CN114959017A - Application of REG1A gene and RUNX3 gene as biomarkers of diabetic nephropathy - Google Patents

Abstract

The invention discloses application of REG1A gene and RUNX3 gene as biomarkers of diabetic nephropathy. Specifically disclosed is the use of a biomarker and/or a substance for detecting said biomarker, which is any one of the following, in the diagnosis of diabetic nephropathy (DKD) or in the manufacture of a product for the diagnosis of DKD: B1) REG1A gene and RUNX3 gene; B2) REG1A gene; B3) RUNX3 gene. The present invention develops DKD biomarkers based on high expression of REG1A gene and RUNX3 gene in blood and kidney of DKD patients. Experiments show that the biomarker has the characteristics of strong sensitivity and high specificity, can provide a quick and effective tool for DKD patient diagnosis, prognosis and risk assessment, further provides an accurate treatment scheme for patients, is favorable for realizing optimal disease management, and has good clinical application value.

Description

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

Technical Field

The invention relates to application of REG1A gene and RUNX3 gene as biomarkers of diabetic nephropathy in the technical field of molecular diagnosis.

Background

Diabetic nephropathy (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), which can progress to End-stage renal disease (ESRD). With the increasing incidence of DM worldwide, DKD has become not only the leading cause of ESRD but also one of the major factors in high disability rate and high mortality rate of diabetes. Early DKD is reversible and rapid and accurate diagnosis at an early stage can prevent further deterioration of the patient's kidney. However, DKD patients are often already in an irreversible state when they visit the clinic because it is extremely easy to ignore it because it is asymptomatic early on. Over time, kidney function gradually fails until it is completely lost. Therefore, the discovery and identification of DKD biomarkers with high sensitivity and specificity is one of the problems to be solved clinically, and has important significance for prediction, diagnosis and prognosis of DKD.

The diagnosis of DKD is based primarily on renal pathology and/or clinical manifestations, including the determination of Glomerular Filtration Rate (GFR) in addition to the course of diabetes, diabetic retinopathy, and urinary protein levels. Although traditional clinical features are readily available, their diagnostic efficacy is limited. With the continuous development of analytical techniques, high throughput techniques and bioinformatics databases, the search for disease-related genes, and the discovery of new biomarkers for assessing DKD risk is a current research hotspot. To date, DKD has no specific therapeutic approach, focusing on prophylaxis, early diagnosis and early intervention therapy. The sensitivity and specificity of currently used diagnostic markers are still not sufficient for the clinical needs of DKD diagnosis and prognosis. In view of this, research and development of more reliable, more sensitive, and more specific biomarkers have important clinical significance for the diagnosis, prediction, prognosis, and the like of DKD.

Disclosure of Invention

The technical problem to be solved by the present invention 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 subject not mentioned herein may be clearly understood by those skilled in the art through the following description.

To solve the above technical problem, the present invention firstly provides any one of the following uses of a biomarker and/or a substance for detecting the biomarker:

A1) the application in the diagnosis of the diabetic nephropathy or the preparation of products for the diagnosis of the diabetic nephropathy;

A2) the application in the auxiliary diagnosis of the diabetic nephropathy or the preparation of products for the auxiliary diagnosis of the diabetic nephropathy;

A3) the application in diabetic nephropathy screening or preparation of products for diabetic nephropathy screening;

A4) use in predicting or assessing the risk of diabetic nephropathy or in the manufacture of a product for predicting or assessing the risk of diabetic nephropathy;

A5) the application in the diabetic nephropathy prognosis evaluation or the preparation of products for the diabetic nephropathy prognosis evaluation;

the biomarker may be any of the following:

B1) REG1A gene and RUNX3 gene;

B2) REG1A gene;

B3) RUNX3 gene.

The REG1A gene described herein may be a human REG1A gene, and the RUNX3 gene may be a human RUNX3 gene.

"REG 1A" and "REG 1A gene" herein have the same meaning and may be used interchangeably, and "RUNX 3" and "RUNX 3 gene" have the same meaning and may be used interchangeably.

The nucleotide sequence of the REG1A gene was 79120488-79123409 (Update Date13-May-2022) of GenBank Accession No. P05451.3. The REG1A gene includes polynucleotides of REG1A gene and any functional equivalents of REG1A gene.

The nucleotide sequence of the RUNX3 gene is 24899511-24965138 th site of GenBank Accession No. Q13761.2 (Update Date 23-Jun-2022). The RUNX3 gene includes polynucleotides of REG1A gene and any functional equivalents of REG1A gene.

In the above application, the substance for detecting the biomarker may be any one of the following substances:

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

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

Specifically, the mRNA expression level refers to the abundance of mRNA transcribed from the gene detected at the transcription level;

the protein expression level refers to the abundance of the protein encoded by the gene measured at the translation level.

