CN111413431A - Method for screening metabolic markers of drug-induced acute kidney injury lesion process - Google Patents
Method for screening metabolic markers of drug-induced acute kidney injury lesion process Download PDFInfo
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Abstract
The invention provides a screening method of a metabolic marker of a drug-induced acute kidney injury process, which is characterized in that a mass spectrum serum metabonomics technology and a mathematical statistics method are adopted to search a progressive metabolic marker related to drug-induced acute kidney injury, an ROC curve is adopted to confirm the diagnostic ability of the marker, and finally the metabolic marker reflecting the drug-induced acute kidney injury process is determined. According to the method, the metabolic markers reflecting the process of the drug-induced acute kidney injury pathological changes are screened through the change process of the content of the endogenous metabolic markers in the serum of the organism, the defects of the existing method for evaluating the process of the drug-induced acute kidney injury pathological changes are overcome, the process of the drug-induced acute kidney injury pathological changes is finally evaluated according to the determined metabolic markers, and the method has the characteristics of quantification, rapidness, high sensitivity, strong specificity and no wound.
Description
Technical Field
The invention belongs to the technical field of marker screening and identification, and particularly relates to a method for screening a metabolic marker of a drug-induced acute kidney injury lesion process.
Background
Drug-induced acute kidney injury is an adverse reaction of the kidney to a therapeutic dose of drug and a toxic reaction that occurs as a result of an overdose or improper use of the drug. The drug-induced acute kidney injury can reach 1/4 of acute kidney injury, and acute kidney injury is easy to cause chronic kidney injury. Therefore, early recognition and diagnosis of drug-induced acute kidney injury are particularly important for preventing the occurrence of late acute kidney injury.
The cisplatin-induced mouse kidney injury model is an animal model simulating human kidney diseases, and the clinical expression of the cisplatin-induced mouse kidney injury model is close to the characteristics of drug-induced acute kidney injury. In the experiment, whether the drug-induced acute kidney injury model is successfully copied or not can be quantitatively judged according to 24h urine protein, the pathological type and the pathological degree of the kidney disease are mainly detected according to kidney histopathology, the subjectivity is strong, the time and the labor are consumed, and a dynamic evaluation method for the drug-induced acute kidney injury process is not available at present, so that a method which is non-invasive in whole, rapid, high in sensitivity and good in specificity and can be used for acute kidney disease diagnosis and dynamically reflecting the disease course needs to be established.
Metabolomics, with its ever-increasing coverage and its inherent high throughput capabilities, finds application in many areas such as disease diagnosis and therapy, drug toxicity research, biomarker discovery, and disease mechanism exploration, among others. Serum contains proto-type drug components and metabolites thereof, and has great advantages in reflecting kidney status.
Disclosure of Invention
The technical problem solved by the invention is to provide a method for screening metabolic markers of a drug-induced acute kidney injury process, wherein potential progressive metabolic markers related to the drug-induced acute kidney injury are searched by a mass spectrum serum metabonomics technology and a mathematical statistics method, and the ROC curve is adopted to confirm the diagnosis capability of the markers, so that the metabolic markers reflecting the drug-induced acute kidney injury process are finally determined, and the method has the characteristics of quantification, rapidness, high sensitivity, strong specificity and no wound.
