Biomarkers of delayed recovery of transplanted kidney function after kidney transplantation and their application

Abstract

This invention relates to the field of biomarkers and their applications, specifically to biomarkers for delayed recovery of transplanted kidney function after kidney transplantation and their applications. Using a combination of metabolomics methods and enzyme-linked immunosorbent assay (ELISA), analysis revealed that the expression of the differentially expressed metabolite 8-hydroxydeoxyguanosine (8-OHdG) was significantly increased in patients with end-stage chronic kidney disease after kidney transplantation. High expression may be related to the severity of pre-transplant chronic kidney disease. The serum 8-OHdG concentration one day after kidney transplantation has a certain diagnostic value for the occurrence of delayed recovery of kidney function (DGF). Serum 8-OHdG levels fluctuate considerably and are correlated with post-operative changes in kidney function, providing a reference for clinically seeking reliable biological indicators for early diagnosis of whether DGF has occurred in kidney transplant patients, and aiding in the clinical evaluation of donor kidney quality and guiding subsequent treatment for patients.

Description

Technical Field

[0001] This invention relates to the field of biomarkers and their uses, specifically to biomarkers for delayed recovery of transplanted kidney function after kidney transplantation and their applications. Background Technology

[0002] In recent years, the increasing incidence of chronic kidney disease has been affecting hundreds of thousands of people worldwide. The ultimate outcome of chronic kidney disease is the development of end-stage renal disease. Although renal replacement therapies such as peritoneal dialysis and hemodialysis alleviate this condition by filtering the patient's blood, they cannot replace other kidney functions, such as hormone release or homeostasis. [1] As the gold standard for treating end-stage renal disease, the number of patients waiting for organs far exceeds the number of donors. Despite changes in organ allocation, the use of broader criteria, and the inclusion of living donors, the number of patients waiting for organs continues to grow at an alarming rate. [2] The severe shortage of existing organ supplies continues to plague the field of solid organ transplantation.

[0003] The host's immune response to transplanted organs is complex and diverse. Immunosuppressive therapy for kidney transplant recipients is routinely administered and adjusted based on functional or histological assessments of the allogeneic graft and / or signs of drug toxicity or infection. Because traditional biomarkers, such as estimating glomerular filtration rate and immunosuppressant blood concentrations, lack sensitivity and specificity in accurately identifying immune and non-immune damage to the transplanted kidney, a significant proportion of patients may receive too much or too little immunosuppression, leading to higher rates of infection, malignancy, and drug toxicity, or an increased risk of acute and chronic graft injury due to rejection. Finding reliable biomarkers is crucial for personalized treatment aimed at prolonging allogeneic graft survival.

[0004] Advances have been made in organ preservation, surgical techniques, intensive care, and immunosuppression in kidney transplantation, leading to improved perioperative graft survival rates. Continuous improvements in immunosuppressive therapy and treatment monitoring have enhanced graft prognosis and patient survival. However, kidney transplantation still faces many challenges. Postoperative complications such as transplant failure, early renal insufficiency, late renal decline, and rejection severely impact transplant survival. Therefore, early and timely diagnosis and management of early post-transplant complications are crucial for graft survival. Summary of the Invention

[0005] The purpose of this invention is to provide biomarkers for delayed recovery of transplanted kidney function after kidney transplantation.

[0006] Another object of the present invention is to provide the use of biomarkers for delayed recovery of transplanted kidney function after kidney transplantation.

[0007] The specific details of the present invention are as follows:

[0008] The biomarker for delayed recovery of transplanted kidney function after kidney transplantation as described in this invention is any one of 8-hydroxydeoxyguanosine, indole-3-carboxylic acid, dopamine, hydroxyindole, indolesulfonate, methoxyacetic acid, acetylcarnitine, and kynurenine.

[0009] Preferably, the biomarker for delayed recovery of transplanted kidney function after kidney transplantation according to the present invention is 8-hydroxydeoxyguanosine.

[0010] The mass-to-charge ratio (m / z) of the 8-hydroxydeoxyguanosine described in this invention is 298.072.

[0011] The application of the biomarker 8-hydroxydeoxyguanosine described in this invention in the preparation of products for the diagnosis of delayed recovery of transplanted kidney function.

[0012] The application of the biomarker 8-hydroxydeoxyguanosine described in this invention in the preparation of products for auxiliary diagnosis of delayed recovery of transplanted kidney function.

[0013] The products described in this invention include reagent kits, test strips, chips, mass spectrometers, and chromatograms.

[0014] The biomarker detection method of this invention involves using the subject's serum as a sample and determining it through serum metabolomics, then exploring the subject's serum biomarkers and serum inflammatory factors through ELISA. The serum sample is incubated at room temperature for 2 hours or overnight at 2-8°C, then centrifuged at 1000×g for 20 minutes. The supernatant is collected and the subject's serum 8-OHdG concentration is detected using a human 8-OHdG enzyme-linked immunosorbent assay kit. The specific steps are as follows:

[0015] 1) Equilibrate the kit to room temperature for 30 minutes, then remove the required strips from the aluminum foil bag. Seal the remaining strips in a resealable bag and return them to 4°C. Add 0.5, 1, 2, 4, 8, and 16 ng / mL of the kit to the standard wells, add the sample to the sample wells, add 50 μL to each of the standard and sample wells, and do not add any to the blank wells.

[0016] 2) Add 100 μL of HRP-labeled detection antibody to each well of the standard and sample wells, seal the membrane and incubate at 37°C for 60 min;

[0017] 3) Discard the liquid, pat dry on absorbent paper, add 350μL of washing solution to each well, let stand for 1 minute, shake off the washing solution, pat dry on absorbent paper, and repeat the washing process 5 times.