The present invention can detect the expression level of REG1A gene and/or RUNX3 gene based on the sequencing techniques of cDNA and RNA. The sequencing technologies are nucleic acid sequencing technologies, including chain terminator (Sanger) sequencing technology and dye terminator sequencing technology, and it is known to those skilled in the art that RNA is usually reverse transcribed into DNA before sequencing because it is less stable in cells and more vulnerable to nuclease in experiments, and in addition, the sequencing technologies also include next generation sequencing technology (i.e. deep sequencing/high throughput sequencing technology), which is a sequencing-by-synthesis technology based on single molecular clusters based on proprietary reversible termination chemical reaction principles. Random fragments of genome DNA are attached to an optically transparent glass surface during sequencing, hundreds of millions of clusters are formed on the glass surface after the DNA fragments are extended and subjected to bridge amplification, each cluster is a monomolecular cluster with thousands of identical templates, and then four kinds of special deoxyribonucleotides with fluorescent groups are utilized to sequence the template DNA to be detected by a reversible edge-to-edge synthesis sequencing technology. The present invention can detect transcriptomes in cDNA using second and third generation sequencing, and further detect the expression levels of REG1A gene and/or RUNX3 gene. The present invention can also quantify the expression level of REG1A gene and/or RUNX3 gene by directly detecting the RNA expression level using Nanopore sequencing.

In the above applications, the substance for detecting the biomarker includes a reagent for detecting the biomarker by reverse transcription-polymerase chain reaction, real-time fluorescence 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 immunoassay, fluorescence immunochromatography, surface plasmon resonance technology, immuno-PCR technology, or biotin-avidin technology.

In the above applications, the product may include a kit, a gene chip, a protein chip, an immunochromatographic diagnostic test paper, a high-throughput sequencing platform, or a biosensor.

The test sample of the product may be derived from a diabetic patient or a suspected diabetic patient.

The test sample of the product may be a blood sample or a tissue sample, the blood sample may be a peripheral blood sample or a peripheral blood mononuclear cell sample, and the tissue sample may be a kidney sample.

The kit can be a gene detection kit or a protein immunodetection kit; the gene detection kit comprises reagents for detecting the transcription level of REG1A gene and/or RUNX3 gene; the protein immunoassay kit comprises a REG1A gene-encoded protein and/or a specific antibody of a RUNX3 gene-encoded protein.

The gene chip comprises a solid phase carrier and oligonucleotide probes fixed on the solid phase carrier, wherein the oligonucleotide probes comprise oligonucleotide probes for detecting REG1A gene and/or RUNX3 gene.

The protein chip comprises a solid phase carrier and an antibody specific to the protein encoded by REG1A gene and/or an antibody specific to the protein encoded by RUNX3 gene immobilized on the solid phase carrier.

The immunochromatographic diagnostic strip comprises an antibody specific to the protein encoded by REG1A gene and/or an antibody specific to the protein encoded by RUNX3 gene.

The high-throughput sequencing platform comprises reagents for detecting the REG1A gene and/or the RUNX3 gene;

the biosensor comprises reagents for detecting REG1A gene and/or RUNX3 gene.

The antibody described herein may be a monoclonal antibody, a polyclonal antibody, an engineered antibody, an antibody variable region Fv, a single chain antibody ScFv, an antigen binding fragment Fab or Fab ', F (ab ') 2, Fab ' -SH, and like antibody fragments, as well as antibody derivatives, and the like.

In the above application, the product comprises a substance for detecting the biomarker.

In the above application, the substance for detecting the biomarker may include at least any one of:

m1) primers for specifically amplifying REG1A gene and/or RUNX3 gene;

m2) a probe that specifically recognizes REG1A gene and/or RUNX3 gene;

m3) binding to the protein encoded by REG1A gene and/or the protein encoded by RUNX3 gene.

Further, the primer may include at least any one of:

p1) nucleotide sequence is the forward primer of the specific amplification RUNX3 gene of SEQ ID No. 1;

p2) nucleotide sequence is the reverse primer of the specific amplification RUNX3 gene of SEQ ID No. 2;

p3) nucleotide sequence is the forward primer of the specific amplified REG1A gene of SEQ ID No. 3;

p4) nucleotide sequence is the reverse primer of SEQ ID No.4 for the specific amplification of REG1A gene.

The present invention also provides a composition, which may be any one of the following:

D1) the composition contains an agent for detecting the mRNA expression level of REG1A gene and an agent for detecting the mRNA expression level of RUNX3 gene;

D2) the composition contains reagents for detecting the protein expression level of REG1A gene and reagents for detecting the protein expression level of RUNX3 gene.

The invention also provides any one of the following applications of the composition:

F1) the application in the diagnosis of the diabetic nephropathy or the preparation of products for the diagnosis of the diabetic nephropathy;

F2) the application in the auxiliary diagnosis of the diabetic nephropathy or the preparation of products for the auxiliary diagnosis of the diabetic nephropathy;

F3) the application in diabetic nephropathy screening or preparation of products for diabetic nephropathy screening;

F4) use in predicting or assessing the risk of diabetic nephropathy or in the manufacture of a product for predicting or assessing the risk of diabetic nephropathy;

F5) the application in the diabetic nephropathy prognosis evaluation or the preparation of products for the diabetic nephropathy prognosis evaluation.

The invention also provides a kit, which can be any one of the following:

G1) the kit contains a reagent for detecting the mRNA expression level of REG1A gene and/or a reagent for detecting the mRNA expression level of RUNX3 gene;

G2) the kit contains a reagent for detecting the protein expression level of REG1A gene and/or a reagent for detecting the protein expression level of RUNX3 gene.

