CN117007703A - Diagnostic marker for urinary calculus, diagnostic kit and application thereof - Google Patents

Abstract

The application relates to a group of diagnostic markers for urinary calculi, and a diagnostic kit and application thereof. The application screens out different markers from metabolites, namely 3- (2-hydroxyphenyl) propionic acid, N-acetyl-L-glutamine, beta-alanyl-L-arginine, erythritol, 5-methylthioadenosine, trans-3-hydroxy ethyl cinnamate, isorhamnetin, 2 (3 h) -benzothiazolone, dibutyl phthalate and fumaric acid, through non-targeted metabonomics detection on the plasma of urinary system stone patients and healthy people, and the markers are used for diagnosing the urinary system stone, thus having important significance for early diagnosis and prevention and treatment of the urinary system stone.

Description

A set of diagnostic markers for urinary tract stones and their diagnostic kits and applications

Technical field

The present invention relates to the technical field of medical biological detection, and specifically relates to a group of diagnostic markers for urinary tract stones and their diagnostic kits and applications.

Background technique

Urinary tract stones, also known as urolithiasis, are one of the most common urological diseases. my country is one of the areas with high incidence of urinary stones in the world. The number of new cases in my country every year is (150-200)/100,000 people, among which 25% of patients require hospitalization. The incidence rate in southern China is as high as 5% to 10%. In the southern part of the country, the incidence rate is 10%. The recurrence rate within 10 years is as high as 80%. According to the latest statistics, 17 Chinese adults One had kidney stones. Since most patients only discover stones after treatment after the onset of symptoms, early evaluation cannot be made, and there is currently a lack of accurate and effective detection methods. Therefore, early detection of urinary system stones is of great significance to patients.

Chinese patent CN113030327A, published on June 25, 2021, discloses a kit for detecting the combined content of markers in biological samples of subjects based on high-performance liquid chromatography-tandem mass spectrometry, which belongs to the field of urinary stone marker detection. Wherein, the kit includes a separation detection reagent for the marker combination, the marker combination includes citric acid and isocitrate, and further, the marker combination also includes oxalic acid and cystine. Using this invention, it is possible to accurately measure the citric acid content in urine, which can be further used to diagnose whether a subject has urinary stones, or to predict whether a subject is at risk of suffering from urinary stones, or to evaluate urinary stones. The effect of patients receiving stone treatment, or being used to predict whether patients with urinary stones have a risk of recurrence after treatment, has great clinical significance and huge economic benefits. Chinese literature (Yan Xiaoyu, Huang Zhihong. Research progress on markers related to urinary tract stones [J]. International Journal of Laboratory Medicine, 2018, 39(7):4.) discusses T-H protein, osteopontin, oxalic acid, and citric acid As well as the correlation between different markers such as susceptibility genes and stones, it is suggested that the detection of markers in urine has certain clinical significance for the use of inhibitors to prevent and treat urolithiasis, and can provide a reference for dose adjustment.

Since the aforementioned detection method (CN113030327A) is for detecting markers in the urine of patients with urinary tract stones, a group of diagnostic markers in the plasma of urinary tract stone patients and their diagnostic kits and applications such as the present invention have not yet been seen. Report.

Contents of the invention

The purpose of the present invention is to provide a set of diagnostic markers for urinary tract stones and their diagnostic kits and applications in view of the deficiencies in the prior art.

In one aspect, a set of diagnostic markers for detecting urinary tract stones is provided, and the diagnostic markers are metabolites in plasma.

As a preferred example, the metabolites include: 3-(2-hydroxyphenyl)propionic acid, N-acetyl-L-glutamine, β-alanyl-L-arginine, and erythritol , 5-methylthioadenosine, trans-ethyl 3-hydroxycinnamate, isorhamnetin, 2(3h)-benzothiazolones, dibutyl phthalate and fumaric acid, fumarate acid.

More preferably, the above-mentioned metabolites that are up-regulated in patients with urinary tract stones are 3-(2-hydroxyphenyl)propionic acid, N-acetyl-L-glutamine, and β-alanyl-L-arginine. acid, erythritol and 5-methylthioadenosine; the above-mentioned metabolites down-regulated in patients with urinary stones are trans-3-hydroxycinnamate ethyl ester, isorhamnetin, 2(3h)-benzo Thiazolones, dibutyl phthalate and fumaric and fumaric acids.