[0018] 4) Add 50 μL of substrate A and 50 μL of substrate B to each well and incubate at 37°C in the dark for 15 min;

[0019] 5) Add 50 μL of stop solution to each well and measure the OD value of each well at a wavelength of 450 nm within 15 min.

[0020] 6) By plotting the logarithm of the average OD obtained from each of the six standards along the vertical Y-axis and the logarithm of the corresponding concentration along the horizontal X-axis, a standard curve is constructed. In order to determine the amount of each index in each sample, the absorbance of the sample is then substituted into the regression equation to form the standard curve.

[0021] The kit described in this invention is an ELISA kit.

[0022] The serum inflammatory factors described in this invention include TNF-α, IL-17, and ROS.

[0023] The 8-OHdG described in this invention showed a significant positive correlation with the concentrations of TNF-α, IL-17α, serum creatinine, and blood urea nitrogen after surgery.

[0024] The 8-OHdG mentioned in this invention is an abbreviation for 8-hydroxydeoxyguanosine.

[0025] Beneficial effects:

[0026] 1. This invention uses metabolomics to screen out the optimal component 8-OHdG as a biomarker for DGF after kidney transplantation. It can be used for early and timely diagnosis of DGF, which is of great significance for early prevention and treatment of DGF.

[0027] 2. This invention uses metabolomics to analyze the differences in metabolic components between kidney transplant patients before and after transplantation. The results showed that the difference in 8-OHdG was the most significant (P<0.001) and the largest fold change (FC=13.947), indicating that 8-OHdG is a stable and reliable biomarker.

[0028] 3. This invention uses ROC analysis of serum 8-OHdG as a biological marker. The results show that: serum 8-OHdG levels one day before surgery cannot be used to diagnose DGF (AUC = 0.515, p = 0.8613); serum 8-OHdG levels one day after surgery can diagnose DGF (AUC = 0.8766, p < 0.0001); and serum 8-OHdG levels one week after surgery can diagnose DGF (AUC = 0.6755, p = 0.0483). This indicates that 8-OHdG can be used as a biomarker for the early clinical diagnosis of DGF after kidney transplantation. Attached Figure Description

[0029] Figure 1 Principal component analysis diagram of positive ion PCA before and after kidney transplantation

[0030] Figure 2 Principal component analysis (PCA) of negative ions after kidney transplantation

[0031] Figure 3 Volcano plot of serum component metabolomics results

[0032] Figure 4 Scatter plot of serum 8-OHdG concentration distribution one day before surgery in normal healthy individuals and kidney transplant patients

[0033] Figure 5 Scatter plot of changes in serum 8-OHdG concentration one day before and one day after surgery

[0034] Figure 6 Scatter plot of changes in 8-OHdG serum concentration one day before surgery and one week after surgery

[0035] Figure 7 Scatter plot of changes in serum 8-OHdG concentration one day and one week post-surgery

[0036] Figure 8 Scatter plot showing the correlation between serum 8-OHdG concentration and [the relationship between these two factors].

[0037] Figure 9 Comparison of 8-OHdG concentrations in the IGF and DGF groups one day before surgery.

[0038] Figure 10 Comparison of 8-OHdG concentrations in the IGF and DGF groups one day post-surgery.

[0039] Figure 11 Comparison of 8-OHdG concentrations in the IGF and DGF groups one week post-surgery.

[0040] Figure 12 ROC analysis of serum 8-OHdG levels one day before surgery to diagnose DGF (digestive disease)

[0041] Figure 13 ROC analysis of serum 8-OHdG levels one day after surgery to diagnose DGF (digestive disease)

[0042] Figure 14 ROC analysis of serum 8-OHdG levels one week post-surgery to diagnose DGF (digestive leukemia) Detailed Implementation

[0043] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0044] Example 1

[0045] Biomarkers for delayed recovery of transplanted kidney function: 8-hydroxydeoxyguanosine.

[0046] Example 2

[0047] 8-Hydroxyguanosine kit for diagnosing delayed recovery of transplanted kidney function.

[0048] Example 3

[0049] 8-Hydroxyguanosine test strips for diagnosing delayed recovery of transplanted kidney function.

[0050] Example 4

[0051] 8-Hydroxyguanosine microarray for diagnosing delayed recovery of transplanted kidney function.

[0052] Example 5: Detection method for 8-hydroxydeoxyguanosine

[0053] After placing the serum sample at room temperature for 2 hours or overnight at 2-8°C, centrifuge it at 1000×g for 20 minutes and collect the supernatant.

[0054] 1) Equilibrate the kit to room temperature for 30 minutes, then remove the required strips from the aluminum foil bag. Seal the remaining strips in a resealable bag and return them to 4°C. Add 0.5, 1, 2, 4, 8, and 16 ng / mL of the kit to the standard wells, add the sample to the sample wells, add 50 μL to each of the standard and sample wells, and do not add any to the blank wells.

[0055] 2) Add 100 μL of HRP-labeled detection antibody to each well of the standard and sample wells, seal the membrane and incubate at 37°C for 60 min;

[0056] 3) Discard the liquid, pat dry on absorbent paper, add 350μL of washing solution to each well, let stand for 1 minute, shake off the washing solution, pat dry on absorbent paper, and repeat the washing process 5 times.

[0057] 4) Add 50 μL of substrate A and 50 μL of substrate B to each well and incubate at 37°C in the dark for 15 min;

[0058] 5) Add 50 μL of stop solution to each well, and measure the OD value of each well at a wavelength of 450 nm within 15 min.