The technical solution for realizing the purpose of the invention is as follows:
a method for screening metabolic markers of a drug-induced acute kidney injury lesion process comprises the following steps:
step 1: performing induction by injecting 20mg/kg cis-platinum into the abdominal cavity, and establishing a drug-induced acute kidney injury mouse model; injecting 20mg/kg of normal saline into the abdominal cavity to establish a blank control group mouse;
step 2: collecting serum of a blank control group mouse; collecting serum of the mouse model respectively at 6h, 12h, 24h, 48h, 72h and 168h after the mouse model is established to form 6h mouse model serum, 12h mouse model serum, 24h mouse model serum, 48h mouse model serum, 72h mouse model serum and 168h mouse model serum, and performing liquid mass combination analysis on the empty white control group mouse serum and the mouse model serum respectively to obtain a metabolite liquid phase fingerprint of the mouse model;
and step 3: carrying out data preprocessing such as peak extraction, peak identification, peak matching, peak alignment, normalization and the like on a metabolite liquid-phase fingerprint of a mouse model by using XCMS software to obtain metabolic profile data containing retention time Rt, mass-to-charge ratio m/z and peak height, and introducing the metabolic profile data into SIMCA-P software for processing to obtain a differential metabolite;
and 4, step 4: calculating the change rate of each differential metabolite, and determining a progressive metabolic marker of the drug-induced acute kidney injury lesion process by combining single-factor variance analysis, random forest algorithm, Pearson correlation coefficient screening and qualitative analysis of the metabolite;
and 5: and (3) evaluating the diagnosis accuracy of the progressive metabolic marker according to the area AUC enclosed by the ROC curve and the coordinate axis: the progressive metabolic markers with AUC > 0.7 are taken as the metabolic markers reflecting the progress of the drug-induced acute kidney injury lesion.
Further, in the method for screening metabolic markers of the process of the drug-induced acute renal injury, the metabolic markers reflecting the process of the drug-induced acute renal injury in step 5 include toxypyrimidine, Phthalic acid, atagabin, 2-Methyl-4,6-dinitrophenol, myrisityl sulfate, Taurine, Benactyzine, Faoracetam, Eicosapentaenoic acid, Trepibutone, 9-Decornylcarnitine, Xanthohumol, 3',4' -methylioxy-alpha-pyrrolidinobiophenol.
Further, the method for screening the metabolic markers of the process of the drug-induced acute kidney injury lesion comprises the following specific steps of step 4:
step 4-1: carrying out primary screening on the differential metabolites obtained in the step (3) by adopting one-factor analysis of variance, and removing differential values which do not meet conditions by taking p <0.05 as a discrimination condition through significant differential comparison to obtain potential metabolic markers;
step 4-2: screening the potential differential metabolites obtained in the step 4-1 by adopting a random forest algorithm, and obtaining potential progressive metabolic markers by taking meanbackground Accuracy >0.001 as a discrimination condition;
step 4-3: performing Pearson correlation analysis on the potential progressive metabolic markers obtained in the step 4-2, and performing R2>0.7, as a discrimination condition, obtaining a time-dependent potential progressive metabolic marker, wherein the time-dependent potential progressive metabolic marker has consistent variation trend in the whole period;
step 4-4: and (4) qualitatively analyzing the time-dependent potential progressive metabolic marker obtained in the step (4-3) by using an HMDB database and related documents and combining secondary fragment ions, eliminating false positive results, namely the false positive results cannot be found in the database and the retention time of the two times is greater than the allowable deviation for 0.2min, and determining the finally screened metabolite as the progressive metabolic marker.
Further, in the method for screening metabolic markers of the process of drug-induced acute kidney injury lesions, the step of searching for differential metabolites in step 3 specifically comprises the following steps: under a positive and negative ion mode, carrying out unsupervised pattern recognition principal component analysis on metabolic profile data of mouse serum of an empty control group and mouse model serum, determining that the profile of the metabolite of the empty control group is significantly different from the metabolic profile of the mouse model by adopting supervised partial least square method discriminant analysis and orthogonal partial least square method discriminant analysis, and finally combining the fact that the VIP value is more than 1 in the orthogonal partial least square method discriminant analysis, the P value in S-plot is more than 0.58 and the independent sample t-test P is less than 0.05 to obtain the metabolite with significance.