In a second aspect, a diagnostic kit for urinary tract stones is provided. The only active ingredient of the kit is a reagent for detecting the expression of any one of the above diagnostic markers.

As a preferred example, the test sample is plasma.

In a third aspect, a diagnostic reagent for urinary tract stones is provided, the reagent being used to detect the expression of any one of the above diagnostic markers.

The fourth aspect provides the application of the above-mentioned reagents in preparing a diagnostic kit for urinary stones and the application of the above-mentioned diagnostic markers in preparing diagnostic reagents or kits for urinary stones.

The advantages of the present invention are:

Untargeted metabolomics was used to identify the 10 most significantly altered metabolites in patients with urinary tract stones and healthy control patients, and the area under the ROC curve of each metabolite was greater than 0.9, indicating high diagnostic accuracy. At present, plasma metabolites have not been used as diagnostic markers for patients with urinary tract stones, and they have good application prospects.

Picture description:

Figure 1 is the base peak chromatogram, A: positive ion mode; B: negative ion mode.

Figure 2 is the PCA analysis chart of QC sample quality control, A: positive ion mode; B: negative ion mode.

Figures 3-5 are diagrams of multivariate statistical analysis results, obtained through principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal-partial least squares discriminant analysis (OPLS-DA) methods. Among them, A: positive ion mode; B: negative ion mode.

Figure 6 is a volcano plot showing the differential metabolite distribution of the two groups of samples, A: positive ion mode; B: negative ion mode.

Figure 7 is a scatter plot of differential metabolite charge ratios and P values, A: positive ion mode; B: negative ion mode.

Figure 8 is a statistical chart of the total number of differential metabolites.

Figure 9 is a volcano plot-MS/MS of the differential metabolite distribution of the two groups of samples.

Figure 10 is a scatter plot-MS/MS of the mass-to-charge ratio and P value of metabolites.

Figure 11 is a box plot of the 10 most significant differential metabolites. A-E are the box plots of the most significantly up-regulated differential metabolites. The metabolites are: 3-(2-Hydroxyphenyl)propanoic acid [3-(2- Hydroxyphenyl) propionic acid], N-Formyl-L-glutamic acid [N-acetyl-L-glutamine], Beta-Alanyl-L-arginine [β-alanyl-L-arginine], Erythritol [Erythritol], 5'-Methylthioadenosine [5-methylthioadenosine]; F-J are the box plots of the most significantly down-regulated metabolites, and the metabolites are Trans-3-Hydroxycinnamate [trans-3-hydroxycinnamate] Ethyl acid ester], Isorhamnetin [Isorhamnetin], 2(3H)-Benzothiazolethione [2(3h)-benzothiazolones], Dibutyl phthalate [dibutyl phthalate], Fumaric acid [fumaric acid, fumaric acid], ****P<0.0001.

Figure 12 is the ROC curve of the most significant differential metabolite, A-E is the ROC curve of the most significantly up-regulated differential metabolite, A: 3-(2-Hydroxyphenyl)propanoic acid [3-(2-hydroxyphenyl)propanoic acid] Acid], the area under the ROC curve is 0.959; B: N-Formyl-L-glutamic acid [N-acetyl-L-glutamine], the area under the ROC curve is 0.998; C: Beta-Alanyl-L-arginine [β -Alanyl-L-arginine], the area under the ROC curve is 0.928, D: Erythritol [erythritol], the area under the ROC curve is 0.994; E: 5'-Methylthioadenosine [5-methylthioadenosine] , the area under the ROC curve is 0.939; F-J are the ROC curves of the most significantly down-regulated differential metabolites, F: Trans-3-Hydroxycinnamate [trans-3-hydroxycinnamate ethyl ester], the area under the ROC curve is 1; G : Isorhamnetin [Isorhamnetin], the area under the ROC curve is 0.996; H: 2(3H)-Benzothiazolethione [2(3h)-benzothiazolethione], the area under the ROC curve is 0.956; I: Dibutyl phthalate [phthalate] Dibutyl dicarboxylate], the area under the ROC curve is 0.997; J: Fumaric acid [fumaric acid, fumaric acid], the area under the ROC curve is 0.930.