[0059] 6) Plot a standard curve by taking the logarithm of the average OD obtained from each of the six standards along the vertical Y-axis and the logarithm of the corresponding concentration along the horizontal X-axis, and then substitute the absorbance of the sample into the regression equation to plot the standard curve.

[0060] To further verify the feasibility of the present invention, the inventors conducted a series of experiments, the steps of which are as follows:

[0061] 1. Materials and Methods

[0062] 1.1 Materials

[0063] 1.1.1 Research Subjects

[0064] With the approval of the Ethics Committee of this hospital, a preliminary metabolomics analysis was conducted on 10 blood samples from 5 patients diagnosed with chronic renal failure and undergoing kidney transplantation at the Department of Organ Transplantation, Affiliated Hospital of Guizhou Medical University, between October 2020 and October 2022. An additional 60 patients diagnosed with chronic renal failure and undergoing kidney transplantation at the same department were selected for exploratory research, and 30 healthy adults were chosen as controls. All participants and their families were informed of the relevant content of this experiment, and informed consent was obtained from them. All relevant clinical data of the recipients were provided by the Department of Organ Transplantation, Affiliated Hospital of Guizhou Medical University, and the research project was approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University.

[0065] 1.1.1.1 Subject Inclusion Criteria

[0066] Healthy control group: Blood samples were collected from 30 healthy adults (18-60 years old) at the Physical Examination Center of the Affiliated Hospital of Guizhou Medical University. None of them had diseases of the liver, kidneys, heart, lungs or other organs.

[0067] Kidney transplant surgery treatment group: 1) Patients who underwent kidney transplant surgery at the Department of Organ Transplantation, Guizhou Medical University during this experiment (October 1, 2020 - December 13, 2022); 2) The donor source was DCD donor or relative donation donor; 3) The patients included in the experiment underwent their first allogeneic kidney transplant surgery during this experiment.

[0068] 1.1.1.2 Case Exclusion Criteria

[0069] The exclusion criteria for this experiment were as follows: 1) Patients under 20 years of age or over 60 years of age were not eligible for enrollment; 2) Patients with primary non-function of the transplanted kidney could not be ruled out; 3) Patients diagnosed with transplanted kidney failure or dysfunction due to hyperacute or acute rejection; 4) Patients who had undergone or were undergoing bone marrow or other organ transplants; 5) Patients using other medications that could significantly affect serum creatinine levels, in addition to prescribed immunosuppressants and other drugs; 6) Patients with other diseases affecting serum creatinine levels besides kidney disease, making it impossible to determine the cause of serum creatinine changes; 7) Patients who, or their families, did not consent to participate in the testing; 8) Patients with poor preoperative or postoperative compliance, or those unable to cooperate with treatment and research due to other mental illnesses.

[0070] 1.1.2 Patient Information

[0071] All patients enrolled in this study were scheduled for kidney transplantation at the Affiliated Hospital of Guizhou Medical University. Routine and surgical data were collected, including: patient gender, age at surgery, BMI, and whether preoperative renal replacement therapy was administered. The immunosuppressive regimen was rabbit anti-human thymocyte globulin / baliximab, and the immunosuppressive regimen was mycophenolate mofetil or mycophenolate mofetil enteric-coated tablets plus tacrolimus plus hormones. Laboratory data on creatinine and blood urea nitrogen 1 day before surgery, 1 day after surgery, and 7 days after surgery were obtained through the Affiliated Hospital of Guizhou Medical University's online HIS system.

[0072] 1.1.3 Sample Collection and Storage

[0073] Five patients who passed all preoperative examinations and were willing to undergo kidney transplantation at the Transplantation Department of the Affiliated Hospital of Guizhou Medical University had their blood drawn intravenously (4 ml each). The blood was collected in ordinary serum tubes, allowed to stand for 2 hours, centrifuged at 3500 rpm for 5 minutes, and 500 μL of the supernatant serum was collected in a 0.5 ml centrifuge tube for later use; this was designated as the pre-transplant group. After surgery, when the patients were in stable condition, blood was drawn again intravenously on the seventh day of the perioperative period to prepare serum for later use; this was designated as the post-transplant group. The samples were stored at -80℃. Samples were collected from 30 healthy adults using the same method. Samples from another 60 patients were collected in the same manner, one day before surgery, one day after surgery, and one week after surgery. The blood was collected in ordinary serum tubes, allowed to stand for 2 hours, centrifuged at 3500 rpm for 5 minutes, and 500 μL of the supernatant serum was collected in a 0.5 ml centrifuge tube and stored at -80℃.

[0074] 1.1.3.1 Serum metabolomics detection

[0075] This study employed UPLC-QTOF-MS technology to perform non-target metabolomics analysis on kidney transplantation status. Raw mass spectrometry data were converted into mzXML format recognizable by the XCMS program using ProteoWizard software. Peak alignment and retention time correction were performed before peak area extraction. Precise mass number matching (<25 ppm) and secondary spectral matching were used to identify the metabolite structures of the collected positive and negative ion peaks. These metabolite components were then determined through matching analysis with a laboratory-built database of standards.

[0076] 1.1.3.2 Detection of serum biomarkers and inflammatory factors

[0077] According to the experimental design, one serum biomarker sample was used to detect the levels of serum 8-OHdG, reactive oxygen species (ROS), IL-17, and three inflammatory cytokines in plasma. These levels were detected using ELISA kits provided by Shanghai Zhuocai Biotechnology Co., Ltd., with product numbers ZC-31674, ZC-33336, ZC-32412, and ZC-35733, respectively.