Further, in the method for screening the metabolic markers of the process of drug-induced acute kidney injury lesions, the conditions of the LC-MS analysis in the step 2 are as follows:
the chromatographic conditions are that a Waters acquisition UHP L CTM BEH C18 chromatographic column is adopted, the mobile phase A is water containing 0.1 percent of formic acid, the mobile phase B is acetonitrile, the volume flow is 0.3 mu L/min, the UV detection wavelength is 254nm, the column temperature is 30 ℃, the sample injection amount is 3 mu L, the elution gradient is 0-2min, 2 percent B, 2-3min, 2-35 percent B, 3-17min, 35-70 percent B, 17-18min, 70 percent B, 18-29min, 70-98 percent B, 29-31min, 98 percent B, 31-33min, 98-2 percent B, 33-35min and 2 percent B;
mass spectrum conditions: adopting an HESI ionization mode, wherein the spraying voltage is 3.5kV at the positive electrode and 2.5kV at the negative electrode, and the collision energy is 12.5 eV, 25 eV or 37.5 eV; the heater temperature is 300 ℃, the capillary temperature is 320 ℃, the volume flow of the auxiliary gas is 10arb, and the volume flow of the sheath gas is 35 arb; the collection mode is positive and negative ion switching, the scanning mode adopts Full Scan/dd-MS2, and the collection range is m/z 100-1500; the resolution was MS Full Scan 35000FWHM, MS/MS 17500 FWHM.
An application of the drug-induced acute kidney injury pathological process metabolic marker obtained by the screening method in preparing a drug-induced acute kidney injury pathological process diagnosis and identification reagent.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
the screening method of the metabolic marker of the pathogenic process of the drug-induced acute kidney injury adopts an acute kidney injury model caused by cisplatin to simulate the drug-induced acute kidney injury, combines a mass spectrum serum metabonomics technology and a mathematical statistics method to search a potential progressive metabolic marker related to the drug-induced acute kidney injury, adopts an ROC curve to confirm the diagnostic capability of the marker, and has the characteristics of high speed, high sensitivity, strong specificity and no wound compared with the traditional serum biochemical index or kidney tissue pathological analysis.
Drawings
FIG. 1 is a liquid phase total ion flux (TICs) of serum metabolites according to an embodiment of the method for screening metabolic markers of the course of drug-induced acute kidney injury pathology of the present invention;
FIG. 2 is a PCA score chart of a blank control group mouse and a mouse model of an embodiment of the method for screening metabolic markers of a drug-induced acute kidney injury lesion process of the present invention;
FIG. 3 is a P L S-DA score chart of blank control mice and mouse models of an embodiment of the method for screening metabolic markers of the process of drug-induced acute renal injury lesions of the present invention;
FIG. 4 is a graph showing HE staining of mouse kidney tissue of blank control mice and mouse models according to an embodiment of the method for screening metabolic markers of the process of drug-induced acute kidney injury.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
A method for screening metabolic markers of a drug-induced acute kidney injury lesion process comprises the following steps:
step 1: performing induction by injecting 20mg/kg cis-platinum into the abdominal cavity, and establishing a drug-induced acute kidney injury mouse model; injecting 20mg/kg of normal saline into the abdominal cavity to establish a blank control group mouse;
step 2: collecting serum of a blank control group mouse; collecting serum of the mouse model respectively at 6h, 12h, 24h, 48h, 72h and 168h after the mouse model is established to form 6h mouse model serum, 12h mouse model serum, 24h mouse model serum, 48h mouse model serum, 72h mouse model serum and 168h mouse model serum, and performing liquid mass combination analysis on the empty white control group mouse serum and the mouse model serum respectively to obtain a metabolite liquid phase fingerprint of the mouse model;
and step 3: carrying out data preprocessing such as peak extraction, peak identification, peak matching, peak alignment, normalization and the like on a metabolite liquid-phase fingerprint of a mouse model by using XCMS software to obtain metabolic profile data containing retention time Rt, mass-to-charge ratio m/z and peak height, and introducing the metabolic profile data into SIMCA-P software for processing to obtain a differential metabolite;
and 4, step 4: calculating the change rate of each differential metabolite, and determining a progressive metabolic marker of the drug-induced acute kidney injury lesion process by combining single-factor variance analysis, random forest algorithm, Pearson correlation coefficient screening and qualitative analysis of the metabolite;
and 5: and (3) evaluating the diagnosis accuracy of the progressive metabolic marker according to the area AUC enclosed by the ROC curve and the coordinate axis: the progressive metabolic markers with AUC > 0.7 are taken as the metabolic markers reflecting the progress of the drug-induced acute kidney injury lesion.