Detailed ways

The present invention will be further described below in conjunction with specific embodiments. It should be understood that these examples are only used to illustrate the invention and are not intended to limit the scope of the invention. In addition, it should be understood that after reading the content described in the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

Example 1

1Experimental method

1.1 Metabolite extraction

Thaw the plasma sample at 4°C. After thawing, the sample is vortexed for 1 minute and mixed evenly; accurately transfer an appropriate amount of sample into a 2ml centrifuge tube; add 400uL methanol solution (stored at -20°C), vortex for 1 minute; 12000rpm 4°C Centrifuge for 10 minutes, take all the supernatant, transfer to a new 2mL centrifuge tube, concentrate and dry; accurately add 150uL of 2-chloro-L-alanine (4ppm) solution prepared with 80% methanol and water (stored at 4°C) to reconstitute. Sample, take the supernatant and filter it through a 0.22um membrane, and add the filtrate into the detection bottle for liquid chromatography-mass spectrometry (LC-MS) detection.

1.2 On-machine detection

1) Chromatographic conditions

Thermo Vanquish (Thermo Fisher Scientific, USA) ultra-high performance liquid phase system, using ACQUITYUPLC HSS T3 (2.1×150mm, 1.8um) (Waters, Milford, MA, USA) chromatographic column, flow rate of 0.25mL/min, column at 40°C The injection volume is 2uL.

In positive ion mode, the mobile phase is 0.1% formic acid acetonitrile (B2) and 0.1% formic acid water (A2). The gradient elution program is: 0 to 1 min, 2% B2; 1 to 9 min, 2% to 50% B2; 9 to 12min, 50% ~ 98% B2; 12 ~ 13.5min, 98% B2; 13.5 ~ 14min, 98% ~ 2% B2; 14 ~ 20min, 2% B2.

Negative ion mode, the mobile phase is acetonitrile (B3) and 5mM ammonium formate water (A3), and the gradient elution program is: 0 to 1 min, 2% B3; 1 to 9 min, 2% to 50% B3; 9 to 12 min, 50% ~98% B3; 12~13.5min, 98%B3; 13.5~14min, 98%~2%B3, 14~17min, 2%B3.

2) Mass spectrometry conditions

Thermo Orbitrap Exploris 120 mass spectrometer detector (Thermo Fisher Scientific, USA), electrospray ion source (ESI), and positive and negative ion modes were used to collect data respectively. The positive ion spray voltage is 3.50kV, the negative ion spray voltage is -2.50kV, the sheath gas is 30arb, and the auxiliary gas is 10arb. The capillary temperature is 325°C, a first-level full scan is performed with a resolution of 60,000, the first-level ion scanning range is m/z 100~1000, and HCD is used for second-level cleavage, the collision energy is 30%, the second-level resolution is 15,000, and the signal is collected The first 4 ions are fragmented, and dynamic exclusion is used to remove unnecessary MS/MS information.

2Data processing and analysis

2.1 Sample data preprocessing

The original mass spectrum offline files were converted into mzXML file format through the MSConvert tool in the Proteowizard software package (v3.0.8789). The RXCMS software package was used for peak detection, peak filtering, and peak alignment processing to obtain a quantitative list of substances. The parameters were set to ppm <30 ppm. The LOESS signal correction method based on QC samples realizes data correction and eliminates systematic errors. In data quality control, substances with RSD>30% in QC samples are filtered out.

The R software package Ropls was used to perform principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) dimensionality reduction analysis on the sample data. The data were scaled and plotted with score plots, load plots, and Splot plots to show the differences in metabolite composition between samples. Use the permutation test method to test the model for overfitting. R2X and R2Y represent the explanation rate of the X and Y matrices of the built model respectively, and Q2 marks the predictive ability of the model. The closer their values are to 1, the better the fit of the model, and the more accurately the samples of the training set can be divided into other categories. Under original ownership. Calculate the P value based on statistical tests, calculate the variable importance of projection (VIP) using the OPLS-DA dimensionality reduction method, and calculate the difference fold between groups using fold change (FC) to measure the intensity and interpretation of the impact of each metabolite component content on sample classification discrimination. Ability to assist in the screening of marker metabolites. Metabolite molecules were considered statistically significant when the P value was <0.05 and VIP>1.