[0078] 1.2 Methods

[0079] 1.2.1 Metabolomics Sample Preprocessing

[0080] Accurately pipette 100 μL of each sample into a 1.5 mL centrifuge tube, add 400 μL of 50% methanol acetonitrile solution pre-cooled at 4 °C, mix the sample by vortexing for 1 min, incubate in an ice bath at -20 °C for 1 h, centrifuge at 14000 r / min at 4 °C for 20 min, and transfer the supernatant to a new centrifuge tube for testing.

[0081] 1.2.2 Chromatographic conditions

[0082] The chromatographic column was a Waters ACQUITY UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm), the injection volume was 1 μL, the column temperature was 25 °C, and the flow rate was 0.3 mL / min. Throughout the analysis, the sample was placed in an autosampler at 4 °C. The mobile phase composition was A: water + 25 mM ammonium acetate + 25 mM ammonia, and B: acetonitrile. The gradient elution program is shown in Table 1 below.

[0083] Table 1 UHPLC Time-Concentration Program Table

[0084]

[0085] 1.2.3 Mass Spectrometry Conditions

[0086] After separation using a UHPLC system, samples were analyzed by mass spectrometry using an Agilent 6550. Following sample analysis, metabolites were identified using an AB Triple TOF 6600 mass spectrometer. Instrument parameter settings are shown in Table 2 below.

[0087] Table 2 Mass Spectrometer Parameter Settings

[0088]

[0089]

[0090] Note: ISVF, Ion Sapar Voltage Floating; CIMP, Candidate Ions to Monitor Percycle; Secondary mass spectrometry was obtained using information-dependent acquisition (IDA) in high-sensitivity mode.

[0091] 1.2.4 Data Processing

[0092] The raw mass spectrometry data were converted to .mzXML format, and the XCMS program was used to identify, calculate, and analyze the material peaks. For the resolved ion peaks, those with a sum of group values ​​greater than 2 / 3 were removed. The retained ion peak data were then matched with pre-built library standard data to identify metabolite structures and determine metabolite components. Multivariate statistical analysis software SIMCA-P was used for pattern recognition analysis. After Pareto-scaling (dividing a variable by the square root of its variance) preprocessing, PCA (principal component analysis), t-tests, fold change analysis, and the results of the difference analysis were visualized.

[0093] 1.2.5PCA (Principal Component Analysis)

[0094] Principal Component Analysis (PCA) is an unsupervised data analysis method that linearly recombines all identified metabolites to form a new set of comprehensive variables. Several comprehensive variables are then selected based on the problem being analyzed to reflect as much information as possible from the original variables, thus achieving dimensionality reduction. Furthermore, PCA on metabolites can also reflect the overall variability between and within sample groups. In this study, groups B and A, representing pre- and post-transplantation outcomes, are used as examples for PCA analysis.

[0095] 1.2.6 Variable Differential Metabolite Analysis

[0096] When analyzing differentially expressed metabolites between two groups of samples, univariate analysis is the simplest and most commonly used experimental data analysis method. In this study, we used a volcano plot, a visualization method for analyzing differentially expressed data. A volcano plot can simultaneously display the number and degree of difference in differentially expressed components, helping researchers identify which components show significant differences in expression between two or more samples. Typically, the horizontal axis represents the degree of differential expression, and the vertical axis represents statistical significance.

[0097] 1.2.7 ELISA detection to explore serum biomarkers and serum inflammatory factors in subjects

[0098] 1.2.7.1 Main ELISA Detection Steps

[0099] After incubating the serum sample at room temperature for 2 hours or at 2-8°C overnight, centrifuge at 1000×g for 20 minutes, collect the supernatant, and use a human 8-OHdG enzyme-linked immunosorbent assay kit to detect the serum 8-OHdG concentration of the subject.

[0100] 1) Equilibrate the kit to room temperature for 30 minutes, then remove the required strips from the aluminum foil bag. Seal the remaining strips in a resealable bag and return them to 4°C. Add 0.5, 1, 2, 4, 8, and 16 ng / mL of the kit to the standard wells, add the sample to the sample wells, add 50 μL to each of the standard and sample wells, and do not add any to the blank wells.

[0101] 2) Add 100 μL of HRP-labeled detection antibody to each well of the standard and sample wells, seal the membrane and incubate at 37°C for 60 min.

[0102] 3) Discard the liquid, pat dry on absorbent paper, add 350μL of washing solution to each well, let stand for 1 minute, shake off the washing solution, pat dry on absorbent paper, and repeat the washing process 5 times.

[0103] 4) Add 50 μL of substrate A and 50 μL of substrate B to each well and incubate at 37°C in the dark for 15 min.

[0104] 5) Add 50 μL of stop solution to each well and measure the OD value of each well at a wavelength of 450 nm within 15 min.

[0105] 6) The average OD obtained for each of the six standards is plotted on the vertical (Y) axis, and the corresponding concentration is plotted on the horizontal (X) axis. To determine the amount of each index in each sample, the absorbance of the sample is then substituted into the regression equation to form a standard curve.

[0106] 1.2.8 Statistical Analysis

[0107] Statistical analysis and graphing were performed using GraphPad Prism 8.3 software. All data were expressed as mean ± standard deviation (x±s). Each experimental group was repeated three times. Independent samples t-tests were used to compare two groups, and one-way ANOVA was used to compare multiple groups. A p-value < 0.05 was considered statistically significant.