The detection sample of 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.

The various reagent components of the kit may be present in separate containers or may be pre-combined in whole or in part into a reagent mixture.

Further, the kit can be a gene detection kit or a protein immunodetection kit.

Further, the kit can be an ELISA kit, a qPCR kit, an electrochemiluminescence detection kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, or an immunohistochemistry detection kit, but is not limited thereto.

Further, the gene detection kit (e.g., qPCR kit) may contain at least any one of:

p1) nucleotide sequence is the forward primer of the specific amplification RUNX3 gene of SEQ ID No. 1;

p2) nucleotide sequence is the reverse primer of the specific amplification RUNX3 gene of SEQ ID No. 2;

p3) nucleotide sequence is the forward primer of the specific amplified REG1A gene of SEQ ID No. 3;

p4) nucleotide sequence is the reverse primer of SEQ ID No.4 for the specific amplification of REG1A gene.

Furthermore, the gene detection kit also comprises Taq DNA polymerase, dNTP, PCR buffer solution and Mg required by PCR amplification ²⁺ One or more of (a).

Further, the gene detection kit can also comprise an internal reference gene detection reagent, and the internal reference gene can be a GAPDH gene and/or a beta-actin gene, but is not limited thereto.

The protein immunoassay kit may contain an antibody that binds to a protein encoded by REG1A gene and/or a protein encoded by RUNX3 gene.

The present invention also provides a device for diagnosis, aided diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy, the device comprising a substance for detecting the biomarker as described herein and a computer readable storage medium having stored thereon a computer program for causing a computer to perform any of the steps of:

H1) diagnosis, auxiliary diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy is carried out according to the expression levels of REG1A gene and RUNX3 gene;

H2) diagnosis, auxiliary diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy according to the expression level of REG1A gene;

H3) and (3) carrying out diagnosis, auxiliary diagnosis, screening, risk prediction or prognosis evaluation of the diabetic nephropathy according to the expression level of the RUNX3 gene.

It is an object of the present invention to establish a simplified blood biomarker to predict risk of DKD. Microarray data obtained from the gene expression integrated database (GEO) were first analyzed, and then blood samples REG1A and RUNX3 were identified in the obtained data set as diagnostic biomarkers of DKD. Blood samples of healthy participants, diabetic patients who did not develop DKD, and DKD patients were further collected and qPCR analysis was performed on these samples to determine the transcript levels of RUNX3 and REG 1A. The obtained blood samples were then used to verify the diagnostic effect of these biomarkers in the GEO database and predict the risk of developing DKD. In summary, RUNX3 and REG1A proved to have the potential to predict DKD risk and may contribute to the prevention and management of DKD.

DKD remains the major cause of ESRD worldwide. Approximately one quarter of DM patients eventually develop DKD. Thus, identifying characteristics of DKD patients may help predict risk of disease progression. The clinical feature is the most widely used DKD predictor at present. Community atherosclerosis risk studies have found that eGFR levels fall most rapidly in diabetic patients at high risk for apolipoprotein L1 genotype, insulin use, high systolic blood pressure, and high glycosylated hemoglobin levels. Another longitudinal observation study at washington university hospital showed that age, obesity, hypertension, high levels of glycated hemoglobin and proteinuria are independent risk factors for the reduction of eGFR in type 2 diabetic patients. Data from the Swedish national diabetes registry also show that independent risk factors for a diabetic patient to develop proteinuria include advanced age, male sex, smoking, high BMI, systolic blood pressure, glycated hemoglobin, TC and LDL-C levels. With the advancement of histological analysis and sequencing technologies, a number of new biomarkers were developed to compensate for the low predicted efficacy of clinical features. The cactus study identified four plasma biomarkers for predicting eGFR reduction in type 1 diabetes patients: beta-2-microglobulin, cystatin C, neutrophil gelatinase-associated lipoprotein and osteopontin. Metabolomics analysis further identified 7 blood metabolites (octanol, oxalic acid, phosphoric acid, benzamide, creatinine, 3, 5-dimethoxymannonamide, and N-acetylglutamine) as predictors of eGFR reduction in diabetic patients. The present invention analyzed the human DKD dataset obtained from the GEO database and determined 38 Differentially Expressed Genes (DEGs) shared in blood and kidney samples. 16 DeGs were further identified by LASSO regression. The expression trends of 9 genes were consistent in blood and kidney samples. These DEGs were used to compare the diagnostic efficacy of lupus nephritis, ANCA-associated nephritis, focal segmental sclerosing nephropathy, IgA nephropathy and minimal disease nephropathy. As a result, only REG1A and RUNX3 were found to be specific for the diagnosis of DKD. Thus, REG1A and RUNX3 are potential biomarkers to differentiate DKD and DM from kidney disease (except DKD). Membranous nephropathy was excluded from our analysis, as anti-phospholipase a2 receptor antibodies have been identified as specific markers for membranous nephropathy. The transcript levels of REG1A and RUNX3 in DKD patients' blood samples (AUC 0.917) and kidney biopsy samples (AUC 0.929) had very high diagnostic efficacy. We selected the GSE142153 dataset (RNAseq from blood samples from DKD patients) as the development set, since blood samples are often the first choice for marker detection. Finally, blood samples from DKD patients and healthy individuals from shenzhen people hospital were used for external validation. As a result, REG1A was found to have the same high diagnostic effect (AUC ═ 0.948) in combination with RUNX 3. Thus, REG1A and RUNX3 are potential biomarkers of DKD.