Example 1
A method for screening metabolic markers of a drug-induced acute kidney injury lesion process comprises the following steps:
the materials required for the preparation of the experiments were as follows:
experimental materials and instruments: a liquid chromatograph; tandem quadrupole-time-of-flight mass spectrometer; Milli-Q ultrapure water systems; a vortex instrument; a centrifugal machine.
The experimental reagents comprise cisplatin, ultrapure water, formic acid, HP L C-grade methanol and acetonitrile.
Experimental animals: male ICR mice of SPF grade; the temperature (23 +/-1.5) DEG C and the relative humidity (45 +/-15) percent are kept in the animal feeding room.
Step 1: replication of drug-induced acute kidney injury model.
A total of 120 male ICR mice were acclimated for one week, and serum was collected from 120 mice over 72h and serum volume was recorded. Mice were randomly divided into blank control group (group C) and model group (group M), 5 groups each, with 12 mice each. Carrying out intraperitoneal injection of 20mg/kg cis-platinum on the model group (group M) for induction, and establishing a drug-induced acute kidney injury mouse model; a blank control group mouse is established by injecting 20mg/kg of normal saline into the abdominal cavity of an empty control group (group C).
Step 2: and (4) collecting data of serum samples.
Collecting serum of a blank control group mouse; collecting serum of the mouse model respectively at 6h, 12h, 24h, 48h, 72h and 168h after the mouse model is established to form 6h mouse model serum, 12h mouse model serum, 24h mouse model serum, 48h mouse model serum, 72h mouse model serum and 168h mouse model serum, and then respectively carrying out liquid-mass combination analysis on the empty white control group mouse serum and the mouse model serum to obtain a metabolite liquid-phase fingerprint of the mouse model.
Firstly, a serum sample is unfrozen at 4 ℃, serum 40 mu L is precisely taken and placed in a 1.5m L EP tube, acetonitrile and methanol (11: 1) are added according to the volume ratio of 1: 3 for protein removal, the mixture is evenly mixed by vortex for 30s, the mixture is centrifuged at 13000r/min for 15min at 4 ℃, the obtained supernatant is centrifuged again at 13000r/min for 15min in a centrifuge, and the supernatant of 100 mu L is transferred to a sample injection vial before testing.
Conditions for LC-MS analysis were:
1) the chromatographic conditions are that a Waters acquisition UHP L CTM BEH C18 chromatographic column is adopted, the mobile phase A is water containing 0.1 percent of formic acid, the mobile phase B is acetonitrile, the volume flow is 0.3 mu L/min, the UV detection wavelength is 254nm, the column temperature is 30 ℃, the sample injection amount is 3 mu L, the elution gradient is 0-2min, 2 percent B, 2-3min, 2-35 percent B, 3-17min, 35-70 percent B, 17-18min, 70 percent B, 18-29min, 70-98 percent B, 29-31min, 98 percent B, 31-33min, 98-2 percent B, 33-35min and 2 percent B;
2) mass spectrum conditions: adopting an HESI ionization mode, wherein the spraying voltage is 3.5kV at the positive electrode and 2.5kV at the negative electrode, and the collision energy is 12.5 eV, 25 eV or 37.5 eV; the heater temperature is 300 ℃, the capillary temperature is 320 ℃, the volume flow of the auxiliary gas is 10arb, and the volume flow of the sheath gas is 35 arb; the collection mode is positive and negative ion switching, the scanning mode adopts Full Scan/dd-MS2, and the collection range is m/z 100-1500; the resolution was MS Full Scan 35000FWHM, MS/MS 17500 FWHM.
Finally, a liquid phase total ion flow graph of serum metabolites was collected, as shown in fig. 1.
And step 3: and (4) processing data of the serum sample.
Using XCMS software to carry out data preprocessing such as peak extraction, peak identification, peak matching, peak alignment, normalization and the like on the metabolite liquid-phase fingerprint of the mouse model to obtain metabolic profile data containing variables (retention time Rt, mass-to-charge ratio m/z) and peak height, and then introducing the metabolic profile data into SIMCA-P software to carry out processing to obtain the differential metabolite.