[0108] 2. Results

[0109] 2.1 Results of metabolomics testing

[0110] 2.1.1 PCA Analysis of Mass Spectrometry Results

[0111] The experimental results data were analyzed using a PCA (Principal Component Analysis) mathematical model. Figures 1-2It can be seen that the metabolic components in the body change significantly before and after kidney transplantation, producing meaningful differential metabolites. The metabolic components under positive and negative ion modes before transplantation (Group A) and after transplantation (Group B) of the same patient can be clearly distinguished by principal component analysis.

[0112] 2.1.2 Analysis of blood components

[0113] Analysis of blood components in kidney transplant patients before and after transplantation revealed a large number of components, displayed as a volcano plot in positive ion mode. A total of 8326 substance signals were detected. Univariate analysis was performed, using FC>1.5 and P<0.05 as the criteria for screening differentially expressed metabolites. Figure 3 As shown by the red dots, there are a total of 407 differential components. This volcano plot can visually show that there are a large number of differential components in the metabolites between the two groups of samples.

[0114] 2.1.3 Serum metabolite analysis in kidney transplantation status

[0115] After analysis by UHPLC-Q-TOF-MS, a positive ion peak of 8326 and a negative ion peak of 7996 were extracted using the XCMS program. This metabolomics study resolved a large number of metabolic components, such as indole-3-carboxylic acid, dopamine, oxindole, indoxyl sulfate, methoxyacetic acid, acetylcarnitine, kynurenic acid, 8-OHdG, and hundreds of other metabolic components. Some of these metabolic components are listed here, and all differences were statistically significant (P < 0.05). Among the listed substances, 8-OHdG showed the most significant difference (P < 0.001) and the largest fold change (FC = 13.947). See Table 3.

[0116] Table 3. Information on differentially metabolites after kidney transplantation

[0117]

[0118] 2.2 Basic information of subjects and ELISA testing

[0119] 2.2.1 Statistical analysis of differences in general characteristics of subjects between groups

[0120] During the study period from December 1, 2020 to December 13, 2022, 60 patients who met the inclusion criteria were collected for general information collection and monitoring of serum creatinine, blood urea nitrogen, and estimated glomerular filtration rate at various time points before and after transplantation. Based on the diagnostic criteria of whether renal replacement therapy was performed within one week after allogeneic kidney transplantation, the subjects were divided into a postoperative DGF group and a postoperative IGF group. Of the 60 enrolled recipients, 14 met the diagnostic criteria for DGF after allogeneic kidney transplantation, with an incidence rate of 23.33%. The mean age of the patients at surgery was 43 ± 11.94 years, with the oldest patient being 69 years old and the youngest being 15 years old. There were 43 males and 17 females. The age of the healthy volunteers ranged from 18 to 60 years, with a mean age of (41.033 ± 5.86) years. There was no significant difference in age between the two groups (P > 0.05). See Table 4.

[0121] Table 4. Basic Information of Subjects

[0122]

[0123] 2.2.2 Serum 8-OHdG ELISA Detection Results and Comparison

[0124] ELISA results showed that in the normal group, 8-OHdG was 1.988±1.025, with a maximum value of 4.182 and a minimum value of 0.981. One day before surgery, 8-OHdG was 4.086±2.830, with a maximum value of 11.56 and a minimum value of 0.970. One day after surgery, 8-OHdG was 6.750±4.142, with a maximum value of 17.77 and a minimum value of 1.133. One week after surgery, 8-OHdG was 4.081±2.045, with a maximum value of 8.178 and a minimum value of 0.9821. Figure 4 It can be seen that the serum 8-OHdG concentration of kidney transplant recipients on the day before the operation was higher than that of normal people. The serum 8-OHdG concentration of the recipients on the day after the operation was higher than that on the day before the operation and one week after the operation, and the change was significant. The serum 8-OHdG concentration of the recipients on the day after the operation was higher than that on the week after the operation, and the change was significant.

[0125] 2.2.3 Correlation analysis between serum ROS, TNF-α, IL-17 ELISA results and 8-OHdG

[0126] The levels of serum ROS with the highest correlation to 8-OHdG (R = 0.8007, P < 0.0001) and the lowest correlation (R = 0.0516, P = 0.6951), as well as serum pro-inflammatory cytokines TNF-α and IL-17, and three plasma inflammatory cytokines, were analyzed. Correlation analysis showed that 8-OHdG was significantly positively correlated with IL-17 preoperatively (R = 0.6994, P < 0.0001), and significantly positively correlated with serum TNF-α concentration one day postoperatively (R = 0.6779, P < 0.0001). See [link to relevant documentation]. Figure 5 .

[0127] 2.2.4 Correlation analysis of 8-OHdG with creatinine and blood urea nitrogen at different time points in kidney transplant recipients

[0128] Preoperatively, 8-OHdG showed no correlation with creatinine (R = 0.0678, P = 0.6064). Postoperatively, 8-OHdG showed a significant positive correlation with creatinine one day after surgery (R = 0.8206). Postoperatively, 8-OHdG showed a significant positive correlation with creatinine one week after surgery (R = 0.3548). Preoperatively, 8-OHdG showed no correlation with blood urea nitrogen (BUN) (R = 0.2227). Postoperatively, 8-OHdG showed a significant positive correlation with BUN one day after surgery (R = 0.9126). Postoperatively, 8-OHdG showed a significant positive correlation with BUN one week after surgery (R = 0.6835).