In DKD patients' blood and kidney samples, we found significantly elevated expression levels of REG1A and RUNX3, suggesting that these genes may have pathogenic effects in DKD. Blood expression levels may also reflect kidney damage. REG1A and RUNX3 levels were positively correlated with serum creatinine to urinary protein creatinine ratio and negatively correlated with eGFR. Therefore, REG1A and RUNX3 levels in the blood may be closely related to glomerular injury and reduced renal function in DKD patients. REG1A levels were also positively correlated with C-peptide, glycated hemoglobin, and fasting blood glucose levels, suggesting that elevated REG1A levels may also be closely correlated with beta cell dysfunction. With the increase of extracellular glucose concentration, the expression level of REG1A rapidly increases, which may be an important physiological feedback loop quality in beta cell regulation. Thus, REG1A and RUNX3 are potential biomarkers of DKD development.

To assess the risk of DKD development, we plotted KM curves for REG1A, RUNX3, and clinical characteristics. The results show that people with high expression of REG1A and RUNX3 have an increased risk of developing DKD after approximately 12 and 8 years of diabetes, respectively. Elevated levels of TC, FBG, SCr, BMI and UACR were found to be risk factors for DKD, while elevated levels of eGFR and HDL-C were found to be protective factors for DKD. Table 3 shows the results of KM analysis of REG1A and RUNX3 at different expression levels. Therefore, the risk of DKD was highest when the expression levels of REG1A and RUNX3 were higher compared to the other groups (HR ═ 6.87). Neither REG1A nor RUNX3 low expression (HR ═ 1) had an increased risk of DKD. The risk of DKD development in diabetic patients 7-8 years after diabetes is rapidly increased in patients with both high expression of REG1A and RUNX3 compared to those with low expression of both genes. Thus, REG1A and RUNX3 are potential biomarkers for predicting the risk of DKD development.

The differential expression of genes is caused by various factors, and is closely related to the occurrence and development of a plurality of diseases, and bioinformatics and biometrical analysis of the differentially expressed genes can provide important theoretical basis for gene diagnosis and treatment. Screening for differential genes is to screen high-throughput gene data by statistical methods and to pick out genes with significant differences between samples.

The inventors of the present invention conducted extensive and intensive studies, and found that REG1A gene and RUNX3 gene are highly expressed in blood and kidney of diabetic nephropathy (DKD) patients by screening and analyzing gene expression profiles of the diabetic nephropathy (DKD), and developed diabetic nephropathy (DKD) biomarkers based on the high expression profiles, so as to achieve the purposes of diagnosis, screening, prognosis evaluation and risk evaluation of diabetic nephropathy (DKD) at mRNA level and protein level. REG1A and/or RUNX3 had a high diagnostic effect on DKD and was confirmed by external validation. REG1A and/or RUNX3 levels were positively and negatively correlated with UACR and eGFR levels, respectively. Therefore, the transcriptional level of REG1A and/or RUNX3 in blood samples has the potential to predict risk of DKD. The biomarker and the kit based on the detection of the REG1A gene and/or the RUNX3 gene have the characteristics of strong sensitivity and high specificity, can provide a quick and effective tool for diagnosis, prognosis and risk assessment of diabetic nephropathy (DKD) patients, further provide accurate treatment schemes for the patients, are favorable for realizing optimal disease management, and have good clinical application value.

Drawings

Fig. 1 is a screen for pegs shared in blood and kidney biopsy samples from DKD patients.

Fig. 2 is a screen for pegs that express consistently in DKD patient blood and kidney biopsy samples. Wherein, A in figure 2 is a lasso coefficient section diagram with 38 characteristics. A coefficient profile is generated from a log (λ) sequence. A vertical line is drawn at the value selected using 5 times cross-validation, where the optimal lambda yields a characteristic that the 16 coefficients are non-zero. B in fig. 2 employs quintupling cross validation by the minimum criterion for optimal parameter (λ) selection in the lasso model. A partial likelihood deviation (binomial deviation) curve is plotted against a log (lambda) curve. By using the minimum standard and the 1SE of the minimum standard (1-SE standard), a vertical dotted line is drawn at the optimum value. C in fig. 2 and D in fig. 2 are the expression of 16 deg's in blood and kidney. Among them 9 varieties of DeGs that are consistently expressed in blood and kidney. In fig. 2E is the distribution of these 9 DEGs in the DKD patient blood samples. In fig. 2F is the distribution of 9 DEGs in DKD patient kidney biopsy samples. Lasso: minimum absolute contraction and select operator; and SE: standard error.