The specific steps for obtaining the differential metabolites include:
1) performing Principal Component Analysis (PCA) of unsupervised pattern recognition on the mouse serum of an empty control group (group C) and the mouse serum of a model group (group M) in a positive and negative ion mode, reflecting the original state of data, and visually displaying the overall difference between different serum samples, wherein M1-M6 represent the 6 th, 12 th, 24h, 48h, 72h and 168h after molding respectively, and C represents the empty control group;
2) supervised partial least squares discriminant analysis (P L S-DA) was performed on the metabolic profile data of the mouse sera of the blank control group (group C) and the mouse sera of the model group (group M) in positive and negative ion mode to characterize whether the model was successful or not in a permutation experiment (permutationtest), as shown in fig. 3, and used for the identification of the subsequent differential metabolites;
3) in a positive and negative ion mode, the metabolic profile data of the mouse serum of an air white control group (group C) and the mouse serum of a model group (group M) are subjected to orthogonal partial least squares discriminant analysis (OP L S-DA), and significant differential metabolites are determined by combining S-plot (P-value >0.58), variable importance VIP >1 and independent sample t-test (P < 0.05).
And 4, step 4: calculating the change rate of each differential metabolite, and determining a progressive metabolic marker of the drug-induced acute kidney injury lesion process by combining single-factor variance analysis, random forest algorithm, Pearson correlation coefficient screening and qualitative analysis of the metabolite, wherein the specific steps comprise:
step 4-1: and (6) primary screening. Carrying out primary screening on the differential metabolites obtained in the step 3 by adopting one-factor analysis of variance, and removing differential values which do not meet conditions by taking p <0.05 as a discrimination condition through significant differential comparison to obtain 396 potential metabolic markers;
step 4-2: and (5) further screening. Screening the 396 potential differential metabolites obtained in the step 4-1 by adopting a random forest algorithm, and obtaining 247 potential progressive metabolic markers by taking Mean increment Accuracy >0.001 as a discrimination condition;
step 4-3: and (5) further screening. Adopting Pearson correlation analysis on 247 potential progressive metabolic markers obtained in the step 4-2, taking time as an abscissa, taking the potential progressive metabolic markers as an ordinate, and taking R as an index2>0.7, as a statistical analysis discrimination condition, obtaining 70 time-dependent potential progressive metabolic markers, wherein the time-dependent potential progressive metabolic markers have consistent variation trend in the whole period;
step 4-4: and (5) performing qualitative analysis. And (3) qualitatively analyzing the 70 time-dependent potential progressive metabolic markers obtained in the step 4-3 by using an HMDB database and related literatures in combination with secondary fragment ions, eliminating false positive results, namely the results cannot be found in the database and the retention time of two times is greater than the allowable deviation for 0.2min, and determining the finally screened 25 metabolites as progressive metabolic markers, wherein the results are shown in the table 1.
TABLE 1 determination of progressive markers
And 5: and evaluating the diagnosis accuracy of the progressive metabolic marker according to the area AUC enclosed by the ROC curve and the coordinate axis. Generally, AUC <0.5 is considered to be non-diagnostic, AUC <0.5 is considered to be less diagnostic, AUC <0.7 is considered to be better diagnostic, AUC <0.9 is considered to be better diagnostic, and AUC >0.9 is considered to be the best diagnostic. The progressive metabolic markers with AUC > 0.7 were used as metabolic markers reflecting the course of drug-induced acute kidney injury lesions, as shown in table 2.
TABLE 2 AUC assessment analysis
The metabolic markers include: toxopyrimide, Phthalic acid, Atagotalin, 2-Methyl-4,6-dinitrophenol, Myristyl sulfate, Taurine, Benactyzine, Fasoracetam, Eicosapenaenoic acid, Treibutone, 9-Decornylcarnitine, Xanthohumol, 3',4' -methylioxy-alpha-pyrrolidinobutophenone.
Pathological tissue section analysis is adopted in the prior art: the blank control group (group C) and the model group (group M) mice were dissected at 6h, 12h, 24h, 48h, 72h and 168h, respectively, and the right kidney was taken out and fixed in 10% formalin.
The specific pathological tissue section analysis process comprises the following steps:
step one, tabletting: the kidney tissue fixed by 10% neutral formalin is modified, dehydrated, transparent, waxed, embedded and sliced according to the conventional method.
