REG1A and RUNX3

[0137]

[0138] Univariate Cox regression was used to determine HR; reported log-rank p values; Bonferroni test for multiple test adjustment for pairwise comparisons. High and low expression cutoffs for REG1A: 1.93; High and low expression cutoffs for RUNX3: 1.79. HR: hazard ratio.

[0139] The application has been described in detail above. For those skilled in the art, the application can be implemented in a wider range with equivalent parameters, concentrations and conditions without departing from the spirit and scope of the application and without unnecessary experiments. Although the application gives specific examples, it should be understood that the application can be further improved. In summary, according to the principle of the application, the present application is intended to include any changes, uses or improvements of the application, including changes made by conventional techniques known in the art, which depart from the scope disclosed in the present application. Some basic features can be applied within the scope of the following attached claims. SEQUENCE LISTING <110> Shenzhen People's Hospital <120> Application of REG1A gene and RUNX3 gene as biomarkers for diabetic nephropathy <160> 4 <170> PatentIn version 3.5 <210> 1 <211> 22 <212> DNA <213> Artificial sequence <400> 1 aggcaatgac gagaactact cc 22 <210> 2 <211> 19 <212> DNA <213> Artificial sequence <400> 2 cgaaggtcgt tgaacctgg 19 <210> 3 <211> 22 <212> DNA <213> Artificial sequence <400> 3 accagctcat acttcatgct ga 22 <210> 4 <211> 20 <212> DNA <213> Artificial sequence <400> 4 ccaggtctca cggtcttcat 20

Claims

1. Use of any one of the following reagents for detecting biomarkers: A1) in the preparation of a product for the diagnosis of diabetic nephropathy; A2) in the preparation of a product for the auxiliary diagnosis of diabetic nephropathy; A3) in the preparation of a product for the screening of diabetic nephropathy; A4) in the preparation of a product for predicting or evaluating the risk of diabetic nephropathy; the biomarkers are the REG1A gene and the RUNX3 gene.

2. Use according to claim 1, characterized in that, the reagents are any one of the following: C1) a reagent for detecting the mRNA expression level of the REG1A gene and a reagent for detecting the mRNA expression level of the RUNX3 gene; C2) a reagent for detecting the protein expression level of the REG1A gene and a reagent for detecting the protein expression level of the RUNX3 gene.

3. Use according to claim 1, characterized in that, the product comprises a kit, a gene chip, a protein chip, an immunochromatographic diagnostic test paper or a high-throughput sequencing platform.

4. Use according to claim 1, characterized in that, the reagents comprise at least any one of the following: M1) primers specific for amplifying the REG1A gene and the RUNX3 gene; M2) probes specific for recognizing the REG1A gene and the RUNX3 gene; M3) antibodies that bind to the REG1A gene-encoded protein and the RUNX3 gene-encoded protein.

5. Use according to claim 4, characterized in that, the primers are as follows P1), P2), P3) and P4): P1) a forward primer specific for amplifying the RUNX3 gene with a nucleotide sequence of SEQ ID No. 1; P2) a reverse primer specific for amplifying the RUNX3 gene with a nucleotide sequence of SEQ ID No. 2; P3) a forward primer specific for amplifying the REG1A gene with a nucleotide sequence of SEQ ID No. 3; P4) a reverse primer specific for amplifying the REG1A gene with a nucleotide sequence of SEQ ID No.

4.

6. Device for the diagnosis, the aided diagnosis, the screening or the risk prediction of diabetic nephropathy, characterized in that, the device comprises the reagent of any one of claims 1-5 and a computer readable storage medium storing a computer program, the computer program causing a computer to perform the following steps: diagnosing, auxiliary diagnosing, screening or risk predicting of diabetic nephropathy according to the expression levels of the REG1A gene and the RUNX3 gene.

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