[0129] 2.2.5 Comparison of serum 8-OHdG levels between the two groups after kidney transplantation

[0130] Subjects were divided into IGF and DGF groups based on whether they underwent renal replacement therapy one week after allogeneic kidney transplantation. Analysis of serum 8-OHdG concentration differences between the IGF and DGF groups revealed no difference in serum 8-OHdG concentration between DGF and IGF patients one day before surgery (P = 0.968). One day after surgery, DGF patients had higher 8-OHdG concentrations than the IGF group (P < 0.05), and one week after surgery, DGF patients also had higher 8-OHdG concentrations than the IGF group (P < 0.05). See [link to article / reference]. Figure 6 .

[0131] 2.2.6 ROC analysis of serum 8-OHdG biological markers

[0132] ROC curves were plotted using the concentrations of 8-OHdG biological markers at different time points and the occurrence of DGF after kidney transplantation. The analysis revealed that serum 8-OHdG levels one day before surgery could not be used to diagnose DGF (AUC = 0.515, p = 0.8613), serum 8-OHdG levels one day after surgery could diagnose DGF (AUC = 0.8766, p < 0.0001), and serum 8-OHdG levels one week after surgery could diagnose DGF (AUC = 0.6755, p = 0.0483).

[0133] 3. Discussion

[0134] In recent years, the number of patients with end-stage renal disease (ESRD) has increased rapidly. As the preferred treatment for ESRD, a major challenge is the severe shortage of kidney organs. Continuous improvements in immunosuppressive therapy and treatment monitoring have improved graft prognosis and patient survival. However, due to the expansion of donor criteria, the pool of donors has broadened, leading to the increasing use of marginal kidneys. This increases the likelihood of delayed graft function (DGF), acute rejection, and primary non-function of the transplanted kidney in the short term post-transplantation. Delayed recovery of transplanted kidney function is generally considered to be caused by an acute kidney injury following transplantation. DGF is a common post-transplant death event. Although DGF is primarily considered an acute and potentially reversible injury to allogeneic grafts caused by ischemia-reperfusion injury, studies have confirmed the link between DGF and short-term and long-term graft dysfunction. Specifically, when DGF complications occur after transplantation, the proportion of patients experiencing acute and chronic rejection is higher than in patients with normal transplanted kidney function recovery. It also frequently leads to decreased early and late post-transplant survival rates, resulting in long-term adverse events following transplantation. [3 [5,6], the presence of DGF indicates undesirable development of future graft function and outcomes. [7] If diagnosed and treated early, DGF can usually recover within 7 to 30 days, so early diagnosis and early intervention are crucial. Currently, internationally, DGF is typically diagnosed as "renal replacement therapy within 7 days after kidney transplantation". [8,9]The diagnosis and treatment of delayed recovery of transplanted kidney function after kidney transplantation are subject to significant subjective influence and are somewhat ambiguous. Some transplant centers still use "a decrease of <10% in serum creatinine for three consecutive days within one week" as the diagnostic criterion. However, further research shows that the peak serum creatinine concentration is not related to the severity of transplanted kidney function damage. Serum creatinine may only rise after graft immune activation and ischemia-reperfusion injury. Therefore, the diagnosis of "a decrease of <10% in serum creatinine for three consecutive days within one week," which involves creatinine clearance rate, is not widely used by clinical organ transplant physicians because it can delay diagnosis and affect treatment. While biopsy is the gold standard, it is not easily accepted by patients due to its invasive nature and the risk of serious complications such as bleeding and even transplanted kidney rupture. Therefore, a more objective and clear definition and diagnostic method for delayed recovery of transplanted kidney function (DGF) is still lacking. Thus, finding biomarkers to predict the occurrence of DGF in the early stages after kidney transplantation, and comprehensive diagnosis and treatment of DGF after kidney transplantation, along with timely symptomatic treatment, are crucial.

[0135] In this study, non-targeted metabolomics analysis based on UPLC-QTOF-MS metabolomics technology identified thousands of metabolic components in the kidney transplant state. PCA analysis of these thousands of metabolic components revealed significant differential metabolites produced before and after kidney transplantation, effectively distinguishing between samples in different states. This suggests significant changes in metabolic components before and after kidney transplantation, demonstrating that certain meaningful molecules can be effectively detected. Based on this, differential metabolites were analyzed, with 8-OHdG being a particularly significant component. 8-OHdG is generated continuously in living cells due to metabolic and biochemical reactions, as well as exposure to physical, chemical, and biological agents. Excessive ROS induces oxidative stress, leading to oxidative DNA damage among various oxidative injuries. The level of DNA oxidative damage tends to increase after toxic damage. Although more than 20 base transgressions have been identified, among different types of ROS in the DNA structural damage formation mechanism, GC-rich sequences appear to be most susceptible to damage.

[10] Guanosine is the most easily oxidized base in DNA. Hydroxyl radicals and superoxide anions directly attack the 8th carbon atom of the guanine base in the DNA molecule, generating the oxidative adduct 8-OHdG. Studies show that 8-OHdG, as a product of DNA oxidative damage, is very stable in the matrix and can be measured using non-invasive samples. It is often used as an indicator for monitoring the body's oxidative stress level, determining the overall impact of oxidative stress, assessing risk, and diagnosing and evaluating the treatment of autoimmune diseases, inflammation, neurodegenerative and cardiovascular diseases, diabetes, cancer, and other age-related diseases.

[0136] ELISA analysis of the experimental subjects showed that the serum expression level of 8-OHdG in kidney transplant patients was significantly higher than that in normal individuals the day before surgery. We believe this may be related to the more severe inflammation experienced by patients with kidney failure.