FIG. 3 is a validation of DKD diagnostic efficacy of REG1A and/or RUNX3 as diagnostic markers. In FIG. 3, A and B in FIG. 3 are DKD diagnostic efficacy analyses of REG1A and RUNX3 blood samples (GSE142153), respectively, and box plots show that the expression levels of REG1A and RUNX3 in blood samples are significantly elevated (DKD vs HC, rank sum test). FIG. 3C is a ROC curve showing the efficacy of REG1A and RUNX3 in combination for the diagnosis of blood samples. D in FIG. 3 and E in FIG. 3 are DKD diagnostic efficacy analyses of REG1A and RUNX3 kidney samples (GSE30122), respectively, and box plots show that the expression levels of REG1A and RUNX3 in kidney samples are significantly elevated (DKD vs HC). FIG. 3, F is a ROC curve showing the efficacy of REG1A and RUNX3 in combination for the diagnosis of kidney samples. P <0.05 (x) indicates a statistical difference, P < 0.01 (x) indicates a significant statistical difference, and P < 0.001 (x) indicates a significant statistical difference.

FIG. 4 is a graph of the expression and diagnostic effect of diagnostic markers in a validation cohort. A in FIG. 4 and B in FIG. 4 are box plots of the expression levels of REG1A and RUNX3, which show that the expression levels of REG1A and RUNX3 are significantly elevated (DKD vs HC). In FIG. 4, C is the ROC curve for REG1A and the ROC curve for RUNX 3. In FIG. 4, D is a ROC curve for the validation of diagnostic efficacy after fitting two diagnostic indicators to one variable. P <0.05 (x) indicates a statistical difference, P < 0.01 (x) indicates a significant statistical difference, and P < 0.001 (x) indicates a significant statistical difference.

FIG. 5 is a calibration curve for diagnostic markers in the development and validation cohorts. FIG. 5A is a calibration curve for diagnostic markers in a development cohort (development set); in fig. 5B is the calibration curve for the diagnostic markers in the validation cohort (validation set).

Fig. 6 is a graph of the expression of diagnostic markers and their correlation with clinical characteristics in DKD and DM cohorts. A in FIG. 6 and B in FIG. 6 are box plots of the expression levels of REG1A and RUNX3, which show that the expression levels of REG1A and RUNX3 are significantly increased (DKD vs DM). C in FIG. 6 is the correlation of REG1A with clinical characteristics. In fig. 6D is the correlation of RUNX3 with clinical features. P <0.05 (x) indicates a statistical difference, P < 0.01 (x) indicates a significant statistical difference, and P < 0.001 (x) indicates a significant statistical difference.

Detailed Description

The present invention is described in further detail below with reference to specific embodiments, which are given for the purpose of illustration only and are not intended to limit the scope of the invention. The examples provided below serve as a guide for further modifications by a person skilled in the art and do not constitute a limitation of the invention in any way.

The experimental procedures in the following examples, unless otherwise indicated, are conventional and are carried out according to the techniques or conditions described in the literature in the field or according to the instructions of the products. Materials, reagents and the like used in the following examples are commercially available unless otherwise specified.

Example 1 screening of biomarkers for diabetic nephropathy

1. Screening for differential genes (DEGs) shared in blood and Kidney samples

Three GEO datasets (GSE30122, GSE142153 and GSE72326) were obtained from a GEO database (Gene Expression integration database, https:// www.ncbi.nlm.nih.gov/GEO /), subjected to preliminary analysis, and subjected to Gene ID and symbol conversion by Perl script. GSE30122, GSE142153, and GSE72326 contained transcriptome data from DKD patient kidney samples, DKD patient blood samples, CKD patient blood samples, and healthy control samples (HC), respectively.

Identification of Differentially Expressed Genes (DEGs): r software (version 4.1.0) was used for data analysis and mapping. The DEGs were screened using the "limma" R package in the R software, and heat and volcanic maps of the DEGs were constructed using the "ggplot 2" package to visualize the expression levels of the DEGs. p-value <0.05, considered statistically significant as a criterion for screening Differentially Expressed Genes (DEGs).

Results as shown in fig. 1, fig. 1 shows the screening process of DEGs shared in blood and kidney biopsy samples of DKD patients. Based on healthy control samples (HC), 679 (GSE 142153: HC, n-10, DKD, n-23) and 499 (GSE 30122: HC, n-50, DKD, n-19) logs were identified in DKD patients' blood and kidney samples, respectively. A total of 38 shared DEGs were found in DKD patients' blood and kidney biopsy samples.

2. Screening for DeGs that express consistently in blood and kidney samples from DKD patients

Dess shared in DKD patient blood samples (GSE142153) were further screened using LASSO regression algorithm. A total of 16 DEGs were identified as diagnostic DKD markers (a in fig. 2, B in fig. 2), with 5 down-regulated genes and 11 up-regulated genes (C in fig. 2), respectively. In the kidney biopsy samples (GSE30122) of DKD patients, there were 2 genes down-regulated and 14 genes up-regulated (D in fig. 2).

Finally, 9 markers with consistent expression profiles were found in DKD patients' blood and kidney biopsy samples: including alpha-2A adrenergic receptor (ADRA2A), C-C motif chemokine 5(CCL5), cholesterol 25-hydroxylase (CH25H), C-X-C chemokine receptor type 4 (CXCR4), hemoglobin subunit delta (HBD), hydroxycarboxylic acid receptor 3(HCAR3), lysophosphatidylcholine acyltransferase 1(LPCAT1), regenerative islet-derived protein 1A (REG1A), and runt-related transcription factor 3(RUNX 3). The distribution of the expression profiles of these DEGs in the blood and kidney of DKD patients (E in fig. 2 and F in fig. 2) shows that their expression is upregulated.