2.2 Pathway analysis

The MetaboAnalyst software package was used to conduct functional pathway enrichment and topology analysis of the screened differential metabolic molecules. The enriched pathways were browsed using the KEGG Mapper visualization tool for differential metabolite and pathway maps.

2.3 Data check

1) Base peak chromatogram

The components flowing out after chromatographic separation continue to enter the mass spectrometer, and the mass spectrometer continuously scans for data collection. A mass spectrum is obtained for each scan, and the ion with the highest intensity in each mass spectrum is selected for continuous scanning. With the ion intensity as the ordinate and time as the abscissa, the obtained spectrum is the base peak chromatogram (see Figure 1).

2) Quality Control (QC)

In metabolomics research based on mass spectrometry technology, in order to obtain reliable and high-quality metabolomics data, quality control (QC) is required. This experiment uses QC samples for quality control during LC-MS detection. Theoretically, QC samples are all the same, but there will be systematic errors in the sample extraction, detection and analysis process, resulting in differences between QC samples. The smaller the difference, the higher the stability of the method and the better the data quality, which is reflected in PCA analysis. The picture shows the density distribution of QC samples, indicating that the data is reliable (see Figure 2).

2.4 Multivariate statistical analysis

Because metabolome data is multidimensional and some variables are highly correlated, traditional univariate analysis cannot quickly, fully, and accurately mine potential information in the data. Therefore, when analyzing metabolome data, it is necessary to use chemometric principles and multivariate statistical methods to perform dimensionality reduction and classification analysis on the collected multidimensional data, so as to mine and extract the most useful information.

Usually, before performing multivariate statistical analysis on metabolomic data, the data need to be transformed by appropriate weights, that is, normalized. This analysis uses adaptive conversion processing on the data before typing this sample to obtain more reliable and intuitive results.

The multivariate statistical analysis (R language Ropls package) methods used in this analysis include: Principal Component Analysis (PCA); Partial Least Squares-Discriminant Analysis (PLS-DA); Orthogonal-Partial Minimum Squares discriminant analysis (Orthognal Partial Least SquaresDiscriminant Analysis, OPLS-DA).

1) Principal component analysis

Principal Component Analysis (PCA) generates new characteristic variables by linearly combining metabolite variables according to certain weights, classifying each set of data through the main new variables (principal components), and removing samples with poor repeatability ( outlier samples) and abnormal samples.

Since there are no external human factors, the resulting PCA model reflects the original state of the metabolome data, which is helpful for grasping the overall situation of the data and grasping the data as a whole, especially for discovering and eliminating abnormal samples, and improving the accuracy of the model. sex. Whether the numerical model calculated by PCA is reliable needs to be rigorously verified. Unreliable mathematical models not only cannot describe the characteristics of metabolomics data well, but may also seriously affect the acquisition of correct results and even mislead the analysis results.

The cross-validation of the model mainly refers to parameters such as R2X, which is the interpretability of the model. Usually, R2 higher than 0.5 is better. The score of each principal component of each sample is its spatial coordinate in the calculated mathematical model, which intuitively reflects the distribution of each sample in the mathematical model space. The degree of aggregation and dispersion of samples can be observed from the PCA score plot. The closer the sample distribution points are, the closer the composition and concentration of the variables/molecules contained in these samples are; conversely, the farther the sample points are, the greater the difference, see Figure 3.

2) Partial least squares discriminant analysis PLS-DA

Unsupervised analysis methods (such as PCA) cannot ignore intra-group errors and eliminate random errors irrelevant to the research purpose. They focus too much on details and ignore the overall pattern and patterns, which is ultimately not conducive to discovering differences and differential compounds between groups. In this case, we need to use the prior knowledge of the sample to further focus the data analysis on the aspects we want to study, and use supervised pattern recognition methods, such as partial least squares-discriminant analysis (PLS-DA).