[11] Studies have found that urinary excretion of 8-OHdG is closely related to the rate of injury and intracellular 8-OHdG levels, but not to cellular clearance, under steady-state conditions (i.e., when the systemic consequences of a given change in injury or cell clearance have stabilized) within approximately 12 hours. In this study, serum 8-OHdG concentrations from kidney transplant patients one day before surgery and from 10 randomly selected healthy adults were compared. The results showed that 8-OHdG concentrations were higher in healthy adults. Studies have shown that end-stage renal disease patients often experience chronic inflammation due to persistent, maladaptive, and uncontrolled protective physiological responses to various harmful stimuli caused by chronic glomerulonephritis, hypertensive nephropathy, obstructive nephropathy, and diabetic nephropathy. In patients with chronic kidney disease, toxin accumulation induces the decoupling of endothelial nitric oxide synthase (eNOS).

[12] Increased activity of nicotinamide adenine dinucleotide phosphate oxidase (NADPH oxidase (NOX)) [13,14] Increased oxidative stress levels in the body, and some patients with chronic kidney disease also experience antioxidant loss due to dietary restrictions, diuretic use, protein and energy waste, and / or reduced intestinal absorption. [15,16] This leads to persistent chronic oxidative stress at lower levels, resulting in higher serum 8-OHdG levels compared to healthy individuals. The results of this study are consistent with this, suggesting that serum 8-OHdG concentration has potential research value in assessing chronic kidney disease. On the first day after kidney transplantation, patients' 8-OHdG levels were significantly higher than pre-operative levels, decreasing after one week, with statistically significant differences between groups. We believe this phenomenon may also be related to ischemia-reperfusion injury caused by kidney transplantation. Ischemia-reperfusion injury (IRI) is an unavoidable and complex pathophysiological phenomenon during kidney transplantation, and is one of the important mechanisms for immediate non-functional or delayed functional renal transplantation. When the transplanted kidney experiences a brief reduction or cessation of blood flow, followed by restoration of blood flow through reperfusion, oxygen undergoes a series of chemical reactions, generating a large number of reactive oxygen species (ROS), such as oxygen free radicals and non-free radical derivatives.

[17] The accumulation of reactive nitrogen species (RNS), lipid peroxides, and other substances leads to the generation of large amounts of reactive oxygen species (ROS). Simultaneously, when tissues and organs experience ischemia, the synthesis of antioxidant enzymes capable of scavenging free radicals is impaired. ROS are highly reactive electrophilic substances that tend to react with molecules of high electron density, such as lipids, proteins, and DNA. The accumulation of large amounts of ROS in organs and the body often triggers a series of inflammatory responses, causing damage to microvessels and parenchymal organs, leading to organ dysfunction. Therefore, ischemia-reperfusion injury and transplant immune responses are often considered the main causes of diuretic oxidase (DGF) during kidney transplantation. Post-transplant IRI generates a large number of oxygen free radicals, and the ability of tissues to synthesize antioxidant enzymes that scavenge free radicals is impaired. Oxygen free radicals attack the 8th carbon atom of the guanine base in DNA to produce an oxidizing adduct, 8-OHdG. DNA damage delays cell function repair and the recovery of antioxidant enzyme synthesis, thus exacerbating the damage of free radicals to ischemic-reperfused tissues. Therefore, the serum 8-OHdG concentration is high on the first day after surgery. One week after surgery, the renal function of kidney transplant patients gradually recovers, the internal environment of the body gradually recovers, and the body's ability to scavenge reactive oxygen species and synthesize antioxidant enzymes is enhanced. Therefore, the serum 8-OHdG concentration of kidney transplant patients one week after surgery is significantly lower than that on the day before surgery and the day after surgery.

[0137] Based on this, correlation analysis was performed on 8-OHdG with some classic inflammatory factors, including those leading to ROS. The correlation between serum ROS and 8-OHdG was 0.8009. This high correlation further confirms that 8-OHdG is a product of ROS attacking DNA. These ROS may originate from the production and accumulation of peroxides caused by oxygen through a series of chemical reactions during the recovery of open blood vessel reperfusion. In addition, we also performed correlation analysis on 8-OHdG with IL-17 and TNF-α. The results showed that the correlation between 8-OHdG and IL-17 before surgery was 0.6994, which may be related to the nephritis manifestation in patients with renal failure. The correlation between 8-OHdG and serum TNF-α concentration one day after surgery was 0.6779, which may be related to the stimulation and induction of the expression of multiple inflammatory genes during kidney transplantation. Studies have shown that TNF-α and IL-17 are expressed at low levels in patients with ordinary chronic renal failure. During renal ischemia-reperfusion injury, ischemia and hypoxia damage to renal small vessel endothelial cells and renal tubular epithelium release inflammatory factors such as IL-17 and TNF-α, recruiting inflammatory cells such as neutrophils, monocytes / macrophages, and lymphocytes to migrate to the transplanted kidney. This leads to an imbalance in the expression of pro-inflammatory and inhibitory factors, which are rapidly upregulated within minutes to hours after kidney transplantation, inducing the expression of various inflammatory genes. Serum 8-OHdG concentrations and serum TNF-α and IL-17 concentrations were positively correlated one day before and one day after surgery. This evidence suggests that 8-OHdG is intrinsically linked to changes in some inflammatory factors and may participate in the process of inducing inflammation, promoting inflammatory responses, and aggravating renal tissue damage. This suggests that 8-OHdG has the potential to be a biomarker reflecting renal injury. This study also conducted a correlation analysis between 8-OHdG and creatinine and blood urea nitrogen, biochemical indicators reflecting renal function. The results showed that 8-OHdG had a strong correlation with creatinine and blood urea nitrogen postoperatively, but this correlation gradually decreased over time. This suggests that early 8-OHdG may have the potential to reflect postoperative renal function, but further investigation is needed.