3. Screening for diagnostic specific DEGs

Screening and validation of diagnostic markers the minimum absolute shrinkage and selection operator (lasso) method implemented in the "glmnet" software package was used for data reduction and selection of predictive features for DKD patients. The diagnostic performance was evaluated by receiver operating characteristic curve (ROC) method. The 'pROC' software package is used for drawing a receiver working characteristic Curve and calculating the Area Under the Curve (AUC) of single-factor or multi-factor ROC, wherein the Area Under the ROC Curve is an important test accuracy index, and the larger the Area Under the ROC Curve is, the higher the diagnostic value of the test is. Calibration curves were drawn using the "rms" software package to assess whether the predicted probability of the model approximates the true probability.

DKD-specific DEGs were further screened specifically using the method described above and two blood transcriptome datasets (GSE142153 and GSE72326) were analyzed to determine the value of DKD-specific DEGs for diagnosis.

In the GSE142153 dataset, several DEGs (ADRA2A, CCL5, CH25H, CXCR4, HBD, HCAR3, LPCAT1, REG1A, and RUNX3) were found to have higher differences, and the AUC of blood samples of other DEGs was greater than 0.7 (table 1) except for the lower diagnostic efficiency of CH25H (AUC ═ 0.67). GSE72326 dataset [ lupus nephritis ═ 48; ANCA vasculitis-associated nephritis 10; focal segmental sclerosing nephropathy ═ 3; IgA nephropathy ═ 5; morbid nephropathy micro ═ 3] was used to validate the specificity of these DEGs for DKD diagnosis, with the exception of HCAR3, which was excluded from the analysis due to not being found in the dataset, and the other 8 DEGs were relatively less effective in diagnosis of LN patients (table 1). CXCR4, LPCAT1, and HBD had higher diagnostic efficacy on ANCA-related nephritis (table 1). Significant diagnostic performance of CXCR4, CCL5, and HBD on FSGS patients was observed (table 1). ADRA2A, LPCAT1, HBD had higher diagnostic efficacy in patients with IgAN (Table 1). CH25H and HBD gave good diagnostic efficacy in MCD patients (Table 1).

In conclusion, the transcriptional levels of REG1A and RUNX3 in blood samples may be used as DKD specific predictors of disease progression, and have good diagnostic value.

Table 1: DKD diagnostic gene specificity analysis

Table 1: the area under the curve (AUC) of the 9 differential genes in various chronic kidney diseases is shown. Diabetic nephropathy data was from GSE 72326; chronic kidney disease data was from GSE 72326. The aim is to screen for genes that are diagnostic for specificity in DKD.

4. Validation of DKD diagnostic potential by REG1A and/or RUNX3 as diagnostic markers

Analysis of DKD diagnostic effects of RUNX3 and REG1A blood samples (GSE142153) revealed significantly elevated levels of REG1A and RUNX3 expression in blood samples of DKD patients compared to healthy control samples (HC) (A in FIG. 3, B in FIG. 3; GSE 142153). The combined diagnostic effect of these genes (RUNX3 and REG1A) was also high (AUC 0.917, 95% CI 0.818-1) (C in FIG. 3).

Similar results were also obtained from kidney samples of DKD patients (GSE30122) in which REG1A and RUNX3 were significantly elevated compared to healthy control samples (HC) (D in FIG. 3, E in FIG. 3; GSE 30122). The combined diagnostic effect of RUNX3 and REG1A was also high (AUC ═ 0.929, 95% CI: 0.846-1) (FIG. 3, F). These results indicate that REG1A and RUNX3 were superior in diagnostic efficacy in combination, and that the diagnostic efficacy in blood was not inferior to that of kidney.

Example 2 verification and application of biomarkers for diabetic nephropathy

This example further verified the diagnostic performance of the biomarkers (REG1A and/or RUNX3) of the present invention on the basis that the biomarkers REG1A and/or RUNX3 for diabetic nephropathy have been screened in example 1.

1. Clinical sample qPCR analytical validation

Clinical statistical data:

from the biological sample bank of Shenzhen's national hospital, a total of 141 human blood samples from DKD patients, diabetic patients (not accompanied by DKD) and healthy individuals (HC) were collected.

Sample inclusion criteria:

healthy Human (HC): is more than or equal to 18 years old; no damage to liver and kidney functions; no history of tumor disease; there is no history of diabetes.

DKD patients: 1) a large amount of albuminuria; 2) diabetic retinopathy with microalbuminuria; 3) microalbuminuria occurs in T1DM patients with a course of diabetes more than 10 years.

Diabetic patients (DKD-free patients) 3 satisfied 1 of them: 1. diabetic symptoms + plasma glucose levels at any time greater than or equal to 11.1mmol/l (200 mg/dl); 2. fasting plasma glucose levels are greater than or equal to 7.0mmol/l (126 mg/dl); in the OGTT test, PG levels at 2 hours were 11.1mmol/l or more (200 ng/dl).