Unlike PCA, which has only one data set, PLS-DA must specify and group samples during analysis, so that the model will automatically add another implicit data set Y, whose number of variables is equal to the number of groups. PLS-DA is currently the most commonly used classification method in metabolomics data analysis. It combines a regression model while reducing dimensionality, and uses a certain discriminant threshold to perform discriminant analysis on the regression results.

The difference between PLS and PCA is that PLS decomposes the independent variable

The cross-validation of the model mainly refers to parameters such as R2X, R2Y, and Q2. R2X is the interpretability of the model X variable (independent variable), R2Y is the interpretability rate of the model Y variable (dependent variable), and Q2 is the predictability of the model ( Theoretically, the closer the R2X and Q2 values are to 1, the better the model, and the lower the value, the worse the fitting accuracy of the model. Under normal circumstances, it is better for R2X and Q2 to be higher than 0.5, and the difference between the two should not be too large. The maximum value of R2 and Q2 is 1). When the R2 value is small, it often means that the repeatability in the test set is poor (when the background noise is high); when the Q2 value is small, it means that the test set has high background noise, or the model has more abnormal samples.

The results are shown in Figure 4. Figure 4A shows the positive ion mode: KS (stone patients), HC (healthy control), R2X=0.384, R2Y=0.987, Q2=0.973. The two groups can be distinguished; Figure 4B shows the negative ion mode KS (Stone patients) HC (healthy control), R2X=0.181, R2Y=0.983, Q2=0.966. It can be seen that the two groups can be distinguished.

3) Orthogonal-Partial Least Squares Discriminant Analysis OPLS-DA

Another commonly used method in metabolomics data analysis is orthogonal partial least squares discriminant analysis (OPLS-DA), which is an extension of PLS-DA. Compared with PLS-DA, this method can effectively reduce the complexity of the model and enhance the explanatory power of the model without reducing the predictive power of the model, thereby maximizing the visibility of differences between groups.

OPLS-DA uses orthogonal signal correction technology to decompose the X matrix information into two types of information related to Y and irrelevant to Y, and then filters out the information irrelevant to the classification. The relevant information is mainly concentrated in the first prediction component. Like the PLS-DA model, OPLS-DA can also use R2X, R2Y, Q2, CV ANOVA and OPLS-DA score plots to evaluate the classification effect of the model. Usually, the significance of variables (feature peaks) in explaining the X data set and the associated Y data set is stated in terms of VIP values. The sum of the squares of all VIP values is equal to the total number of variables in the model. Therefore, its average value is 1. When the VIP of a variable is >1, it means that the variable is important and is usually used as a screening for potential biomarkers. One of the conditions.

The results are shown in Figure 5. Figure 5A shows the positive ion mode, R2X=0.384, R2Y=0.987, Q2=0.965. Figure 5B shows the negative ion mode, R2X=0.181, R2Y=0.983, Q2=0.965. Both groups can be seen. Clearly differentiated.

4) Differential metabolite screening

Find differential metabolites from the sample primary substance list and filter with the preset P value and VIP threshold in the statistical test. (P<0.05, VIP>1)

The volcano plot can be used to intuitively display the distribution of differential metabolites between the two groups of samples. Usually the abscissa is represented by log2(FC), with metabolites with greater differences distributed at both ends. The ordinate is represented by -log10(Pvalue), which is the negative logarithm of the significant P value of the statistical test. In the figure, FC and P value And the VIP filtering parameters are preset thresholds for analysis.

The results are shown in Figure 6. Each point in the figure represents a metabolite. The abscissa represents the logarithm of Log2 of the quantitative difference fold of a certain metabolite in the two samples; the ordinate represents the logarithm of -log10 of the P value. The larger the absolute value of the abscissa, the greater the difference in the expression fold of a certain metabolite between the two samples; the larger the value of the ordinate, the more significant the differential expression, and the more reliable the differentially expressed metabolites obtained by screening.