[0138] This invention further divided patients into an IGF group and a DGF group based on whether renal replacement therapy was required one week after kidney transplantation. Results showed that 8-OHdG expression differed significantly between the groups. ROC analysis revealed that the accuracy of serum 8-OHdG level detection for diagnosing DGF one day post-transplantation was 0.876, with a sensitivity of 76.67%, a specificity of 86.65%, and a cutoff value of 75.59 ng / ml. This suggests that early postoperative serum 8-OHdG concentration has diagnostic significance for postoperative DGF. DGF typically refers to acute kidney injury occurring in the first week after kidney transplantation, requiring dialysis. The incidence of DGF varies considerably, averaging 31% in US transplant centers.

[18] Studies have shown that DGF is a risk factor for acute cellular rejection and short-term graft loss.

[19] The development of DGF is associated with an increased risk of long-term chronic allogeneic transplant nephropathy and a shortened allogeneic transplant survival.

[20] Meanwhile, DGF often affects maintenance immunosuppressive therapy, especially the use of calcineurin inhibitors (CNIs). The mechanism of DGF is extremely complex, and the influencing factors of graft DGF come from both the donor and recipient, with ischemia and reperfusion injury being the most common influencing factors. This acute kidney injury is caused by the complex interaction of ischemic hypoxic injury and events that alter repair mechanisms. In the field of kidney transplantation, many studies have focused on the clinical characteristics and risks of DGF, its mechanisms, and treatment methods. The exploration of potential biomarkers for the diagnosis of DGF has also gradually become a focus of researchers. Current clinical characteristics are insufficient to predict post-transplant DGF, and there is a need to find biomarkers to better diagnose the impact of DGF on patient prognosis and the significance of early DGF biomarkers. However, there are currently no suitable early biomarkers. Our study found that 8-OHdG has good biomarker potential through receiver operating characteristic curve analysis. However, the limited sample size that met the experimental requirements and the fact that only samples were collected at three time points, as well as the failure to consider the rate of change of 8-OHdG, are limitations of this study. Future experiments should further investigate the relationship between 8-OHdG and transplanted kidney function, extending the experimental time, increasing the sample size, and linking it to transplanted kidney biopsy results. More detailed grouping of subjects could be conducted for inter-group comparisons to scientifically assess the relationship between donor kidney biomarkers and the risk of postoperative DGF.

[0139] 4. Conclusion

[0140] Compared with healthy individuals, serum 8-OHdG expression is significantly increased in patients with end-stage chronic kidney disease (CKD), and this high expression may be related to the severity of CKD before transplantation. In the early post-transplant period, serum 8-OHdG concentrations fluctuate considerably and are correlated with post-transplant DGF (digestive glomerulonephritis), suggesting that it can predict post-transplant DGF to some extent and has the potential to serve as a biomarker for early clinical diagnosis of DGF in kidney transplant patients.

[0141] Although the present invention has been described in detail above with general descriptions, specific embodiments, and experiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

[0142] References:

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Claims

1. Use of a reagent of a biomarker 8-hydroxydeoxyguanosine in the preparation of a product for the diagnosis or auxiliary diagnosis of delayed graft function of a transplanted kidney after kidney transplantation.

2. Use according to claim 1, characterized in that, The product includes a kit, a test paper, a chip, a mass spectrum, and a chromatogram.

3. Use according to claim 2, characterized in that, The product is a kit.

4. Use according to claim 3, characterized in that, The method for using the kit is as follows: taking serum of a subject as a sample, determining by a serum metabolomics detection method, and then detecting the serum biomarker and serum inflammatory factors of the subject by the kit, the serum sample is placed at room temperature for 2 hours or at 2-8℃ overnight, then centrifuged at 1000xg for 20 min, the supernatant is taken and detected by a human 8-OHdG enzyme-linked immunosorbent assay kit to determine the concentration of 8-OHdG in the serum of the subject, and the specific steps are as follows: 1) The kit is equilibrated at room temperature for 30 min, then the required strips are taken out from the aluminum foil bag, the remaining strips are sealed with a self-sealing bag and put back at 4℃, 0.5, 1, 2, 4, 8, and 16 ng / mL standard samples are added to the standard sample wells in the kit, the sample is added to the sample well, 50 μL is added to each of the standard sample well and the sample well, and no addition is made to the blank well; 2) 100 μL of HRP-labeled detection antibody is added to each of the standard sample well and the sample well, the membrane is sealed and incubated at 37℃ in an incubator for 60 min; 3) Discard the liquid, pat dry on a blotting paper, add 350 μL of washing solution to each well, stand for 1 min, shake off the washing solution, pat dry on a blotting paper, and repeat the plate washing for 5 times; 4) Add 50 μL of substrate A and B to each well, and incubate at 37℃ in the dark for 15 min; 5) Add 50 μL of stop solution to each well, and measure the O.D. value of each well at 450 nm within 15 min; 6) Draw a standard curve by taking the logarithm of the average O.D. obtained from each of the six standards in the vertical Y-axis and the logarithm of the corresponding concentration in the horizontal X-axis, and then substitute the absorbance of the sample into the regression equation standard curve.

5. Use according to claim 4, characterized in that, The kit is an ELISA kit.

6. Use according to claim 4, characterized in that, The serum inflammatory factors include TNF-α, IL-17, and ROS.

7. Use according to claim 6, characterized in that, The 8-OHdG shows a significant positive correlation with TNF-α, IL-17α, blood creatinine, and urea nitrogen concentration after the operation.

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