All sample acquisitions for experimental purposes were informed by the patient and approved by the ethical committee of the national hospital, shenzhen. For continuous variables, data are expressed as mean ± Standard Deviation (SD) or median and quartile range, and as a percentage of categorical variables. The Mann-WhitneyU test or the t test was used to compare the differences between the two groups according to whether the data fit a normal distribution.

Real-time fluorescent quantitative pcr (qpcr) analysis: total RNA was extracted from Peripheral Blood Mononuclear Cells (PBMCs) using trizol (invitrogen) according to the manufacturer's instructions. Reverse transcription of RNA reverse transcription was performed using a reverse transcription RT reverse transcription kit (ThermoFisher science). Quantitative PCR was performed using PowerUpSYBR Green Master mix (Thermo science). The results of the study were normalized using the GAPDH method. qPCR employs the ABI real-time fluorescent quantitative PCR system (applied biosystems, foster city, CA, usa). Gene expression level utilization 2 ^-△△Ct And calculating by the method. Gene-specific PCR primers are shown in Table 2.

TABLE 2 primer sequences for qRT-PCR analysis

Validation of centralized evaluation of the diagnostic properties of REG1A and RUNX3 qPCR analysis of REG1A and RUNX3 was performed using 141 blood samples from the human biological specimen bank of Shenzhen Renshimi Hospital.

DKD (n-50) and HC (n-41) groups were included in the validation set (table 3). In the DKD group, REG1A and RUNX3 were significantly up-regulated and compared to the HC group (A in FIG. 4, B in FIG. 4). AUC of REG1A and RUNX3 were 0.912 and 0.859, respectively (C in FIG. 4). When REG1A and RUNX3 were fitted as univariates, the diagnostic efficiency of the development set was 0.917 (C in FIG. 3), and the diagnostic efficiency of the validation set (AUC 0.948, 95% CI: 0.989-0.998) was higher (D in FIG. 4), indicating that REG1A and RUNX3 have higher diagnostic value. In the development and validation sets, the predicted and true values of the calibration curve are also highly consistent (a in fig. 5, B in fig. 5), and the dashed diagonal line represents the perfect prediction of an ideal model. The solid line indicates the performance of the model, and the fit of the solid line to the dashed line indicates the more accurate the model. The dashed and solid lines are very good in both the evolving set (a in fig. 5) and the validated set (B in fig. 5). The results show that REG1A and RUNX3 have satisfactory predictive power in both validation set and development set, and have significant effect in predicting DKD.

Table 3, verification of baseline information for diagnostic markers and clinical features in healthy controls versus DKD groups in cohort

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

2. Correlation between diagnostic DeGs expression levels and clinical features

Correlation of diagnostic markers with clinical characteristics the diagnostic markers were subjected to a spearman correlation analysis with clinical characteristics using the "ggplot 2" package in the R software, followed by visualization of the results using the "ggplot 2" package in the R software. The diagnostic markers and baseline data for clinical features for the DKD and DM (no DKD) groups in the validation set are shown in table 4.

Table 4, Baseline information for DM and DKD panel diagnostic markers and clinical features

Note: n is the number of samples. Data are expressed as mean (SD), median (25% quartile, 75% quartile) or number (percentage). For short: BMI: body mass index, FBG: fasting blood glucose, C-P: c peptide, HbA1 c: glycated hemoglobin A1c, SCr: serum creatinine, glomerular filtration rate (eGFR): estimation of 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.

As a result of analysis, the expression levels of REG1A and RUNX3 were found to be significantly higher in the DKD group than in the DM group (A in FIG. 6, B in FIG. 6). REG1A was positively correlated with Serum Creatinine (SCR), C-peptide (C-P), HbA1C, fasting plasma glucose (FBG), Urinary Albumin Creatinine Ratio (UACR), and negatively correlated with eGFR levels (C in FIG. 6). RUNX3 was positively correlated with UACR, SCr levels and negatively correlated with eGFR levels (D in fig. 6). The results showed that both REG1A and RUNX3 were positively correlated with the clinical characteristic Urinary Albumin Creatinine Ratio (UACR) and Serum Creatinine (SCR) and negatively correlated with glomerular filtration rate (eGFR) level. However, the current diagnosis of DKD is mainly based on the clinical diagnosis of increased UACR or decreased eGFR, suggesting that clinical examination of the expression levels of REG1A and/or RUNX3 has guiding significance for the diagnosis of DKD.

3. KM analysis of diagnostic markers and clinical features

Prognostic potential analysis of the identified biomarker Kaplan-meier (km) curve was constructed using the R software "Survival" software package to assess the probability of DKD occurring over a specific time period, and the log rank test was used to determine differences between groups. Prognostic value of diagnostic markers was assessed using single-factor and multi-factor Cox proportional hazards models (table 5). The probability of DKD occurrence in the corresponding time period was analyzed using the Kaplan-meier (km) method, variables including diagnostic DEGs and clinical characteristics. With DKD as the endpoint event, variables associated with poor prognosis included REG1A, RUNX3, Total Cholesterol (TC), FBG, SCr, Body Mass Index (BMI) and UACR, while increases in high density lipoprotein cholesterol (HDL-C), Age (Age) and eGFR all suggested a good prognosis (table 5). Finally, we divided REG1A and RUNX3 into 4 groups according to the expression level, and the results showed that patients with both high expression groups had the worst prognosis (HR 6.87) (Table 6). In conclusion, REG1A and RUNX3 not only served as diagnostic markers for DKD patients, but REG1A and RUNX3 also correlated well with DKD prognosis.