Figure 6A shows the positive ion mode. The top five metabolite names with the smallest P value are displayed by default. The up-regulated metabolites are: 219.0431, 175.0533; the down-regulated metabolites are: 259.1101, 202.0893, 162.0546. The lightest colors represent metabolites that were detected but did not meet the filtering parameters. Figure 6B shows the negative ion mode, and the down-regulated metabolites are: 207.1023, 251.1407, 164.0355, 243.0656, 340.1538.

The scatter plot of charge ratio and P value of differential metabolites is shown in Figure 7. Drawing a scatter plot based on the mass to charge ratio and P value of metabolites can clearly see the distribution of differential substances in the sample. Figure 7A shows the positive ion mode. The top five most significant up-regulated differential substances are: 219.0431, 175.0533, 153.0544, 309.2259, 367.2686; the top five most significant down-regulated differential substances are: 259.1101, 202.0893, 162.0546, 180.0649, 339.23 15. Figure 7B shows the negative ion mode. The top five most significant up-regulated differential substances are: 460.0339, 165.0556, 166.0591, 245.1026, 257.0932; the top five most significant down-regulated differential substances are: 207.1023, 251.1407, 164.0355, 243.0656, 340.1 538.

Differential metabolite identification-MS/MS: Metabolite identification is first confirmed based on the precise molecular weight, and then the metabolites are obtained by confirming the annotations of Human Metabolome Database (HMDB), massbank, LipidMaps, and mzcloud based on the MS/MS fragmentation pattern.

Find differential metabolites from the first-level substance list of the sample, and filter with the preset P value and VIP threshold in the statistical test. The statistical chart of the total number of differential metabolites after screening is shown in Figure 8, of which 122 were up-regulated and 122 were down-regulated. There are 96.

Volcano plot-MS/MS is shown in Figure 9: it can intuitively show the distribution of differential metabolites between the two groups of samples. Usually the abscissa is represented by log2(FC). Metabolites with greater differences are distributed at both ends, and the ordinate is - Log10 (P value) represents the negative logarithm of the significant P value of the statistical test. The names of the top five metabolites with the smallest P value are: 3-(2-Hydroxyphenyl)propanoic acid [3-(2-hydroxyphenyl) ) propionic acid], N-Formyl-L-glutamic acid [N-acetyl-L-glutamine], Erythritol [erythritol], Trans-3-Hydroxycinnamate [trans-ethyl 3-hydroxycinnamate] , Isorhamnetin [Isorhamnetin].

The scatter plot of charge ratio and P value of differential metabolites - MS/MS is shown in Figure 10, which can clearly see the distribution of differential substances in the sample.

Among them, the top five most significantly up-regulated differential metabolites are: 3-(2-Hydroxyphenyl)propanoic acid [3-(2-hydroxyphenyl)propionic acid], N-Formyl-L-glutamic acid [N-acetyl-L -Glutamine], Beta-Alanyl-L-arginine [β-alanyl-L-arginine], Erythritol [erythritol], 5'-Methylthioadenosine [5-methylthioadenosine];

The top five most significantly down-regulated differential metabolites are: Trans-3-Hydroxycinnamate [trans-3-hydroxycinnamate ethyl ester], Isorhamnetin [isorhamnetin], 2(3H)-Benzothiazolethione [2(3h) - Benzothiazolone, Dibutyl phthalate [dibutyl phthalate], Fumaric acid [fumaric acid, fumaric acid].

5) Box plot display of the most significantly different substances

The results are shown in Figure 11, where Figure 11A-E shows the box plot of the top five most significantly up-regulated differential metabolites, respectively A: 3-(2-Hydroxyphenyl)propanoic acid [3-(2-hydroxyphenyl) Propionic acid], B: N-Formyl-L-glutamicacid [N-acetyl-L-glutamine], C: Beta-Alanyl-L-arginine [β-alanyl-L-arginine], D: Erythritol [erythritol], E: 5'-Methylthioadenosine [5-methylthioadenosine], ****P＜0.0001.

Figure 11F-J shows the box plot of the top five most significantly down-regulated differential metabolites, respectively F: Trans-3-Hydroxycinnamate [trans-3-hydroxycinnamate ethyl ester], G: Isorhamnetin [Isorhamnetin] [Principle], H: 2(3H)-Benzothiazolethione [2(3h)-benzothiazoleone], I: Dibutyl phthalate [dibutyl phthalate], J: Fumaric acid [fumaric acid, fumaric acid] acid], ****P＜0.0001.