Step two, dehydration and embedding: kidney tissue is washed with tap water and fixed liquid, after alcohol gradient dehydration (70%, 80%, 95% I, 95% II, 100% I, 100% II), the tissue is transparent (xylene I, xylene II), and embedded after wax immersion (paraffin I, paraffin II).
Thirdly, slicing: and slicing the embedded tissue with the thickness of 3-5 mu m, and baking the slices at the temperature of 60 ℃ for dyeing.
Step four, dyeing: HE staining (xylene I, xylene II deparaffinization, 100% I, 100% II, 95% I, 95% II, 80%, 70% gradient alcohol, hematoxylin staining, differentiation, reversed blue, eosin staining, 70%, 80%, 95% I, 95% II, 100% I, 100% II gradient alcohol).
Fifthly, transparent and sealing: and sealing the transparent xylene I and xylene II by using gum, and performing microscopic examination.
And sixthly, performing optical lens inspection.
As shown in fig. 4, from the histopathological section analysis results, it was found that the kidney injury of the model group gradually worsened with the increase of time from 0h after the model creation. Partial renal tubular dilatation with different degrees appears at the skin-marrow junction, more renal tubular epithelial cells are degenerated, necrosed and shed, basement membranes are exposed, a large amount of red granular materials without structures are contained in the cavities, more renal tubular cavities are provided with protein tubes, the death rate of 168h mice is too high, and pathological tissue section analysis is not carried out.
According to the method, the metabolic markers reflecting the process of the drug-induced acute kidney injury pathological changes are screened through the change process of the content of the endogenous metabolic markers in the serum of the organism, the defects of the existing method for evaluating the process of the drug-induced acute kidney injury pathological changes are overcome, and the process of the drug-induced acute kidney injury pathological changes is finally evaluated according to the determined metabolic markers.
Example 2
An application of the drug-induced acute kidney injury pathological process metabolic marker obtained by the screening method in preparing a drug-induced acute kidney injury pathological process diagnosis and identification reagent.
The foregoing is directed to embodiments of the present invention and, more particularly, to a method and apparatus for controlling a power converter in a power converter, including a power converter, a power.
Claims (6)
1. A method for screening a metabolic marker of a drug-induced acute kidney injury lesion process is characterized by comprising the following steps:
step 1: performing induction by injecting 20mg/kg cis-platinum into the abdominal cavity, and establishing a drug-induced acute kidney injury mouse model; injecting 20mg/kg of normal saline into the abdominal cavity to establish a blank control group mouse;
step 2: collecting serum of a blank control group mouse; collecting serum of the mouse model respectively at 6h, 12h, 24h, 48h, 72h and 168h after the mouse model is established to form 6h mouse model serum, 12h mouse model serum, 24h mouse model serum, 48h mouse model serum, 72h mouse model serum and 168h mouse model serum, and performing liquid mass combination analysis on the empty white control group mouse serum and the mouse model serum respectively to obtain a metabolite liquid phase fingerprint of the mouse model;
and step 3: carrying out data preprocessing such as peak extraction, peak identification, peak matching, peak alignment, normalization and the like on a metabolite liquid-phase fingerprint of a mouse model by using XCMS software to obtain metabolic profile data containing retention time Rt, mass-to-charge ratio m/z and peak height, and introducing the metabolic profile data into SIMCA-P software for processing to obtain a differential metabolite;
and 4, step 4: calculating the change rate of each differential metabolite, and determining a progressive metabolic marker of the drug-induced acute kidney injury lesion process by combining single-factor variance analysis, random forest algorithm, Pearson correlation coefficient screening and qualitative analysis of the metabolite;
and 5: and (3) evaluating the diagnosis accuracy of the progressive metabolic marker according to the area AUC enclosed by the ROC curve and the coordinate axis: the progressive metabolic markers with AUC > 0.7 are taken as the metabolic markers reflecting the progress of the drug-induced acute kidney injury lesion.