TABLE 5 univariate COX regression of diagnostic markers and clinical features

HR: the hazard ratio; CI: a confidence interval; TC: total cholesterol (mmol/L); FBG: fasting plasma glucose (mmol/L); SCr: serum creatinine (μmol/L); HDL-C: high density lipoprotein (mmol/L); age: age (year); eGFR: estimation of glomerular filtration Rate (ml/min/1.73 m) ² ) (ii) a BMI: body mass index (kg/cm) ² ) (ii) a UCR: urinary protein creatinine ratio (mg/g).

Table 6: HR values for combinations of different expression levels of REG1A and RUNX3

Determining HR using one-way Cox regression; reported log-rank p values; bonferroni test multiple test adjustments were compared pairwise. High and low expression cut-points for REG 1A: 1.93; high and low expression cut-points for RUNX 3: 1.79. HR: hazard ratio.

The present invention has been described in detail above. It will be apparent to those skilled in the art that the invention can be practiced in a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation. While the invention has been described with reference to specific embodiments, it will be appreciated that the invention can be further modified. In general, this application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. The use of some of the essential features is possible within the scope of the claims attached below.

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Claims (10)

1. Use of any of the following biomarkers and/or substances for detecting said biomarkers:

A1) the application in the diagnosis of the diabetic nephropathy or the preparation of products for the diagnosis of the diabetic nephropathy;

A2) the application in the auxiliary diagnosis of the diabetic nephropathy or the preparation of products for the auxiliary diagnosis of the diabetic nephropathy;

A3) the application in diabetic nephropathy screening or preparation of products for diabetic nephropathy screening;

A4) use in predicting or assessing the risk of diabetic nephropathy or in the manufacture of a product for predicting or assessing the risk of diabetic nephropathy;

A5) the application in the diabetic nephropathy prognosis evaluation or the preparation of products for the diabetic nephropathy prognosis evaluation;

the biomarker is any one of the following:

B1) REG1A gene and RUNX3 gene;

B2) REG1A gene;

B3) RUNX3 gene.

2. The use according to claim 1, wherein the substance for detecting the biomarker is any one of:

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

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

3. The use according to claim 1 or 2, wherein the means for detecting the biomarker comprises 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 immunoassay, fluorescence immunochromatography, surface plasmon resonance, immuno-PCR or biotin-avidin.

4. The use of any one of claims 1-3, wherein the product comprises a kit, gene chip, protein chip, immunochromatographic diagnostic strip, high-throughput sequencing platform, or biosensor.

5. Use according to any of claims 1 to 4, wherein the product comprises a substance for detecting the biomarker of claim 1.

6. The use according to claim 5, wherein the substance for detecting the biomarker of claim 1 comprises at least any one of:

m1) primers for specifically amplifying REG1A gene and/or RUNX3 gene;

m2) a probe that specifically recognizes REG1A gene and/or RUNX3 gene;

m3) binding to the protein encoded by REG1A gene and/or the protein encoded by RUNX3 gene.

7. A composition, characterized in that the composition is any one of the following:

D1) the composition contains an agent for detecting the mRNA expression level of REG1A gene and an agent for detecting the mRNA expression level of RUNX3 gene;

D2) the composition contains reagents for detecting the protein expression level of REG1A gene and reagents for detecting the protein expression level of RUNX3 gene.

8. Use of the composition of claim 7, wherein the use comprises any of the following:

F1) the application in the diagnosis of diabetic nephropathy or the preparation of products for the diagnosis of diabetic nephropathy;

F2) the application in the auxiliary diagnosis of the diabetic nephropathy or the preparation of products for the auxiliary diagnosis of the diabetic nephropathy;

F3) the application in diabetic nephropathy screening or preparation of products for diabetic nephropathy screening;

F4) use in predicting or assessing the risk of diabetic nephropathy or in the manufacture of a product for predicting or assessing the risk of diabetic nephropathy;

F5) the application in the diabetic nephropathy prognosis evaluation or the preparation of products for the diabetic nephropathy prognosis evaluation.

9. The kit is characterized by comprising any one of the following components:

G1) the kit contains a reagent for detecting the mRNA expression level of REG1A gene and/or a reagent for detecting the mRNA expression level of RUNX3 gene;

G2) the kit contains a reagent for detecting the protein expression level of REG1A gene and/or a reagent for detecting the protein expression level of RUNX3 gene.

10. An apparatus for diagnosis, aided diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy, the apparatus comprising a substance for detecting the biomarkers as claimed in any one of claims 1 to 6 and a computer readable storage medium storing a computer program for causing a computer to perform any one of the steps of:

H1) diagnosis, auxiliary diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy is carried out according to the expression levels of REG1A gene and RUNX3 gene;

H2) diagnosis, auxiliary diagnosis, screening, risk prediction or prognosis evaluation of diabetic nephropathy according to the expression level of REG1A gene;

H3) and (3) carrying out diagnosis, auxiliary diagnosis, screening, risk prediction or prognosis evaluation of the diabetic nephropathy according to the expression level of the RUNX3 gene.

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