2.5 Verification of sensitivity and specificity

The detected significantly different metabolites were used to draw the ROC curve. The results are shown in Figure 12. Figure 12A-E are the ROC curves of the top 5 most significantly up-regulated differential metabolites, respectively A: 3-(2-Hydroxyphenyl)propanoic acid [3-(2-Hydroxyphenyl)propionic acid], the area under the ROC curve is 0.959, with high diagnostic value; B: N-Formyl-L-glutamic acid [N-acetyl-L-glutamine], ROC curve The area under the ROC curve is 0.998, and the diagnostic value is high; C: Beta-Alanyl-L-arginine [β-alanyl-L-arginine], the area under the ROC curve is 0.928, and the diagnostic value is high; D: Erythritol [red Physitol], the area under the ROC curve is 0.994, and the diagnostic value is high; E: 5'-Methylthioadenosine [5-methylthioadenosine], the area under the ROC curve is 0.939, and the diagnostic value is high.

Figure 12F-J are the ROC curves of the top five most significantly down-regulated differential metabolites, respectively F: Trans-3-Hydroxycinnamate [trans-3-hydroxycinnamate ethyl ester], the area under the ROC curve is 1, diagnosis High value; G: Isorhamnetin [Isorhamnetin], the area under the ROC curve is 0.996, high diagnostic value; H: 2(3H)-Benzothiazolethione [2(3h)-benzothiazolethione], the area under the ROC curve is 0.956, High diagnostic value; I: Dibutyl phthalate [dibutyl phthalate], the area under the ROC curve is 0.997, high diagnostic value; J: Fumaricacid [fumaric acid, fumaric acid], the area under the ROC curve is 0.930 , high diagnostic value.

In summary, non-targeted metabolomics was used to identify the 10 most significantly changed metabolites in patients with urinary tract stones and healthy control patients, and the area under the ROC curve of each metabolite was greater than 0.9. The diagnostic accuracy of the test is high. Can be used for clinical diagnosis of urinary stones.

The above are only the preferred embodiments of the present invention. It should be pointed out that those of ordinary skill in the art can also make several improvements and supplements without departing from the method of the present invention. These improvements and supplements should also be regarded as It is the protection scope of the present invention.

Claims (8)

1. A set of diagnostic markers for detecting urinary tract stones, characterized in that the diagnostic markers are metabolites in plasma. 2. The diagnostic marker according to claim 1, characterized in that the metabolites include: 3-(2-hydroxyphenyl)propionic acid, N-acetyl-L-glutamine, β-alanine Acyl-L-arginine, erythritol, 5-methylthioadenosine, trans-ethyl 3-hydroxycinnamate, isorhamnetin, 2(3h)-benzothiazolones, phthalic acid Dibutyl ester and fumaric acid, fumaric acid. 3. The diagnostic marker according to claim 1, characterized in that the metabolites that are up-regulated in patients with urinary tract stones are 3-(2-hydroxyphenyl)propionic acid and N-acetyl-L-glutamine. Aminoamide, β-alanyl-L-arginine, erythritol and 5-methylthioadenosine; the metabolite down-regulated in patients with urinary tract stones is trans-3-hydroxycinnamic acid ethyl ester, isorhamnetin, 2(3h)-benzothiazolones, dibutyl phthalate and fumaric and fumaric acids. 4. A diagnostic kit for urinary tract stones, characterized in that the only active ingredient of the kit is: a reagent for detecting the expression of the diagnostic marker described in any one of claims 1-3. 5. The diagnostic kit according to claim 5, wherein the test sample is plasma. 6. A diagnostic reagent for urinary tract stones, characterized in that the reagent is used to detect the expression level of the diagnostic marker according to any one of claims 1-3. 7. Application of the reagent according to claim 6 in the preparation of a urinary tract stone diagnostic kit. 8. Application of the diagnostic marker according to any one of claims 1 to 3 in the preparation of urinary tract stone diagnostic reagents or kits.