2. The method for screening metabolic markers of drug-induced acute kidney injury pathological process according to claim 1, wherein the metabolic markers reflecting the drug-induced acute kidney injury pathological process in step 5 comprise toxypyrimidine, Phthalic acid, atagabin, 2-Methyl-4,6-dinitrophenol, myrisityl sulfate, Taurine, Benactyzine, Fasoracetm, Eicosapentaenoic acid, Trepibutone, 9-Decenoylcarnitine, Xanthohumol, 3',4' -methyendixy-alpha-pyrrolidiniophenol.
3. The method for screening the metabolic markers of the course of drug-induced acute kidney injury according to claim 1, wherein the step 4 comprises the following steps:
step 4-1: carrying out primary screening on the differential metabolites obtained in the step (3) by adopting one-factor analysis of variance, and removing differential values which do not meet conditions by taking p <0.05 as a discrimination condition through significant differential comparison to obtain potential metabolic markers;
step 4-2: screening the potential differential metabolites obtained in the step 4-1 by adopting a random forest algorithm, and obtaining potential progressive metabolic markers by taking meanbackground Accuracy >0.001 as a discrimination condition;
step 4-3: for the potential obtained in step 4-2Progressive metabolic markers were analyzed by Pearson correlation with R2>0.7, as a discrimination condition, obtaining a time-dependent potential progressive metabolic marker, wherein the time-dependent potential progressive metabolic marker has consistent variation trend in the whole period;
step 4-4: and (4) qualitatively analyzing the time-dependent potential progressive metabolic marker obtained in the step (4-3) by using an HMDB database and related documents and combining secondary fragment ions, eliminating false positive results, namely the false positive results cannot be found in the database and the retention time of the two times is greater than the allowable deviation for 0.2min, and determining the finally screened metabolite as the progressive metabolic marker.
4. The method for screening the metabolic markers of the course of drug-induced acute kidney injury according to claim 1, wherein the step of searching for the differential metabolites in step 3 comprises: under a positive and negative ion mode, carrying out unsupervised pattern recognition principal component analysis on metabolic profile data of mouse serum of an empty control group and mouse model serum, determining that the profile of the metabolite of the empty control group is significantly different from the metabolic profile of the mouse model by adopting supervised partial least square method discriminant analysis and orthogonal partial least square method discriminant analysis, and finally combining the fact that the VIP value is more than 1 in the orthogonal partial least square method discriminant analysis, the Pvalue is more than 0.58 in S-plot and the independent sample t-test P is less than 0.05 to obtain the metabolite with significance.
5. The method for screening the metabolic markers of the process of drug-induced acute kidney injury according to claim 1, wherein the conditions of the LC-MS in step 2 are as follows:
the chromatographic conditions are that a Waters acquisition UHP L CTM BEH C18 chromatographic column is adopted, the mobile phase A is water containing 0.1 percent of formic acid, the mobile phase B is acetonitrile, the volume flow is 0.3 mu L/min, the UV detection wavelength is 254nm, the column temperature is 30 ℃, the sample injection amount is 3 mu L, the elution gradient is 0-2min, 2 percent B, 2-3min, 2-35 percent B, 3-17min, 35-70 percent B, 17-18min, 70 percent B, 18-29min, 70-98 percent B, 29-31min, 98 percent B, 31-33min, 98-2 percent B, 33-35min and 2 percent B;
mass spectrum conditions: adopting an HESI ionization mode, wherein the spraying voltage is 3.5kV at the positive electrode and 2.5kV at the negative electrode, and the collision energy is 12.5 eV, 25 eV or 37.5 eV; the heater temperature is 300 ℃, the capillary temperature is 320 ℃, the volume flow of the auxiliary gas is 10arb, and the volume flow of the sheath gas is 35 arb; the collection mode is positive and negative ion switching, the scanning mode adopts Full Scan/dd-MS2, and the collection range is m/z 100-1500; the resolution was MS Full Scan 35000FWHM, MS/MS 17500 FWHM.
6. Use of the metabolic marker of the process of the drug-induced acute renal injury pathological changes obtained by the screening method of any one of claims 1 to 5 in the preparation of a reagent for diagnosing and identifying the process of the drug-induced acute renal injury pathological changes.
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